The Impact of Acute Oral Sodium Propionate Supplementation ...

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The Impact of Acute Oral

Sodium Propionate

Supplementation on Energy

Metabolism

A thesis submitted for the degree of Doctor of Philosophy, Imperial College London

Alia Sukkar

Clinical Medicine Research

Department of Metabolism, Digestion and Reproduction

Imperial College London

2021

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For Mom and Dad...

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Abstract:

Background:

Previous research has demonstrated that acute propionate supplementation in humans can

have favourable effects on energy metabolism by raising energy expenditure and lipid

oxidation. Moreover, acute propionate supplementation in humans has shown to affect

subjective appetite by increasing nausea and triggering the release of the anorectic hormone

glucagon-like peptide 1 (GLP-1). Studies investigating the acute effects of propionate

administration on glucose homeostasis in humans report conflicting outcomes. However,

previous research has generally only investigated the acute impact of propionate

supplementation in the overnight fasted state and for relatively short observation periods

(<180 min).It is, therefore, presently unknown how raised bioavailability of gut-derived

propionate modulates energy metabolism during physical activity and in the postprandial

state. Consequently, the aim of the present trial is to investigate the acute effect of sodium

propionate supplementation on energy expenditure, substrate oxidation, appetite response

and glucose homeostasis in three different energy states (overnight fasted, sub-maximal

exercise and post-prandial) and over longer time-periods. Moreover, NMR (nuclear magnetic

resonance) spectroscopy was used to investigate changes in serum metabolite phenotype

after sodium propionate ingestion.

Methodology:

The thesis is comprised of three separate randomized controlled double-blind cross-over

studies (overnight fasted, submaximal exercise, and postprandial). In each study, following an

overnight fast, tablets containing either sodium propionate or sodium chloride (Control) were

first administered over 180 min.

Overnight Fasted study: 19 volunteers (11 males and 8 females; age: 34.6 ± 4.1 years; BMI

(body mass index): 23.1 ± 0.7 kg/m2) completed the two study visits after an overnight fast.

The study extended over a total period of 360 min while volunteers remained fasted for the

duration of the study.

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Sub-maximal exercise study: 19 volunteers (14 males and 5 females; age: 42.7 ± 3.5 years;

BMI: 24.5 ± 0.7 kg/m2) completed a maximal exercise test visit and two study visits. The study

extended over a total period of 240 min. At time-point 180 min, participants exercised at 40%

of VO2 max for 60 min.

Post-prandial study: 19 volunteers (12 males and 7 females; age: 45.0 ± 3.5 years; BMI: 24.8

± 0.8 kg/m2) completed two study visits. The study extended over a total period of 300 min.

At time-point 180 min, a mixed calorie liquid meal (Ensure Original Vanilla Nutrition Shake:

72.7 g carbohydrate, 13.6 g fat and 20.5 g protein; 500 kcal) was provided to volunteers.

Energy expenditure and substrate oxidation were measured throughout these visits using

indirect calorimetry. Participants were asked to complete 100mm visual analogue scales

(VAS) that assessed subjective appetite (hunger, thirst and nausea) throughout these visits.

Insulin resistance and insulin sensitivity were assessed via HOMA-IR and Matsuda Index

respectively. The oral disposition index (ODI) was used to assess β-cell function. In the post-

prandial trial, GLP-1 release was measured, and 1H-NMR spectroscopy was used to analyse

serum metabolite profile associated with propionate supplementation.

Results:

Propionate supplementation increased energy expenditure in the overnight fasted state,

which was mainly observed within the first 180 min of ingestion, and in the post-prandial

state. A consistent increase in lipid oxidation was found in the overnight fasted state,

however, these effects were not observed during submaximal exercise or in the post-prandial

state. A decrease in carbohydrate (CHO) oxidation was also found in the overnight fasted

state. Propionate ingestion increased subjective nausea in the overnight fasted and post-

prandial states and increased subjective thirst during submaximal exercise. However, no

effect on subjective hunger was found was found in the three different energy states. GLP-1

secretion was significantly increased in the overnight fasted state, however, insulin sensitivity

and β-cell function were unaffected with propionate ingestion. In the overnight fasted state,

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low density lipoprotein (LDL)/ very low-density lipoprotein (VLDL), lactate and methanol were

upregulated and 3-hydroxybutyrate and lysine were supressed following propionate

supplementation. LDL/VLDL, lactate and alanine were upregulated following propionate

supplementation in the postprandial state.

Conclusion:

This thesis is the first to demonstrate that acute oral sodium propionate supplementation in

healthy human volunteers can have favourable effects on energy metabolism in different

energy states. Should these effects be replicated over longer time periods would suggest

that increasing systemic levels of gut-derived propionate appears would be a promising

strategy to improve long term energy balance and body weight management.

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Copyright Declaration

‘The copyright of this thesis rests with the author. Unless otherwise indicated, its contents are licensed under a Creative Commons Attribution-Non Commercial 4.0 International Licence (CC BY-NC).

Under this licence, you may copy and redistribute the material in any medium or format. You may also create and distribute modified versions of the work. This is on the condition that: you credit the author and do not use it, or any derivative works, for a commercial purpose.

When reusing or sharing this work, ensure you make the licence terms clear to others by naming the licence and linking to the licence text. Where a work has been adapted, you should indicate that the work has been changed and describe those changes.

Please seek permission from the copyright holder for uses of this work that are not included in this licence or permitted under UK Copyright Law.’

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Declaration of Contributors:

The majority of this work has been done by the author. All other collaborations and assistance

are listed below.

Supplements:

Sodium propionate and sodium chloride tablets were prepared by Quay Pharma (UK).

Study Visits:

All study visits were performed with the help of the research nurses and administration staff

at the NIHR/Wellcome Trust Imperial Clinical Research Facility.

Overnight Fasted Study:

Horia Schiopu, Hui Guo and Harry Kingsley-Smith helped with the overnight fasted study

visits.

Sub-maximal Exercise Study:

Delphine Lim, Rita Dos Santos and Chun Cheng and helped with the sub-maximal exercise

study visits.

Post-prandial Study:

Callum Riley and Jess Wang helped with the post-prandial study.

Radioimmunoassay:

Radioimmunoassay was performed with the assistance of Dr. Edward Chambers, Dr. Georgia

Franco Becker, Horia Schiopu, Hui Guo and Harry Kingsley-Smith.

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NMR Analysis:

Samples were prepared with the assistance of Dr. Edward Chambers, Professor Jonathan R

Swann and Horia Schiopu. Dr. Jose Ivan Serrano Contreras performed the NMR statistics

analysis.

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Acknowledgements:

Firstly, I would like to thank my parents for giving me the opportunity to achieve this

milestone and their constant support and push to challenge myself and bring out the best in

me. Mom and Dad thank you... this whole work is dedicated to you. You have raised me up

to more than I can be.

I would also like to thank my supervisor Professor Gary Frost for providing me with this

beautiful opportunity to join his team and getting the chance to learn from his unique

expertise. I would also like to give a very special thank you to my second supervisor, Dr.

Edward Chambers, for being the most supportive mentor and for guiding me throughout my

PhD since day one. Honestly, this work could not have been possible without your support ...

thank you!

I would also like to give a very big thank you for my sister Dania, my grandparents, Yara and

Hania for being my biggest supporters and a special one goes to my little sister Nour for

cheering me on every step of the way and tolerating all the ups and downs throughout this

journey.

I would also like to thank my wonderful students Horia Schiopu, Hui Guo, Delphine Lim,

Callum Riley, Chun Cheng, Harry Kingsley-Smith, Rita Dos Santos and Jess Wang for being an

excellent addition to the team. I am grateful for each and every participant and the NIHRF

Imperial CRF staff for making this trial possible. I would also like to thank Katerina for her kind

and eager assistance when needed.

I am very fortunate to have met a unique group of friends Mai, Haya, Souad, Madawi, Shoukri,

Ahamd and Khalifa who became my Imperial family and played a memorable role during my

PhD journey.

Finally, I would like to give a massive thank you for my life-partner, Fares, for always wanting

to play a pivotal role in my becoming a Dr. and indeed doing so by being super patient,

developing an earnest interest in ‘propionate’ and my progress, and giving all the support and

encouragement only an ideal partner would so willingly give. Thank you!

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Achievements:

Publication:

SUKKAR, A. H., LETT, A. M., FROST, G. & CHAMBERS, E. 2019. Regulation of energy expenditure

and substrate oxidation by short chain fatty acids. J Endocrinol.

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Abbreviations:

ACC: Acetyl-CoA carboxylase

Acetyl- CoA: Acetyl coenzyme A

AMP: Adenosine monophosphate

AMPK: 5ʹ adenosine monophosphate- activated protein kinase

AgRP: Agouti-related peptide

ARC: Arcuate nucleus

ATP: Adenosine triphosphate

BCAA: Branched chain amino acid

BF%: Body fat percentage

BMI: Body mass index

BMR: Basal metabolic rate

BOLD: Blood oxygen level dependent

BW: Body weight

C-13: Carbon-13

CHO: Carbohydrate

CNS: Central nervous system

CoA: Coenzyme A

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CPT- 1: Carnitine palmitoyltransferase I

CPT1B: Carnitine palmitoyl transferase 1B

CYC1: Cytochrome c1

CV: Coefficient of variation

DC: Direct calorimetry

DIT: Diet-induced thermogenesis

DF: Dietary fibres

DHA: Docosahexaenoic acid

DVC: Dorsal vagal complex

ECG: Electrocardiogram

EE: Energy expenditure

EPA: Eicosapentaenoic acid

ESI: Electrospray ionization

FABP4: Fatty acid–binding protein 4

FerF: Fermentable Fibre

FFA: Free fatty acid

FFAR-2: Free-fatty acid receptor 2

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FFAR-3: Free-fatty acid receptor 3

GC: Gas chromatography

GI: Gastrointestinal tract

GLP-1: Glucagon-like peptide 1

G-protein: Guaninenucleotide-binding protein

GLUT-4: Glucose transporter

HDL: High-density lipoprotein

HIEC: Hyperinsulinemic Euglycemic Glucose Clamp

HFD: High fat diet

HOMA: Homeostasis Model Assessment

HPLC: High-performance liquid chromatography

HR: Heart rate

HLD: High lipid diet

IC: Indirect calorimetry

IGN: Intestinal gluconeogenesis

IPE: Inulin Propionate Ester

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i.p.: intraperitoneal injection

ISIMatsuda: Matsuda Insulin Sensitivity Index

KO: Knock-out

LC: Liquid Chromatography

LCFAs: Long-chain fatty acids

LDL: Low density lipoprotein

M: Glucose disposal rate

MAP: Mean arterial pressure

MALDI: Matrix-assisted laser desorption ionization

MCT: Monocarboxylate transporter

MC4R: Melanocortin 4 receptor

Min: Minutes

MS: Mass spectrometry

MTT: Meal Tolerance Test

m/z: Mass-to-charge ratio

NMR: Nuclear magnetic resonance

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NPY: Neuropeptide Y

OAT: Organic anion transporter

OGTT: Oral Glucose Tolerance Test

PEE: Physical activity energy expenditure

PME: Pectin methylesterase

POMC: pro-opiomelanocortin

Pparg: Peroxisome proliferator-activated receptor gamma

PYY: Peptide tyrosine tyrosine

QUICKI: Quantitative insulin sensitivity check index

REE: Resting energy expenditure

RER: respiratory exchange ratio

SCD1: Stearoyl-CoA desaturase

SCFA: Short chain fatty acids

SD: Standard deviation

SEM: Standard error of mean

SF: Sweet potato

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SNS: Sympathetic nervous system

SR: Sweet potato residue

TBA: Total bile acid

TCA: Tricarboxylic acid

TEE: Total energy expenditure

TG: Triglycerides

TNF-a: Tumour necrosis factor-a

ToF: Time of flight

T2D: Type 2 Diabetes

UCP1: Mitochondrial uncoupling protein 1

UCP2: Mitochondrial uncoupling protein 2

uN2: Urinary nitrogen component

WAT: White adipose tissue

VAS: Visual analogue scales

VLDL: Very-low-density lipoprotein

VMH: Ventromedial hypothalamus

VCO2: Carbon dioxide production

VO2: Oxygen consumption

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WT: Wild-type

w/w: weight by weight

1D-NMR: One-dimensional NMR

2D-NMR: Two-dimensional NMR

3OHB: 3-hydroxybutyrate

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Table of Contents

Abstract: ....................................................................................................................................................... 3

Copyright Declaration ................................................................................................................................... 6

Declaration of Contributors: .......................................................................................................................... 7

Acknowledgements: ...................................................................................................................................... 9

Achievements: ............................................................................................................................................. 10

Abbreviations: ............................................................................................................................................. 11

Chapter 1: Introduction ................................................................................................................................ 26 1.1 Obesity: ..................................................................................................................................................... 26

1.1.1 Definition: .......................................................................................................................................... 26 1.1.2 Prevalence and economic consequences: ......................................................................................... 26 1.1.3 Causes: .............................................................................................................................................. 27

1.2 Dietary Fibre: ............................................................................................................................................. 29 1.2.1 Definition: .......................................................................................................................................... 29 1.2.2 Effect of dietary fibre on non-communicable diseases and current intakes: .................................... 29

1.3 Fermentable dietary fibre: ......................................................................................................................... 30 1.3.1 Definition: .......................................................................................................................................... 30 1.3.2 Effect of fermentable dietary fibre on body weight: ......................................................................... 30

1.4 Short-Chain Fatty Acids (SCFA): ................................................................................................................. 32 1.4.1 Definition: .......................................................................................................................................... 32 1.4.2 Sources: ............................................................................................................................................. 33 1.4.3 Absorption and transport .................................................................................................................. 37 1.4.4 Metabolism: ...................................................................................................................................... 38 1.4.5 Receptors: ......................................................................................................................................... 40

1.5 The role of propionate on energy metabolism: ......................................................................................... 42 1.6 Thesis purpose and rationale: ................................................................................................................... 48

1.6.1 Chapter 3: .......................................................................................................................................... 48 1.6.2 Chapter 4: .......................................................................................................................................... 48 1.6.3 Chapter 5: .......................................................................................................................................... 49 1.6.4 Chapter 6: .......................................................................................................................................... 49

Chapter 2: Methodology .............................................................................................................................. 51 2.1 Methodology: ............................................................................................................................................ 51 2.2 Recruitment: .............................................................................................................................................. 51

2.2.1 Inclusion Criteria: .............................................................................................................................. 52 2.2.2 Exclusion Criteria: .............................................................................................................................. 52

2.3 Sample Size: ............................................................................................................................................... 53 2.4 Randomization: ......................................................................................................................................... 54 2.5 Supplements: ............................................................................................................................................. 54 2.6 Study Visits Design: ................................................................................................................................... 55

2.6.1 Prior Study Visits Requirements: ....................................................................................................... 55 2.6.2 Study Visit Protocol: .......................................................................................................................... 56

2.7 Energy Expenditure and Substrate Oxidation Measurement: ................................................................... 61

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2.8 Homeostasis Model Assessment (HOMA): ................................................................................................ 62 2.9 Matsuda Index: ......................................................................................................................................... 62 2.10 Visual Analogue Scales (VAS): ................................................................................................................. 63 2.11 Samples Collection and Analysis: ............................................................................................................ 63

2.11.1 Blood samples: ................................................................................................................................ 63 2.11.2 NMR Analysis: .................................................................................................................................. 66

2.12 Statistics: ................................................................................................................................................. 67 2.12.1 Data analysis: ................................................................................................................................... 67 2.12.2 NMR Spectral data analysis: ............................................................................................................ 68

2.13 Participant’s Characteristics: ................................................................................................................... 69 2.13.1 Overnight Fasted Study: .................................................................................................................. 69 2.13.2 Sub-maximal exercise Study: ........................................................................................................... 71 2.13.3 Post-Prandial Study: ........................................................................................................................ 73

Chapter 3: Energy Expenditure and Substrate Oxidation .............................................................................. 75 3.1 Abstract: .................................................................................................................................................... 75 3.2 Energy Expenditure: Definition and Measurement Tools .......................................................................... 78

3.2.1 Indirect Calorimetry .......................................................................................................................... 80 3.3 Effect of Propionate on EE and Substrate Oxidation: ................................................................................ 83

3.3.1 Effect of Propionate on EE and Substrate in Rodents: ...................................................................... 83 3.3.2 Effect of Propionate on EE and Substrate in Humans: ...................................................................... 83

3.4 Hypothesis: ................................................................................................................................................ 84 3.5 Aims: .......................................................................................................................................................... 84 3.6 Outcome Measures: .................................................................................................................................. 85 3.7 Methods: ................................................................................................................................................... 85 3.8 Results: ...................................................................................................................................................... 85

3.8.1 Overnight fasted Trial: ....................................................................................................................... 85 3.8.2 Sub-maximal Exercise Trial: ............................................................................................................. 100 3.8.3 Post-prandial Trial: .......................................................................................................................... 112

3.9 Key Findings: ........................................................................................................................................... 127 3.9.1 Overnight fasted Trial: ..................................................................................................................... 127 3.9.2 Sub-maximal exercise Trial: ............................................................................................................. 127 3.9.3 Post Prandial Trial: ........................................................................................................................... 127

3.10 Summary: .............................................................................................................................................. 127 3.11 Discussion: ............................................................................................................................................. 128

3.11.1 Impact of propionate supplementation on energy expenditure and substrate oxidation in different energy states: ........................................................................................................................................... 128 3.11.2 Acute impact of propionate supplementation on resting energy expenditure and substrate oxidation in humans: ................................................................................................................................ 129 3.11.3 Acute impact of propionate supplementation on post-prandial energy expenditure and substrate oxidation in humans: ................................................................................................................................ 130 3.11.4 Acute impact of propionate supplementation on energy expenditure and substrate oxidation in humans during sub-maximal exercise: ..................................................................................................... 132 3.11.5 The impact of Propionate on energy metabolism: ........................................................................ 133

3.12 Study Limitations: .................................................................................................................................. 136 3.13 Conclusion: ............................................................................................................................................ 138

Chapter 4: Effect of Sodium Propionate on Appetite Regulation ................................................................. 140

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4.1 Abstract: .................................................................................................................................................. 140 4.2 Dietary Fibres and Appetite Regulation: ................................................................................................. 142 4.3 Short Chain Fatty Acids and Appetite: ..................................................................................................... 143

4.3.1 SCFA and Central Nervous System: ................................................................................................. 144 4.3.2 SCFA receptors and anorectic gut hormone release: ...................................................................... 145 4.3.3 SCFA and leptin secretion: ............................................................................................................... 147 4.3.4 SCFA and Gastric Tract Motility: ...................................................................................................... 148 4.3.5 Propionate and Hepatic Metabolism: ............................................................................................. 150 4.3.6 Conclusion: ...................................................................................................................................... 151

4.4 Propionate and Appetite: ........................................................................................................................ 152 4.4.1 The impact of Propionate on appetite in non-humans: .................................................................. 152 4.4.2 The impact of Propionate on appetite in humans: .......................................................................... 153

4.5 Hypothesis: .............................................................................................................................................. 156 4.6 Aims: ........................................................................................................................................................ 156 4.7 Outcome Measures: ................................................................................................................................ 156 4.8 Measurement Tools: ................................................................................................................................ 157

4.8.1 Objective Methodologies: ............................................................................................................... 157 4.8.2 Subjective Methodologies: .............................................................................................................. 158 4.8.3 Conclusion: ...................................................................................................................................... 160

4.9 Methods: ................................................................................................................................................. 160 4.10 Results: .................................................................................................................................................. 160

4.10.1 Overnight fasted Trial: ................................................................................................................... 160 4.10.2 Sub-maximal exercise Trial: ........................................................................................................... 168 4.10.3 Post-prandial Trial: ........................................................................................................................ 174

4.11 Key Findings: ......................................................................................................................................... 183 4.11.1 Overnight fasted Trial: ................................................................................................................... 183 4.11.2 Sub-maximal exercise Trial: ........................................................................................................... 183 4.11.3 Post-prandial Trial: ........................................................................................................................ 184

4.12 Summary: .............................................................................................................................................. 184 4.13 Discussion: ............................................................................................................................................. 184 4.14 Study Strengths: .................................................................................................................................... 186 4.15 Study Limitations: .................................................................................................................................. 186 4.16 Conclusion: ............................................................................................................................................ 187

Chapter 5: Effect of Sodium Propionate on Glucose Homeostasis ............................................................... 189 5.1 Abstract: .................................................................................................................................................. 189 5.2 Glucose Regulation: ................................................................................................................................. 191 5.3 Indices: .................................................................................................................................................... 192

5.3.1 Direct Measures: ............................................................................................................................. 192 5.3.2 Indirect Measures: ........................................................................................................................... 194

5.4 Simple surrogate indexes: ....................................................................................................................... 196 5.4.1 Indices obtained from fasting blood sample: .................................................................................. 196 5.4.2 Indices obtained after dynamic testing such as OGTT/MTT (Muniyappa and Madan, 2018): ........ 199

5.5 Methods of Choice: .................................................................................................................................. 202 5.6 Dietary Fibres and Type 2 Diabetes: ........................................................................................................ 202 5.7 Propionate and Glucose Homeostasis: .................................................................................................... 203

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5.7.1 The impact of Propionate on glucose homeostasis in non-ruminants: ........................................... 204 5.7.2 The impact of Propionate on glucose homeostasis in humans: ...................................................... 207

5.8 Propionate’s mechanism of action on glucose ........................................................................................ 210 5.8.1 The Impact of Propionate on enzymatic activity: ............................................................................ 210 5.8.2 The Impact of Propionate on Free-fatty acid receptors and Gut hormones: .................................. 210 5.8.3 The Impact of Propionate on gut microbiota, systemic inflammation and plasma metabolome: .. 214 5.8.4 The Impact of Propionate on hepatic tissue: .................................................................................. 215 5.8.5 The Impact of Propionate on adipose tissue: .................................................................................. 215 5.8.6 The Impact of Propionate on β-cell: ................................................................................................ 216 5.8.7 Conclusion: ...................................................................................................................................... 217

5.9 Hypothesis: .............................................................................................................................................. 217 5.10 Aims: ...................................................................................................................................................... 217 5.11 Outcome Measures: .............................................................................................................................. 218 5.12 Methods of Choice: ................................................................................................................................ 218 5.13 Methods: ............................................................................................................................................... 218 5.14 Results: .................................................................................................................................................. 218

5.14.1 Overnight fasted Trial: ................................................................................................................... 218 5.14.2 Sub-maximal exercise Trial: ........................................................................................................... 224 5.14.3 Post-prandial Trial: ........................................................................................................................ 228

5.15 Key Findings: ......................................................................................................................................... 236 5.15.1 Overnight fasted Trial: ................................................................................................................... 236 5.15.2 Sub-maximal exercise Trial: ........................................................................................................... 236 5.15.3 Post-prandial Trial: ........................................................................................................................ 237

5.16 Summary: .............................................................................................................................................. 237 5.17 Discussion: ............................................................................................................................................. 237

5.17.1 Impact of Propionate on glucose profile: ...................................................................................... 237 5.17.2 Impact of Propionate on GLP-1 levels: .......................................................................................... 241 5.17.3 Impact of Propionate on Insulin Resistance and β-cell function: .................................................. 241

5.18 Study Strengths: .................................................................................................................................... 242 5.19 Study Limitations: .................................................................................................................................. 242 5.20 Conclusion: ............................................................................................................................................ 243

Chapter 6: Effect of Sodium Propionate on Serum Metabolic Phenotypes .................................................. 244 6.1 Abstract: .................................................................................................................................................. 244 6.2 Metabolomics: ........................................................................................................................................ 245

6.2.1 Definition: ........................................................................................................................................ 245 6.2.2 Design: ............................................................................................................................................. 245 6.2.3 Analytical Tools: .............................................................................................................................. 246

6.3 Fermentable Fibre and Metabolite Profiling: .......................................................................................... 251 6.4 Propionate and Metabolic Profiling: ....................................................................................................... 254 6.5 Hypothesis: .............................................................................................................................................. 255 6.6 Aims: ........................................................................................................................................................ 256 6.7 Outcome Measures: ................................................................................................................................ 256 6.8 Methods: ................................................................................................................................................. 256 6.9 Results: .................................................................................................................................................... 256

6.9.1 Post-Prandial Trial: .......................................................................................................................... 256

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6.10 Discussion: ............................................................................................................................................. 263 6.10.1 3-hydroxybutyrate: ........................................................................................................................ 263 6.10.2 Lysine: ............................................................................................................................................ 264 6.10.3 Methanol: ...................................................................................................................................... 264 6.10.4 Lactate: .......................................................................................................................................... 265 6.10.5 Alanine: ......................................................................................................................................... 268 6.10.6 VLDL/LDL: ...................................................................................................................................... 270

6.11 Study Limitations: .................................................................................................................................. 271 6.12 Conclusion: ............................................................................................................................................ 272

Chapter 7: General Discussion .................................................................................................................... 274 7.1 Thesis aims: ............................................................................................................................................. 274

Chapter 3: ................................................................................................................................................. 274 Chapter 4 .................................................................................................................................................. 274 Chapter 5 .................................................................................................................................................. 275 Chapter 6 .................................................................................................................................................. 275

7.2 Summary of results: ................................................................................................................................ 275 7.3 Conclusion, Limitations and Future Work: .............................................................................................. 276

References: ................................................................................................................................................ 280

Appendices ................................................................................................................................................ 304 Appendix 1: ................................................................................................................................................... 304 Appendix 2: ................................................................................................................................................... 312 Appendix 3: ................................................................................................................................................... 321 Appendix 4: ................................................................................................................................................... 330 Appendix 5: ................................................................................................................................................... 331 Appendix 6: ................................................................................................................................................... 333 Appendix 7: ................................................................................................................................................... 335

List of Figures

Figure 1-1 Major SCFA ........................................................................................................... 33

Figure 1-2: Gut derived SCFA metabolic pathways ................................................................ 37

Figure 1-3 Peripheral propionate levels ................................................................................. 45

Figure 2-1: Peripheral propionate levels ................................................................................ 54

Figure 2-2: Overnight fasted Study Protocol .......................................................................... 57

Figure 2-3: Sub-maximal Exercise Study Protocol ................................................................. 59

Figure 2-4: Post-prandial Study Protocol ............................................................................... 60

Figure 3-1: Overnight Fasted Study: Effect of oral sodium propionate supplementation on

EE ............................................................................................................................................. 87

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Figure 3-2 Overnight Fasted Study: Effect of oral sodium propionate supplementation on

RER .......................................................................................................................................... 90


lipid oxidation .......................................................................................................................... 92


CHO oxidation: ........................................................................................................................ 95


HR ............................................................................................................................................ 97


MAP ....................................................................................................................................... 100

Figure 3-7: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation

on EE ...................................................................................................................................... 102


on RER ................................................................................................................................... 104


on lipid oxidation ................................................................................................................... 106


on carbohydrate oxidation ..................................................................................................... 108


on HR ..................................................................................................................................... 110


on MAP .................................................................................................................................. 112

Figure 3-13: Post-prandial Study: Effect of oral sodium propionate supplementation on EE

................................................................................................................................................ 114

Figure 3-14: Post-prandial Study: Effect of oral sodium propionate supplementation on RER

................................................................................................................................................ 117

Figure 3-15: Post-prandial Study: Effect of oral sodium propionate supplementation on lipid

oxidation ................................................................................................................................ 119

Figure 3-16: Post-prandial Study: Effect of oral sodium propionate supplementation on

carbohydrate oxidation ........................................................................................................... 122

Figure 3-17: Post-prandial Study: Effect of oral sodium propionate supplementation on HR

................................................................................................................................................ 124

Figure 3-18: Post-prandial Study: Effect of oral sodium propionate supplementation on MAP

................................................................................................................................................ 126

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Figure 4-1: Mechanisms of how SCFA suppress appetite and energy intake: ...................... 151


hunger: ................................................................................................................................... 162


thirst: ...................................................................................................................................... 165


nausea: .................................................................................................................................... 167


on hunger: .............................................................................................................................. 169


on thirst .................................................................................................................................. 171


on nausea: ............................................................................................................................... 173


hunger: ................................................................................................................................... 176

Figure 4-9: Post-prandial Study: Effect of oral sodium propionate supplementation on thirst:

................................................................................................................................................ 178


nausea: .................................................................................................................................... 180

Figure 4-11: Post-prandial Study: Effect of oral sodium propionate supplementation on GLP-

1 levels: .................................................................................................................................. 183


glucose levels: ........................................................................................................................ 220


insulin levels: ......................................................................................................................... 223


HOMA-IR levels .................................................................................................................... 223


on glucose levels: ................................................................................................................... 225


on insulin levels: .................................................................................................................... 227


on HOMA-IR ......................................................................................................................... 228

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glucose levels: ........................................................................................................................ 230

Figure 5-8: Post-prandial Study: Effect of oral sodium propionate supplementation on insulin

levels: ..................................................................................................................................... 232

Figure 5-9:Post-prandial Study: Effect of oral sodium propionate supplementation on GLP-1

levels ...................................................................................................................................... 235

Figure 5-10:Post-prandial Study: Effect of oral sodium propionate supplementation on

Matsuda Index ........................................................................................................................ 235

Figure 5-11: Post-prandial Study: Effect of oral sodium propionate supplementation on Oral

Disposition Index ................................................................................................................... 236

Figure 6-1: RM-MCCV‐PLS‐DA score plot at baseline ....................................................... 257

Figure 6-2: RM-MCCV‐PLS‐DA score plot at 180 min ....................................................... 258

Figure 6-3: RM-MCCV‐PLS‐DA score plot at 240 min ....................................................... 260

List of Tables

Table 1-1: SCFA concentrations in human blood .................................................................... 39

Table 2-1: Sodium and Propionate Tablets Composition ........................................................ 55

Table 2-2: Overnight Fasted Study Participant Characteristics ............................................... 69

Table 2-3: Overnight Fasted Study Participant Baseline Values ............................................. 70

Table 2-4: Sub-maximal Study Participant Characteristics ..................................................... 71

Table 2-5: Sub-maximal Study Participant Baseline Values ................................................... 71

Table 2-6: Post-prandial Study Participant Characteristics ..................................................... 73

Table 2-7: Post-prandial Study Participant Baseline Values ................................................... 73

Table 6-1: Serum Metabolite Changes: ................................................................................. 261

Table 6-2: List of 1H NMR peak assignments ...................................................................... 262

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Chapter 1: Introduction

1.1 Obesity:

1.1.1 Definition:

Obesity is a complex, chronic disease that has reached pandemic levels globally. According to

the World Health Organization, obesity can be defined as an abnormal or excess accumulation

of fat that may impair health (Organization, 1 April 2020). The most common method of

assessing obesity and overall weight status is by calculating body mass index (BMI) which can

be determined by dividing body weight in kilograms by the square of height in meters. For

adults, a BMI <18.5 kg/m2 is considered underweight, a BMI between 18.5 to 24.9 kg/m2 is

classified as normal, while a BMI ≥ 25 kg/m2 is considered to be overweight, and a BMI ≥ 30

kg/m2 or a BMI ≥ 40 kg/m2 are defined as obese and severely obese respectively. Although

BMI may not always reflect body fat percentage (BF%) in varying individuals, it is still the most

useful diagnostic method for assessing obesity at the population level given its simplicity and

equal use for both genders and all adult ages (Organization, 1 April 2020). Moreover, it has a

positive relationship with BF%, measured by accurate methods such as dual x-ray

absorptiometry (Flegal et al., 2009) and bioelectrical impedance (Romero-Corral et al., 2008).

Nevertheless, other simple anthropometric measures exist that could be used to assess

obesity and associated metabolic risks such as waist circumference and waist-to-hip-ratio

(Valsamakis et al., 2004, Mokha et al., 2010).

1.1.2 Prevalence and economic consequences:

Recent data suggests that nearly a third of the global population is overweight and around

13% is considered obese (Organization, 1 April 2020). Given the current rise in obesity rates,

projections show a steady increase in obesity levels for at least the next decade (OECD, 2017).

In 2017, the Health Survey for England reported a 64.3% prevalence of either overweight

(35.6%) or obesity (28.7%) i.e. more than half of the population has a surplus amount of body

27

fat (Baker, 2019). Indeed, today, the majority of the global population reside in countries

where obesity tends to kill more people than underweight thus rendering this as a global

problem rather than one related to high income countries as was previously thought

(Organization, 1 April 2020).

The economic burden of obesity is substantial. In 2014, research put forth by the McKinsey

Global Institute estimated that the global impact of obesity is around 3% of the global GDP ,

equivalent to $2 trillion (Institute, 2014). This perhaps is not surprising since obesity is

associated with a multitude of diseases such as cardiovascular disease, renal diseases,

hypertension, type 2 diabetes and overall mortality (Whitlock et al., 2009). In the UK alone,

the NHS spends an estimated £6.1 billion each year on obesity and obesity related costs and

this is projected to increase by one and a half times by 2050 (England, 2017).

1.1.3 Causes:

Obesity is a multifactorial disease with a number of factors contributing to the growing

obesity pandemic, shifting mainly between biological, environmental and societal factors.

The study of twins, especially those reared apart, is an excellent study design that can

distinguish between environmental and genetic influences on BMI. Stunkard et al.

demonstrated in 93 pairs of monozygotic twins who naturally share 100% of their genes, a

high intrapair correlation of BMI heritability of ~70% (0.70 for men and 0.66 for women) that

was also supported by similar estimates from maximum-likelihood model fitting analysis of

0.74 and 0.69 for men and women respectively (Stunkard et al., 1990). Indeed, mutations in

certain genes such as melanocortin 4 receptor (MC4R) gene, can induce hyperphagia and

obesity in human subjects (Farooqi et al., 2000). Although genetic conditions could play a role

in the aetiology of obesity, these conditions are quite rare (Hebebrand et al., 2010, Savona-

Ventura and Savona-Ventura, 2015) and cannot explain the rising prevalence of obesity seen

in the last 50 years at the population level. The rise in obesity has coincided with substantial

environmental changes that promote an obese lifestyle such as reductions in home cooking,

reduced physical activity, greater reliance on convenience food and an increase in sedentary

or desk-job work and an overall ‘westernized’ lifestyle (Caballero, 2007, Blüher, 2019, Lee et

al., 2019). Moreover, socioeconomic differences and societal influences also play an

important role in the development of obesity. For instance, food insecurity has been

28

suggested to be associated with reduced resting metabolic rate, increased energy intake and

increased fat storage as a defence mechanism against food scarcity (Dhurandhar, 2016). Also,

ethnic differences can explain the heterogenicity in obesity observed between different

countries where obesity prevalence is as little as ~5% in countries such as Japan and Korea

with levels rising to 30-40% in others such as the United States and Canada (OECD, 2017).

Thus, given all these factors, it appears that an individual’s susceptibility to obesity is mainly

determined from societal interrelationships, local environment and genetic inheritance.

Indeed, intergenerational BMI transmission or adiposity shaped by family environment and

parental genetic inheritance can account for 35-40% of a child’s BMI (Dolton and Xiao, 2017).

Nevertheless, although the pathogenesis of obesity is complex and multifactorial, the root

cause of obesity stems from a chronic imbalance between energy intake and energy

expenditure, where daily energy intake exceeds energy expenditure (Jebb and Prentice, 1995,

Caballero, 2007, Sharma and Padwal, 2010, Blüher, 2019). In a longitudinal study on 5119

perimenopausal Scottish women aged 45-54 years that commenced between 1990 and 1994,

with follow-up visits at 1997–1999 and 2009– 2011, indicated an average yearly weight gain

of 0.27 kg/year (Yang et al., 2017). Also, data from the National Health and Nutrition

Examination Survey (NHANES) that is a national representation of adults in the United States,

demonstrated that average weight gain between 1999–2000 through 2015–2016 in adults

over 20 years of age, was 0.78 kg/year and 0.62 kg/year men and women respectively (Fryar

et al., 2018). Thus, considering the average weight gain of 0.3-1 kg/year observed in middle-

aged adults, which can become substantial over the course of decades, equates to a

surprisingly minor positive energy balance of 50-100 kcal/day (Hill et al., 2003, Zhai et al.,

2008). Therefore, interventions targeted to halt this gradual increase in annual weight gain

and subsequent development of obesity need only to promote a minimal reduction in daily

energy intake and/or increase in energy expenditure.

29

1.2 Dietary Fibre:

1.2.1 Definition:

The definition of dietary fibre may differ between organizations and depends on both

analytical and nutritional perceptions. However, it is generally accepted that dietary fibre are

carbohydrates polymers with three or more monomeric units, which are neither digested nor

absorbed in the human intestine while also conferring physiological health benefits upon

consumption. Dietary fibre can also be further classified according to varying physiochemical

properties such as viscosity in either a solution or in the digestive tract, fermentability in the

colon as assessed by rate of fermentation or production of short-chain fatty acids (SCFA) or

by its bulking effects in the colon (Stephen et al., 2017). Therefore, it is not surprising that

different subtypes of fibre will not confer the same physiological benefits on intestinal

functions such as constipation relief or contribution to the SCFA pool. For instance, an

insoluble fibre such as cellulose is poorly fermented by gut microbiota but can increase faecal

weight which can in turn help in relieving chronic constipation (Danjo et al., 2008, Nagano et

al., 2018). On the other hand, intake of a soluble and fermentable fibre such as pectin can

enhance the gastrointestinal immune barrier by preventing the adhesion of pathogens to

epithelial cells and strengthening epithelial integrity while also favouring the production of

beneficial gut microbes (Bang et al., 2018, Beukema et al., 2020).

1.2.2 Effect of dietary fibre on non-communicable diseases and current intakes:

Numerous studies have shown that increased intake of dietary fibre is associated with

reduced body weight and sustained weight loss (Liu et al., 2003, Du et al., 2010, Bozzetto et

al., 2018).For instance, the Finnish Diabetes prevention study, which included overweight

men and women (n=522) with impaired glucose tolerance, showed that individuals with high

fibre and low fat intake lost around 2 kg more after 3 years than subjects consuming a high

fat and low fibre intake. Moreover, fibre density was independently associated with sustained

weight reduction (>5%) even after accounting for potential cofounders (Lindström et al.,

2006). In addition, evidence from prospective cohort studies and randomized controlled trials

demonstrate a decrease in incidence of several non-communicable disease such as coronary

30

heart disease, stroke incidence and mortality, type 2 diabetes and colorectal cancer as well

as lower body weight with high intakes of dietary fibre. The greatest risk reduction was

associated with dietary fibre intakes between 25-29g/d (Reynolds et al., 2019). Nonetheless,

it must be noted that the effects of dietary fibres on obesity and overall body weight are not

always consistent and may depend on a number of factors including the type of dietary fibre,

the amounts used, diet composition and the metabolic phenotype of individuals (Blaak,

2016). However, despite the multiple benefits associated with high fibre consumption, intake

of dietary fibres has dropped drastically since the ‘hunter gatherers’ era when >100g of

dietary fibre was estimated to be consumed per day (Eaton, 2006). Most countries today

including the United States, United Kingdom and other European countries recommend an

average dietary fibre intake of 25–35 g for adults (25–32 g/d for adult women and 30–35 g/d

for adult men). However, the majority of adults fail to achieve the recommended dietary fibre

due to increased adoption of a ‘Westernized diet’, where estimated daily intakes in adult

females range from 14 to 21g/d and between 15 to 25 g/d in adult males, which fall well

below recommended intakes (McGill and Devareddy, 2015, Stephen et al., 2017).

1.3 Fermentable dietary fibre:

1.3.1 Definition:

Fermentable fibres are those that resist digestion in the small intestine but are readily

fermented in the large intestine by gut microbiota. Fermentation here refers to the anaerobic

metabolic process in which microorganisms extract energy for bacterial growth and

development. Examples of fermentable fibres include pectin, gums and inulin-type fructans

(Lattimer and Haub, 2010).

1.3.2 Effect of fermentable dietary fibre on body weight:

Increasing evidence suggests that the fermentable component of dietary fibre may be

responsible for the benifitial effects of dietary fibre on body weight regulation and decreased

adiposity (Wanders et al., 2011, Adam et al., 2014, Adam et al., 2015b). Indeed, rodents fed

a high fat diet supplemented with fermentable fibre were shown to be protected against high

fat diet associated weight gain and related morbidities (Adam et al., 2015a, Bray et al., 2018).

31

This may be since animal studies shows a clear indication that increasing the fermentable

fibre content in food is related to increased production of satiety gut hormones such as PYY

and GLP-1 levels that may lead to long term improvements in body weight and composition

(Massimino et al., 1998, Delzenne et al., 2005, Parnell and Reimer, 2012, Adam et al., 2016,

Singh et al., 2018). Moreover, fermentable fibres have also shown to reduce appetite by

acting on central appetite mechanisms (So et al., 2007, Arora et al., 2012).

Similarly, human trials also show that long term supplementation of fermentable fibre can

induce weight loss and fat accumulation whilst enhancing metabolic parameters (Parnell and

Reimer, 2009, Guess et al., 2015) which may be related to changes in appetite hormone

secretion. In a double-blind randomized placebo- controlled trial, Parnell et al. reported in

overweight/obese adults that supplementation of 21 g of oligofructose vs. placebo

(maltodextrin) for 21 days results in weight loss of 1.03 ±0.43 kg versus a weight gain of 0.45

± 0.31 kg (P = 0.01) in the placebo group. Moreover, the oligofructose group had higher

circulating levels of PYY and reduced levels of ghrelin (hunger signalling hormone) and energy

intake than the control group, although GLP-1 levels remained similar across both groups

(Parnell and Reimer, 2009). Similarly, Cani et al. showed that gut fermentation of prebiotics

(a mixture of glucosyl-(fructosyl)n- fructose and (fructosyl)m-fructose extracted from chicory

roots) can influence appetite hormones in healthy adults. Prebiotics refer to dietary fibres

that can selectively stimulate the growth of certain bacteria that have a favourable role on

host’s health. Supplementation of 16g of prebiotics per day in comparison to 16g of non-

fermentable dextrin-maltose control for two weeks, resulted in decreased hunger ratings as

reported by volunteers and increases in plasma PYY and GLP-1 levels (Cani et al., 2009). Also,

Whelan et al. showed in a cross-over trial involving 11 healthy adults, who consumed enteral

formulas as a sole source of nutrition for two weeks that when receiving a standard formula

supplemented with either pea fibre (10g/l) or fructooligosaccharide (5g/l) volunteers had

higher subjective scores of fullness and satiety in comparison to when fed a standard formula

alone (Whelan et al., 2006). Comparably, in hyperinsulinemic subjects, Freeland et al.

demonstrated that supplementation of a high-wheat fibre cereal (24g fibre/d) that increases

colonic fermentability, versus a low-fibre cereal intake resulted in increased GLP-1 levels after

1 year of supplementation (Freeland et al., 2010).

32

It must be noted that animal studies that have demonstrated favourable effects of

fermentable fibre supplementation on overall body composition/weight and gut hormone

secretion utilize large amounts of fibre that are considerably larger than habitual fibre intake

in humans (>7% of the total weight of food consumed in animals versus <1% in human studies)

(Chambers et al., 2011). Moreover, as encouraging as human trials are on fermentable fibre

benefits, common side effects of increased fibre intake include bloating, flatulence, cramps

and soft stools. Therefore, identifying the mechanism of how fermentable dietary fibres can

reduce adiposity and promote weight loss, may prove a more effective strategy at weight

regulation than the use of unsustainable high fibre diets.

1.4 Short-Chain Fatty Acids (SCFA):

Increasing evidence suggests that the the benifitial effects of fermentable dietary fibre on

body weight and energy homeostasis may be due to the major metabolites generated from

microbial fermentation in the gut, the short chain fatty acids (SCFA): acetate, propionate and

butyrate that are shown to modulate metabolic pathways and receptor-mediated

mechanisms at diverse organ sites (Sukkar et al., 2019).

1.4.1 Definition:

SCFA are small organic monocarboxylic acids with varying chain lengths, ranging from two to

six carbon atoms. They can be found in low quantities in food sources but are predominantly

produced by gut microbiota as end-products of fermentation of indigestable carbohydrates

or protein. The major SCFA (90-95%) found in the human gastrointestinal tract are the

straight chain organic fatty acids: acetic acid (C2H4O2), propionic acid (C3H6O2) and butyric acid

(C4H8O2) produced via carbohydrate fermentation (Figure 1-1). Protein fermentation, on the

other hand, can produce brached chain SCFA such as isobutyrate, valerate and hexanoate

accounting for £ 5% of total SCFA production (Cook and Sellin, 1998, Ríos-Covián et al., 2016).

These branched chain SCFA often have unfavorable outcomes on host’s health by negatively

affecting epitheilial barrier and function (Corpet et al., 1995, Canfora et al., 2015) In this text,

the term SCFA will be used to represent the predominant three SCFA: acetate, propionate

and butyrate.

33

Figure 1-1 Major SCFA Acetic Acid, Propionic acid and Butyric acid respectively.

1.4.2 Sources:

1.4.2.1.1 Dietary Sources:

SCFA are naturally present in dietary sources such as milk, cheese and yogurt and can also be

used as food additives and preservatives. Fermentation increases the concentration of SCFA

in foods and levels of SCFA are shown to be considerably higher in fermented versus

unfermented food products due to the greater abundance of SCFA generating

microorganisms (Annunziata et al., 2020).Indeed, SCFA levels can progressively increase with

fermentation days. For instance, Utoiu et al. showed that fermentation of Kombucha

beverage and a symbiotic culture of bacteria and yeast (SCOBY) with pollen can lead to a

steady increase in SCFA concentrations with the highest levels found at the end of the study

period at 17 days of fermentation (Day 0-17 respectively: acetate: 0.415 ± 0.005 g/L to 3.51 ±

0.11 g/L, propionate from 0.095 ± 0.012 g/L to 0.56 ± 0.041 g/L, butyrate from 0.12 ± 0.038

g/L to 1.78 ± 0.054 g/L ) (Uțoiu et al., 2018).

According to the Codex General Standard for Food Additives, acetate can be used safely as an

acidity regulator, preservative or sequestrant and can thus be found in commonly consumed

food products such as fresh and dried pastas, fermented vegetables as well as in dairy

products (Additives, 2019a). Similarly, propionate can be used as a food preservative due to

its antimicrobial properties (P.M. Davidson, 2005) and can commonly be found in ripened and

unripen cheese, fat spreads and ready-prepared meals (Additives, 2019b). Butyrate too has

34

antibacterial properties (Levison, 1973) and can be used as a food flavouring (Nations, 2010)

or added to animal feed to improve meat quality (Lan et al., 2020) or even as an alternative

to antibiotic use in animal feed (Bedford and Gong, 2018).

SCFA concentrations in food are quite tightly regulated during quality control processes in the

food and beverage industry mainly due their odorous characteristics (Gill et al., 2018) as well

as for safe consumption issues. Thus, their concentrations in food products is minimal. For

instance, a maximum safe level of 5000 mg of sodium propionate can be added to 1 kg meat

and fish products (Additives and Food, 2016). Similarly, up to 6000 mg of sodium acetate can

be found in fresh pastas and noodles (Additives, 2019a). Therefore, the major source of

dietary SCFA in humans stems from the fermentation of dietary fibres by colonic microbiota,

rather than oral intake in foods.

1.4.2.1.2 Microbial-derived SCFA:

The human gastrointestinal tract is host to a huge array of micro-organisms (1013 –1014) which

can ferment dietary fibres, or unabsorbed carbohydrates providing a minor energy

contribution (1.2-6%) to our daily energy needs (Popovich et al., 1997, Byrne et al., 2015).

Microbial density and even bacterial diversity increase steadily along the gastrointestinal tract

with concentrations of only 101 bacteria per gram content in the stomach and reaching

1012 bacteria per gram in the colon (Dieterich et al., 2018). In fact, bacteria residing in the

human colon account for 70% of all bacteria present in the body and is considered the main

site of bacterial fermentation of indigestible food components (Hillman et al., 2017). Human

microbiota are mostly strictly anaerobic with the vast majority belonging to three main phyla:

Bacteroidetes, Firmicutes, and Proteobacteria (Dieterich et al., 2018). Short-chain fatty acids

(SCFA), acetate, propionate and butyrate are the principal metabolic end products of gut

microbial saccharolytic fermentation of dietary fibre and are present in an approximate molar

ratio of 60:20:20. Data from suddenly deceased humans demonstrated that the

concentration of SCFA could reach around 200 mmol/kg in the colon with the greatest

amounts present in the cecum (Cummings et al., 1987). However, the total amount, ratio and

rate of SCFA production in the gut depends on several factors such as microbiome diversity,

gut transit time and type and amount of dietary fibre ingested (Byrne et al., 2015). Intake of

inulin, for example, has shown to increase total SCFA concentrations in both the cecum and

35

portal vein in comparison to cellulose intake (Weitkunat et al., 2015). Pectin fermentation, on

the other hand, can promote the growth of bacterial species that lead to increased production

of both acetate and butyrate (Bang et al., 2018).

Carbohydrate fermentation ultimately results in pyruvate production. However, very little

pyruvate is found in the gut as the majority is metabolised to SCFA, CO2, H2, methane and

water (Cummings, 1981). Acetate (C2H3O2) formation by intestinal microbiota is widely

distributed among bacterial groups and is mainly produced via pyruvate decarboxylation to

acetyl-coA or reductive methylation of CO2, also known as Wood-Ljungdahl pathway

(Cummings, 1981, Schönfeld and Wojtczak, 2016). Some homoacetogenic bacteria or

acetogens include Akkermansia Muciniphila (A. muciniphila) and Bifidobacteria which

generate acetate from CO2 and H2 (González Hernández et al., 2019). Moreover, acetate can

be synthesized in the liver after a prolonged fast or alcohol consumption (Scheppach et al.,

1991). Daily acetate production in humans is estimated to be 9g/d (~150 mmol/d) (Wolever

et al., 1995) of which 35% is estimated to be produced via colonic acetate production

(Pouteau et al., 1998).

Propionate (C3H6O2), on the other hand, is produced from pyruvate via three mechanisms:

Succinate decarboxylation pathway, the acrylate pathway and the propanodiol pathway

depending on the type of propionic bacteria. In the former pathway, CO2 is fixed to pyruvate

and forms succinate, which is ultimately decarboxylated to produce propionate. In the

acrylate pathway, propionate is produced from acrylate using lactate as a precursor (Sa'ad et

al., 2010). Finally, the propanodiol pathway present in phylogenetically distant bacteria

involves the conversion of deoxy-sugars to propionate. Propiogenic bacteria include

Lactobacillus plantarum, Bacteroides thetaiotaomicron and Akkermansia muciniphila (Ríos-

Covián et al., 2016, El Hage et al., 2019). Daily propionate production is estimated to be 2.5

g/d (~33.7 mmol/d) (Morrison and Preston, 2016).

Butyrate (C4H8O2) is produced by the condensation of two acetyl-CoA molecules that generate

acetoacetyl-CoA which subsequently, by the reductive conversion of acetoacetyl-CoA, is

converted to butyryl-CoA (Schönfeld and Wojtczak, 2016). Another less common route of

metabolism involves the butyrate kinase pathway which butyryl-CoA into butyrate via

phosphotransbutyrylase and butyrate kinase enzymes (Ríos-Covián et al., 2016).

36

Faecalibacterium prausnitzii and Roseburia are some butyrate generating microorganisms

which also demonstrate cross-feeding between acetate-producing and butyrate-producing

bacteria (Duncan et al., 2004). Indeed, a stable isotope study in humans confirmed that

interconversion of acetate to butyrate (24%) was a result of bacterial rather than human

metabolism since when 13C-labelled acetate capsules that ensured absorption of acetate in

the proximal gastrointestinal tract (before it reaches substantial bacterial sites) were used, 13C-labelled butyrate was no longer detectable in plasma (Boets et al., 2017). Average

butyrate production in healthy individuals is estimated to be 5.5–7.5 g/d (62.4 – 85.1 mmol)

(Banasiewicz et al., 2020).

All in all, it appears that between bacterial species, there is high interconversion from acetate

to butyrate (24%), a low interconversion from butyrate to acetate (10%) and from propionate

to acetate (8%) and almost no interconversion between propionate and butyrate (5%) and

between acetate to propionate (3%) and between butyrate and propionate (1%) (Boets et al.,

2017).

A summary of the major gut derived SCFA metabolic pathways is below: adopted from

(Frampton et al., 2020):

37

Figure 1-2: Gut derived SCFA metabolic pathways

Dietary fibres or non-digestible carbohydrate enter the colon to be fermented by the resident microbiota into

monosaccharides. These monosaccharides are then metabolised via various metabolic pathways to produce

the primary SCFA: acetate, propionate and butyrate.

1.4.3 Absorption and transport

A number of mechanisms have been proposed for SCFA absorption across the colonic

epithelium. Apical uptake of SCFA involves either passive diffusion of undissociated SCFA

across cellular membranes or active transport which is facilitated by various transporters.

However, passive diffusion appears to play a minor role in the uptake of SCFA since the

average luminal pH (5.5–6.5) entails that only a small amount of SCFA (~1%) are present in

the protonated form. Thus, active transport appears to play the major role in SCFA absorption

via three main transporters: 1) an unidentified transporter that catalyses SCFA uptake with

HCO3 secretion 2) members of monocarboxylate transporters (MCT) family that cotransport

SCFA with cations such as H+ or with lactate or pyruvate 3) sodium-dependent

monocarboxylate transporter (SMCT)1 that couples SCFA uptake with Na+ transport and Cl

and water absorption due to a rise in luminal pH with SCFA uptake (Cook and Sellin, 1998,

den Besten et al., 2013b). The remaining SCFA not absorbed and metabolised by colonocytes

38

are then transported across the basolateral membrane into the portal vein via SCFA HCO3

antiport,MCT4 and possibly via MCT5 (den Besten et al., 2013b). Although little is known

about the transporters required to transfer SCFA from blood into peripheral tissues,

transporters organic anion transporter 2 (OAT2) found in the kidneys and liver, and OAT7

expressed in the liver ,were identified as transporters of propionate and butyrate respectively

across the sinusoidal membrane of hepatocytes (Islam et al., 2008, Shin et al., 2007).

1.4.4 Metabolism:

Once SCFA enter the portal vein, they are available for uptake and metabolism by the liver.

The SCFA not extracted by the liver are then released into peripheral circulation via the

hepatic vein. Substantial differences in SCFA concentrations is seen between initial

concentrations in the lumen and peripheral blood, which indicate varying metabolism of

these metabolites across different organ sites. Butyrate appears to be the preferred fuel for

colonocytes as approximate molar fractions of acetate, propionate and butyrate fluctuate

between 60:20:20 in the colonic lumen to 70:20:10 in the portal vein (Cummings et al., 1987).

The majority of propionate (~90%) and remaining butyrate are then extracted by the liver

which leaves acetate as the only SCFA present in substantial amounts (>50 μmol/) in

peripheral circulation (Cummings et al., 1987, Bloemen et al., 2009, Boets et al., 2017). It must

be noted that varying levels of SCFA can be found in circulation due to the health or nutritional

status of volunteers during trials or even due to the difference in rate in colonic fermentation

due to the varying timing of exposure to food (Wolever et al., 1997). Moreover, plasma SCFA

can be derived from both exogenous sources i.e. microbial fermentation, and endogenous

sources. Acetate and butyrate for instance can arise from fatty acid oxidation (Schönfeld and

Wojtczak, 2016) while propionate can be produced from amino acid catabolism (Walter et al.,

1989). The following table (Table 1-1)adopted from findings of (Cummings et al., 1987,

Bloemen et al., 2009) represents estimated SCFA concentrations in human portal and hepatic

veins and periphery:

39

Table 1-1: SCFA concentrations in human blood

Average SCFA concentrations in human blood μmol/l:

In mammals, SCFA can be used as substrates for lipid or carbohydrate synthesis and can also

be oxidized in the TCA (tricarboxylic acid) cycle. Acetate and butyrate can enter the TCA cycle

as acetyl-coA and can be used as precursors for de novo lipogenesis where they can be

incorporated into various long chain fatty acids such as palmitate and stearic acid as well as

cholesterol and ketone bodies. Propionate, on the other hand, can act as a precursor for

hepatic gluconeogenesis where it enters the TCA cycle at the level of succinyl-CoA which can

be converted to oxaloacetate and ultimately converted into glucose. Although acetate and

butyrate can also contribute to glucose metabolism, this is mainly reflected in the enrichment

of the triose phosphate precursor pool and with no net glucose production (den Besten et al.,

2013a, Boets et al., 2017). Complete oxidation of acetate, propionate and butyrate yields a

maximum ATP (adenosine triphosphate) of 10, 18 and 27 ATP/M , equivalent to 73, 131.4 and

197.1 kcal/M, respectively (Baldwin, 1995).

Around 90-95% of SCFA are absorbed in the gastrointestinal tract with only ~5% excreted in

the faeces (Cummings, 1981). However, overweight and obese individuals seem to have

higher total faecal SCFA concentrations than lean individuals (Duncan et al., 2008, Schwiertz

et al., 2010, Kim et al., 2019) which may indicate more colonic SCFA production and increased

energy harvest in overweight and obese individuals. However, Rozenbloom et al. has shown

in obese subjects that despite having higher faecal content of SCFA in comparison to lean

volunteers, this was not due increases in SCFA absorption or differences in diet (Rahat-

Rozenbloom et al., 2014) . An increase in SCFA faecal content could be due for instance to

reduced colonic transit time (El Oufir et al., 2000) or in fact reduced absorption of colonic

SCFA (Vogt and Wolever, 2003). Also, the differing microbial profile seen with obese

individuals vs lean subjects such as a higher Firmicutes abundance (Rahat-Rozenbloom et al.,

2014) or sometimes a higher Bacteroidetes presence (Schwiertz et al., 2010) can indicate

Portal Hepatic Periphery

Acetate 258 - 262.8 115 - 219.5 70 - 172.9

Propionate 30.3 - 88 6.9 - 21 3.6 - 5

Butyrate 29 - 30.1 12 4 - 7.5

40

greater efficiency in fermenting available substrates and a resulting higher faecal SCFA

content. However, a recent meta-analysis has shown that faecal gut microbial richness at the

phylum level is in fact lower in obese versus non-obese subjects (Kim et al., 2019). In any case,

it appears that gut production of acetate, propionate and butyrate (Bloemen et al., 2009) as

well as plasma SCFA levels (Sowah et al., 2020) seem to be quite similar across individuals

with varying BMIs. Also, since SCFA are readily absorbed by the host, faecal concentrations

may rarely reflect intestinal SCFA metabolism or any additional energy harvest (den Besten

et al., 2013a). It must be noted that no research so far has investigated or corrected for energy

or fibre intake in obese versus lean individuals, thus it may be that the raised faecal SCFA

levels reported in obese subjects may be a simple reflection of greater energy intake whereby

more dietary substrate is being fermented by the colonic microbiota. However, a recent

meta-analysis has found inconsistent results with the effects of weight loss achieved through

dietary, physical activity–based, and surgical weight-loss interventions in overweight and

obese subjects on faecal SCFA levels with some studies reporting a decrease in SCFA

concentrations while others observing no significant effect (Sowah et al., 2019). Since studies

included in this review were of small sample size, further studies are needed to accurately

assess the effect of lower energy intake on faecal SCFA concentrations or to compare SCFA

production in lean versus obese subjects given the same diet. Noteworthy, however, plasma

SCFA have been shown to remain remarkably stable in obese subjects even after weight loss

and thus lower energy intake (Sowah et al., 2020).

1.4.5 Receptors:

SCFA have been identified as ligands for two previously orphaned G-protein-coupled

receptors, GPR43 and GPR41 now termed FFAR-2 (free fatty acid receptor 2) and FFAR-3 (free

fatty acid receptor 3) respectively. FFARs are seven-transmembrane receptors that activate

heterotrimeric G protein (guaninenucleotide-binding protein) and can act as therapeutic

targets for various diseases (Hara et al., 2013). Although both FFAR-2/3 have similar

endogenous ligands, their G-protein activating mechanisms can differ. The pertussis toxin-

sensitive Gi/o pathway is activated by both FFA2 and FFA3, whereas Gq/11 is mainly activated

via FFAR-2 (Le Poul et al., 2003). Propionate appears to have the highest affinity to both FFAR-

2 and FFAR-3 while acetate and butyrate are more selective to FFAR-2 and FFAR-3 respectively

41

(Le Poul et al., 2003). However, these results differ depending on species and thus, must be

interpreted with caution. The receptors are widespread throughout different tissues. FFA3 is

mainly found in adipose tissue, pancreas, spleen, lymph nodes, bone marrow, and peripheral

blood mononuclear cells including monocytes whereas FFA2 is greatly present in the distal

ileum, colon, and adipose tissue, with the highest expression found in immune cells such as

monocytes and neutrophils. Nevertheless, both receptors are mostly expressed in the colon

epithelial and enteroendocrine cells such as L-cells (Xiong et al., 2004, Tolhurst et al., 2012,

Byrne et al., 2015, Tang et al., 2015). As ligands of such widespread receptors, SCFAs have

been shown to affect host metabolism at different organ sites. It is noteworthy however that

SCFA have a low potency (EC50 of around 0.5 mM) in activating these receptors in comparison

to other ligands and hence may limit the activation of these receptors to certain areas in the

human body such as the gut lumen where SCFA are relatively abundant (Ang and Ding, 2016).

Tolhurst et al. have shown that SCFA can stimulate GLP-1 (glucagon-like peptide 1) secretion

from intestinal L-cells via FFA2 and can therefore enhance glucose tolerance (Tolhurst et al.,

2012). Indeed, GLP-1 as a potent incretin hormone can lower glucose levels and improve

insulin sensitivity while inducing proliferation of pancreatic b-cells (Vilsbøll, 2009). Through

FFA2 as well, SCFA can, independent of GLP-1 activation, enhance insulin secretion while

inhibiting b-cell apoptosis (Pingitore et al., 2017). Other studies have shown that SCFA can

increase PYY (peptide tyrosine tyrosine) secretion, an anorexigenic hormone and regulator of

gut motility, via FFA3 (Samuel et al., 2008). Moreover, SCFA, especially propionate, can

substantially increase leptin secretion from white adipose tissue through FFA3 even at

physiologically attained levels (Xiong et al., 2004). These SCFA also seem to regulate energy

expenditure and induce adipogenesis and can thus protect from ectopic fat accumulation and

the subsequent improvement in insulin sensitivity through these receptors (Hong et al., 2005,

Kimura et al., 2011, Kimura et al., 2013, Lu et al., 2016).Nevertheless, contradictory studies

have found that these metabolic changes could occur independent of FFA2/3 activation and

thus other mechanistic pathways may be accountable (Lin et al., 2012, Bjursell et al., 2011,

Frost et al., 2014a, den Besten et al., 2015).

42

1.5 The role of propionate on energy metabolism:

In rodent models, propionate has been shown to stimulate the secretion of anorectic gut

hormones such as PYY and GLP-1 from enteroendocrine L-cells via FFAR-2 (Tolhurst et al.,

2012, Psichas et al., 2015). These hormones have been shown to regulate appetite and food

intake via central appetite regulating mechanisms (Perry and Wang, 2012). Indeed, effects of

PYY and GLP-1 on food intake seem to be abolished with disruption of the vagal-brainstem-

hypothalamic pathway (Abbott et al., 2005). Among the SCFA, propionate seems to have the

highest affinity to FFAR-2 (Le Poul et al., 2003). Moreover, propionate is an end-product of

bacterial metabolism and does not undergo substantial bacterial interconversions like other

SCFA such as acetate (den Besten et al., 2013a). Interestingly, gut microbial transplantation

from human twins discordant for obesity to germ free mice show that mice who received

microbiota from the lean twin had higher cecal levels of propionate in comparison to mice

who received the obese transplant. Moreover, phenotypic and body composition differences

to resemble that of their transplant donors were apparent therefore suggesting positive

effects of raised colonic propionate on energy balance (Ridaura et al., 2013). In line with this,

our research group investigated the effects of targeted delivery of propionate to the colon on

appetite and body weight regulation in human subjects (Chambers et al., 2015).

However, since oral propionate is rapidly absorbed and in order to avoid the unpalatability of

orally administered propionate and ensure that propionate is directly delivered to the colon

where L-cells are abundant, a novel delivery system was designed in the form of an inulin

propionate ester (IPE) that targets the release of propionate in the proximal colon in gram

quantities. This carrier involves a molecule where propionate is chemically bound via an ester

linkage to inulin and the majority (>80%) of propionate is released once the IPE is fermented

by colonic microbiota, thereby targeting colonic delivery of propionate with very little

dissociated in the upper gastrointestinal tract. 10g of inulin alone is estimated to produce

15.0 mmol of propionate from microbial fermentation whereas fermentation of 10g of IPE is

estimated to deliver an additional 36.2 mmol from bound propionate. Isotope labelling

studies, using a 13C-propionate variant of the IPE, ensured the stability of propionate along

the gastrointestinal tract till it reaches the colon (Chambers et al., 2015).

43

In an acute study, which was a double-blind randomized cross-over study involving 20

overweight (BMI: 25.4 ± 0.8 kg/m2) but otherwise healthy adults, showed that acute

supplementation of 10 g of IPE in a mixed calorie breakfast in comparison to 10g inulin control

did not alter subjective markers of appetite or glucose homeostasis but significantly

decreased food intake by 13.8% in the IPE group that was coupled with a significant increase

in post-prandial PYY and GLP-1 levels.

Subsequently, a chronic study was initiated where overweight subjects were supplemented

with 10g of IPE (n=25) or an inulin control (n=24) for 24 weeks in a randomised parallel design.

It was hypothesised that the raised PYY and GLP-1 following IPE intake would translate into

long-term reductions in energy intake and produce improvements in body weight. At the end

of the supplementation period, marked differences in weight gain were observed between

the two groups. In the IPE group, none of the participants experienced significant weight gain

(>5% of baseline weight) in contrast to 17% in the inulin control. Moreover, body composition

was significantly altered in the IPE group in comparison to control whereby intraabdominal

adipose tissue was significantly reduced and a trend (p=0.061) for decreased

intrahepatocellular content was observed in the IPE group that was significant in participants

that met the diagnostic criteria for non-alcoholic fatty liver disease. This was also coupled

with prevention of the deterioration of postprandial glucose response. Intriguingly, food

intake was surprisingly similar between the two groups as no apparent effect on food intake

was observed after an ad libitum meal at the end of the supplementation period. Moreover,

no effect on appetite regulating hormones (PYY and GLP-1) levels was apparent. Thus, it

appears that over time, a desensitisation of FFAR-2/3 response may occur, and propionate

hence seems to regulate energy balance independent of appetite hormone secretion. It is

highly possible since no effect on energy intake was observed, that propionate may regulate

body weight by increasing energy expenditure. Indeed, animal studies do suggest that

increasing propionate bioavailability can increase energy expenditure and substrate oxidation

by acting on various tissues and organ sites (Sukkar et al., 2019). For instance, propionate has

been shown to stimulate sympathetic nervous system activity and oxygen consumption in

mice when given at high doses (1g/kg) intraperitoneally thereby increasing energy

expenditure (Kimura et al., 2011). Moreover, propionate has been shown to upregulate

44

metabolic pathways in both the liver and adipose tissue that favour an increase in energy

expenditure and lipid oxidation (den Besten et al., 2015).

Thus, in order to investigate the effects of increased systemic propionate concentrations on

energy expenditure in humans, a follow up pilot study was conducted that examined the

effect of acute propionate supplementation on resting energy expenditure (REE) in healthy

human volunteers (Chambers et al., 2018). Eighteen healthy volunteers (BMI: 24.1 ± 1.2

kg/m2) were recruited who completed two study visits. In a randomized, cross-over design,

participants received tablets at multiple timepoints throughout the 180 min of the trial,

containing either 1369mg sodium propionate (Propionate) or 833mg sodium chloride

(sodium matched control) whereby the total amount of sodium propionate ingested at the

intervention visit was 6845mg (71mmol). REE and substrate oxidation were also measured

during visits using indirect calorimetry. Findings were encouraging since a small but a

significant increase in REE was observed with propionate supplementation (mean difference:

0.045±0.020 kcal/min; p=0.036) that coincided with a significant increase in lipid oxidation

(mean difference:0.012 ± 0.006 g/min; p=0.048).

The positive effects of raising gut-derived propionate on energy expenditure were only

demonstrated in the overnight fasted state. It would be of great interest to investigate the

acute effect of propionate supplementation on energy expenditure in other physiological

states such as post-prandially and during physical activity. Moreover, in this study (Chambers

et al., 2018), peripheral propionate concentrations only became significantly raised at the end

of the 180 min trial Figure 1-3 therefore the complete absorption or bioavailability of oral

propionate may not have been fully measured. Therefore, examining if these positive effects

on energy expenditure can be replicated in different energy states as well as determining how

long the acute metabolic effects of propionate can persist beyond the 180 min would fulfil

this gap in the literature and would be of interest for future research studies. Consequently,

the first aim of this thesis is to explore the acute effect of increased propionate bioavailability

on energy expenditure and substrate oxidation in different energy states (overnight fasted,

submaximal exercise and post-prandial). It will also examine how long the metabolic effect of

propionate can persist in these states and if sympathetic nervous system activity, measured

45

through heart rate and mean arterial pressure, is stimulated following propionate

supplementation. This will be addressed in (Chapter 3:)

Figure 1-3 Peripheral propionate levels The acute effect of oral sodium propionate supplementation on propionate levels in peripheral blood. A.

Propionate (Time×Trial: P= 0.043) and B. Propionate iAUC (P=0.021) (Chambers et al., 2018).

Findings of Chambers et al. also highlight a promising effect of gut derived propionate on

appetite regulation in healthy volunteers (Chambers et al., 2015). Food intake and meal size

were significantly reduced with acute 10 g IPE supplementation to a mixed calorie breakfast

in comparison to inulin control that coincided with a greater post-prandial anorectic gut

hormone PYY and GLP-1 release. However, surprisingly, this did not coincide with any

significant decrease in subjective hunger ratings. In the follow up pilot study of Chambers et

al. that acutely supplemented healthy individuals in the overnight, fasted state with

propionate tablets (71 mmol) similarly confirmed that propionate seems to have no effect on

subjective hunger although energy intake was not assessed in that study (Chambers et al.,

2018). Intriguingly, however, propionate supplementation did elicit a significant increase in

subjective nausea response over the 180 min of the trial. Therefore, it is possible that the

effect of propionate on appetite may partially be driven by the higher nauseating effect

46

induced by propionate that can result in reduced energy intake and a larger satiation response

without influencing subjective feelings of hunger. It would thus be quite interesting to

determine whether acutely raising propionate bioavailability can maintain a nauseating effect

or suppress subjective appetite over a longer time-period and whether this effect persists in

different energy states. Therefore, the second aim of this thesis is to fulfil this gap in the

literature and will be discussed in (Chapter 4:).

Propionate has previously been shown to stimulate GLP-1 release from enteroendocrine L-

cells via FFAR-2 which subsequently increases insulin secretion from β-cells (Tolhurst et al.,

2012). GLP-1, as an incretin hormone secreted from intestinal L-cells, has shown to have a

distinct impact on β-cell function and glucose homeostasis. For instance, in β-cells, GLP-1 has

shown to stimulate insulin gene expression and biosynthesis and also to enhance glucose

stimulated insulin secretion and to restore glucose competence in glucose resistant β-cells.

Moreover, it can act as a growth factor and can thus increase β-cell mass (Buteau, 2008).

Furthermore, GLP-1 has been shown to delay gastric emptying and can slow down the

absorption of nutrients which can in turn reduce post-prandial glucose response (Van

Bloemendaal et al., 2014). In addition, independent of GLP-1 secretion, propionate has also

shown to modulate glucose homeostasis and insulin sensitivity via FFAR-2 in rodent in vitro

models (Han et al., 2014). Thus, increasing propionate bioavailability seems to be an attractive

therapeutic target in regulating glucose homeostasis.

Indeed, our research group investigated the effects of long-term colonic delivery of

propionate in the form IPE on glucose homeostasis in healthy overweight individuals

(Chambers et al., 2015). Participants received 10g of IPE every day for 24 weeks. At the end

of the supplementation period, subjects taking the IPE in comparison to inulin control had

significant improvements in β-cell function as assessed by the oral disposition index that was

independent of GLP-1 release (Pingitore et al., 2017). The acute increase in insulin secretion

observed in the intervention group and the lack of effect on insulin sensitivity was suggestive

of propionate acting directly on pancreatic β-cells. Certainly, a follow up in vitro trial on

human islets demonstrated that propionate can directly potentiate glucose stimulated insulin

secretion and maintain β-cell mass by inhibiting apoptosis.

47

Our research group then investigated the effects of acute sodium propionate

supplementation (71 mmol) on glucose homeostasis in healthy individuals in the overnight,

fasted state (Chambers et al., 2018). Glucose and insulin levels were unchanged with

propionate supplementation in comparison to sodium chloride control. However, in that

study, propionate levels only became significantly increased at the end of the trial at 180 min

which may not have been a sufficient timeframe to assess the impact on glucose and insulin

profile. Moreover, this was only assessed in the fasted state and thus whether propionate can

favourably effect glucose homeostasis in other energy states such as during submaximal

exercise or post-prandial remains to be determined. Therefore, the third aim of this thesis is

to build on this previous work and examine the acute effect of oral sodium propionate

supplementation on glucose homeostasis in all three energy states and over an extended

timeframe as detailed in (Chapter 5:).

Recent advances in metabolomics has provided key opportunities in various research settings.

Metabolomics can be defined as the study of metabolites that serve as direct representatives

of biological activity and allow reflection of an organism’s phenotypic state (Patti et al., 2012).

Thus, metabolomics can be used to identify objective biomarkers that could be changed as a

result of an intervention in research trials. The main analytical tools applied in metabolomics

include nuclear magnetic resonance (NMR) and mass spectrometry (MS) (González-Peña and

Brennan, 2019). Hence, employing metabolomic techniques in the present thesis seemed to

be an attractive tool in order to objectively assess the metabolite changes that could be

induced with acute sodium propionate supplementation. This seems to be a necessary step

in this direction since no human trial up to date has employed metabolomic techniques to

identify biomarkers influenced by propionate administration and relate those changes with

the observed phenotype. Moreover, it can allow a base for future studies to build on to

determine why those changes occurred and how can that be related to improved metabolic

profile and body homeostasis that is observed with propionate administration.

48

1.6 Thesis purpose and rationale:

In summary, the overall purpose of this research is to examine the acute effect of oral sodium

propionate supplementation on energy expenditure and substrate oxidation in different

energy states and to investigate how long the metabolic effects of propionate can persist in

these states.

1.6.1 Chapter 3:

1.6.1.1.1 Aims: The first aim of this thesis is to explore the acute effect of increased propionate bioavailability

on energy expenditure and substrate oxidation in different energy states (overnight fasted,

sub-maximal exercise and post-prandial). It will also examine how long the metabolic effect

of propionate can persist in these states and if sympathetic nervous system activity, measured

through heart rate and mean arterial pressure, is stimulated following propionate

supplementation. This will be discussed in Chapter 3.

1.6.1.1.2 Hypothesis:

I hypothesised that acute oral intake of sodium propionate would increase energy

expenditure and rates of lipid oxidation in different physiological states (overnight fasted,

sub-maximal exercise and post-prandial) and would modulate energy production pathways

to support increases in energy expenditure and lipid oxidation via increases in heart rate and

blood pressure.

1.6.2 Chapter 4:

1.6.2.1.1 Aims:

The second aim of this thesis is to determine the acute effect of increased propionate

bioavailability on appetite in different energy states (overnight fasted, sub-maximal exercise

49

and post-prandial) by means of visual analogue scales and GLP-1 measurements. This will be

discussed Chapter 4.


I hypothesised that acute oral intake of sodium propionate would reduce subjective appetite

during overnight fasted, sub-maximal exercise and post-prandial states that is mainly related

to an increase in subjective nausea and would also increase circulating GLP-1 levels that was

measured in the post-prandial state.

1.6.3 Chapter 5:

1.6.3.1.1 Aims:

The third aim of this thesis is to explore the acute effect of increased propionate

bioavailability on glucose homeostasis in different energy states (overnight fasted, sub-

maximal exercise and post-prandial). This will be discussed in Chapter 5.


I hypothesised that acute oral intake of sodium propionate would improve insulin resistance

during overnight fasted and sub-maximal exercise states and would improve β-cell function

and increase insulin sensitivity and GLP-1 secretion in the post-prandial state.

1.6.4 Chapter 6:

1.6.4.1.1 Aims:

The fourth aim of this thesis is to examine the acute effect of increased propionate

bioavailability on serum metabolite profile related to gluconeogenesis. This will be explored

in Chapter 6.

50


I hypothesised that acute oral intake of sodium propionate would modulate metabolic

pathways to support suppression of gluconeogenesis from gluconeogenic substrates and

therefore an increase in gluconeogenic substrates would be observed in serum.

51

Chapter 2: Methodology

2.1 Methodology:

This clinical trial (Registration No: NCT04093453) was approved by the London-Bloomsbury

Research Ethics Committee (18/LO/0433) and carried out in accordance with the Declaration

of Helsinki. The trial consisted of three separate studies:

1. Overnight fasted study

2. Sub-maximal exercise study

3. Post-prandial study

Each of these studies was a randomized controlled double-blind cross-over study. The fasting,

exercise and postprandial studies consisted of two, three and two study visits respectively.

2.2 Recruitment:

Subjects were recruited via e-mail using the Healthy Volunteer Database NIHR Imperial

Clinical Research Facility and via posters spread around Imperial College Campuses.

Responders to posters were sent an e-mail which contained a pre-screening questionnaire

and the participant information sheets (Appendices). Potential candidates were then invited

to a screening visit at the NIHR Imperial Clinical Research Facility to assess full eligibility.

During screening, the participants were assessed via an interview which discusses medical

and drug history. All subjects then provided written informed consent (Appendices).

Afterwards, a blood test (fasting blood glucose and full blood count), an electrocardiogram

(ECG), blood pressure and weight and height and body composition measurements were

recorded, and all women of child-bearing age underwent a urinary pregnancy test. Results

were then assessed as per the following criteria and approved by the study clinician before

the participants were formally enrolled in the study.

52

The inclusion and exclusion criteria were set up to identify and recruit healthy adult

participants which is defined as below:

2.2.1 Inclusion Criteria:

• Men and women aged 18-65 years (inclusive)

• Body mass index (BMI) of 18-35 kg/m2.

2.2.2 Exclusion Criteria:

• Weight change of ≥ 3kg in the preceding 2 months

• Current smokers

• Substance abuse

• Excess alcohol intake

• Pregnancy/Breastfeeding

• Diabetes

• Cardiovascular disease

• Cancer

• Gastrointestinal disease e.g. inflammatory bowel disease or irritable bowel syndrome

• Kidney disease

• Liver disease

• Pancreatitis

• Started new medication within the last 3 months likely to interfere with energy

metabolism, appetite regulation and hormonal balance including: anti-inflammatory

drugs or steroids, antibiotics, androgens, phenytoin, erythromycin or thyroid

hormones.

• Involved in current research or have recently been involved in any research or donated

blood prior to recruitment in the past 12 weeks.

53

2.3 Sample Size:

Chapter 3:

The total sample size for the three separate studies was estimated using data from (Chambers

et al., 2018). This preliminary study identified that resting energy expenditure was raised by

0.045 ± 0.068 kcal/min (mean ± SD) over 180 min following sodium propionate

supplementation. A power calculation confirmed that 20 participants would be adequate to

detect the same difference (α=0.05, power=0.80).

Chapter 4:

The total sample size for the three separate studies was estimated using data

from (Chambers et al., 2018). This preliminary study identified that mean Nausea VAS was

raised from 5 ± 9 mm (mean ± SD) to 9 ± 9 mm over 180 min following sodium propionate

supplementation. A power calculation confirmed that 17 participants would be adequate to

detect the same difference (effect size= 0.75, α=0.05, power=0.80).

Chapter 5:

Data from (Chambers et al., 2015) identified a HOMA-IR of 2.0 ± 0.3 (mean ± SD) in 25

healthy adults. A power calculation confirmed that 20 participants would be adequate to

detect a 10% change in HOMA-IR (1.8 ± 0.3) following sodium propionate

supplementation (effect size= 0.67, α=0.05, power=0.80).

Based on the above information from the separate power calculations 20 volunteers would

be needed. 25 participants were recruited to each study to allow a drop-out rate of 20%.

54

2.4 Randomization:

Randomization was done via an internet- based randomization service (sealedenvelope.com).

Both the participants and the researchers were blinded to the type of tablets ingested on

each study visit.

2.5 Supplements:

Sodium propionate tablets (6845 mg) were used as the intervention product whereas sodium

chloride tablets (4164 mg) were used as a sodium control. This dose was chosen since it was

previously shown that peripheral concentrations of propionate can significantly be increased

after 180 minutes using this strategy (Figure 2-1) (Chambers et al., 2018). The tablets were

prepared by Quay Pharma (UK) and have passed the Ph Eur disintegration for gastro-resistant

tablets (less than 10% of the tablet dissociates in 0.1 M HCl after 120 minutes and completely

disintegrates in a pH 6.8 phosphate buffer solution within 60 minutes). The tablet’s enteric

coating (Acryl-EZE, Colorcon, UK) contributed 11% of the overall tablet weight (ColorCon,

2019). Components of the tablets can be found in Table 2-1.

Figure 2-1: Peripheral propionate levels

55

The acute effect of oral sodium propionate supplementation on propionate levels in peripheral blood. A.

Propionate (Time×Trial: P= 0.043) and B. Propionate iAUC (P=0.021) (Chambers et al., 2018).

Table 2-1: Sodium and Propionate Tablets Composition

Amount (mg)

Component Control Propionate

Sodium Propionate 684.5

Sodium Chloride 416.4

Kollidon K90 6.9

Avicel pH 102 523.6 248.6

Magnesium Stearate 10 10

Kollidon VA64 50 50

Total 1000 1000

2.6 Study Visits Design:

2.6.1 Prior Study Visits Requirements:

On the day before each study visit, participants were asked to avoid alcohol and strenuous

exercise and to consume a standardized evening meal (ready-prepared or shop-prepared

meal) of their choice before an overnight fast for ³ 6 hours (water was allowed). These

measures were taken to promote consistency between visits. Female participants were asked

to complete the two study visits <7 days apart during the early phase of the menstrual cycle

56

days (1-7 days) to avoid any possible effect of the menstrual cycle on energy metabolism

(Solomon et al., 1982).

2.6.2 Study Visit Protocol:

For the first 180 min of the study visit, all three separate trials (fasting, exercise and post-

prandial) commenced as follows.

Participants were asked to arrive to the NIHR Imperial CRF at 9:00 a.m. Compliance of the

prior study visit requirements was first assessed via participants’ oral confirmation and by the

nutritional packaging and receipt of purchase of the evening meal. The volunteers were then

asked to void their bladder and urine was afterwards collected for the remainder of the visit

to provide an estimate of protein oxidation from urea concentration (Frayn, 1983). Body

weight (to the nearest 0.1 kg) and composition as well as resting energy expenditure were

measured using the bioelectrical impedance analysis (BC-418 Segmental Body Composition

Analyzer, Tanita UK Ltd, Middlesex,UK).

A cannula was then inserted into the volunteers’ antecubital vein and two fasting baseline

blood samples (8 ml each) ³ 5 minutes apart were collected. Further 8 ml blood samples were

taken at 60, 120 and 180 minutes. Of note, to ensure the cannula remains functional for the

duration of the study, a saline flush was used after each cannula use. Thus, prior to each blood

sample collection, a 2 ml blood sample was taken and discarded to ensure a fresh blood

sample was collected each time. Participants were given 1369 mg sodium propionate or 833

mg sodium chloride tablets every 30 minutes for the first 120 minutes of the study at 0, 30,

60, 90 and 120 minutes. The total amount of sodium propionate ingested over the study visit

was 6845mg (71mmol). Mean arterial pressure (MAP) and heart rate (HR) measurements

were also collected at baseline and at 60, 120 and 180 min (SureSigns VM4, Phillips, USA).

Participants also completed visual analogue scales (VAS) at baseline, 30, 60, 90, 120, 150 and

180 min that asked the following three questions: “How hungry do you feel right now?” and

“How thirsty do you feel right now?” and “How sick do you feel right now?”.

57

2.6.2.1.1 Overnight Fasted Study Protocol:

The Fasting study visit protocol is depicted in Figure 2-2: Overnight fasted Study Protocol.

Each study visit extended over 360 minutes. All participants completed the two study visits ³

2 days apart. The mean ± SD (range) period between these study visits was 5±3 (3-14) days.

Figure 2-2: Overnight fasted Study Protocol Sodium propionate or sodium chloride tablets were administered every 30 min for the first 120 min of the study.

Energy expenditure and substrate oxidation measurements were assessed in the last 15 min of every hour of

the study using indirect calorimetry. Blood samples, HR and MAP were assessed hourly. VAS were filled every

30 min of the study.

Energy expenditure and substrate oxidation were measured using an indirect calorimeter

(Gas Exchange Monitor, GEM, GEMNutrition, Daresbury,UK) for 15 minutes at baseline (-15-

0 min) and in the last 15 minutes of every hour of the study (45-60 min; 105-120 min; 165-

180 min; 225-240 min; 285-300; 345-360 min). The calorimeter was calibrated with “zero”

(0.00% O2 and 0.00% CO2) and “span” (20.00% O2, 1.00% CO2) gases (BOC Gases, U.K.) upon

participant’s arrival. During measurements, the participants lay fully supine under the

58

calorimeter’s hood. Volunteers were allowed to listen to soft music during measurements as

long as they remained in a relaxed position with no movement nor talking. Physical activity

was also restricted between energy expenditure measurements and participants remained

mainly rested on their beds but were allowed to read, work on laptops or listen to music.

Further fasting blood samples were then taken at 240, 300 and 360 min.

Blood pressure and heart rate measurements (SureSigns VM4, Phillips, USA) were taken every

hour at 240, 300 and 360min. VAS were also filled at 210, 240, 270, 300, 330 and 360 min.

2.6.2.1.2 Sub-maximal exercise Study Protocol:

Participants initially completed a maximal exercise test visit at the NIHR Imperial Clinical

Research Facility performed on a cycle ergometer to determine maximal oxygen uptake

(VO2max) and maximal aerobic power output (Wmax). This visit did not require the prior visit

requirements outlined in section Prior Study Visits Requirements:. After adjusting the cycle’s

handle and saddle to each participant’s comfort, the participants were asked to cycle at 40 W

for a period of 5 min for warm-up and acclimatization. Then every following min, the workload

was increased by 20W until voluntary exhaustion (Lanzi et al., 2015). VO2 was considered

maximal if a respiratory exchange ratio (RER) ³ 1.05 was recorded during the test. The mean±

SD RER at VO2max equated to 1.15 ± 0.03.

Subjects then completed two study visits as per Figure 2-3: Sub-maximal Exercise Study

Protocol outlined below. The study visits extended to 240 min. All participants completed the

two study visits ³ 2 days apart. The mean ± SD (range) period between these study visits was

5±2 (3-12) days.

59

Figure 2-3: Sub-maximal Exercise Study Protocol Sodium propionate or sodium chloride tablets were administered every 60 min for the first 120 min of the study.


the study and for 60 min during exercise using indirect calorimetry. MAP was measured hourly. Blood HR and

MAP were assessed hourly for a period of 180 min and every 15 min in the final hour of the study during exercise.

Indirect calorimetry (Quark CPET) was used to measure energy expenditure and substrate

oxidation. The calorimeter was calibrated with Cosmed calibration compressed gas, N.O.S (5%

CO2, 16% O2, BAL. N2) upon participant’s arrival. Flow rate via the bi-directional turbine was

also calibrated using a 3-litre calibration syringe. Resting energy expenditure measurements

were taken at baseline (-15-0 min) and at (45-60 min; 105-120 min; 165-180 min). Energy

expenditure was then measured for a period of one hour at time-point 180 min while

participants were cycling on a cycle ergometer at 30% Wmax (equivalent to~40%VO2max)

(Arts and Kuipers, 1994) determined from their maximal exercise test visit. This low to

moderate level intensity was used in order to confirm that both carbohydrate and lipid were

being oxidized for energy expenditure (Fletcher et al., 2017).

60

Blood samples were collected during exercise at 195, 210,225 and 240 min.

MAP (SureSigns VM4, Phillips, USA) was taken after cycling at 240 min. HR (SureSigns VM4,

Phillips, USA) was recorded at 195-, 210-, 225- and 240-min. VAS were also filled at 210 and

240 min.

2.6.2.1.3 Post-Prandial Study Protocol:

The post-prandial study visit protocol is depicted in the below Figure 2-4: Post-prandial Study

Protocol. Each study visit extended over 300 minutes. All participants completed the two study

visits ³ 2 days apart. The mean ± SD (range) period between these study visits was 6 ±2 (3-7)

days.

Figure 2-4: Post-prandial Study Protocol Sodium propionate or sodium chloride tablets were administered every 30 min for the first 120 min of the study.


the study using indirect calorimetry. HR and MAP were assessed hourly. VAS were filled every 30 min of the

61

study. An Ensure drink was administered at time-point 180 min. Blood samples were taken hourly for the first

180 min of trial and were then taken at 195, 210,240 and 300 min.

Energy expenditure and substrate oxidation were measured using GEM as described for the

fasting study. Measurements were taken at baseline (-15-0 min) and in the last 15 minutes of

every hour of the study (45-60 min; 105-120 min; 165-180 min; 225-240 min; 285-300 min).

At timepoint 180 min, a mixed calorie liquid meal (Ensure Original Vanilla Nutrition Shake:

72.7 g carbohydrate, 13.6 g fat and 20.5 g protein; 500 kcal) was provided. Postprandial blood

samples were then taken at 195, 210,240 and 300 min.

MAP and HR measurements (SureSigns VM4, Phillips, USA) were taken every hour at 240- and

300-min. VAS were also filled at 210, 240, 270 and 300 min.

2.7 Energy Expenditure and Substrate Oxidation Measurement:

Energy expenditure and substrate oxidation were estimated based on oxygen consumption

(VO2) and carbon dioxide production (VCO2). Energy expenditure was calculated based on the

modified Weir equation (Weir, 1949):

EE (kcal/day) = ([VO2 × 3.941] + [VCO2 × 1.11]) ×1440

Carbohydrate and lipid oxidation rates were calculated from stochiometric equations of

glucose and triacylglycerol oxidation respectively. As for protein oxidation rates, empirical

values were used and are based on urinary nitrogen excretion (uN2)since most uN2 (>80%) is

in the form of urea (Frayn, 1983). The following equations were thus used:

CHO (g/min) = (4.55* VCO2) -(3.21*VO2) -(2.87* uN2)

Lipid (g/min) = (1.67* VO2) -(1.67* VCO2) -(1.92* uN2)

Protein (g/min) = uN2 * 6.25

62

2.8 Homeostasis Model Assessment (HOMA):

HOMA, a measure for quantifying insulin resistance and β-cell function, was calculated based

on fasting plasma glucose and insulin values according to the following equation (Gutch et al.,

2015):

HOMA= Fasting Insulin (IU/ml) x Fasting Glucose(mmol/l) / 22.5

where 22.5 is a normalizing factor obtained from the product of normal fasting plasma insulin

of 5 IU/ml and normal fasting plasma glucose of 4.5mmol/l of an “ideal and normal”

individual.

2.9 Matsuda Index:

In the postprandial study, the Matsuda Index (ISIMatsuda) for measurement of postprandial

insulin sensitivity was calculated as follows (Patarrão et al., 2014):

ISIMatsuda= !","""

$(&'×)'×&*+,-×)*+,-)

10,000: Simplifying constant to get numbers from 0 to 12;

√ : Correction of the nonlinear values distribution;

G0 – fasting plasma glucose concentration (mg/dl);

I0 – fasting plasma insulin concentration (mIU/l);

Gmean – mean plasma glucose concentration (mg/dl);

Imean – mean plasma insulin concentration (mIU/l);

63

2.10 Oral Disposition Index:

In the postprandial study, the oral disposition index (ODI) for measurement of postprandial

insulin sensitivity was calculated as follows (Utzschneider et al., 2009) :

Oral disposition index = (DI0 –120/D G0 –120) x (1/fasting insulin)

2.11 Visual Analogue Scales (VAS):

Visual Analogue Scales were composed of three questions: “How hungry do you feel right

now?” and “How thirsty do you feel right now?” and “How sick do you feel right now?”.

Subjects were asked to mark their answers for each question with vertical lines on 100 mm

horizontal straight lines that had the following words anchored at each end “Not at all” and

“Extremely”, corresponding to 0 mm and 100 mm respectively. Measurements were then

computed by measuring the distance from the left end of the line up to the mark.

2.12 Samples Collection and Analysis:

2.12.1 Blood samples:

A total of 8 ml of venous blood sample was collected at each time-point. 5 ml were collected

for serum (SST Vacutainer tube); 2 ml for gut hormones (Lithium Heparin tube: 20 μl aprotinin

(Trasylol, Nordic Pharma) per 1 ml blood was added to the tube prior to sample collection.

Aprotinin is a pancreatic trypsin inhibitor that was used to prevent degradation of the gut

hormones) and 1 ml for glucose (Grey Vacutainer). Serum was left to clot for 10 minutes

before processing. All other samples were put on ice immediately upon collection prior to

processing. Samples were then centrifuged at 2,500 relative centrifugal force (RCF) at 4°C for

10 min. The resulting supernatant was then aliquoted into eppendorfs and stored in -20°C

freezers until further processing.

1.1.1.1 Glucose Analysis:

Glucose was analysed using the Randox Glucose (GLUC/PAP) kits supplied by Randox as per

the manufacturer’s instructions at Imperial College London Hammersmith Campus. Samples

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were done in duplicates to ensure better accuracy. The principle behind this analysis relies on

the oxidation of glucose via glucose oxidase. The resulting hydrogen peroxide reacts with

phenol and 4-aminophenazone to form a red - violet quinoneimine dye that can be detected

via a spectrometer.

Microsoft Excel Version 16.30 was used to calculate the unknown glucose levels in

participants’ samples.

The coefficient of variation (CV), a statistical measure of the dispersion of the data around

the mean, was calculated for all three studies. A CV £ 10% was considered acceptable (Sacks

et al., 2002, Pleus et al., 2019). If the CV of the samples was outside this range, the samples

were either repeated. The mean CV in duplicates for the three trials was £ 10%: Fasting Study:

6.2%; Post-prandial study: 4.6%; and Exercise study: 5.5%. The CV of the trials was calculated

from the average CV of all the assays performed in each study.

1.1.1.2 Radioimmunoassay:

A radioimmunoassay (RIA) is a sensitive and specific in vitro assay that can measure substance

concentrations, often antigens, through the use of antibodies.

In an RIA, a known concentration of antigens are radiolabelled, often via gamma-

radioactive isotopes of iodine, such as 125-I, attached to tyrosine. The radiolabelled antigens

are then incubated with a constant dilution of anti-serum, thereby a specific number of

antigens will be bound to the available antibody sites. When unlabelled antigens are added

to the system, from a human serum sample for instance, they will compete with the labelled

antigens for the antibody sites. Hence, as the concentration of the unlabelled antigen

increases, the amount of bound tracer antigens will accordingly decrease. The amount of

tracer can then be determined via a gamma counter after centrifuging and separating the

antibody bound antigens from the free tracers and counting either one or both fractions. A

standard curve is also set up with increasing concentrations of standard unlabelled antigen

and through interpolation, the amount of antigen in unknown samples can be determined.

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Thus, the 4 basic requirements for an RIA include an antiserum to the unknown antigen to be

measured; a radioactive form of the antigen; a method to separate the antibody bound tracer

from the unbound tracer; and finally, an available equipment to measure radioactivity.

2.12.1.1.1 Insulin Analysis:

The insulin RIA was carried out at Imperial College London Hammersmith Campus using

Millipore Human Insulin Specific RIA kit (HI-14K) (Millipore Corporation, Billerica, USA). This

assay measures ‘true’ insulin levels since it does not cross-react with Human Proinsulin

(<0.2%). Moreover, it utilizes 125I-labeled Human Insulin and a Human Insulin antiserum to

determine the level of Insulin in serum. The assay was performed according to the

manufacturer’s specifications and guidelines.

Gamma counter (LB2111 Multy Crystal Gamma Counter, Berthhold Technologies, Bad

Wildbad, Germany) was used for counting in this assay. Microsoft Excel Version 16.30 was

used to calculate the unknown insulin levels in participants’ samples.

If the CV% between the duplicate samples was >10%, the samples were either repeated or

great caution was taken in selecting the appropriate value. Indeed, a CV <10% is often

considered an acceptable value for insulin RIA (Besch et al., 1987, Deberg et al., 1998, Rasmi

et al., 2014).

The mean intra assay CV’s for the current three trials was <10%: Fasting Study: 7.9%; Post-

prandial study: 3.9%; and Exercise study: 1.0%.

2.12.1.1.2 GLP-1 Analysis:

GLP-1 was measured using a previously established in house specific and sensitive RIA

(Kreymann et al., 1987). The antibody used does not cross-react with other gut hormones

such as glucagon or GIP or pancreatic peptides.

66

GLP-1 was counted using Gamma counter (LB2111 Multy Crystal Gamma Counter, Berthhold

Technologies, Bad Wildbad, Germany). Microsoft Excel Version 16.30 was used to calculate

the unknown GLP-1 levels in participants’ samples.

If the CV% between the duplicate samples was >10%, the samples were either repeated or

caution was taken in selecting the appropriate value. Indeed, a CV% < 10% is often considered

an acceptable range for GLP-1 RIA (Näslund et al., 1999, Roberts et al., 2011) The mean intra-

assay CV of the post-prandial trial was 6%.

2.12.2 NMR Analysis:

2.12.2.1.1 Sample Preparation:

Serum samples were thawed, vortexed and remained at room temperature for 10 minutes.

Samples were then centrifuged at 18,000 g at 4°C for 10 min. A 300-μL aliquot of the

supernatant was mixed with 300 μL of 75 mM Na2HPO4 buffer (pH 7.4, LC-MS water/D2O

(80:20)), 2 mM sodium azide and 0.08% w/v of TSP (3-trimethylsilyl-[2,2,3,3,-2H4]-propionic

acid sodium salt).

2.12.2.1.2 1H-NMR spectroscopy:

Water-suppressed 1H NMR spectroscopy was performed at 310 K on a Bruker 600 MHz

Avance III HD spectrometer equipped with a 5-mm BBI Z-gradient probe and automated

tuning and matching (Bruker Biospin, Karlsruhe, Germany). The 1D 1H NMR spectra were

acquired using standard one-dimensional (1D) pulse sequence, with saturation of the water

resonance (noesygppr1d, standard Bruker pulse program) during both the relaxation delay

(RD = 4s) and mixing time (tm = 10ms). The two magnetic field z-gradients implemented by

this pulse sequence are applied for 1 ms, and the receiver gain was set to 90.5 and acquisition

time (ACQ) to 2.73s for all experiments. Each 1D 1H NMR spectrum was acquired using 4

dummy scans, 32 scans, 64K time domain points and with a spectral window of 20 ppm.

67

Following the acquisition of the 1D NOESY-presat, 1D CPMG with water suppression was

acquired using the Carr–Purcell–Meiboom–Gill pulse sequence (cpmgpr1d). The acquisition

parameters are set up in the same way as the 1D NOESY-presat, with the addition of the spin-

echo delay (D1/D2) that is set at 0.3 ms and the implementation of 128 loops for T2 filter (L4).

Prior to Fourier transformation of 1D experiments, free induction decays were multiplied by

an exponential function corresponding to a line broadening of 0.3 Hz (Dona et al., 2014).

2.13 Statistics:

2.13.1 Data analysis:

Participants in all three studies were excluded from data analysis if the coefficient of variation

(CV) of their baseline REE from the two study visits was >10%. In healthy adult humans, under

standard conditions following a 12 hour fast and abstinence from exercise, within subject REE

CV is approximately 3-8% (Donahoo et al., 2004). A CV >10%has been shown to be an indicator

of within machine variability or participants’ failure to comply with study protocol such as an

overnight fast and avoidance of strenuous physical activity (Adriaens et al., 2003).

The mean ± SEM (range) within-subject CV of baseline REE for the fasting trial (n=19) was 4.6

± 0.5 (0.8-8.4) %; Post-prandial trial (n=20):3.5 ±3.1 (0.1-11.8) %; Exercise trial (n=19): 3.4 ±

0.6 (0.1-8.6) %.

For energy expenditure and substrate oxidation measurements, the first 5 minutes of every

energy expenditure measurement, were discarded and the remaining 10 minutes were used

to calculate energy expenditure and substrate oxidation rates, in order to allow for subject

habituation to the testing procedure. During the one-hour exercise energy expenditure

measurement, the first 15 minutes were discarded to allow participants to reach a steady-

state. Data was then analysed from 195-210 min, 210-225 min and 225-240 min to compute

changes over time during exercise.

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The incremental area under the curve (iAUC) and the positive incremental area under the

curve (+iAUC) were calculated using the trapezoid rule while considering the area above

baseline for +iAUC. Normality was checked using the Shapiro-Wilk test and based on the

normality distribution, iAUC and +iAUC were analysed with either parametric t-tests or non-

parametric Wilcoxon signed rank tests. GraphPad Prison Version 8 was used for this analysis.

Repeated measures ANOVA was used to analyse time-course data using time and trial as

within subject variables. Post hoc Fishers LSD tests were then used when a significant

interaction (Trial x Time) was identified. IBM SPSS Statistics version 24 for Windows was

utilized for this statistical analysis. Data is presented as means ± standard error of mean (SEM)

unless stated otherwise and p<0.05 is considered significant. For baseline measures, the

average of the two baseline values was used during analysis. In cases of missing time-points,

the corresponding values from the other trial were used.

Normality for baseline measurements across the propionate and control visits was checked

using the Shapiro-Wilk test and based on the normality distribution, baseline values were

analysed with either parametric t-tests or non-parametric Wilcoxon signed rank tests. P<0.05

is considered significant.

2.13.2 NMR Spectral data analysis:

The multivariate data analysis was performed on the 1D 1H CPMG spectra. Each spectrum

(~21K spectral variables) was automatically phased and baseline corrected and then digitized

over the range δ -0.5 to 11 and imported into MATLAB (2014a, MathWorks, Natick, U.S.A.).

The spectral regions corresponding to the internal standard (δ -0.5 to 0.7), water (δ 4.3 to 5.2)

and noise (δ 8.5 to 11.00) were excluded. The spectra of plasma were referenced to the

doublet of the anomeric proton signal of a-glucose at δ 5.23 ppm. Prior to multivariate data

analysis, the spectra from both data sets were normalized using the probabilistic quotient

method (Dieterle et al., 2006).

The data set was auto-scaled and modelled using Partial Least Squares Discriminant Analysis

(PLS-DA) in a Monte-Carlo Cross-Validation (MCCV) framework using repeated measures (RM)

design. Storey-Tibshirani method was used for correction of multiple testing; variables with q

69

< 0.05 were considered as significant (Posma et al., 2018). The goodness of fit (R2Y) and the

goodness of prediction (Q2Y) were calculated using the training and the test data,

respectively.

2.13.2.1.1 Identification of Metabolites:

Subset optimization by reference matching (STORM) was used to identify metabolites using

the correlation structure of the 1D 1H CPMG data set (Posma et al., 2012). Internal and

external databases such as the Human Metabolome Data Base (HMDB; http://hmdb.ca/) or

the Biological Magnetic Resonance Data Bank (BMRB; http://www.bmrb.wisc.edu) were used

for confirmation of assignments.

2.14 Participant’s Characteristics:

2.14.1 Overnight Fasted Study:

31 participants were screened for eligibility. Of those, 5 were ineligible and 1 dropped out

before the start of the trial. The remaining 25 participants were fully eligible and commenced

with the trial. Three participants failed to attend both study visits. One other participant

dropped out due to failure in cannula insertion after several attempts. 21 participants thus

completed the two study visits. Of those, 2 participants were excluded from analysis since

their CV of their baseline REE from the two study visits was >10%. Thus, data was analysed

from the remaining 19 participants.

The following table represents participants’ (n=19) characteristics at screening:

Table 2-2: Overnight Fasted Study Participant Characteristics

Variable Value

Male n (%) 11 (57.9)

Age (y) 34.6 ± 4.1

Weight (kg) 67.0 ± 3.4

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BMI (kg/m2) 23.1 ± 0.7

Body Fat (%) 20.6 ± 1.8

*Values are presented as n (%) or means ± SEMs.

The following table represents baseline values of measurements in the Control and

Propionate trials (n=19):

Table 2-3: Overnight Fasted Study Participant Baseline Values

Control Propionate P-value

Body Weight

(kg)

68.0 ± 2.6 67.6 ± 2.7 0.4

REE (kcal/min) 1.012 ± 0.035 1.021 ± 0.039 0.610

RER (VCO2/VO2) 0.882 ± 0.019 0.870 ± 0.018 0.221

Lipid Oxidation

(g/min)

0.024 ± 0.007 0.029 ± 0.006 0.203

Carbohydrate Oxidation

(g/min)

0.145 ± 0.020 0.136 ± 0.019 0.153

Heart Rate

(bpm)

63.8 ± 1.955 65 ± 2.3 0.524

Mean Arterial Pressure

(mmHg)

76.9 ± 2.6 74.3 ± 2.4 0.033*

Glucose

(mmol/L)

4.41 ± 0.12 4.28 ± 0.09 0.062

Insulin

(µU/mL)

10.20 ± 0.86 9.41 ± 1.02 0.182

Hunger VAS

(mm)

31.2 ± 25.1 32 ± 25.0 0.868

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Thirst VAS

(mm)

27.2 ± 25.8 21.5 ± 25.2 0.148

Nausea VAS

(mm)

4.7 ± 9.1 6 ± 14.2 0.688

All data are expressed as mean ± SEM. Normality was checked using the Shapiro-Wilk test and based

on the normality distribution, baseline values were analysed with either parametric t-tests or non-

parametric Wilcoxon signed rank tests. *p<0.05 was considered significant. Abbreviations: REE:

resting energy expenditure; RER: respiratory exchange ratio.

2.14.2 Sub-maximal exercise Study:

30 participants were screened. Of those, two were ineligible and three withdrew from the

study after completion of visit one. 25 volunteers completed the three study visits. Six

volunteers were excluded during analysis as 4 had a CV>10% of the baseline REE between the

study visits and two had measurement errors during visits.


Table 2-4: Sub-maximal Study Participant Characteristics

Variable Value

Male n (%) 14 (73.7)

Age (y) 42.7 ± 3.5

Weight (kg) 73.2 ± 3.3

BMI (kg/m2) 24.5 ± 0.7

Body Fat (%) 21.8 ± 1.5#


#: This was calculated from n=18 since body fat (%) data collection from one volunteer was missed.

Table 2-5: Sub-maximal Study Participant Baseline Values

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Body Weight

(kg)

72.5 ± 2.9 72.5 ± 2.9 0.9

REE (kcal/min) 1.224 ± 0.050 1.248 ± 0.047 0.129

RER (VCO2/VO2) 0.824 ± 0.015 0.827 ± 0.012 0.798

Lipid Oxidation

(g/min)

0.054 ± 0.007 0.051 ± 0.006 0.671


(g/min)

0.103 ± 0.018 0.112 ± 0.017 0.768

Heart Rate

(bpm)

63.1 ± 4.2 63.4 ± 3.9 0.602


(mmHg)

80.9 ± 1.1 78.6 ± 1.1

0.164

Glucose

(mmol/L)

4.5 ± 0.1

4.6 ± 0.1

0.561

Insulin

(µU/mL)

6.1 ± 0.6

6.8 ± 0.8

0.353

Hunger VAS

(mm)

31.2 ± 25.1 43.6 ± 6.4 0.649

Thirst VAS

(mm)

29 ± 5.8 35.8 ± 5.7 0.198

Nausea VAS

(mm)

2.8 ± 1.4 3.4 ± 2.0

0.844



parametric Wilcoxon signed rank tests. *p<0.05 was considered significant. Abbreviations: REE:

resting energy expenditure; RER: respiratory exchange ratio.

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2.14.3 Post-Prandial Study:

27 volunteers were assessed for eligibility. 26 were deemed eligible. However, one participant

dropped out before the first study visit due to time conflicts. Of the remaining participants,

one volunteer dropped out during second study visit due to illness. Thus, 24 volunteers

completed the two study visits. Data was analysed from 20 volunteers, as one was excluded

from analysis due to having a CV >10% of the baseline REE from the two study visits, three

had technical errors during visits such as calorimeter or cannula failure.


Table 2-6: Post-prandial Study Participant Characteristics

Variable Value

Male n (%) 12 (60.0)

Age (y) 45.0 ± 3.5

Weight (kg) 71.7 ± 2.9

BMI (kg/m2) 24.8 ± 0.8

Body Fat (%) 24.2 ± 1.7


Table 2-7: Post-prandial Study Participant Baseline Values


Body Weight

(kg)

71.6 ± 2.9 70.8 ± 2.9 0.3

REE (kcal/min) 1.019 ± 0.046 1.011 ± 0.046 0.572

RER (VCO2/VO2) 0.867 ± 0.015 0.882 ± 0.015 0.354

Lipid Oxidation

(g/min)

0.030 ± 0.005 0.025 ± 0.006 0.409


(g/min)

0.132 ± 0.016 0.141 ± 0.015 0.946

74

Heart Rate

(bpm)

64.6 ± 2.6 67.9 ± 2.7 0.006 *


(mmHg)

82.6 ± 2.0 84.3±2.0 0.222

Glucose

(mmol/L)

4.62 ± 0.10 4.72 ± 0.10 0.220

Insulin

(µU/mL)

10.1 ± 1.1 10.5 ± 0.9 0.368

GLP-1 (pmol/L) 65.8 ± 8.4 57.1 ± 6.3 0.177

Hunger VAS

(mm)

36.3 ± 5.6 31.6 ± 6.4 0.475

Thirst VAS

(mm)

18.7 ± 4.4 19.8 ± 4.8 0.558

Nausea VAS

(mm)

3.4 ± 1.4 3.1 ± 2.2 0.171



parametric Wilcoxon signed rank tests. *p<0.05 was considered significant. Abbreviations: GLP-1:

glucagon like peptide 1; REE: resting energy expenditure; RER: respiratory exchange ratio.

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Chapter 3: Energy Expenditure and Substrate Oxidation

3.1 Abstract:

Background:

Earlier work in humans have demonstrated that acute ingestion of 71 mmol of sodium

propionate can increase energy expenditure and lipid oxidation in healthy human volunteers

over period of 180 min after an overnight fast. However, what is yet unknown is whether

these effects are sustained under different physiological states. Therefore, the aim of the

present trial is to investigate the effect of oral sodium propionate supplementation on energy

expenditure and substrate oxidation in all three energy states (overnight fasted, sub-maximal

exercise and post-prandial) and whether any effect is maintained over longer (>180 min) time

periods.

Methodology:

The trial consisted of three separate studies:

Overnight fasted study: 19 volunteers (11 males and 8 females; age: 34.6 ± 4.1 years; BMI



BMI: 24.5 ± 0.7 kg/m2) completed a maximal exercise test visit and two study visits.


± 0.8 kg/m2) completed two study visits.

Each of these studies was a randomized controlled double-blind cross-over study. In each

study, following an overnight fast, tablets containing either 6845mg sodium propionate or

4164mg sodium chloride (Control) were first administered over 180 min.

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Overnight Fasted study: The study extended over a total period of 360 min while volunteers

remained fasted for the duration of the study.

Sub-maximal exercise study: The study extended over a total period of 240 min. At time-

point 180 min, exercise was introduced where participants started cycling on a cycle

ergometer at 40% of VO2 max determined from their maximal exercise test visit for a period of

one hour.

Post-prandial study: The study extended over a total period of 300 min. At time-point 180

min, a mixed calorie liquid meal (Ensure Original Vanilla Nutrition Shake: 72.7 g carbohydrate,

13.6 g fat and 20.5 g protein; 500 kcal) was provided to volunteers.

Energy expenditure and substrate oxidation were measured throughout these visits using

indirect calorimetry. Heart rate (HR) and mean arterial pressure (MAP), as measures of

sympathetic nervous system (SNS) activity, were also recorded.

Results:

Overnight fasted study:

Oral sodium propionate supplementation increased energy expenditure (EE) (Control

=1.013± 0.034kcal/min; Propionate= 1.040 ± 0.039kcal/min; Effect of trial p=0.024) and lipid

oxidation (Control 0.126 ± 0.019 g/min vs 0.110 ± 0.019 g/min respectively; Effect of trial

p=0.039) and decreased carbohydrate (CHO) oxidation (Control= 0.032± 0.007g/min;

Propionate = 0.042 ± 0.007g/min; Effect of trial p=0.009) after an overnight fast over a period

of 360 min. Sodium propionate supplementation increased HR (Control= 62.1± 1.7 bpm;

Propionate= 64.9± 2.0 bpm; Effect of trial: p=0.030) over 360 min. MAP was also increased

with propionate supplementation during the last 180 min of the trial (iAUC180-360 min p=0.041).

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Sub-maximal exercise study:

Oral sodium propionate supplementation increased lipid oxidation at 180 min (Control= 0.062

± 0.007; Propionate= 0.078 ± 0.007; time x trial p=0.023) and decreased CHO oxidation at 120

min (Control= 0.092 ± 0.022; Propionate= 0.050 ± 0.017; time x trial p=0.041) in the overnight

fasted state with no effect on EE (Effect of trial: p=0.756), HR (Effect of trial: p=0.918)and MAP

(Effect of trial: p= 0.092). During sub-maximal exercise (Control: 48 ± 2 % ; Propionate: 47 ±

2% VO2max), propionate supplementation had no effect on EE (Effect of trial: p= 0.537), lipid

oxidation (Effect of trial: p=0.893), CHO oxidation (Effect of trial: p=0.762) and markers of

sympathetic nervous system activity (HR: Effect of trial: p=0.979; MAP: Effect of trial:

p=0.883).

Post-prandial study:

Oral sodium propionate supplementation increased EE (+iAUC0-300 min p=0.023), lipid oxidation

(+iAUC0-300 p=0.036) and HR (Control: 66.1bpm ± 2.4; Propionate: 68.6± 2.3 bpm; Effect of

trial: p=0.029) over of the 300 min study period. Changes in lipid oxidation were exclusively

observed during the initial 180 min overnight fasted state (+iAUC0-180 p= 0.036), whereas

changes in EE were observed post-prandially (iAUC180-300 min p=0.003 and +iAUC180-300 min

p=0.003).

Conclusion:

This was the first trial in humans to examine the acute ingestion of oral sodium propionate

(71 mmol) in healthy human volunteers in three separate energy states (overnight fasted,

sub-maximal exercise and post-prandial). Findings revealed that in the overnight fasted state,

propionate supplementation can increase energy expenditure and lipid oxidation and

decrease CHO oxidation which was allied to differences in SNS activity. Sodium propionate

also raised EE in the post-prandial state with no preferred use of a substrate. However, acute

oral sodium propionate ingestion had no effect on EE and substrate oxidation during sub-

maximal exercise. Future follow-on studies should use a 24-h respiratory chamber and a

continuous measurement of SNS activity to assess the full effect of oral sodium propionate

supplementation on energy expenditure and substrate oxidation. Furthermore, future studies

78

are warranted to determine whether the acute effects of oral sodium propionate on energy

metabolism are sustained following chronic supplementation.

3.2 Energy Expenditure: Definition and Measurement Tools

Total energy expenditure (TEE) is composed of three main components: basal metabolic rate

(BMR) or resting energy expenditure (REE), diet-induced thermogenesis (DIT) and physical

activity energy expenditure (PEE). BMR/REE comprise the majority of energy needs (60-80%)

and are often used interchangeably in the literature and clinical practice. BMR can be defined

as the minimum amount of energy required to maintain homeostatic processes such as

respiration and body temperature maintenance and must be measured under strict

conditions, such as, in a complete resting posture, post a >12-hour overnight fast, 24 hours

after exercise and with minimal lighting. REE, reflects energy needs at the time of

measurement (~10% higher than BMR).Fewer restrictions apply during REE measurements

where the subject may be in a supine or sitting position and measurements can be recorded

in post-prandial conditions (Gupta et al., 2017, Psota and Chen, 2013). DIT accounts for 8-12%

of TEE and can be defined as the rise in EE associated with meal ingestion and metabolism of

the food and is mainly affected by meal composition and energy content of the ingested food

(Psota and Chen, 2013).Finally, PEE is the energy expended during physical activity and is the

most variable component of TEE ranging from 10-30%, depending on the physical activity

status of the individual (Johnson and McKenzie, 2001, Psota and Chen, 2013).

Several methods exist to measure energy expenditure (EE), and the preferred method of use

relies mainly on the outcome measure, practicality and availability. Direct calorimetry (DC) is,

as the name implies, the direct measurement of heat exchange between the individual and

the environment via a calorimeter. As the heat produced by the body directly correlates with

EE and thus metabolic rate, DC allows the quantification of EE by measuring the simultaneous

measurement of heat produced by an organism. The direct calorimeter, often termed whole-

room calorimeter, involves placing an individual in a confined, insulated chamber that allows

measurement of heat exchange (Kenny et al., 2017). DC, however, has its drawbacks that

limits its use such as technical challenges, high cost of operation and maintenance, and

discomfort for subjects due to the restricted space available during measurements.

79

Moreover, DC cannot convey information on the type of substrate being oxidized for energy

metabolism (Kenny et al., 2017) (Johnson and McKenzie, 2001). EE can also be assessed via

gas exchange measurement, whereby a direct relationship exists between oxygen

consumption and heat liberation by the body that allows an ‘indirect’ yet highly accurate

estimation of EE (Kenny et al., 2017). Indirect calorimetry (IC) is indeed considered the ‘gold

standard’ in measuring EE in clinical research due to its more practical use and lower

equipment cost concomitant with technological advancement (Kenny et al., 2017). Different

equipment is available for measurement; however, the ventilated hood system is most often

used where a clear plastic hood is placed over a subject’s head in an airtight form and then

oxygen consumption (VO2) and carbon dioxide production (VCO2) are quantified. IC can also

provide an index for substrate utilization and can indicate if an individual is in a fasted or post-

prandial state via the ratio of CO2 produced to O2 consumed. This ratio, called the respiratory

exchange ratio (RER), can vary between 0.67 and 1.20 depending on the predominant

substrate metabolized. For instance, during carbohydrate oxidation, RER=1.0 since an equal

amount of CO2 is produced for O2 consumed, whereas during lipid oxidation this ratio drops

to 0.7 since less CO2 is produced for O2 consumed (Johnson and McKenzie, 2001). IC is not

without its limitations where it is vitally important during IC measurements that gas analysers

are appropriately calibrated and that the gases flowing into the analysers are exposed to the

same level of humidity, temperature, pressure and flow rate. Moreover, appropriate

education and training is required for personnel conducting the measurements to obtain

accurate results (Gupta et al., 2017). When comparing DC with IC, it is noteworthy that

discrepancies can occur since these methodologies differ in both experimental design and

since they measure different forms of energy i.e. DC measures heat dissipation while IC

measures mechanical/chemical/heat energy. Moreover, during rapid changes in metabolism

such as during dynamic exercise, DC lags behind IC since initial heat loss in that state is not

paralleled by the rapid increase in heat production. However, after this initial lag and with

exercise continuation, elevation in body temperature is no longer maintained and heat loss

would eventually equate to heat production. Thus, when tested under steady-state

conditions, DC and IC can yield coherent results (Webb et al. 1988) (Kenny et al., 2017).

Finally, predictive equations can also be used to estimate EE. The most commonly used are

Harris–Benedict, Mifflin–St Jeor, Owen and World Health Organization/ Food and Agriculture

Organization/United Nations University (WHO/FAO/UNU) equations. As each equation was

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derived based on a specific population and most often on normal weight individuals renders

them inappropriate in many clinical settings (Psota and Chen, 2013) . For instance, the Harris

and Benedict equation has been shown to overestimate REE by 5-13% in a population of

normal weight and obese adults in comparison to IC (Garrel et al., 1996). Also, in a diverse

and heterogeneous German population, the WHO equation has been shown to

underestimate EE at low REE and overestimate REE at high level REE (Müller et al., 2004).

Since the purpose of this research relies on the accurate measurement of both energy

expenditure and substrate oxidation in different energy states, indirect calorimetry was the

preferred method of choice and will be discussed in more detail in the next section.

3.2.1 Indirect Calorimetry

Humans extract the chemical energy stored in foods by oxidizing the available nutrients,

namely carbon-based fuels (carbohydrates (CHO), protein and lipid) into CO2 and water.

Therefore, the ultimate pathway for all biochemical reactions occurring in the body is either

combustion or synthesis of CHO, lipid and protein resulting in a specific ratio of VO2 and VCO2

that can be used to calculate REE. The chemical energy liberated from food is either lost as

heat via oxidation or stored in high-energy molecules such as adenosine triphosphate (ATP).

IC measures this energy by taking into account minute ventilation and calculating the

difference in O2 and CO2 content of inspired and expired air. Total EE is then computed using

the modified Weir equation (Mtaweh et al., 2018, Gupta et al., 2017):

EE (kcal/day) = ([VO2 × 3.941] + [VCO2 × 1.11] + [uN2 × 2.17]) ×1440

However, urinary nitrogen component (uN2) which conveys the contribution of protein

oxidation to TEE is often excluded from the equation since protein oxidation is relatively

constant in various physiological states i.e. fasting, post-prandial and during physical activity

(Melzer, 2011) contributing to ~20% of daily metabolic rate (Schutz, 2011) . Hence, the

following equation is more often used:

EE (kcal/day) = ([VO2 × 3.941] + [VCO2 × 1.11]) ×1440

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Carbohydrate and lipid oxidation rates can be estimated from stochiometric equations of

glucose and triacylglycerol oxidation respectively. As for protein oxidation rates, empirical

values are used and are based on urinary nitrogen excretion since most uN2 (>80%) is in the

form of urea (Frayn, 1983). The following equations are thus commonly used:

CHO = (4.55* VCO2) -(3.21*VO2) -(2.87* uN2)

Lipid = (1.67* VO2) -(1.67* VCO2) -(1.92* uN2)

Protein = uN2 * 6.25

A number of modalities are present for IC and can be summarized into the following

components (Gupta et al., 2017, Westerterp, 2017):

• Confinement Systems: Subjects are held in a sealed chamber and rates of gas

exchange in a fixed volume are monitored. A major limitation of this method is that it

only allows a short period of measurement before O2 depletion occurs.

• Closed-circuit systems: Subjects breathe from a pre-filled container (most often

composed of 100% O2) and the CO2 exhaled is absorbed by CO2 absorbers. The

quantity of O2 consumed is then quantified from the reduction in O2 volume.

Drawbacks include poor portability and possibility of increased work of breathing due

to an increase in compression on the breathing system. Also, RER cannot be

calculated.

• Total collection systems: Expired respiratory gases of subjects are collected and

analysed for volume and composition. The most common example of this system is

the Douglas bag method. However, shortcomings to this method include frequent gas

leaks and technical skills requirements.

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• Open circuit systems: This method is one of the oldest methods used in IC and is also

the one that is most currently used up to date. Metabolic carts are the standardized

equipment used and involve a gas collection system which can take the form of a

facemask, mouthpiece or a canopy; a flow rate measuring system; O2 and CO2 gas

analysers; calibration gases, environmental assessors for temperature, pressure and

humidity; and finally a central computer that can combine all measurement outcomes

and presents it to the user. Although this method is most commonly used, it is not

without some drawbacks. For instance, the use of a facemask or mouthpiece can elicit

increased work of breathing due to discomfort. Also, the canopy system can cause

claustrophobia and hyperventilation in some subjects which again can affect outcome

measures.

• Other methods: These include whole room calorimeters where EE can be measured

continuously for 24 hours or longer via VO2 and VCO2. Doubly labelled water is also

another method that allows EE measurement by VCO2. These two methods, however,

are often limited to specific research settings due to high cost, technical challenges

and time span. Other novel methods are now introduced which include portable

devices and heat sensors that can be used to assess PEE or REE. However, long term

data validating these methods is still lacking and need further study. A simpler and

more economical method that could be used for estimating EE ,especially in large

population studies, is via HR monitoring where in most circumstance, HR highly

correlates with VO2 and EE (Wicks et al., 2011, Keytel et al., 2005). Similarly, MAP can

be used as an estimate for REE due to the established link or strong association

between blood pressure and resting energy expenditure (Luke et al., 2004, Creber et

al., 2018)

Given the purpose of this research, the open circuit system (face mask in the sub-maximal

exercise trial and the canopy for the overnight fasted and post-prandial trial) was chosen as

detailed previously in the methods section. HR and MAP were also used as a proxy measure

to assess changes in EE related to stimulation in sympathetic nervous system (SNS) activity.

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3.3 Effect of Propionate on EE and Substrate Oxidation:

As mentioned earlier in Chapter 1:, propionate has been shown to have an effect on EE and

substrate oxidation in both rodent and human studies. For energy expenditure to increase,

there needs to be a parallel elevation in substrate oxidation, namely lipid and/or

carbohydrate oxidation to support the increase in energy metabolism. Indeed, findings have

suggested that propionate can increase energy expenditure while promoting lipid oxidation

(den Besten et al., 2015, Chambers et al., 2018, Canfora et al., 2017).

3.3.1 Effect of Propionate on EE and Substrate in Rodents:

Only one rodent study has examined the direct chronic effect of oral propionate

supplementation on energy expenditure. Den Besten et al., has shown that when propionate

is incorporated to a high lipid diet (HLD) at 5% (w/w) for 12 weeks, mice were protected from

diet-induced obesity via increases in energy expenditure and a switch from carbohydrate

oxidation to lipid oxidation, as verified by a lower RER, in contrast to mice who were on an

HFD only. This change in metabolism was also maintained in both 12 h light and dark phases,

so whether the mice were engaging in physical activity, rested or post-prandial (den Besten

et al., 2015). Another rodent study has investigated the acute effect of propionate on energy

expenditure via intraperitoneal injection at a supraphysiological rate of 1g/kg BW. Oxygen

consumption and thus energy expenditure was significantly higher after propionate

supplementation in WT mice versus FFAR-3 KO mice. Moreover, HR was also significantly

increased in WT mice albeit KOs. Physical activity however was comparable between WT and

KO mice which hence indicates that the observed metabolic changes with propionate

administration were mediated via FFAR-3 dependent SNS activation

3.3.2 Effect of Propionate on EE and Substrate in Humans:

A single human study has investigated the acute effect of propionate on energy expenditure

and substrate oxidation via oral supplementation. In 18 healthy males (n=9) and female (n=9)

subjects, Chambers et al., has shown that acute oral sodium propionate supplementation (71

mmol) over 180 time period can increase resting energy expenditure and resting lipid

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oxidation in comparison to a sodium chloride control. (Chambers et al., 2018). Peripheral

propionate concentrations, however, only became significantly increased at 180 min and thus

the total impact of propionate on energy metabolism may not have been fully measured in

this study. Meanwhile, Canfora et al. examined the acute effect of propionate administration

on energy expenditure and substrate oxidation via rectal infusion to the gut in 12

normoglycemic adult men (Canfora et al., 2017). Using a high propionate SCFA infusion (18

mmol sodium acetate (45%), 14 mmol sodium propionate (35%), 8 mmol sodium butyrate

(20%) dissolved in 200 mL sterile water)), Canfora et al. have shown an increase in energy

expenditure and lipid oxidation and a decrease in CHO oxidation in the rested fasted state but

not post-prandially after an oral glucose load (75g). However, due to the infusion of all three

principal SCFAs, it is not possible to attribute the changes in energy metabolism specifically

to propionate.

Thus, it appears that increasing propionate bioavailability in both animals and humans can

increase energy expenditure while promoting lipid oxidation. However, a gap remains in the

literature on the acute effect of oral supplementation of propionate on energy expenditure

and substrate oxidation in different energy states and also how long the metabolic effect of

propionate can persist in these states.

3.4 Hypothesis:

I hypothesised that acute oral intake of sodium propionate would increase energy

expenditure and rates of lipid oxidation in different physiological states (overnight fasted,

sub-maximal exercise and post-prandial) and would modulate energy production pathways

to support increases in energy expenditure and lipid oxidation.

3.5 Aims:

This chapter will aim to present and discuss findings related to determining the acute effect

of propionate bioavailability on energy metabolism and substrate oxidation in different

energy states (overnight fasted, sub-maximal exercise and post-prandial). It will also examine

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how long the metabolic effect of propionate can persist in these states and if SNS activity,

measured through HR and MAP, is raised following propionate supplementation.

3.6 Outcome Measures:

The primary outcome measures of the trial were changes in energy expenditure and rates of

lipid oxidation via indirect calorimetry. Secondary measures included effects on sympathetic

nervous system activity measured through heart rate and mean arterial pressure.

3.7 Methods:

Please refer to Chapter 2:

3.8 Results:

3.8.1 Overnight fasted Trial:

3.8.1.1.1 Energy Expenditure:

Energy expenditure between 0-360 min was significantly higher in the Propionate trial in

comparison to Control (Control =1.013± 0.034kcal/min; Propionate= 1.040 ± 0.039kcal/min;

Effect of trial p=0.024) (Figure 3:1: A). Similarly, in the Propionate trial, iAUC0-360 min and

+iAUC0-360 min was significantly higher in comparison to the Control (p= 0.050; p=0.007) (Figure

3:1: B and C) respectively.

iAUC0-180 min and +iAUC0-180 min in the Propionate trial was significantly higher than in the

Control trial (p= 0.026; p=0.013) respectively (Figure 3:1: D and E). However, there was no

significant difference in iAUC180-360 min and +iAUC180-360 min between trials (Control: p= 0.154;

Propionate: p=0.083) (Figure 3:1: F and G).

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A

B

C

87

D

E

F

G

Figure 3-1: Overnight Fasted Study: Effect of oral sodium propionate supplementation on EE

The effect of oral sodium propionate supplementation on energy expenditure A. Overnight fasted energy

expenditure (Time×Trial: p= 0.377, Trial: p=0.024, Time: p= 0.248) and B. Overnight fasted energy expenditure

iAUC0-360 min (p= 0.050). C. Overnight fasted energy expenditure +iAUC0-360 min (p= 0.007). D. Overnight fasted

energy expenditure iAUC0-180 min (p= 0.026) E. +iAUC0-180 min (p=0.013). E. iAUC180-360 min (p= 0.154). F. +iAUC180-

360 min (p=0.083). All data expressed as mean ± SEM (n=19).

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3.8.1.1.2 RER:

RER between 0-360 min was significantly higher in the Control trial (Control= 0.862 ± 0.018;

Propionate= 0.839 ± 0.018; Effect of trial p=0.018) (Figure 3:2: A). RER iAUC0-360 and +iAUC0-

360 ,however, were not significantly different between trials (p= 0.100; p=0.473) (Figure 3:2: B

and C).

Similarly, iAUC0-180 min and +iAUC0-180 min were not significantly different between trials (p=

0.095; p=0.331) respectively (Figure 3:2: D and E). Also, iAUC180-360 min and +iAUC180-360 min were

not significantly different between trials (p= 0.184; p=0.652) (Figure 2: F and G) respectively.

A

89

B

C

D

E

90

F

G

Figure 3-2 Overnight Fasted Study: Effect of oral sodium propionate supplementation on RER The effect of oral sodium propionate supplementation on respiratory exchange ratio A. RER (Time×Trial:

p=0.737; Trial: p=0.018; Time: p= 0.000) and B. RER iAUC0-360min (p=0.100). C. RER +iAUC0-360 (p=0.473). D. RER

iAUC 0-180 min (p=0.095). E. RER +iAUC 0-180 min (p=0.331). F. RER iAUC 180-360 min (p=0.184). G. RER +iAUC 180-360 min

(p=0.652). All data expressed as mean ± SEM (n=19).

3.8.1.1.3 Lipid Oxidation:

Lipid oxidation between 0-360 min was significantly higher in the Propionate trial (Control=

0.032± 0.007g/min; Propionate = 0.042 ± 0.007g/min; Effect of trial p=0.009) (Figure 3:3 A).

Similarly, iAUC0-360 min and +iAUC0-360 min were significantly higher in the Propionate trial (p=

0.035; p=0.046) respectively (Figure 3:3 B and C).

iAUC0-180 min and +iAUC0-180 were significantly higher in the Propionate trial (p= 0.021; p=0.016)

respectively (Figure 3: 3 D and E). iAUC180-360 min and +iAUC180-360min were not significantly

different between trials (p= 0.113; p=0.185) (Figure 3: 3 F and G) respectively.

0.035; p=0.046) respectively (Figure 3:3 B and C).

iAUC0-180 min and +iAUC0-180 were significantly higher in the Propionate trial (p= 0.021; p=0.016)

respectively (Figure 3: 3 D and E). iAUC180-360 min and +iAUC180-360min were not significantly

different between trials (p= 0.113; p=0.185) (Figure 3: 3 F and G) respectively.

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A

B

C

92

Figure 3-3: Overnight Fasted Study: Effect of oral sodium propionate supplementation on lipid oxidation

The effect of oral sodium propionate supplementation on lipid oxidation A. Lipid oxidation (Time×Trial: p=0.506;

Trial: p=0.009 Time: p= 0.000) and B. Lipid oxidation iAUC0-360 min (p= 0.035). C. Lipid oxidation +iAUC0-360 (p=

0.046). D. Lipid oxidation iAUC0-180 min (p= 0.021). E. Lipid oxidation +iAUC0-180 (p= 0.016). F. Lipid oxidation

iAUC180-360 min (p= 0.113). G. Lipid oxidation +iAUC180-360min (p=0.185). All data expressed as mean ± SEM (n=19).

D

E

F

G

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3.8.1.1.4 Carbohydrate Oxidation:

Carbohydrate oxidation rates between 0-360 min was significantly lower in the Propionate

trial in comparison to the Control (0.126 ± 0.019 g/min vs 0.110 ± 0.019 g/min respectively;

Effect of trial p=0.039) (Figure 3:4 A). iAUC0-360 min and +iAUC0-360 min were not significantly

different between trials (p= 0.679; p=0.275) respectively (Figure 3:4: B and C).

iAUC0-180 min and + iAUC0-180 were not significantly different between groups (p= 0.189; p=

0.266) (Figure 3:4: D and E). Similarly, iAUC180-360 min and + iAUC180-360 min were not significantly

different between Propionate and Control (p= 0.426; p= >0.999) (Figure 3:4: F and G).

A

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B

C

D

E

95

F

G

Figure 3-4: Overnight Fasted Study: Effect of oral sodium propionate supplementation on CHO oxidation: The effect of oral sodium propionate supplementation on carbohydrate oxidation A.

Carbohydrate oxidation (Time×Trial: p=0.949; Trial: p=0.039; Time: p= 0.000) and B.

Carbohydrate oxidation iAUC 0-360 min (p= 0.679). C. Carbohydrate oxidation +iAUC 0-360 min (p=

0.275) D. Carbohydrate oxidation iAUC 0-180 min (p= 0.189) E. carbohydrate oxidation + iAUC 0-

180min (p= 0.266) F. Carbohydrate oxidation iAUC 180-360 min (p= 0.426) G. Carbohydrate

oxidation + iAUC 180-360 min (p>0.999). All data expressed as mean ± SEM (n=19).

3.8.1.1.5 Protein Oxidation:

Protein oxidation between 0-360 min was similar between trials (Control Trial= 0.053 ± 0.004

g/min; Propionate Trial= 0.053 ± 0.004 g/min; p= 0.744).

3.8.1.1.6 Heart Rate:

HR between 0-360 minutes was significantly higher in the Propionate trial in comparison to

the Control (Control= 62.1± 1.7 bpm; Propionate= 64.9± 2.0 bpm; Effect of trial: p=0.030)

(Figure 3:5: A). iAUC0-360 min and +iAUC 0-360 min, however, were similar between trials (p=0.192;

p=0.057) respectively (Figure 3:5: B and C).

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iAUC0-180 min was not significantly different between the two trials (p=0.113) respectively

(Figure 3:5: D). +iAUC 0-180 however, was significantly higher in the Propionate trial in

comparison to Control (p=0.038) (Figure 3:5: E). HR iAUC180-360 min and +iAUC 180-360 however

were not significantly different between the two trials (p=0.385; p= 0.504) respectively

(Figure 3:5: F and G).

A

B

C

97

D

E

F

G

Figure 3-5: Overnight Fasted Study: Effect of oral sodium propionate supplementation on HR The effect of oral sodium propionate supplementation on Heart Rate A. HR (Time×Trial: p=0.281; Trial: p=0.030;

Time: p= 0.008) and B. HR iAUC0-360 min (p=0.192). C. HR +iAUC 0-360 min (p=0.057); D. HR iAUC0-180 min (p=0.113) E.

HR +iAUC 0-180 min (p=0.038) F. HR iAUC180-360 min (p=0.385) G. HR +iAUC 180-360 min (p=0.504) All data expressed

as mean ± SEM (n=19).

3.8.1.1.7 Mean Arterial Pressure (MAP):

MAP between 0-360 minutes was not significantly different between trials (Control= 77.1 ±2.1

mmHg; Propionate= 76.1± 2.0 mmHg; p=0.180) (Figure 3:6: A). iAUC0-360 min and +iAUC 0-360 min

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MAP were not significantly different between the trials (p=0.122; p=0.306) (Figure 3:6: B and

C)

iAUC 0-180 min and +iAUC 0-180 min MAP were not significantly different between the trials

(p=0.501; p=0.548) (Figure 3:6: D and E). iAUC 180-360 min was significantly higher in the

Propionate trial (p=0.041) (Figure 3:6: F). +iAUC 180-360 min, however, was not significantly

different between trials (p=0.248) (Figure 3:6: G).

A

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B

C

D

E

100

F

G

Figure 3-6: Overnight Fasted Study: Effect of oral sodium propionate supplementation on MAP The effect of oral sodium propionate supplementation on Blood Pressure A. MAP (Time×Trial: p=0.197; Trial:

p=0.180; Time: p= 0.076) and B. MAP iAUC0-360 min (p=0.122). C. MAP +iAUC 0-360 min (p=0.306). D. MAP iAUC0-180

min (p=0.501). E. MAP +iAUC 0-180 (p=0.548). F. MAP iAUC180-360 min (p=0.041). G. MAP +iAUC180-360 min (p=0.248). All

data expressed as mean ± SEM (n=19).

3.8.2 Sub-maximal Exercise Trial:


Resting energy expenditure was similar in both trials (Control =1.239 ± 0.048 kcal/min;

Propionate= 1.245 ± 0.050 kcal/min; Effect of trial p=0.756) (Figure 3:7: A). Similarly, iAUC0-

180 min and +iAUC0-180 min showed no significant differences between the two trials (p= 0.535;


Volunteers were exercising at 48 ± 2 % and 47 ± 2% of their VO2max in the Control and

Propionate trials, respectively. Energy expenditure throughout exercise was comparable

between trials (Control=5.085 ± 0.330 kcal/min; Propionate= 4.991± 0.328 kcal/min; Effect of

trial p= 0.537) (Figure 3:7: A). iAUC195-240 min and +iAUC195-240 min were also similar (p= 0.311;

p=0.311) (Figure 3:7: D and E).

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A

B

C

102

D

E

Figure 3-7: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on EE The effect of oral sodium propionate supplementation on energy expenditure A. Resting energy expenditure

(Time×Trial: p= 0.281; Trial: p=0.756; Time: p= 0.544) and Energy Expenditure during exercise (Time×Trial: p=

0.537) (Time: p= 0.000) B. Resting energy expenditure iAUC0-180 min (p= 0.535). C. Resting energy expenditure

+iAUC0-180 min (p= 0.389). D. Energy expenditure during exercise iAUC195-240 min (p= 0.311). E. Energy expenditure

during exercise iAUC195-240 min (p= 0.311) All data expressed as mean ± SEM (n=19).

3.8.2.1.2 RER:

Resting RER was comparable between the Control and Propionate trials (Control= 0.814 ±

0.015; Propionate= 0.795 ± 0.012; Effect of trial p=0.186). However, there was a significant

Time x Trial interaction (p=0.015) with post hoc analysis revealing a significant difference at

180 min (Control= 0.807 ± 0.015; Propionate= 0.773 ± 0.015; p=0.040) (Figure 3:8: A). iAUC0-

180 min for the resting period was also significantly lower in the Propionate trial (p= 0.001)

(Figure 3:8: B). However, +iAUC0-180 min was not significantly different between trials

(p=0.233) (Figure 3:8: C).

RER throughout exercise was not significantly different (Effect of trial p=0.593) between the

Control (0.843 ± 0.015) and the Propionate (0.835 ± 0.010) trial. RER iAUC195-240 and +iAUC180-

240 were not significantly different between the trials (p= 0.189; p=0.542) (Figure 3:8: D and

E).

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A

B

C

104

D

E

Figure 3-8: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on RER

The effect of oral sodium propionate supplementation on respiratory exchange ratio A. Resting RER (Time×Trial:

p=0.015; Trial: p=0.186; Time: p= 0.000) and RER during exercise (Time×Trial: p= 0.537) B. Resting RER iAUC0-180

min (p= 0.001). C. Resting RER +iAUC0-180 min (p=0.233). D. RER during exercise iAUC195-240 (p= 0.189). E. RER during

exercise +iAUC195-240 (p= 0.542). All data expressed as mean ± SEM (n=19).


Resting lipid oxidation was not significantly different between the two trials (Control= 0.059

± 0.007 g/min; Propionate= 0.066 ± 0.006 g/min; Effect of trial p=0.288). However, there was

a significant Time x Trial interaction (p=0.023) with post hoc analysis revealing a significant

difference at 180 min (Control= 0.062 ± 0.007; Propionate= 0.078 ± 0.007) (Figure 3:9: A).

iAUC0-180 min and +iAUC0-180 for the resting period was significantly higher in the Propionate

trial in comparison to control (p= 0.001; p= 0.007) respectively (Figure 3:9: B and C).

Lipid oxidation during exercise was not significantly different between the trials (Control=

0.284 ± 0.032 g/min; Propionate = 0.287 ± 0.030 g/min; Effect of trial p=0.893) (Figure 3:9: A)

and iAUC195-240 min and +iAUC180-240 were not significantly different (p= 0.712; p=0.775)

respectively (Figure 3:9: D and E).

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A

B

C

106

D

E

Figure 3-9: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on lipid oxidation

The effect of oral sodium propionate supplementation on lipid oxidation A. Resting lipid oxidation (Time×Trial:

p=0.040; Trial: p=0.288; Time: p= 0.000) and Lipid oxidation during exercise (Time×Trial: p=0.523); (Time: p=

0.000) B. Resting lipid oxidation iAUC0-180 min (p= 0.001). C. Resting lipid oxidation +iAUC0-180 min (p= 0.007). D.

Lipid oxidation during exercise iAUC195-240 min (p= 0.712). E. Lipid oxidation during exercise +iAUC195-240 (p= 0.775).

All data expressed as mean ± SEM (n=19).


Resting carbohydrate oxidation rates were comparable between trials (Control= 0.094 ± 0.018

g/min vs Propionate=0.073 ± 0.017 g/min; Effect of trial p=0.219), but there was a significant

Time x Trial interaction (p=0.032) at timepoint 120 min (Control= 0.092 ± 0.022; Propionate=

0.050 ± 0.017; p=0.041) (Figure 3: 10: A). iAUC0-180 min for the resting period was significantly

lower in the Propionate trial in comparison to Control (p<0.001) (Figure 3:10: B). This

coincides with no significant change in +iAUC0-180 for resting CHO oxidation between trials (p=

0.278) (Figure 3: 10: C).

Carbohydrate oxidation rates during exercise was not significantly different between Control

and the Propionate (0.626 ± 0.062 g/min vs 0.594 ± 0.058 g/min respectively; Effect of trial

p=0.762) (Figure 3: 10: A) and iAUC195-240 min and +iAUC195-240 were not different between trials

(p=0.821; p=0.860) respectively) (Figure 3:10: D and E).

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A

B

C

108

D

E

Figure 3-10: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on carbohydrate oxidation The effect of oral sodium propionate supplementation on carbohydrate oxidation A. Resting carbohydrate

oxidation (Time×Trial: p=0.032); (Time: p= 0.000) and Carbohydrate oxidation during exercise (Time×Trial:

p=0.845; Time: p=0.219; Time: p= 0.479) B. Resting carbohydrate oxidation iAUC0-180 min (p<0.001). C. Resting

carbohydrate oxidation +iAUC0-180 min (p= 0.278) D. Carbohydrate oxidation during exercise iAUC195-240 min (p=

0.821). E. Carbohydrate oxidation during exercise +iAUC195-240 min (p= 0.860). All data expressed as mean ± SEM

(n=19).



g/min; Propionate Trial= 0.078 ± 0.009g/min; p= 0.622).


Resting HR was similar between trials (Control: 59.4 bpm ± 2.400; Propionate: 59.6 ± 2.8 bpm;

Effect of trial: p=0.918) (Figure 3:11: A) and resting HR iAUC0-180 min and +iAUC 0-180 were also

not statistically significant between the two trials (p=0.640; p=0.380) (Figure 3:11: B and C),

respectively.

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HR during exercise was not significantly different between the two trials (Control= 95.7 ±

3.1bpm; Propionate= 95.7 ± 3.4 bpm; Effect of trial= p=0.979) (Figure 3:11: A ).iAUC180-240 and

+iAUC 180-240 were also similar between trials (p=0.890; p=0.963) respectively (Figure 3: 11: D

and E).

A

B

C

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D

E

Figure 3-11: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on HR

The effect of oral sodium propionate supplementation on Heart Rate A. Resting HR (Time×Trial: p=0.428; Trial:

p=0.918; Time: p= 0.061) and HR during exercise (Time×Trial: p=0.159) B. Resting HR iAUC0-180 min (p=0.640). C.

Resting HR +iAUC 0-180 (p=0.380) D. HR concentration during exercise iAUC180-240 (p=0.890). E. HR concentration

during exercise +iAUC180-240 (p=0.963). All data expressed as mean ± SEM (n=19).

3.8.2.1.7 Mean Arterial Pressure:

Resting mean arterial blood pressure (MAP) was not significantly different between the

Control and the Propionate trials (Control= 85.2 ± 2.0 mmHg; Propionate=83.4 ± 1.9 mmHg;

Effect of trial: p= 0.092) (Figure 3:12: A) and iAUC0-180 min and +iAUC 0-180 MAP were not

statistically different between the trials (p=0.840; p=0.734) (Figure 3:12: B and C).

MAP following exercise was not significantly different between trials (Control= 88.7 ± 2.7

mmHg; Propionate= 89.1 ± 2.6 mmHg; p=0.883) (Figure 3:12: A). iAUC180-240 min and +iAUC

180-240 MAP were not statistically different between the trials (p=0.502; p=0.700) (Figure 3:12:

D and E).

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A

B

C

112

D

E

Figure 3-12: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on MAP The effect of oral sodium propionate supplementation on Blood Pressure A. Resting MAP (Time×Trial:

p=0.596; Trial: p= 0.092; Time: p= 0.023) and BP during exercise (p=0.883) B. Resting MAP iAUC 0-180

(p=0.840). C. Resting MAP +iAUC 0-180 (p=0.734). D. BP iAUC180-240 min during exercise (p=0.502). E. MAP

+iAUC180-240 min during exercise (p=0.700). All data expressed as mean ± SEM (n=19).

3.8.3 Post-prandial Trial:


Overall energy expenditure between 0-300 min was similar in both trials (Control =1.057 ±

0.044 kcal/min; Propionate= 1.072± 0.046 kcal/min; Effect of trial p=0.289).However, there

was a significant Time x Trial interaction (p=0.010) with post hoc analysis revealing a

significant difference at time-point 300 min (p=0.026). (Figure 3:13: A). iAUC0-300 min was not

significantly different between trials (p=0.063). However, +iAUC0-300 min was significantly

higher in the Propionate trial (p=0.023) (Figure 3:13: B and C).

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Resting iAUC0-180 min and +iAUC0-180 min showed no significant differences between the two

trials (p= 0.219; p=0.064) respectively (Figure 3:13: D and E). Post-prandial iAUC180-300 min and

+iAUC180-300 min were significantly higher in the Propionate trial (p=0.003; p=0.003) (Figure

3:13: F and G).

A

B

C

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D

E

F

G

Figure 3-13: Post-prandial Study: Effect of oral sodium propionate supplementation on EE The effect of oral sodium propionate supplementation on energy expenditure A. Energy expenditure

(Time×Trial: p= 0.010; Trial: p=0.289; Time: p= 0.000) and B. Energy expenditure iAUC 0-300 min (p= 0.063). C.

Energy expenditure +iAUC0-300 min (p= 0.023). D. Overnight fasted energy expenditure iAUC0-180 min (p= 0.219) E.

Overnight fasted energy expenditure +iAUC0-180 min (p=0.064). F. Post-prandial iAUC180-300 min (p= 0.003). G. Post-

prandial +iAUC180-300 min (p=0.003). All data expressed as mean ± SEM (n=19).

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3.8.3.1.2 RER:

RER between 0-300 min was comparable between the Control and Propionate trials

(Control= 0.881± 0.012; Propionate= 0.887± 0.011; Effect of trial p=0.581) (Figure 3:14: A).

RER iAUC0-300min and +iAUC0-300min was not significantly different between trials (p=0.232;

p=0.610) (Figure 3:14: B and C).

iAUC0-180 min and +iAUC0-180 min for the overnight fasted period were not significantly different

between trials (p= 0.202; p=0.946) respectively (Figure 3:14: D and E). RER iAUC180-300 min and

+iAUC180-300 min were not significantly different between the trials (p=0.676; p=0.417) (Figure

3:14: F and G).

A

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B

C

D

E

117

F

G

Figure 3-14: Post-prandial Study: Effect of oral sodium propionate supplementation on RER The effect of oral sodium propionate supplementation on respiratory exchange ratio A. RER (Time×Trial:

p=0.733; Trial: p=0.581; Time: p= 0.000) and B. RER iAUC0-300 (p=0.232). C. RER +iAUC0-300 (p=0.610). D. Overnight

fasted RER iAUC0-180 min (p=0.202). E. Overnight fasted RER +iAUC0-180 min (p=0.946). F. Overnight fasted RER

iAUC180-300 min (p=0.576). G. Overnight fasted RER +iAUC180-300 min (p=0.417). All data expressed as mean ± SEM

(n=19).


Lipid oxidation between 0-300 min was not significantly different between the two trials

(Control= 0.027 ± 0.005 g/min; Propionate= 0.025 ± 0.005 g/min; Effect of trial p=0.668)

(Figure 3:15: A). Also, iAUC0-300 min was not significantly different between trials (p=0.186)

(Figure 3:15: B). However, +iAUC0-300 was significantly higher in the Propionate trial (p=0.036)

(Figure 3:15: C).

iAUC0-180 min was not significantly higher between groups (p=0.177). However, +iAUC0-180 for

the overnight fasted period was significantly higher in the Propionate trial in comparison to

control (p= 0.036) respectively (Figure 3:15: D and E). Post-prandial iAUC180-300 min and

+iAUC180-300 for lipid oxidation were not significantly different (p= 0.706; p=0.765) respectively

(Figure 3:15: F and G).

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A

B

C

119

D

E

F

G

Figure 3-15: Post-prandial Study: Effect of oral sodium propionate supplementation on lipid oxidation

The effect of oral sodium propionate supplementation on lipid oxidation A. Lipid oxidation (Time×Trial: p=0 .645;

Trial: p=0.668; Time: p= 0.000) and B. Lipid oxidation iAUC0-300 min (p= 0.186). C. Lipid oxidation +iAUC0-300 (p=

0.036). D. Overnight fasted lipid oxidation iAUC0-180 min (p= 0.177). E. Overnight fasted lipid oxidation +iAUC0-180

(p= 0.036). F. Post-prandial lipid oxidation iAUC180-300 min (p= 0.706). G. Post-prandial lipid oxidation +iAUC180-

300min (p=0.765). All data expressed as mean ± SEM (n=19).

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Carbohydrate oxidation rates between 0-300 min were comparable between trials (Control=

0.151± 0.013 g/min vs Propionate=0.157± 0.010 g/min; Effect of trial p=0.527) (Figure 3:16:

A). iAUC0-300 min and +iAUC0-300 were not significantly different between trials (p=0.571;


iAUC0-180 min and + iAUC0-180 for the overnight fasted were similar between the Propionate and

Control trial (p= 0.294; p>0.999) (Figure 3:16: D and E). Also, iAUC180-300 min and +iAUC180-300

were not significantly different between trials (p=0.612; p=0.734) respectively (Figure 3:16: F

and G).

A

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B

C

D

E

122

F

G

Figure 3-16: Post-prandial Study: Effect of oral sodium propionate supplementation on carbohydrate oxidation

The effect of oral sodium propionate supplementation on carbohydrate oxidation A. Carbohydrate oxidation

(Time×Trial: p=0.452; Trial: p=0.527; Time: p= 0.000) and B. Carbohydrate oxidation iAUC0-300 min (p= 0.571). C.

Carbohydrate oxidation +iAUC0-300 min (p= 0.953) D. Overnight fasted carbohydrate oxidation iAUC0-180 min (p=

0.294) E. Overnight fasted carbohydrate oxidation + iAUC0-180 (p>0.999) F. Post-prandial carbohydrate oxidation

iAUC180-300 min (p= 0.612) G. Post-prandial carbohydrate oxidation + iAUC180-300 min (p=0.734). All data expressed

as mean ± SEM (n=19).



g/min; Propionate Trial= 0.055 ± 0.005 g/min; p=0.425).


HR between 0-300 min was significantly higher in the Propionate trial (Control: 66.1bpm ±

2.4; B: 68.6± 2.3 bpm; Effect of trial: p=0.029) (Figure 3:17: A). However, baseline HR was

significantly different between trials (p= 0.006). Overnigth fasted HR iAUC0-180 min and +iAUC 0-

180 were not statistically significant between the two trials (p=0.735; p=0.946) respectively

(Figure 3:17: B and C).

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HR iAUC0-300 and +iAUC 0-300 were not significantly different between the two groups

(p=0.502; p=0.200) respectively (Figure 3:17: D and E). Post-prandial HR iAUC180-300 was

similar between trials (p=0.254). However, +iAUC 180-300 was significantly higher in the Control

trial in comparison to the Propionate (p=0.017) (Figure 3:17: F and G).

A

B

C

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D

E

F

G

Figure 3-17: Post-prandial Study: Effect of oral sodium propionate supplementation on HR

The effect of oral sodium propionate supplementation on Heart Rate A. HR (Time×Trial: p=0 .681; Trial: p=0.029;

Time: p= 0.000) and B. HR iAUC0-300 min (p=0.735). C. HR +iAUC 0-300 min (p=0.946); D. Overnight fasted HR iAUC0-

180 min (p=0.502) E. Overnight fasted HR +iAUC 0-180 (p=0.200) F. Post-prandial HR iAUC180-300 min (p=0.254) G.

Post-prandial HR +iAUC 180-300 (p=0.017). All data expressed as mean ± SEM (n=19).

3.8.3.1.7 MAP:

Mean arterial blood pressure (MAP) between 0-300 min was not significantly different

between the Control and the Propionate trials (Control= 82.9± 2.0 mmHg; Propionate=83.7±

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1.7 mmHg; Effect of trial: p= 0.320) (Figure 3:18: A). iAUC0-300 min and +iAUC 0-300 were not

significantly different between trials (p=0.411; p=0.800) respectively (Figure 3:18: B and C).

iAUC0-180 min and +iAUC 0-180 MAP were not statistically different between the trials (p=0.412;

p=0.865) (Figure 3;18: D and E). Post-prandial iAUC180-300 min and +iAUC 180-300 were not

significantly different between trials (p= 0.367; p>0.999) (Figure 3:18: F and G) respectively.

A

B

C

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D

E

F

G

Figure 3-18: Post-prandial Study: Effect of oral sodium propionate supplementation on MAP

The effect of oral sodium propionate supplementation on Blood Pressure A. MAP (Time×Trial: p=0.864; Trial: p=

0.320; Time: p= 0.004) B. MAP iAUC0-300 min (p=0.411). C. MAP +iAUC 0-300 min (p=0.800). BP iAUC0-180 min (p=0.412).

D. Overnight fasted MAP +iAUC 0-180 (p=0.865). E. Post-prandial MAP iAUC180-300 min (p=0.367). F. Post-prandial

MAP +iAUC180-300 min (p>0.999). All data expressed as mean ± SEM (n=19).

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3.9 Key Findings:


• Overnight fasted state: Propionate increased EE and lipid oxidation and decreased

CHO oxidation over a period of 360 min. Changes in EE and lipid oxidation are mainly

seen within the first 180 min following propionate ingestion. HR can similarly be

stimulated within the first 180 min of propionate ingestion. MAP also increased with

propionate supplementation after a prolonged fast.

3.9.2 Sub-maximal exercise Trial:

• Overnight fasted state: Propionate increased lipid oxidation and decreased CHO

oxidation after an overnight fasted state with no effect on energy expenditure.

• Submaximal exercise state: Propionate had no effect on EE and substrate oxidation

during sub-maximal exercise.

3.9.3 Post Prandial Trial:

• Overnight fasted state: Propionate had no effect on EE and CHO oxidation in the

overnight fasted state. Propionate stimulated lipid oxidation in the fasted state.

• Post-prandial state: Propionate increased EE in the post-prandial state with no

preferential use of a substrate.

3.10 Summary:

Acute ingestion of oral sodium propionate in healthy human volunteers may increase energy

expenditure in the overnight fasted state within the first 180 min of ingestion and in the post-

prandial state. In the fasted state, it can also stimulate HR and MAP. A consistent increase in

lipid oxidation has also been found in the overnight fasted state, however, these effects are

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not observed during sub-maximal exercise nor in the post-prandial state. A decrease in CHO

oxidation can also found in the overnight fasted state.

3.11 Discussion:

3.11.1 Impact of propionate supplementation on energy expenditure and substrate oxidation

in different energy states:

No human study apart from this trial has examined the effect of acute/chronic propionate

supplementation on energy expenditure and substrate oxidation in the three energy states

(overnight fasted, sub-maximal exercise and post-prandial) typically experienced in a 24 h

period. A single rodent study, however, has examined the effect of chronic oral sodium

propionate supplementation on energy expenditure and substrate oxidation in different

energy states (den Besten et al., 2015). Den Besten et al. demonstrated in mice that

incorporation of sodium propionate chronically to an HFD can increase energy expenditure

and lipid oxidation in different energy states by promoting changes in hepatic and adipose

tissue that support a lipid switch between lipid synthesis to oxidation (den Besten et al.,

2015). This corroborated with some of the present findings where energy expenditure was

significantly increased with propionate supplementation in both the overnight fasted state

and post-prandially, but not during physical activity. Lipid oxidation as well was only found to

be stimulated in the overnight fasted state. This may be since the magnitude of metabolic

processes differ significantly between murine models and humans where, for instance, EE per

unit mass or per gram of body weight as well as rates of substrate utilization (lipid and CHO)

are substantially higher in mice relative to humans despite sharing comparable physiological

and metabolic functions at different bodily organs/tissues (Kummitha et al., 2014). Therefore,

caution is needed when translating these murine model findings directly to humans. Also,

differences may have occurred due to chronic versus acute supplementation strategies.

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3.11.2 Acute impact of propionate supplementation on resting energy expenditure and

substrate oxidation in humans:

In the present trial, acute ingestion of 71 mmol of sodium propionate over 180 min was able

to consistently increase overnight fasted lipid oxidation (~ 0.01 g/min) which if maintained

over a 24 hour period would equate to an increase in lipid oxidation of 14.4 g/d . Moreover,

a simultaneous decrease in CHO oxidation was found in two out of the three trials. An increase

in energy expenditure after an overnight fasted state, however, was only found in the

overnight fasted trial (9.72 kcal over the course of 6 hours) which is nevertheless greater than

the energy yield of the complete oxidation of 71 mmol of sodium propionate (7.1 kcal) hence

signifying that the increase in EE was above the energy content of the propionate tablet and

is not simply oxidizing an endogenous fuel source. . Of note, in the sub-maximal exercise trial,

due to feasibility purposes, a face mask was used instead of the canopy, and the use of a

facemask can overestimate oxygen consumption and resting energy expenditure by ~ 7%

(p<0.05) and carbon dioxide production by 4.1% (p>0.05) in comparison to the canopy. RER

however can be similar across both methods (Forse, 1993). As for the overnight fasted and

postprandial trials, where the same calorimeter was used in both, the inconsistent effect on

resting energy expenditure cannot be attributed to methodological differences but could be

explained by differences in population characteristics in key variables such as BMI and body

lipid content. However, this is perhaps unlikely since the differences in participant

characteristics between the three trails were small. A more likely reason is that the rise in

REE (4-5%) that is expected with propionate supplementation in humans based on current

evidence (Chambers et al., 2018) falls at the very limits of detection of IC (4-10%) (Mtaweh

et al., 2018) based on day to day variability and error in measurements and can therefore be

left undetected using the current gold-standard technique.

Similar to our findings, Chambers et al. have also shown that in healthy human volunteers, an

acute oral administration of 71 mmol of sodium propionate over a 180 min period can

increase energy expenditure by ~4% in an overnight fasted state while shifting substrate

metabolism to favour an increase in lipid oxidation rates however with no substantial effect

on carbohydrate oxidation (Chambers et al., 2018). The lack of effect on CHO oxidation may

be due to the statistical test used to analyse it. In that study, only the +iAUC was examined

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(which only calculates the area above baseline i.e a stimulatory effect) and thus any decrease

in CHO oxidation would not have been detected. Canfora et al., on the other hand, have

shown in twelve normoglycemic men that rectally infused SCFA mixture high in propionate

(18 mmol sodium acetate (45%), 14 mmol sodium propionate (35%), 8 mmol sodium butyrate

(20%) dissolved in 200 mL sterile water) which can typically be achieved with high fibre intake,

increased REE and fasting lipid oxidation and can decrease fasting carbohydrate oxidation as

assessed by iAUC tests (Canfora et al., 2017). Results from all these trials seem to be feasible

since for EE to increase, there needs to be a parallel increase in substrate oxidation, which in

this case seems to be fuelled by an elevation in lipid oxidation. It is also possible that the

increase in lipid oxidation is matched by a decrease in carbohydrate oxidation which explains

the lack of change of REE in the first 180 min in the sub-maximal exercise trial after propionate

supplementation. In any case, as human trials are quite scare, further intervention studies are

needed to confirm the impact of acute propionate administration on REE and substrate

oxidation. However, given the current evidence, it appears that propionate supplementation

can consistently increase resting lipid oxidation while its effects on stimulating REE are yet

inconclusive and require further investigation.

3.11.3 Acute impact of propionate supplementation on post-prandial energy expenditure and

substrate oxidation in humans:

In the post-prandial state, the present trial demonstrated that acute ingestion of 71 mmol of

sodium propionate over 180 min can increase post-prandial EE with no substantial effect on

substrate oxidation which coincides with the findings of Canfora et al. who showed in twelve

normoglycemic men that rectally infused SCFA mixture high in propionate (18 mmol sodium

acetate (45%), 14 mmol sodium propionate (35%), 8 mmol sodium butyrate (20%) dissolved

in 200 mL sterile water) has no significant effect on substrate oxidation, however, contrary to

the present findings, they found no significant effect on post-prandial energy expenditure

(Canfora et al., 2017). In addition, a study on overweight women showed that

supplementation of propionate (36.2 mmol) in the form of an inulin propionate ester along

with a moderate exercise program for 4 weeks has no effect on energy expenditure but can

increase post-prandial whole-body lipid oxidation and decrease CHO oxidation (Malkova et

al., 2020). A direct comparison between the studies is challenging since multiple variables

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including the dosing, supplementation strategy, duration of the post-prandial period

measurement, participants’ characteristics as well as acute versus chronic supplementation

are variable. The former study included only overweight men BMI (30.3 ± 0.8 kg/m2), whilst

the latter study included only overweight women (BMI: 29.0 ± 1.9 kg/m2).The present study

included both normal and overweight individuals (BMI: 24.7 ± 0.8 kg/m2) with the majority

being in the normal range BMI. DIT peak in overweight individuals or people with higher lipid

content tends to be lower and occurs at a later time than normal weight subjects (Melzer,

2011). Thus, too short measurements are likely to give inaccurate DIT differences between

lean and overweight/obese subjects. Indeed, in the Canfora and the present study, the post-

prandial time-frame extended over two hours whereas in the Malkova trial, the post-prandial

period covered 7 hours. Thus, whether the studies would have yielded similar results had the

post-prandial time-frame been extended remains questionable and requires further

investigation. Of note as well, Malkova et al., conducted EE and substrate oxidation

measurements ³ 18 hours post the last inulin-propionate dose (Malkova et al., 2020)

therefore whether this translates to an acute effect remains questionable. Differences

between studies may also occur due to differences in the composition and energy content of

the meal provided. Protein content is the main nutrient that can drive an increase in DIT

where an increase in 1% of protein fraction results in a 0.22 ± 0.42 % elevation in DIT

(Westerterp, 2004) whereas the main substrate oxidized post-prandially depends on glucose

availability (Melzer, 2011). In the present study, as mentioned in the methods section, all

participants received an Ensure drink (72.7 g carbohydrate, 13.6 g lipid and 20.5 g protein;

500 kcal) whereas in the Canfora et al. study, the meal consisted of a 75g oral glucose load

and finally, in the Malkova et al. trial, the meals were tailored per body weight of individuals

and included a mixed meal breakfast (1g lipid, 1.2 g carbohydrate, 0.25 g protein and 15 kcal

/BW) and lunch (0.8 g lipid, 1.1 g carbohydrate, 0.35 g protein, 13 kcal/BW). Also, as the

propionate mode of supplementation was different between studies, where it was it was via

an oral supplementation in the fasted state in the current trial, via food in the Malkova trial

(Malkova et al., 2020) and via rectal infusions in the Canfora experiment (Canfora et al., 2017),

this is likely to impact energy homeostasis differently as various organs trigger varying

hormonal and regulatory processes which would ultimately impact the effects on energy

metabolism differently (Havel, 2004, Kuliczkowska-Plaksej et al., 2012).

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In summary, the post-prandial trial examined the acute ingestion of 71 mmol of sodium

propionate on energy expenditure and substrate oxidation and found a positive effect on EE

after ingestion of a mixed meal which is physiologically relevant in comparison to an

experimental 75 g oral glucose load. However, since a paucity of data exists in this aspect,

future studies are therefore warranted to investigate and consolidate those findings further.

3.11.4 Acute impact of propionate supplementation on energy expenditure and substrate

oxidation in humans during sub-maximal exercise:

This study appears to be the only human trial that examined the effect of acute oral

propionate supplementation on energy expenditure and substrate oxidation during exercise

and therefore comparing the results with other findings for further confirmation is limited.

The present findings display no effect of acute supplementation of 71 mmol of sodium

propionate on energy expenditure or substrate oxidation during sub-maximal exercise. In this

study, during exercise, energy expenditure and lipid oxidation increased substantially in

comparison to the overnight fasted state by ~4 times. Thus, this may have overridden the

minor increase in REE and lipid oxidation observed with propionate supplementation.

However, the present study only tested a single exercise intensity (~40% of VO2 max) and

whether the same effect holds across a broader range of exercise intensities where substrate

oxidation varies remains to be elucidated and is an area for future research. Fitness level also

is another factor to be considered when assessing substrate oxidation. Trained individuals are

known to reach maximal lipid oxidation at intensities between 59%-64% of VO2 max in contrast

to the general population who reach maximal lipid oxidation at a lower intensity range

between (47-52% VO2 max) (Achten and Jeukendrup, 2004) hence had the cohort solely

included fitter individuals who are more prone to increases in lipid oxidation due to increases

in intramuscular triglycerides and mitochondrial/cellular protein and hormonal regulations,

perhaps propionate supplementation may have increased lipid oxidation in those individuals

(Purdom et al., 2018).

Moreover, gender plays an important role in determining substrate oxidation during exercise.

Indeed, studies consistently show that pre-menopausal women have a significantly greater

ability than men in oxidizing lipids during exercise (Carter et al., 2001, Venables et al., 2005,

Dasilva et al., 2011). For instance, in a cross-sectional study including 300 healthy men (n=157)

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and pre-menopausal women (n=143), maximal lipid oxidation per kg of fat free mass was

significantly higher in women than in men, and this also occurred at a significantly higher

exercise intensity ( 45.1% versus 52.1% VO2 max;p< 0.01 in women versus men respectively).

In addition, the contribution of lipid oxidation to total energy expenditure at all exercise

intensities ranging from 41-61%, was significantly higher in women (p<0.01) (Venables et al.,

2005). In the sub-maximal exercise trial, the majority of participants were men (73.7%) which

may also explain why a lack of effect on lipid oxidation was seen with propionate

supplementation.

In summary, however, since this was the first in human trial to determine the effects of

propionate supplementation on energy expenditure and substrate oxidation during exercise

and no effect was present, it is currently difficult to draw any conclusions at this point. Future

studies are henceforth needed to confirm these effects bearing as well the above-mentioned

factors.

3.11.5 The impact of Propionate on energy metabolism:

Several propositions exist that can explain the effect of propionate on whole-body energy

expenditure and lipid oxidation identified in the current studies. Available data from animal

research suggests that propionate can increase energy expenditure via increases in lipid

oxidation by acting on several tissues and organ sites (Sukkar et al., 2019) as discussed below.

3.11.5.1.1 The impact of Propionate on hepatic energy metabolism:

At the hepatic level, propionate has been shown to decrease Pparg activity which would

ultimately increase energy expenditure and lipid oxidation by activating the UCP2-AMPK-ACC

pathway. Pparg acts as a potent regulator of UCP2 expression, fatty acid oxidation and overall

lipid metabolism. Increased expression of mitochondrial UCP2 in hepatocytes leads to an

increase in proton leak and a decrease in AMP: ATP ratio which serves as a reflection of the

energy state of the cell as well as a direct activator of AMPK. Activation of AMPK in turn leads

to a downregulation of malonyl-CoA, an endogenous inhibitor of CPT- 1, the first enzyme in

the b-oxidation of lipids thereby acting as a switch from lipid synthesis to oxidation. Indeed,

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this mechanism was confirmed when using liver specific Pparg-KO mice where the KO mice

no longer exhibited the propionate induced increase in hepatic lipid oxidation, decrease in

hepatic steatosis and any other effect on hepatic Pparg target genes (den Besten et al., 2015).

This proposed mechanism may uphold in humans and in the present findings since previous

investigations in suddenly deceased individuals have shown that the majority of propionate

(~90%) is taken up and metabolised by the liver with very little entering the systemic

circulation (Cummings et al., 1987). Indeed, this may be since long term supplementation of

propionate in the form of inulin propionate ester has been shown to decrease obesity-related

fatty liver disease in rodents (Zhu et al., 2020) Furthermore, this was also seen in humans

where long term supplementation of propionate for 24 weeks in the form of an inulin

propionate ester was shown to decrease intrahepatocellular lipid content in overweight

individuals with non-alcoholic fatty liver disease (Chambers et al., 2015).

3.11.5.1.2 The impact of Propionate on adipose tissue energy metabolism:

Propionate also seems to induce changes in adipose tissue that can promote increases in

energy expenditure and substrate oxidation which can explain the increase in resting and

postprandial energy expenditure and overnight fasted lipid oxidation observed in this trial. In

white adipose tissue, den Besten et al. showed that propionate can elicit a UCP2-AMPK-ACC

cascade similar to that observed in hepatic tissue via downregulation of Pparg, which

ultimately increases both energy expenditure and lipid oxidation. By using adipose specific

Pparg-KO mice, this theory was confirmed since the propionate fed KO mice no longer

exhibited reductions in white adipose tissue mass and body weight in comparison to the wild-

type propionate fed mice. Also, the increased lipid oxidation observed in the wild-type mice,

was abolished in the knock-outs (den Besten et al., 2015). Propionate was also shown to

improve overall lipid homeostasis by enhancing the lipid buffering capacity of WAT. Jocken et

al. showed that treatment of human adipocytes with a high propionate SCFA mixture at (1

μmol/L) and supraphysiological (1 mmol/L) concentrations for 6 hours can attenuate

intracellular lipolysis as assessed via decreased basal glycerol release in comparison to control

cells (Jocken et al., 2018). Since WAT is not typically classified as a metabolically active tissue,

the effects of propionate on EE and substrate oxidation may be driven by more metabolically

active tissue or processes such as ‘beiging of WAT” (Carpentier et al., 2018). Indeed, in a

murine mice model, Lu et al has shown that propionate when supplemented to an HFD can

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significantly suppress typical metabolic changes associated with an HFD such as diet induced

weight gain, increases in triglyceride and cholesterol concentrations and inflammatory

markers in comparison to an HFD control. This was mainly mediated via FFAR2/3 and the

upregulation expression of rate limiting enzymes involved in lipid oxidation such as cpt1 and

cpt2 in adipose tissue and the expression of genes related to mitochondrial biogenesis namely

(PGC-1a, NRF-1, Tfam, β-F1-ATPase, COX IV and cyt-c) ) as well as promoting beige

adipogenesis as evidenced by emerging beige adipocytes in white lipid depots and increases

in beige adipocyte markers (Tbx1, Tmem26, CD137) which result in increased energy

expenditure and lipid oxidation (Lu et al., 2016).

3.11.5.1.3 The impact of Propionate on skeletal muscle energy metabolism:

The observed increase in energy expenditure and lipid oxidation in this trial can also be

explained by the effects of propionate on skeletal muscle. Murakami et al. showed that

propionate at physiological concentrations (0·1and 0·3mM) typically seen in humans and

rodents can increase mRNA and protein expressions of UCP-1 in C2C12 cells which hence can

increase energy expenditure by uncoupling the oxidation of fuels, especially lipids, from ATP

production to be released as heat (Murakami et al., 2015) .

3.11.5.1.4 The impact of Propionate on SNS activity:

Another likely mechanism that can explain the effects of propionate supplementation on

energy expenditure in this study can be attributed to changes in the SNS activity. Studies have

previously reported that propionate can regulate the vagal system to support increases in

energy expenditure. For instance, in a rodent model, Kimura et al. showed that

intraperitoneal injection of a high dose of propionate (1g/kg) can stimulate SNS via FFAR-3.

This was mainly seen via significant increases in heart rate and oxygen consumption which

reflect increases in energy expenditure in wild type mice in contrast to FFAR-3 (-/-) KO mice

(Kimura et al., 2011). In the present investigation, there was a significant impact of propionate

on SNS activity in the overnight, fasted state as seen with a stimulatory increase in heart rate

and blood pressure. This was however only seen in the overnight fasted state in one of the

trials (Overnight fasted trial), but not in others. This may be since the dose used in the animal

study was much higher than the one used presently where daily propionate production in

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humans equates to 29.5 mg/kg/d (Overnight fasted trial: 1977 ± 100 mg/d; Post-prandial trial:

2115 ± 86 mg/d; Sub-maximal Exercise trial: 2159 ± 97 mg/d as estimated from participants’

body weight) and may also be due to the difference in mode of supplementation. Another

more likely reason is that HR and MAP measurements were only measured once by the end

of each hour, albeit the sub-maximal exercise study where they were also taken every 15

minutes during the exercise period, and thus a continuous measurement of both factors may

have yielded varying results given the encouraging effect on heart rate and blood pressure in

one of the trials. With respect to the PP trial, HR was decreased with propionate

supplementation in comparison to control. However, since baseline HR measurements were

significantly different between trials, whether propionate can actually decrease HR post-

prandially remains questionable and must be interpreted with caution. Future studies will

hopefully clarify this further perhaps by using more reliable methodologies such as

continuous heart rate variability or microneurography (Zygmunt and Stanczyk, 2010,

Seravalle et al., 2013),

3.11.5.1.5 Other potential mechanisms:

Another proposed mechanism that can explain the positive effects of propionate on energy

homeostasis is via intestinal gluconeogenesis (IGN). De Vadder et al. showed that mice on a

standard diet supplemented with propionate at (5% wt/wt) can reduce body weight and

adiposity when compared with mice on standard diet control despite similar energy intake.

This was mediated via the gut-brain neural circuit and FFAR-3 where propionate as a substrate

of intestinal gluconeogenesis can be converted into glucose before reaching the liver and

sensed by the brain which ultimately promotes the observed metabolic benefits. This was

also later confirmed using IGN KO mice where the KO mice on an HFD rich in propionate were

no longer protected from diet induced obesity in comparison to WT mice (De Vadder et al.,

2014).

3.12 Study Limitations:

This study is not without its limitations. For one, due to feasibility, it was not possible to use

the same mode of indirect calorimetry for all studies. In the EX trial, a face mask was used

instead of the canopy, and the use of a facemask can overestimate oxygen consumption and

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resting energy expenditure by ~ 7% (p<0.05) and carbon dioxide production by 4.1% (p>0.05)

in comparison to the canopy as mentioned earlier. REE however can be similar across both

methods (Forse, 1993). Further, the present methodology could not provide direct evidence

on whether the perceived increase in lipid oxidation was due to propionate oxidation or due

to oxidation of endogenous sources of lipids. Indeed, RER is a rough index and cannot

distinguish between the oxidation of CHO and lipids mixture and that of SCFA (Caballero et

al., 2003). Also, the current research as well as all previous work involving the effect of

propionate on energy expenditure and substrate oxidation have not included an energy

matched control. Therefore, introducing an energy matched control such as palmitate (4

mmol) can provide a better overview of the predominant source of lipid oxidation. Moreover,

carbon-13 labelled propionate can also be used to assess when propionate is being oxidized

in relation to when the increase in whole body lipid oxidation occurs which again clarifies the

main source of lipid oxidation. Another limitation in the PP trial is that the post-prandial phase

was measured for only two hours which is shorter than the recommended post-prandial 6-

hour measurement. However, if an effect of propionate on energy expenditure and substrate

oxidation were to occur, it ought to be within the early post-prandial phase as the majority of

DIT(~60%) occurs in this phase (Melzer, 2011). Also, in the PP trial, the meal provided via the

Ensure drink was not tailored to participants’ individual energy requirements and thus ranged

from (5.1-9.4 kcal/BW). An additional limitation of the study is that HR and MAP

measurements, as secondary outcomes, were only marginally recorded throughout the trials.

Therefore, future follow up studies, using more reliable methodologies such as continuous

heart rate variability or microneurography (Zygmunt and Stanczyk, 2010, Seravalle et al.,

2013), can better determine the effects of gut absorbed propionate on SNS activity.

Thus, as per the positive present findings, future human studies are warranted to assess the

full effect of propionate supplementation on energy expenditure and substrate oxidation

using a 24-hour respiratory chamber.

Another limitation of the trial is that it only examined acute effects of sodium propionate

supplementation on energy expenditure and substrate oxidation so whether the same effects

can be replicated with long-term propionate supplementation or would the body adapt

chronically is yet to be determined. This could be a very interesting area for future research.

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Previous work by Chambers et al. that supplemented overweight individuals over a period of

24 weeks with 10g of IPE demonstrated that body weight gain was prevented in the

intervention group in comparison to individuals who received 10g of inulin ester control,

where none of the participants experienced significant weight gain of >5% of baseline weight

in the IPE group in comparison to 17% in the inulin control, despite instructions on

maintaining the same physical activity throughout the supplementation period. Moreover,

energy intake seemed to be unchanged between the two groups as assessed by an ad libitum

meal after 24 weeks. Thus, it could be assumed that energy expenditure was increased with

IPE supplementation that prevented the weight gain observed in the inulin group. Also, body

composition was significantly altered in the IPE group in comparison to control whereby

intraabdominal adipose tissue was significantly reduced and a trend (p=0.061) for decreased

intrahepatocellular content was observed in the IPE group that was significant in participants

that met the diagnostic criteria for non-alcoholic fatty liver disease which indicate that lipid

oxidation was enhanced in the IPE group (Chambers et al., 2015). These findings hence signify

that the effects of propionate supplementation on energy metabolism may uphold even

chronically. However, future long-term studies are needed to determine that whereby direct

measures of energy expenditure and substrate oxidation such as use of indirect calorimetry

at the end of supplementation period as well as energy intake are assessed to determine

changes in energy metabolism.

3.13 Conclusion:

Acute ingestion of oral sodium propionate in healthy human volunteers can increase energy

expenditure and lipid oxidation and decrease CHO oxidation after an overnight fasted state

which seems to be mediated via SNS activity stimulation. It can also increase energy

expenditure post-prandially with no preferred use of a substrate. However, acute oral sodium

propionate ingestion seems to have no effect on energy expenditure and substrate oxidation

during sub-maximal exercise. These findings support my hypothesis in the overnight, fasted

state and post-prandially but not during sub-maximal exercise. As discussed earlier, there are

multiple factors that may have contributed to the discrepancy in findings. Nevertheless, this

study is the first in human study to provide direct evidence that acute oral ingestion of sodium

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propionate can modulate energy expenditure and substrate metabolism in different energy

states positively and thus, given these findings, future follow up studies can use a 24-hour

respiratory chamber and perhaps a continuous measurement of SNS activity to assess the full

effect of oral sodium propionate supplementation on energy expenditure and substrate

oxidation. Moreover, future interventions can utilize an energy-matched control since all

previous trials, both rodent and human studies including the current study, that have

investigated the effects of propionate on energy metabolism have not employed an energy

matched control which would be very useful in order to clarify whether the effects of

propionate on energy and substrate metabolism are sustained independent of the

propionate’s caloric value.

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Chapter 4: Effect of Sodium Propionate on Appetite Regulation

4.1 Abstract:

Background:

Previous research in healthy humans has demonstrated that acute oral sodium propionate

supplementation can affect appetite by increasing subjective nausea in an overnight fasted

state. Raised levels of propionate in the gut lumen have also been shown to stimulate release

of the anorectic hormone glucagon-like peptide 1 (GLP-1). However, currently, no human

study has examined if propionate supplementation can affect appetite during physical activity

and in the postprandial state. Therefore, the objective of this chapter was to investigate the

acute effect of oral sodium propionate supplementation (71 mmol) on subjective measures

of appetite, nausea and GLP-1 levels in different energy states (overnight fasted, sub-maximal

exercise and post-prandial) in healthy human volunteers.

Methodology:


Overnight fasted study: 19 volunteers (11 males and 8 females; age: 34.6 ± 4.1 years; BMI






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Overnight fasted study: The study extended over a total period of 360 min while volunteers





one hour.




Participants were asked to complete 100mm visual analogue scales (VAS) that assessed

subjective appetite (hunger, thirst and nausea) throughout these visits. GLP-1 release was

also measured during the post-prandial trial.

Results: Overnight fasted study:

Oral sodium propionate supplementation increased subjective nausea in the overnight fasted

state (Control= 3.5 ± 1.39 mm; Propionate= 8.9 ± 2.70 mm; Effect of trial p= 0.029;+iAUC0-360

min p=0.006) over a period of 360 min with no effect on subjective hunger (Control= 48.0± 4.11

mm; Propionate= 44.8± 5.25 mm; Effect of trial p= 0.417) or thirst (Control= 24.7± 5.43 mm;

Propionate= 23.2± 5.25 mm; Effect of trial p= 0.580).

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Oral sodium propionate supplementation increased subjective thirst (Control= 39.8 ± 5.3 mm;

Propionate= 46.8 ± 5.4 mm; Effect of trial; p=0.015) during sub-maximal exercise (Control: 48

± 2 % ; Propionate: 47 ± 2% VO2max) with no effect on subjective hunger (Control= 51.7 ± 5.7

mm; Propionate: 57.2 ± 5.3 mm; Effect of trial: p=0.258) or nausea (Control= 2.9 ± 1.1 mm;

Propionate: 6.7 ± 3.4 mm; Effect of trial: p=0.295).


Oral sodium propionate supplementation increased GLP-1 secretion (iAUC0-180 min p= 0.004;

and +iAUC0-180 p=0.016) in the overnight fasted state, but not during the postprandial period.

Propionate supplementation increased subjective nausea (+iAUC180-300 p=0.049) but had no

effects on subjective hunger or thirst.

Conclusion:

This was the first trial in humans to examine the impact of acute ingestion of oral sodium

propionate (71 mmol) in healthy human volunteers in different energy states (overnight

fasted, sub-maximal exercise and post-prandial). Findings revealed that acute propionate

supplementation stimulated feelings of nausea during fasted and post-prandial states and

increased subjective thirst during sub-maximal exercise. Moreover, oral sodium propionate

can stimulate GLP-1 release in the overnight fasted state.

4.2 Dietary Fibres and Appetite Regulation:

It is estimated that around 40% of the world’s population is either overweight or obese.

Europe has identified to be amongst the top two regions worldwide, along with the Americas,

to have the highest prevalence of overweight and obesity (Chooi et al., 2019). Evidence

suggests that the average weight gain (0.5-1 kg/year) observed in middle-age adults could be

the result of a relatively small surplus of 50 kcal /d, which could be reversed by decreasing

excess energy intake and/or increasing energy expenditure by a 100 kcal/d while assuming a

50% efficiency rate (Hill et al., 2003, Zhai et al., 2008). However, even such a small increase in

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habitual weight gain is significantly associated with multiple comorbidities such as type 2

diabetes, cardiovascular disease and some types of cancers (Zheng et al., 2017). Therefore,

interventions generally accepted at the population level to suppress appetite and energy

intake are highly attractive and urgently needed.

Epidemiological studies consistently describe an inverse association between dietary fibre

(DF) intake and weight gain (Bozzetto et al., 2018). A prospective, European cohort study

including 89,432 individuals who were followed up for 6.5 years showed that total fibre intake

was inversely associated with weight gain and waist circumference increase. For instance,

every 10g increase in total fibre intake was associated with a 39g/year reduction in body

weight (BW) and 0.08cm/year decrease in waist circumference. Also, every 10g increase in

cereals intake was associated with a 77 g/year and 0.10 cm/year decrease in BW and waist

circumference, respectively (Du et al., 2010). Also, the Nurses’ Health study, which included

74,091 female subjects, showed over a 12-year period, an increase in 12g of dietary fibre

intake, is associated with 3.5 kg less weight gain (Liu et al., 2003). Increased intake of DF is

also shown to have favourable effects on appetite regulation (Slavin and Green, 2007,

Wanders et al., 2011). This is mainly due to the different physiological characteristics of DF

that enable it to have a profound effect on both satiation and satiety. Satiation can be defined

as an intrameal satiety that results in meal termination and therefore controls meal size,

whereas satiety can be defined as inter-meal satiety that develops after an eating episode

and results in inhibition of further eating and increase in fullness thereby influencing the

timings of the next meals (Blundell et al., 2010, Hervik and Svihus, 2019)

4.3 Short Chain Fatty Acids and Appetite:

A growing body of evidence suggests that the effect of DF on appetite regulation may be

related to the fermentability of DF and the production of the short chain fatty acids (SCFA):

acetate, propionate and butyrate (Wanders et al., 2011, Hervik and Svihus, 2019). Various

mechanisms have been proposed to explain how raised production of SCFA in the gut could

supress short-term appetite response and energy intake:

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4.3.1 SCFA and Central Nervous System:

The Central Nervous System (CNS) plays a pivotal role in the complex interplay between gut

hormones and CNS to influence food intake and appetite regulation. Within the CNS, the

hypothalamus is identified to be the key regulator of appetite. A semipermeable blood–brain

barrier also exists that allows peripheral signals, such as hormones and nutrients, to gain

access to the CNS. The hypothalamic arcuate nucleus (ARC), found at the base of the

hypothalamus, receives long-term and short-term humoral signals which communicate

general health, and meal initiation and termination respectively. Moreover, the ARC contains

gut hormone receptors and two sets of appetite regulating neurons: the pro-

opiomelanocortin (POMC) appetite-inhibiting neurons, and the neuropeptide Y (NPY) and

agouti-related peptide (AgRP) appetite- stimulating co-expressing neurons. Along with the

hypothalamus, the brainstem plays an integral role in appetite regulation where it receives

neural, nutrient and hormonal signals from the gastrointestinal tract (GI) in a manner similar

to the hypothalamus. In particular, the DVC (dorsal vagal complex) in the brainstem relays

signals from the periphery and hypothalamus to control energy intake. Also, the vagus

communicates the transmission of afferent and efferent neural signals between the nucleus

of the tractus solitarus in the DVC and the GI (Perry and Wang, 2012, Hussain and Bloom,

2013). Li et al. showed that acute oral butyrate administration via an intragastric tube in mice

can significantly decrease food intake by inhibiting orexigenic neuron activity in the

hypothalamus such as NPY. To confirm that the gut-brain neural circuit is responsible for the

apparent decrease in energy intake and not increased butyrate concentrations in circulation,

butyrate was administered directly into circulation by intravenous injection. However,

although butyrate concentrations were significantly raised, this did not influence acute

refeeding or food intake within 24 hours which implicates that the main mechanism involved

is via the direct gut-brain neural circuit. Moreover, chronic butyrate supplementation to an

HFD in mice in the form of sodium butyrate for 9 weeks resulted in a significant (22 %)

decrease in energy intake and BW (27%) in comparison to mice on an HFD control, without

influencing physical activity. This was also confirmed via subdiaphragmatic vagotomy where

mice receiving the sham surgery reduced cumulative food intake whereas those receiving the

subdiaphragmatic vagotomy failed to reduce food intake (Li et al., 2018).

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Frost et. al showed that acetate whether administered via colonic or i.v. infusion in mice

results in acute suppression of energy intake. The authors demonstrated that i.p.

(intraperitoneal injection) of acetate at a rate of 500 mg/kg was associated with a significant

decrease in acute food intake 1- and 2-hours post-injection with no association with aversive

behaviour or changes in gut hormone secretion such as PYY and GLP-1 levels. Moreover,

central administration of acetate (2.5 µmmol of sodium acetate) directly into the third

ventricle also resulted in suppression of food intake primarily 1-2 hours post-injection

although the effect was less potent than i.v. administration. Subsequent investigations

showed that mice given an i.p. injection of acetate resulted in a significant increase in signal

intensity in ARC, a four-fold increase in POMC expression and a strong reduction in AgRP

expression after hypothalamic take up and metabolism of acetate. This was mainly mediated

via acetate induced reduction of hypothalamic AMPK catalytic activity (Frost et al., 2014b).

Moreover, Byrne et. provided the first in human evidence that increased colonic propionate

production via an inulin propionate ester (IPE) (10g) that directly delivers propionate to the

colon, can influence energy intake in healthy non-obese men by attenuating reward-based

eating behaviour by decreasing blood oxygen level dependent (BOLD) signal via central

appetite regulation. Volunteers experienced reduced BOLD signals when evaluating pictures

of food with high energy density in comparison with food of lower energy density when

colonic propionate levels were acutely increased which was associated a significant decrease

in subsequent ad libitum energy intake (~10%) (Byrne et al., 2016).

In summary, it appears that SCFA’s effect on appetite in both humans and animals may be

facilitated by the CNS.

4.3.2 SCFA receptors and anorectic gut hormone release:

The SCFA are ligands of FFAR-2 (free-fatty acid receptor 2) and FFAR-3 (free-fatty acid

receptor 3) present on L cells in the intestine, which are endocrine cells that produce and

secrete the anorexigenic hormones peptide YY (PYY) and glucagon-like peptide-1(GLP-1)

(Byrne et al., 2015). GLP-1 is an anorectic hormone mainly produced in the ileum with levels

rising in parallel to energy intake, particularly after a high carbohydrate meal. GLP-1 is shown

to inhibit energy intake directly via central mechanisms and also indirectly by affecting gut

motility. Indeed, GLP-1 acts as an ‘ileal brake’ whereby stomach and gut motility are

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synchronised to ensure a stable flow of nutrients into the small intestine thus regulating

appetite (De Graaf et al., 2004, De Silva and Bloom, 2012, Van Bloemendaal et al., 2014).

Similarly, PYY, an anorexigenic hormone, is a peptide produced by L-cells in the gut and is

found in the highest concentrations in the distal small intestine and colon. PYY concentrations

peak post-prandially, the magnitude of which rises in proportion to the energy content

ingested and is mainly stimulated by a high-fat diet. PYY is also thought to affect gut motility

and can act as an ‘ileal brake’ delaying gastric emptying thereby promoting sensations of

fullness and satiety. Moreover, it can affect appetite directly via vagal and central

mechanisms to inhibit energy intake and suppress appetite (Karra et al., 2009, De Silva and

Bloom, 2012). FFAR-2 seems to be mainly activated by acetate and propionate while FFAR-3

is mainly activated by propionate and then by acetate and butyrate to a similar extent.

However, this mainly depends on interspecies variability (Byrne et al., 2015). FFARs initiate

their signalling via coupling with G-proteins. FFAR-3 is mainly coupled with Gi/Go family.

FFAR-2, on the other hand, is mainly coupled with Gq -proteins and possibly with Gi/Go

pathways (Covington et al., 2006). Rodent and human studies have portrayed SCFA’s ability

to induce gut hormone release and subsequent energy intake predominately via these

receptors. Using primary murine colonic cultures, Tolhurst et al. showed that acetate and

propionate are able to stimulate GLP-1 secretion via both receptors, co-expressed on L- cells,

but to a much greater extent via FFAR-2. This was confirmed since the SCFA triggered the

elevation of intracellular Ca2+ in L-cells that is specific to Gq signalling pathway and hence

FFAR-2 activation. Moreover, this was further confirmed in vivo where mice lacking FFAR-2

exhibited diminished basal and glucose-stimulated GLP-1 levels (Tolhurst et al., 2012). Using

physiological concentrations of SCFA (2 mM), Larraufie et al. showed that propionate and

butyrate in particular, are able to stimulate PYY gene expression and secretion in human but

not rodent enteroendocrine cells. This was mediated via two mechanisms: one via stimulation

of FFAR-2 by all the SCFA, and the second and more dominant pathway is via propionate and

butyrate inhibition of histone deacetylases. The increased PYY gene expression resulted in

increased PYY secretion under basal and stimulated conditions since the observed effect was

time and dose dependent, which indicates that SCFA may induce PYY secretion in both the

fasted and fed states (Larraufie et al., 2018). Also, Psichas et al. showed in primary murine

colonic crypts that physiological concentrations of propionate (1–50mmol/L) can significantly

increase PYY and GLP-1 secretion, with higher doses eliciting a greater response (~ 2-fold).

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This was mediated via FFAR-2 since primary colonic cultures obtained from FFA2- deficient

mice had signfciant attenuation of gut hormone release. Similar observations were also

confirmed in vivo, where an intra-colonic injection of 180 mmol/L of propionate vs saline

control in rats resulted in a significant 1.3- and 1.6-fold increase in portal vein PYY and GLP-1

levels respectively, while no change was observed in FFAR-2 deficient mice (Psichas et al.,

2015).

Independent of FFARs activation, SCFA have also shown to influence appetite and regulate

gut hormone expression. Lin et al. showed that mice on a high fat diet (HFD) supplemented

with butyrate or propionate in the form of sodium salts and 5% wt/wt can lead to a reduction

in food intake. A follow up titration study confirmed that the effective dose for food

suppression is 5% and 4.3% for butyrate and propionate respectively. Moreover, acutely,

sodium butyrate supplementation was shown to significantly increase GLP-1 concentration.

Using FFAR-3 KO mice and comparing them with controls showed that the SCFA induced

suppression of energy intake was independent of FFAR-3 activation since the KO and controls

exhibited similar behaviour. However, the KO mice displayed attenuated GLP-1 secretion with

butyrate supplementation which portrays that butyrate stimulation of gut hormones is

partially mediated via FFAR-3 activation. Interestingly as well, butyrate supplementation was

able to significantly increase ghrelin expression in the KO mice. The authors suggested that in

addition to the influence of the SCFA on gut hormone secretion, it is possible that the impact

of food intake could be related to a decrease in palatability with butyrate and propionate

supplementation, although mice did not exhibit any signs of overt malaise (Lin et al., 2012).

In summary, it is quite evident that SCFA are able to stimulate anorexigenic gut hormone

release, mainly PYY and GLP-1 expression that are known to reduce appetite. However,

intriguingly, whether this is achieved via FFAR-2/3 activation remains to be established and

given the current evidence, it appears SCFA are able to stimulate PYY and GLP-1 expression

by acting on these receptors and also independent of them.

4.3.3 SCFA and leptin secretion:

Apart from the influence of SCFA on gut hormone expression, SCFA have been shown to

stimulate other anorexigenic hormones such as leptin secretion. Leptin is a potent

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anorexigenic hormone that is released into circulation from adipose tissue as a function of

energy stores and can cross the blood brain barrier and suppress energy intake by acting on

hypothalamic leptin receptors in the central nervous system. Indeed, Satoh et al.

demonstrated that the ventromedial hypothalamus (VMH) plays a vital role in mediating

leptin effects. After acute exogenous leptin infusion via iv (1.0 mg/rat) or

intracerebroventricular (2.0 g/rat) injection, rats exhibited a 57% and 91% decrease in food

intake versus vehicle-treated groups. In contrast, this effect was no longer present in VMH

lesioned rats (Satoh et al., 1997). Leptin has also been shown to regulate the expression of

orexigenic such as neuropeptide Y and agouti-related protein as well as anorexigenic peptides

such as pro-opiomelanocortin (POMC) (Klok et al., 2007).

Xiong et al. showed that SCFA can stimulate leptin secretion from a primary culture of mouse

white adipose tissue and adipocyte cell line through FFAR-3 activation via Gai pathway. Also,

in vivo, oral propionate supplementation (200 µl of 2.5 M sodium propionate solution) was

shown to increase leptin concentrations in mice by ~80%, however, this was not sufficient to

suppress food intake (Xiong et al., 2004). On a contradictory note, Zaibi et al. showed that

FFAR-3 is not expressed in murine adipose tissue although leptin secretion in the presence of

SCFA was decreased in FFAR-3 KO mice. The authors suggested that this might be due to

downregulation of FFAR-2 expression in the adipose tissue of the KO mice. Moreover, they

showed that acetate and propionate are able to stimulate leptin secretion from mesenteric

and epididymal adipose tissue respectively via FFAR-2 Gai signalling (Zaibi et al., 2010).

Similarly, Hong et al. showed that FFAR-3 is not present on mice adipose tissue whereas FFAR-

2 is expressed on 4 different adipose tissue. Moreover, they showed that treating 3T3-L1 cells

with propionic acid can elevate FFAR-2 and leptin mRNA levels (Hong et al., 2005).

In summary, increased leptin secretion associated with SCFA administration may also provide

an explanation of SCFA apparent suppression of appetite.

4.3.4 SCFA and Gastric Tract Motility:

The gut, as the primary organ where food is processed, plays a key role in the regulation of

satiety and satiation via different mechanisms such as meal volume, nutrient composition and

gastric motility. Moreover, the GI tract relays neural signals to the brainstem regarding the

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chemical, mechanical and nutritive properties of ingested food via afferent vagus nerves

mechanoreceptors and chemoreceptors that are sensitive to intraluminal distention and

chemical stimuli respectively. Gastric motility appears to be the major determinant of satiety

and satiation where delayed gastric emptying has been associated with increased feeling of

satiety and termination of food intake (Janssen et al., 2011, Hussain and Bloom, 2013). SCFA

have been shown to influence gastrointestinal motility throughout the digestive tract. In the

stomach, cecal infusions of a SCFA mixture has shown to delay gastric emptying in humans in

a dose dependent manner by promoting the relaxation of the proximal stomach. This thus

implicates that SCFA can contribute to the ileocolonic brake (the inhibition of gastric emptying

upon nutrient reach to the ileo-colonic junction) (Cherbut et al., 1997). Moreover, in the

terminal ileum, Richardson et al. showed that an ileal infusion of an acetic acid solution (20

mM, 50 mM, and 100 mM) and butyric solution (100 mM) in rats can significantly stimulate

ileal emptying thus protecting ileal mucosa from colonic content reflux. This effect was also

more pronounced at higher concentrations and for shorter chain fatty acids (Richardson et

al., 1991). Yajima et al. also showed that intravenous injection of propionate (1.2 mg/kg) and

butyrate (1.4 mg/kg) can increase ileal motility by stimulating a biphasic contraction

consisting of an initial phase and subsequent tonic contraction (YAJIMA, 1984). As for the

effect on colonic motility, SCFA may have an inhibitory effect. Squires et al. showed that in

vitro that SCFA delivered as a mixture or alone can significantly reduce the contractile activity

across the different ranges of the large bowel of rats. The effect seemed to be dose

dependent where colonic motor activity was prominently reduced at 100 mM in comparison

to 10 mM concentrations. However, sodium salts of the SCFA displayed no effect on colon

contractile activity (Squires et al., 1992). In healthy human volunteers, Jouet et al. showed

that intracolonic infusions of a SCFA mixture has no effect on colonic motility (Jouet et al.,

2013).

The mechanisms of SCFA on gastrointestinal motility is not yet fully understood. However,

one possible mechanism is via neuro-hormonal pathway that involves PYY release (Cherbut,

2003). Indeed, Cherbut et al. showed that intracolonic infusion of SCFA at a rate of 2 mmol/h

in mice can reduce colonic motility and increase transit time while stimulating PYY peripheral

concentrations. The effect on gastric motility seems to be mediated by PYY since PYY anti-

serum suppressed the colonic myoelectrical activity and inhibited the SCFA induced decrease

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in colonic motility (Cherbut et al., 1998). Similarly, Ropert et al. showed that intracolonic

infusions of SCFA in healthy human volunteers can significantly decrease proximal gastric

tone that coincided with a significant increase in PYY but not GLP-1 concentrations (Ropert et

al., 1996). It is quite plausible that PYY can mediate such effects since PYY has been shown to

act as an ‘ileal brake’ and can thus inhibit gastric emptying (De Silva and Bloom, 2012).

In summary, SCFA may have varying effects on gastrointestinal motility that can hence reduce

appetite. This seems to be mainly mediated by SCFA delay of gastric emptying and colonic

motility possibly via PYY release.

4.3.5 Propionate and Hepatic Metabolism:

In addition to the proposed mechanisms mentioned, propionate in comparison to other SCFA,

has a distinct effect on hepatic metabolism that could in turn influence appetite regulation.

Propionate is a known precursor for hepatic gluconeogenesis where it enters the TCA

(tricarboxylic acid) cycle at the level of succinyl-CoA and is then converted to oxaloacetate

and then ultimately into glucose (den Besten et al., 2013b). Indeed, the liver in both humans

and animals is estimated to take up a substantial amount of propionate (90%-95%) from the

portal vein (Cummings et al., 1987, Bergman, 1990) however the contribution of propionate

to hepatic gluconeogenesis depends on the species. In ruminants, it is estimated that

between 50-75% of the glucose requirements of the animal is met by hepatic gluconeogenesis

from propionate (Bergman, 1990). Evidence from animal studies also suggests that the liver

plays an important role in communicating energy status to the brain and vice versa via hepatic

vagal afferents (Arora et al., 2011, Fam et al., 2012). Sodium propionate infusion in ruminants

have consistently shown to have hypoghagic effects (Farningham and Whyte, 1993, Elliot et

al., 1985, Oba and Allen, 2003) which seems to be mediated via hepatic-brain-axis since

effects on appetite are abolished with vagotomy and vagal nerve blockage (Forbes, 1988,

Allen, 2000, Arora et al., 2011, Sa'ad et al., 2010). As the vast majority of evidence on the role

of the liver and hepatic gluconeogenesis in mediating the hypoghagic effects of propionate

comes from ruminant studies, great caution must be used when translating these results to

humans. In humans, limited data exists on estimating hepatic gluconeogenesis from

propionate. However, a stable isotope study has shown that only a small fraction (6 %) of gut

derived propionate can be utilized for hepatic gluconeogenesis rather than be directly

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oxidized, although the post-prandial nature of the study may have underestimated the

amount of propionate that could be used for gluconeogenesis after an overnight, fasted state

(Boets et al., 2017). It is noteworthy, however, that the liver is a key anabolic organ in humans

contributing ~20% of resting energy expenditure (Kummitha et al., 2014). Therefore, it is

highly possible that changes in hepatic glucose production and metabolism could be sensed

centrally by the brain which can in turn influence appetite and feeding behaviour.

4.3.6 Conclusion:

A review of available evidence highlights that SCFA can influence appetite by exerting multiple

effects on various organs and tissue sites, as summarized in Figure A. The majority of available

mechanistic evidence comes from animal models and in vitro experiments. Hence, further

human studies are needed in order to strengthen the proposed mechanisms.

Figure 4-1: Mechanisms of how SCFA suppress appetite and energy intake:

Increasing gut-derived SCFA can: (1) stimulate the release of peptide tyrosine tyrosine (PYY) and glucagon-like-

peptide-1 (GLP-1) via the activation of free fatty acid receptor 2 and 3 (FFAR2/3) on colonic L-cells which can

inhibit gastric motility. Increased peripheral PYY and GLP-1 levels can stimulate the activity of the appetite-

suppressing pro-opiomelanocortin (POMC)/ neurons and inhibit appetite-stimulating neuropeptide Y

(NPY)/agouti-related peoptide (AgRP) neurons in the hypothalamus and brainstem (2) increase portal

concentrations of propionate which are taken up by the liver and can stimulate hepatic gluconeogenesis. This

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can increase hepatic energy state which via vagal nerve afferents can modulate feeding behaviour. (3) increase

acetate concentrations which can cross the blood– brain barrier and increase POMC and reduce AgRP

expression. (4) increase peripheral propionate and acetate concentrations which induce leptin release from

adipocytes via activation of FFAR2. Leptin in turn inhibits NPY neurons and activates POMC neurons.

4.4 Propionate and Appetite:

Studies have highlighted how SCFA can mechanistically affect appetite. Therefore, the aim of

this section is to specifically investigate available evidence on propionate’s actual impact on

subjective appetite and energy intake.

4.4.1 The impact of Propionate on appetite in non-humans:

4.4.1.1.1 Chronic Propionate Supplementation:

4.4.1.1.1.1 Oral:

Several rodent studies have examined the chronic effect of oral propionate supplementation

on appetite, all but one of which have found no significant effect on feed intake. Boillot et al.

showed that rats fed a standard diet supplemented with 74.1 mmol/kg of propionic acid for

3 weeks had no significant effect on feed intake in comparison to mice fed a diet

supplemented with a corresponding amount of poorly fermentable cellulose. Of note, only

after week 1 on the diet, rats on the propionate supplemented diet had decreased feed

intake. The authors explained this may be due to poor palatability of the diet. However, after

adaptation to the diet, feed intake after week 1 was similar between the diets (Boillot et al.,

1995). Similarly, Berggren et al. demonstrated that oral sodium propionate ingestion in mice

fed an HFD supplemented with 26mmol/kg BW/d of sodium propionate for 19 days has no

effect on feed intake in comparison to mice who were on an HFD only (Berggren et al., 1996).

Also, De Vadder et al. showed that mice fed a standard diet supplemented with sodium

propionate (5% wt/wt) for 10 days had no effect on food intake (De Vadder et al., 2014). In

addition, Den Besten et al. showed that mice on an HFD supplemented with sodium

propionate at 5% wt/wt for 12 weeks had no effect on food intake (den Besten et al., 2015).

Moreover, Tirosh et al. showed that supplementing drinking water of mice with sodium

propionate (15 mg/kg) for 6 weeks has no effect on water intake in comparison to an

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equivalent amount of sodium chloride added to the drinking water of the control group

(Tirosh et al., 2019). However, very recently, Zhu et al. showed that obese mice on an HFD

supplemented with IPE or propionate solutions of various concentrations can significantly

decrease food intake after 4 weeks of supplementation in comparison to mice on an HFD

alone (Zhu et al., 2020).

4.4.1.1.1.2 Infusion:

Only two non-ruminant studies have examined the effect of chronic propionate

supplementation via infusion on appetite and both have found no substantial effect on feed

intake. McBurney et al. showed that pigs receiving a portal infusion of an isomolar propionic

acid solution at a rate of 0.01 kg/BW/min, which is similar to a typical SCFA post-prandial

absorption rate, for seven days had no significant effect on feed intake (McBurney et al.,

1995). Also, Berggren et al. showed in obese hyperinsulinemic rats with a metabolic profile

similar to subjects at risk of developing metabolic disease, that rectal infusion of sodium

propionate at a rate of 4 mmol/kg BW/d for 19 days has no effect on feed intake (Berggren

et al., 1996).

In summary, animal studies demonstrate that propionate supplementation in most cases

does not have a signification impact on appetite regulation when administered orally or via

infusion for a chronic period of time.

4.4.2 The impact of Propionate on appetite in humans:

4.4.2.1.1 Acute Propionate Supplementation:

4.4.2.1.1.1 Oral:

Several human studies have examined the acute effect of propionate supplementation on

appetite and found varying but significant impact of propionate on different appetite

measures. Liljeberg et al. showed that acute ingestion of barely bread baked with a high

concentration of sodium propionate (1.9 mol) can significantly prolong the duration of satiety

in 11 healthy adults, as assessed via subjective satiety ratings in comparison to control bread

(Liljeberg et al., 1995). A subsequent study by Liljeberg et al. also showed that sourdough

bread supplemented with 1.9 mol of sodium propionate in 12 healthy volunteers prolonged

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subjective satiety in comparison to control bread. The authors suggested this may be due to

delayed gastric emptying observed with propionate supplementation, as evidenced by

lowered blood concentrations of paracetamol, an indirect marker of gastric emptying

(Liljeberg and Björck, 1996). However, Frost et al. showed in 10 healthy volunteers that the

consumption of pasta with tomato sauce supplemented with 30g sunflower oil and 30 mmol

of sodium propionate has no effect on subjective hunger but can significantly increase post-

prandial GLP-1 levels, delay gastric emptying and increase subjective nausea (Frost et al.,

2003). Similarly, in a double blind, cross-over trial, Chambers et al. showed that

supplementing 18 healthy volunteers acutely with 71 mmol of sodium propionate over 180

min significantly raised PYY levels at 180 min in comparison to a sodium chloride control.

Moreover, subjective nausea was significantly raised with propionate supplementation

whereas subjective hunger was unchanged as assessed by the VAS (Chambers et al., 2018).

Also, in a randomized controlled trial, Chambers et al. were able to demonstrate in 20

overweight adults that acute supplementation of propionate in the form of an IPE (36.2

mmol), in comparison to an inulin control, can significantly increase post-prandial PYY and

GLP-1 levels, although subjective ratings of appetite and nausea as measured by VAS and

rates of gastric emptying remained unchanged. Moreover, they found a decrease in food

intake by around 14% in the IPE group (Chambers et al., 2015). In contrast, Ruijschop et al.

demonstrated in 43 healthy, normal-weight women that consumption of 150 ml of a non-

fermented dairy beverage containing 0.6 % of calcium propionate has no effect on ad libitum

energy intake but can significantly increase subjective feelings of fullness and decrease

subjective feelings of hunger and desire to eat (Ruijschop et al., 2008). Furthermore, in a

randomized cross-over trial involving 20 healthy subjects, Darzi et al. showed that acute

ingestion of propionate (6 mmol) in a palatable sourdough in comparison to a control bread

of similar palatability had no effect on energy intake 180 min after the bread breakfast via an

ad libitum meal and 24 hour energy intake. However, VAS (visual analogue scales) displayed

no significant effect on subjective appetite ratings except for the desire to eat something

sweet (Darzi et al., 2012). Also, Byrne et al showed in a randomized cross-over trial involving

20 healthy non-obese men, that acute supplementation of propionate in the form of an IPE

(10g) can reduce anticipatory food hedonic responses and associated BOLD signal changes

that resulted in a significantly reduced ad libitum energy intake ~10% with no effect on gut

hormone secretion (Byrne et al., 2016).

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In summary, acute human studies demonstrate that oral propionate supplementation can

have a significant impact on various appetite measures.


4.4.2.1.2.1 Oral:

Two human studies have examined the effect of chronic oral propionate supplementation on

appetite measures and found no significant effect on energy intake. Todesco et al. showed

that incorporation of 9.9 g of sodium propionate to bread has no significant effect on energy

intake in six healthy adults after one week of intervention in comparison to control white

bread (Todesco et al., 1991). Also, in a randomized controlled trial, Chambers et al. were able

to demonstrate in 60 overweight adults that chronic supplementation of propionate in the

form of an IPE (36.2 mmol) has no apparent effect on food intake as assessed by an ad libitum

meal. However, with IPE supplementation, there was a trend towards decreased food intake

by ~ 10%. Subjective ratings of appetite were significantly decreased with IPE with no effect

of subjective ratings of nausea. Also, PYY and GLP-1 levels remained unchanged with IPE

supplementation (Chambers et al., 2015).

In summary, few chronic human studies exist that examine the effect of oral propionate

supplementation on appetite measures, yet current evidence indicates that propionate

administration has no significant impact on appetite or energy intake.

Conclusion:

As can be deduced from rodent and human studies, the effect of propionate on appetite is

still debatable. Animal studies have focused on chronic effects of supplementation and found

no significant effect on feed intake whether propionate was administered orally or via

infusion. Human studies, on the other hand, examined the effect of acute and chronic effect

of oral propionate supplementation on appetite measures. Chronic oral supplementation

appeared to have no significant impact on energy intake although results are deduced from

only two human studies which examined this effect. Acute studies, on the other hand, were

numerous and all human studies found an effect of acute oral supplementation on appetite

however with no consensus with respect to a specific appetite measure. Noteworthy, also,

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no acute study up to date has examined the effect of sodium propionate supplementation on

appetite in different energy states. Earlier work of Chambers et al. have demonstrated that

acute sodium propionate supplementation can increase subjective nausea in healthy

volunteers as assessed by VAS in the overnight fasted state over 180 min (Chambers et al.,

2018). However, circulating propionate levels only became significantly increased by the end

of the trial at 180 min which hence may not have been a sufficient timeframe to capture the

full effect of propionate on appetite. Moreover, this was only examined after an overnight

fasted state. Thus, the aim of this work would be to build on this previous work by extending

the timeframe and also by assessing the acute effects of sodium propionate on appetite in

healthy volunteers in different physiological states (overnight fasted, sub-maximal exercise

and post-prandial).

4.5 Hypothesis:

I hypothesised that acute oral intake of sodium propionate would reduce subjective appetite

during overnight fasted, sub-maximal exercise and post-prandial states that is mainly related

to an increase in subjective nausea. The impact of oral sodium propionate on subjective

appetite would be related to increased circulating GLP-1 levels.

4.6 Aims:

This chapter will aim to determine the acute effect of propionate bioavailability on appetite

in different energy states (overnight fasted, sub-maximal exercise and post-prandial) by

means of VAS and GLP-1 measurements.


Measuring the acute effects of sodium propionate on appetite was a secondary outcome of

this trial. This will be assessed via VAS in all three studies: overnight fasted, sub-maximal

exercise and post-prandial. In addition, circulating levels of the anorectic hormone, GLP-1,

will be measured in the post-prandial study.

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4.8 Measurement Tools:

Numerous methods, both objective and subjective, have been developed in order to assess

appetite, food intake and their underlying mechanism.

4.8.1 Objective Methodologies:

Objective methodologies include ad libitum food intake that evaluates in-laboratory energy

and macronutrient intake and has been shown to be a reliable and reproducible method in

different contexts such as in healthy, normal weight individuals as well as in obese subjects

(Arvaniti et al., 2000, Venti et al., 2010, Thivel et al., 2016). However, the main drawback that

could arise from a buffet -style ad libitum food intake, is overeating due to the presence of a

variety of foods which would not be an accurate representation of daily energy intake

(Arvaniti et al., 2000, Venti et al., 2010, Gibbons et al., 2019). Yet, increasing the variety

present at multi-item ad libitum test meals may not necessarily decrease the sensitivity to

evaluate perception changes in hunger and fullness (Wiessing et al., 2012). Other factors that

must be considered when employing ad libitum meals in experimental settings include

palatability, food texture, hunger state of subjects and also internal factors such as dietary

restraints (De Graaf et al., 2004, Blundell et al., 2010).

Additional objective methods for assessing appetite include measurements of biological

satiety markers such as gastrointestinal gut hormones (PYY, GLP-1 and ghrelin) that give an

indication of short and long term energy balance and other markers such as leptin changes

for longer term negative energy balance (>2-4 days) and decreases in blood glucose levels as

a marker for short-term energy balance (< 5 min). These tools are useful as they can provide

an index for the satiating effect of food as well as the physiological mechanism behind food

intake regulation (De Graaf et al., 2004, Blundell et al., 2010). GLP-1 in particular has shown

to be a reliable peripheral biomarker associated with meal termination and a predictor of

subjective appetite decrease (De Graaf et al., 2004). Gibbons et al. showed in a cross-over

trial involving 16 healthy, overweight and obese adults that GLP-1, and not PYY, is associated

with short term energy intake after an isoenergetic meal consisting of either a high fat/low

carbohydrate meal ( 50% energy from fat) or a high-carbohydrate/low-fat ( 4% energy from

fat). GLP-1 levels were negatively associated with subjective hunger measured via VAS after

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a high fat meal and was also negatively associated with energy intake as assessed by ad

libitum energy intake after either a high fat or high carbohydrate meal. PYY concentrations,

although significantly increased after either meal, were not associated with feelings of hunger

or with energy intake (Gibbons et al., 2013). A meta-analysis including 115 individuals that

examines the acute effect of GLP-1 concentrations on acute energy intake, however, revealed

a dose dependent reduction in energy intake after an ad libitum meal with a mean of ~12 %

during GLP-1 infusions in comparison to a saline control, although the effect was more

pronounced in lean subjects rather than overweight individuals. Moreover, infusion rates

were correlated with gastric emptying where higher infusion rates resulted in greater

reductions in gastric emptying rates. Increases in plasma GLP-1 levels, however, were only

postively and negatively correlated with subjective feelings of fullness and prospective

consumption respectively, but not to differences in ad libitum energy intake (Verdich et al.,

2001).

Biological satiety markers are not without limitations. For instance, due to the rapid

degradation of the peptides, stringent procedures must be followed when collecting blood

samples such as mixing blood samples immediately with the right dose of protease inhibitors.

Moreover, post-prandial studies evaluating appetite using appetite related hormones are

often expensive and can be quite challenging to carry out. Therefore, it remains debatable as

to whether subjective markers can offer a more robust overview of appetite ratings (Gibbons

et al., 2019).

4.8.2 Subjective Methodologies:

VAS are the most commonly used subjective appetite rating scale in clinical and research

settings and their main advantage relies on their ease of design and data handling, and they

are also shown to be quite useful in monitoring appetite sensations that could be challenging

to assess via alternative methods (Livingstone et al., 2000, Stubbs et al., 2000, Gibbons et al.,

2019). VAS include a variety of appetite related questions that encompass several parameters

such as hunger, fullness, desire to eat and prospective food consumption. In most cases, VAS

are composed of horizontal straight lines 100 mm in length with words or questions at each

end describing extremes such as “I am not hungry at all” or “I am extremely hungry”. Subjects

are then asked to mark across the line their corresponding feelings. Measurement is then

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calculated by measuring the distance between the left end of the line up to the mark. VAS

have shown to have reliable and valid results in predicting feeding behaviour in subjects

across different age groups (Barkeling et al., 1995, Flint et al., 2000, Stubbs et al., 2000, Parker

et al., 2004). However, when assessing the relationship or validity of VAS as a direct predictor

of energy intake, the correlation between the two is not quite robust. A systemic literature

review including 462 papers that examine the relationship between appetite ratings and

energy intake demonstrated that only half of the relevant papers showed a link between self-

reported appetite scores and energy intake while the other half showed that the subjective

ratings did not reliably predict energy intake (Holt et al., 2017). Moreover, from the included

studies, only a handful (6%) actually undertook statistical analysis to analyse this relationship,

and the majority failed to show a link between them. Noteworthy, however, although the

relationship between appetite scores and energy intake were independent of study factors

such as age, sex and sample size, it was dependent on ‘intervention’ type where most studies,

particularly those involving administration of GLP either subcutaneously or via intravenous

administration, demonstrated that VAS appetite scores can reliably predict energy intake.

Another issue that can arise with use of the VAS approach is reproducibility i.e. variation in

results measured at different time-points and on different test days but under the same

standard conditions. Flint et al. demonstrated that appetite ratings of VAS can be reproduced

after single test meals and are not affected by prior diet standardization when considering

the specific parameters being measured, as well as statistical aspects including sensitivity and

power calculations (Flint et al., 2000). Raben et al., on the other hand, showed that

reproducibility of appetite scores after two different test meals assessed twice on different

occasions preceded by 3 days on a standard diet, is low. The authors, however, did note that

palatability may have had an impact on the appetite ratings since hunger and food

consumption rate decreased and fullness increased when palatability decreased (Raben et

al., 1995). Further factors that may lead to poor reproducibility of VAS include biological day

to day variation in subjective hunger as well as methodological variations in study protocols

such as the order of test meals, or prior meal familiarization which can result in conditioned

satiation and even the overlap between physiological and cognitive factors, all of which must

be considered carefully when drawing conclusions using VAS (Raben et al., 1995, Flint et al.,

2000, Holt et al., 2017, Gibbons et al., 2019). Nevertheless, despite the varying issues that

could arise with the use of VAS, when analysed and interpreted cautiously bearing these

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factors in mind, they can provide valuable information regarding processes involved in eating

behaviour regardless of their correlation with food intake or not (Blundell et al., 2010,

Livingstone et al., 2000).

4.8.3 Conclusion:

As human appetite is a quite complex process that depends on a wide range of factors, it

seems that both subjective and objective markers of appetite should ideally be combined and

interpreted together in order to give a better overview and understanding of human appetite

control.

Since measuring the acute effects of sodium propionate on appetite was a secondary

outcome of this trial, VAS were chosen as a marker for subjective appetite in all three studies

given their simplicity and minimal invasiveness. Moreover, this trial involves a repeated

measures design where within-subject treatment effects under controlled laboratory

conditions are examined, and VAS have shown to be best employed under those conditions

(Stubbs et al., 2000, Gibbons et al., 2019). GLP-1, a robust objective marker of appetite, will

also be evaluated in combination with the VAS in the post-prandial trial.

4.9 Methods:

Please refer to the Chapter 2:

4.10 Results:


4.10.1.1.1 Hunger:

Mean hunger concentrations between 0-360 min was not significantly different between the

Propionate and Control trials (Control= 48.0± 4.11 mm; Propionate= 44.8± 5.25 mm; Effect of

trial p= 0.417) (Figure 4:2: A). There was also no significant difference in iAUC0-360 min and

+iAUC0-360 min between trials (p= 0.336; p=0.241) respectively (Figure 4:2: B and C).

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iAUC0-180 min and+iAUC0-180 min were similar between trials (p=0.494; p=0.369) respectively;

(Figure 4:2: D and E). There was also no significant difference in iAUC180-360 min and +iAUC180-

360 min between trials (p=0.260; p= 0.293) (Figure 4:2: F and G).

A

B

C

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D

E

F

G

Figure 4-2: Overnight Fasted Study: Effect of oral sodium propionate supplementation on hunger:

The effect of oral sodium propionate supplementation on subjective hunger: 0 mm corresponds with “Not at

all” and 100 mm with “Extremely” A. Hunger (Time×Trial: p=0.709; Trial: p=0.417; Time: p= 0.000) B. Hunger

iAUC0-360 (p=0.336). C. Hunger +iAUC0-360 (p=0.241). D. Hunger iAUC0-180 min (p=0.494). E. Hunger +iAUC0-180 min

(p=0.369). F. Hunger iAUC180-360 min (p=0.260). G. Hunger +iAUC180-360 min (p=0.293). All data expressed as mean ±

SEM (n=19).

4.10.1.1.2 Thirst:

Mean thirst concentrations between 0-360 min was not significantly different between the

Propionate and Control trials (Control= 24.7± 5.43 mm; Propionate= 23.2± 5.25 mm; Effect of

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trial p= 0.580) (Figure 4:3: A). Similarly, iAUC0-360 min and +iAUC0-360 min were not significantly

different between trials (p= 0.257; p=0.413) respectively (Figure 4:3: B and C).

iAUC0-180 min and +iAUC0-180 min in the Control trial was not significantly different between trials

(p= 0.207; p=0.359) respectively (Figure 4:3: D and E). There was also no significant difference

in iAUC180-360 min and +iAUC180-360 min between trials (p=0.372; p= 0.413) respectively (Figure

4:3: F and G).

A

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B

C

D

E

165

F

G

Figure 4-3: Overnight Fasted Study: Effect of oral sodium propionate supplementation on thirst:

The effect of oral sodium propionate supplementation on subjective thirst: 0 mm corresponds with “Not at all”

and 100 mm with “Extremely” A. Thirst (Time×Trial: p=0.033; Trial: p=0.580; Time: p= 0.346) B. Thirst iAUC0-360

(p=0.257). C. Thirst +iAUC0-360 (p=0.413). D. Thirst iAUC0-180 min (p=0.207). E. Thirst +iAUC0-180 min (p=0.359). F.

Thirst iAUC180-360 min (p=0.372). G. Thirst +iAUC180-360 min (p=0.413). All data expressed as mean ± SEM (n=19).

4.10.1.1.3 Nausea:

Mean nausea concentrations between 0-360 min was significantly higher in the Propionate

trial (Control= 3.5 ± 1.39 mm; Propionate= 8.9 ± 2.70 mm; Effect of trial p= 0.029) (Figure 4:2:

A). iAUC0-360 min was not significantly different between trials (p= 0.159) (Figure 4:3: B).

However, +iAUC0-360 min was significantly higher in the Propionate trial (p=0.006) (Figure 4:4:

C).

iAUC0-180 min and +iAUC0-180 min in the Control trial were not significantly different between trials


in iAUC180-360 min between trials (p=0.159). However, +iAUC180-360 min was significantly higher in

the Propionate trial (p= 0.043) respectively (Figure 4:4: F and G).

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A

B

C

167

D

E

F

G

Figure 4-4: Overnight Fasted Study: Effect of oral sodium propionate supplementation on nausea:

The effect of oral sodium propionate supplementation on subjective nausea: 0 mm corresponds with “Not at

all” and 100 mm with “Extremely” A. Nausea (Time×Trial: p=0.481; Trial: p=0.029; Time: p= 0.612) B. Nausea

iAUC0-360 (p=0.159). C. Nausea +iAUC0-360 (p=0.006). D. Nausea iAUC0-180 min (p=0.210). E. Nausea +iAUC0-180 min

(p=0.084). F. Nausea iAUC180-360 min (p=0.159). G. Nausea +iAUC180-360 min (p=0.043). All data expressed as mean ±

SEM (n=19).

4.10.1.1.4 Correlation Analysis:

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There was a significant negative correlation between nausea and hunger between 0-360 min: iAUC 0-360 min. r= -0.21 95% confidence interval (CI: 0.01478 to 0.3842) p= 0.0302. 4.10.2 Sub-maximal exercise Trial:

4.10.2.1.1 Hunger:

Mean hunger concentrations between 0-180 min was not significantly different between

trials (Control: 49.8 ± 6.1 mm; Propionate= 54.5± 5.6 mm; Effect of trial p=0.354) (Figure 4:5:

A). iAUC0-180 min and +iAUC0-180 min were also similar between trials (p=0.252; p=0.144)

respectively (Figure 4:5: B and C).

Volunteers also reported no significant differences in subjective hunger (Control= 51.7 ± 5.7

mm; Propionate: 57.2 ± 5.3 mm; Effect of trial: p=0.258) (Figure 4:5: A). There was also no

significant difference in iAUC180-240 min and +iAUC180-240 min between trials (p=0.184; p= 0.463)

(Figure 4:5: D and E).

A

169

B

C

D

E

Figure 4-5: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on hunger:


all” and 100 mm with “Extremely”. A. Hunger (Time×Trial: p=0.783; Trial: p=0.354; Time: p= 0.000) and

subjective hunger during exercise (Time×Trial: p=0.329; Trial: p=0.258; Time: p= 0.004) B. Hunger iAUC0-180

(p=0.252). C. Hunger +iAUC0-180 (p=0.144). D. Hunger iAUC180-240 min (p=0.184). E. Hunger +iAUC180--240 min

(p=0.463). All data expressed as mean ± SEM (n=19).

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4.10.2.1.2 Thirst:

Mean subjective thirst ratings between 0-180 min was not significantly different between

trials (Control: 26.7 ± 5.3 mm; Propionate= 29.6 ± 5.4 mm; Effect of trial; p=0.268) (Figure 4:6:



Mean subjective ratings of thirst were significantly higher in the Propionate trial than Control

during exercise (Control= 39.8 ± 5.3 mm; Propionate= 46.8 ± 5.4 mm; Effect of trial; p=0.015)

(Figure 4:6: A). However, there was no significant difference in iAUC180-240 min and +iAUC180-240

min between trials (p=0.984; p= 0.403) (Figure 4:6: D and E).

A

171

B

C

D

E

Figure 4-6: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on thirst


and 100 mm with “Extremely”. A. Thirst (Time×Trial: p=0.248; Trial: p=0.268; Time: p= 0.195) and subjective

thirst during exercise (Time×Trial: p=0.946; Trial: p=0.015; Time: p= 0.000) B. Thirst iAUC0-180 (p=0.164). C. Thirst

+iAUC0-180 (p=0.569). D. Thirst iAUC0-240 min (p=984). E. Thirst +iAUC0-240 min (p=0.403). All data expressed as mean

± SEM (n=19).

:

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4.10.2.1.3 Nausea:

Mean nausea concentrations between 0-180 min was not significantly different between

trials (Control: 1.9 ± 0.7 mm; Propionate= 5.8 ± 2.3 mm; Effect of trial; p=0.115) (Figure 4:7:



Volunteers also reported no significant differences in subjective nausea (Control= 2.9 ± 1.1

mm; Propionate: 6.7 ± 3.4 mm; Effect of trial: p=0.295) (Figure 4:7: A). There was also no

significant difference in iAUC180-240 min and +iAUC180-240 min between trials (p=0.977; p= 0.938)

(Figure 4:7: D and E).

A

173

B

C

D

E

Figure 4-7: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on nausea:


all” and 100 mm with “Extremely”. A. Nausea (Time×Trial: p=0.369; Trial: p=0.115; Time: p= 0.193) and

subjective nausea during exercise (Time×Trial: p=0.365; Trial: p=0.295; Time: p= 0.367) B. Nausea iAUC0-180

(p=0.518). C. Nausea +iAUC0-180 (p=0.641). D. Nausea iAUC0-240 min (p=0.977). E. Nausea +iAUC0-240 min (p=0.938).

All data expressed as mean ± SEM (n=19).


There was no significant correlation between nausea and hunger between 0-180 min: iAUC 0-180 min r= -0.07 95% confidence interval (CI: --0.5088 to 0.3959) p= 0.7725.

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There was also no significant correlation between nausea and hunger between 180-240 min: iAUC 180-240 min: r= 0.06 95% confidence interval (CI: -0.4015 to 0.5039) p= 0.7932. 4.10.3 Post-prandial Trial:

4.10.3.1.1 Hunger:

Hunger between 0-300 min was comparable between the Control and Propionate trials

(Control= 48.8± 5.0mmol/L; Propionate= 43.7± 5.4 mmol/L; Effect of trial p=0.359) (Figure

4:8: A). Hunger iAUC0-300 and +iAUC0-300 was not significantly different between trials

(p=0.840; p=0.768) (Figure 4:8: B and C).

iAUC0-180 min and +iAUC0-180 for the 180 min fasted period were not significantly different

between trials (p= 0.159; p=0.353) respectively (Figure 4:8: D and E). Hunger iAUC180-300 and

+iAUC180-300 were not significantly different between the trials (p= 0.718; p=0.932) (Figure 4:8:

F and G).

A

175

B

C

D

E

176

F

G

Figure 4-8: Post-prandial Study: Effect of oral sodium propionate supplementation on hunger:


all” and 100 mm with “Extremely” A. Hunger (Time×Trial: p=0.389; Trial: p=0.359; Time: p= 0.004) B. Hunger

iAUC0-300 (p=0.840). C. Hunger +iAUC0-300 (p=0.768). D. Hunger iAUC0-180 min (p=0.159). E. Hunger +iAUC0-180 min

(p=0.353). F. Post-prandial hunger iAUC180-300 min (p=0.718). G. Post-prandial hunger +iAUC180-300 min (p=0.932). All


4.10.3.1.2 Thirst:

Thirst between 0-300 min was comparable between the Control and Propionate trials

(Control= 15.7± 4.3 mmol/L; Propionate= 14.5± 4.0 mmol/L; Effect of trial p=0.316) (Figure

4:9: A). Thirst iAUC0-300 and +iAUC0-300 was not significantly different between trials (p=0.325;

p=0.454) (Figure 4:9: B and C).


between trials (p= 0.304; p=0.525) respectively (Figure 4:9: D and E). Thirst iAUC180-300 and


F and G).

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A

B

C

178

D

E

F

G

Figure 4-9: Post-prandial Study: Effect of oral sodium propionate supplementation on thirst:


and 100 mm with “Extremely” A. Thirst (Time×Trial: p=0.444; Trial: p=0.316; Time: p= 0.186) B. Thirst iAUC0-300

(p=0.325). C. Thirst +iAUC0-300 (p=0.454). D. Thirst iAUC0-180 min (p=0.304). E. Thirst +iAUC0-180 min (p=0.525). F. Post-

prandial thirst iAUC180-300 min (p=0.700). G. Post-prandial thirst +iAUC180-300 min (p=0.850). All data expressed as

mean ± SEM (n=20).

4.10.3.1.3 Nausea:

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Nausea between 0-300 min was comparable between the Control and Propionate trials

(Control= 4.7± 1.8 mm; Propionate= 5.3± 1.8 mm; Effect of trial p=0.499) (Figure 4:10: A).

Nausea iAUC0-300 and +iAUC0-300 was not significantly different between trials (p=0.194;

p=0.062) (Figure 4:10: B and C).


between trials (p= 0.278; p=0.273) respectively (Figure 4:10: D and E). Nausea iAUC180-300 was

not significantly different between the trials (p= 0.080). However, +iAUC180-300 was

significantly higher in the Propionate trial (p=0.049) (Figure 4:10: F and G).

A

180

B

C

D

E

F

G

Figure 4-10: Post-prandial Study: Effect of oral sodium propionate supplementation on nausea:

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all” and 100 mm with “Extremely” A. Nausea (Time×Trial: p=0 .116; Trial: p=0.499; Time: p= 0.166) B. Nausea

iAUC0-300 (p=0.194). C. Nausea +iAUC0-300 (p=0.062). D. Nausea iAUC0-180 min (p=0.278). E. Nausea +iAUC0-180 min

(p=0.273). F. Post-prandial nausea iAUC180-300 min (p=0.080). G. Post-prandial nausea +iAUC180-300 min (p=0.049). All


4.10.3.1.4 GLP-1:

For detailed results please refer to 5.14.3.1.3.

In summary, GLP-1 between 0-300 min was comparable between the Control and Propionate

trials (Control= 67.9± 7.1 mmol/L; Propionate= 70.5± 6.2 mmol/L; Effect of trial p=0.626). GLP-

1 iAUC0-300 and +iAUC0-300 was not significantly different between trials (p=0.095; p=0.114).

Similarly, GLP-1 iAUC180-300 and +iAUC180-300 were not significantly different between the trials

(p= 0.081; p=0.312). However, iAUC0-180 min and +iAUC0-180 for the overnight fasted period was

significantly higher in the Propionate trial (p= 0.004; p=0.016) respectively.

A

182

B

C

D

E

183

F

G

Figure 4-11: Post-prandial Study: Effect of oral sodium propionate supplementation on GLP-1 levels:


There was a significant negative correlation between nausea and hunger between 0-300 min: iAUC 0-300 min : r= 0.41 95% confidence interval (CI: 0.2333 to 0.5553) p <0.0001. 4.11 Key Findings:


• Overnight fasted state: Acute propionate ingestion can significantly stimulate

subjective nausea over a prolonged fasted period of 360 min. This is mainly driven by

the second half of the fasting period (180-360 min). However, it has no effect on

subjective hunger and thirst.


• Overnight fasted state: Acute propionate ingestion has no effect on subjective

markers of appetite (hunger, thirst and nausea).

• Sub-maximal exercise state: Acute propionate can significantly stimulate subjective

thirst during exercise.

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• Overnight fasted state: Acute propionate ingestion in the fasted state can stimulate

GLP-1 secretion but has no effect on subjective markers of appetite (hunger, thirst and

nausea).

• Post-prandial state: Acute propionate ingestion can significantly stimulate subjective

nausea but has no effect on GLP-1 secretion and subjective hunger and thirst post-

prandially.

4.12 Summary:

Acute ingestion of oral sodium propionate in healthy human volunteers can stimulate GLP-1

secretion in the overnight fasted state. It can also significantly increase subjective thirst

during sub-maximal exercise and subjective nausea in the overnight fasted and post-prandial

states. However, it has no effect on subjective hunger.

4.13 Discussion:

The aim of this work was to build on previous research which demonstrated that acute

sodium propionate supplementation can increase subjective nausea in healthy volunteers as

assessed by VAS in the overnight fasted state over 180 min (Chambers et al., 2018). Thus, the

acute impact of propionate supplementation on appetite in different energy states (overnight

fasted, sub-maximal exercise and post-prandial) were assessed by means of VAS and GLP-1

measurements and over an extended period of time as discussed below.

4.13.1.1.1 Impact of acute propionate supplementation on appetite measures:

This trial has demonstrated that acute ingestion of 71 mmol of sodium propionate in healthy

human volunteers can have an effect on appetite where it can increase subjective nausea and

GLP-1 levels in the overnight fasted state and can also increase subjective thirst and nausea

during sub-maximal exercise and post-prandial states respectively. However, it seems to have

no effect on subjective hunger in different physiological states. This is in agreement with

previous research of Chambers et al. who showed that acute ingested of 71 mmol of sodium

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propionate over 180 min in the overnight fasted state in healthy volunteers can significantly

increase subjective nausea by means of VAS with no effect on subjective hunger (Chambers

et al., 2018). Similar to this trial, in the post-prandial state, Darzi et al. showed in 20 healthy

volunteers that acute ingestion of propionate (6 mmol) in a palatable sourdough in

comparison to a control bread of similar palatability has no effect on subjective hunger (Darzi

et al., 2012). Also, Chambers et al showed in 20 overweight adults that acute supplementation

of 36.2 mmol of propionate in the form of an IPE has no effect on subjective hunger, however

they also found no effect on subjective nausea. Moreover, in contrast to the present findings,

they found an increase in post-prandial GLP-1 levels (Chambers et al., 2015). Likewise, Frost

et al. showed in 10 healthy volunteers that the consumption of pasta with tomato sauce

supplemented with 30g sunflower oil and 3 mmol of sodium propionate can significantly

increase post-prandial GLP-1 levels and delay gastric emptying. However, comparable with

this trial, they did find an increase in post-prandial subjective nausea and no effect on post-

prandial subjective hunger (Frost et al., 2003). Also, Ruijschop et al. demonstrated in 43

healthy, normal-weight women that consumption of 150 ml of a non-fermented dairy

beverage containing 0.6 % of calcium propionate can significantly decrease subjective feelings

of hunger with no effect on ad libitum energy intake (Ruijschop et al., 2008). Liljeberg et al.,

on the other hand, showed that the addition of 1.9 mol of sodium propionate to bread can

significantly prolong subjective satiety i.e. hunger ratings in comparison to control bread in

12 healthy volunteers (Liljeberg and Björck, 1996, Liljeberg et al., 1995). However, the

unpleasant taste of sodium propionate added to bread was not accounted for and palatability

may have been a cofounder. Henceforth, it can be deduced that the effect of propionate

supplementation on subjective measures of appetite and GLP-1 levels are inconsistent. A

plausible explanation for the discrepancies in findings may be due to dosing varieties as well

as differing mode of administration. For instance, while propionate was administered in the

solid form in most studies, Ruijschop et al. administered propionate in an aqueous solution.

This may have resulted in a varying gastric emptying response and hence appetite effects

since liquids empty from the stomach at a significantly faster rate than solids (Fisher et al.,

1982). Also, where propionate in the gastrointestinal tract is dissociated is also a factor that

must be considered. If, for example, sodium propionate was dissociated in the stomach, the

acidity of the stomach may convert the propionate to its corresponding organic acid

(propionic acid) and organic acids have been shown to delay gastric emptying (Liljeberg and

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Björck, 1998, Liljeberg and Björck, 1996, Hunt and Knox, 1969, Darwiche et al., 2001).

Whereas, if it remains intact as an organic salt it may or may not have an effect on gastric

emptying (Liljeberg and Björck, 1996). Another factor to be considered is the effect of

propionate dissociation on gut hormone release. That is, if propionate is delivered to distal

parts of the gastrointestinal tract, it is more likely to stimulate gut hormone release since L-

cells are more densely found in the colon. Chambers et al. for instance has shown that delivery

of propionate via an IPE is likely to deliver propionate directly to the colon and can stimulate

GLP-1 release (Chambers et al., 2015). However, most of the studies mentioned including this

trial did not report or have an accurate estimation on where in the gastrointestinal tract the

propionate is expected to be delivered and absorbed, hence a direct comparison between

studies on the effect of propionate on gut hormone release is challenging.

4.14 Study Strengths:

This study is unique in that it examined the effect of sodium propionate on subjective appetite

in healthy human volunteers in the three different physiological states. No study, to the best

of knowledge, has examined the effect of sodium propionate on appetite during exercise

while also considering ‘thirst’ as a marker of subjective appetite, although thirst is more

closely linked to increased drinking than hunger is to eating (Mattes, 2010). Thus, it is difficult

to draw conclusions based on the current literature. However, the present findings

demonstrate that sodium propionate has no effect on subjective markers on appetite during

exercise except thirst. The addition of sodium may have increased osmolarity in the

gastrointestinal tract which can draw water from blood circulation into the tract thus

increasing thirst. However, since the control used was ‘sodium chloride’, the sodium is

controlled for, and hence it is highly unlikely that this may have caused the increased

subjective thirst reported. Future studies are therefore needed to further examine the effect

of sodium propionate on appetite ratings during exercise.


The acute effect of sodium propionate supplementation on appetite was a secondary

outcome of the study. Thus, VAS were chosen as a non-invasive, subjective marker of

appetite. However, subjective markers, given their drawbacks, should ideally be combined

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with objective markers of appetite such as ad libitum energy intake and biological satiety

makers in order to give a better and more accurate overview of appetite changes and food

intake. Chambers et al. for instance showed in 20 overweight adults that acute

supplementation of 36.2 mmol of propionate in the form of an IPE can significantly reduce

energy intake by ~14% despite no apparent effect on subjective ratings of appetite and

nausea. This suggests that propionate may act as a satiation signal, rather than a satiety

signal.

Another limitation of the study is that GLP-1 levels were also only measured in the post-

prandial trial, however, post-prandial conditions are more likely to induce GLP-1 secretion in

comparison to other physiological conditions. Nevertheless, future studies should examine

satiety markers in all three energy states.

4.16 Conclusion:

Acute ingestion of oral sodium propionate in healthy human volunteers can influence

subjective markers of appetite, mainly by increasing subjective feelings of nausea during

overnight fasted and post-prandial states and increasing subjective thirst during exercise.

Moreover, it can also stimulate biological satiety markers such as GLP-1 concentrations in the

overnight fasted state all of which could have modulated energy intake had it been measured.

Of note as well, several of the human studies (Frost et al., 2003, Chambers et al., 2018)

including this trial have demonstrated an increase in subjective nausea with propionate

supplementation. Thus, future research aimed at developing therapeutic strategies to

increase propionate availability to reduce appetite would need to develop ways in order to

overcome this limitation. Noteworthy too, an increase in subjective feelings of nausea with

propionate supplementation may be an indicator that propionate can influence satiation

rather than satiety. Future studies can hence assess this by employing VAS continually during

an ad libitum test meal and observe if propionate promotes an early termination of meals.

Furthermore, future intervention studies using a combination of subjective and objective

markers of appetite are clearly needed to give a better overview of the effect of sodium

propionate on appetite regulation. It would be quite interesting as well to assess the impact

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of sodium propionate delivery on appetite in different areas in the gastrointestinal tract. One

way of achieving this is by using tablets similar to the ones used in the present trial but can

disintegrate at varying pH along the tract. For instance, the current tablets are most likely to

disintegrate in the proximal intestine where pH is 6.6 (Evans et al., 1988) since these tablets

were previously shown to completely dissociate at pH 6.8 (Supplements:) whereas other

tablets could be designed that disintegrate at pH 7.5 and 6.4 that would ideally deliver

propionate to the terminal ileum and cecum respectively.

Moreover, very importantly, future studies can quantify both energy intake and energy

expenditure in order to best determine the acute effect of oral sodium propionate

supplementation on overall energy balance.

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Chapter 5: Effect of Sodium Propionate on Glucose Homeostasis

5.1 Abstract:

Background:

Previous studies investigating the acute effects of oral propionate supplementation on

glucose homeostasis in humans have reported inconsistent outcomes. Moreover, studies

have mainly focused on examining these effects during fasted or post-prandial states.

Therefore, this study aimed to investigate in healthy human volunteers, the acute effect of

oral sodium propionate supplementation (71 mmol) on glucose homeostasis in three

separate energy states (overnight fasted, sub-maximal exercise and postprandial).

Methodology:


Overnight Fasted study: 19 volunteers (11 males and 8 females; age: 34.6 ± 4.1 years; BMI









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Overnight Fasted study: The study extended over a total period of 360 min while volunteers





one hour.




Insulin resistance and insulin sensitivity were assessed via HOMA-IR and Matsuda Index

respectively. The oral disposition index (ODI) was used to assess β-cell function. Glucagon-like

peptide 1 (GLP-1), an incretin hormone, was also measured.

Results:

Overnight Fasted study:

Oral sodium propionate supplementation attenuated the decrease in glucose concentrations

over a prolonged fasted period of 360 min (iAUC0-360 min p= 0.012;). However, propionate

supplementation had no effect on insulin concentrations (Control= 7.70 ± 0.48 μU/mL;

Propionate= 7.87± 0.59 μU/mL; Effect of trial p= 0.759), or insulin resistance (HOMA-IR:

Control: 1.42 ± 0.10; Propionate: 1.46 ± 0.12; p=0.698).


Oral sodium propionate supplementation had no effects on glucose (Control= 4.40 ± 0.10

mmol/L; Propionate= 4.37 ± 0.09 mmol/L; Effect of trial: p=0.654) or insulin concentrations

(Control= 5.34 ± 0.39 μU/mL; Propionate= 5.63 ± 0.55 μU/mL; Effect of trial: p=0.379) during

sub-maximal exercise (Control: 48 ± 2 % ; Propionate: 47 ± 2% VO2max).

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Oral sodium propionate supplementation had no effect on glucose concentrations (Control=

5.38± .133 mmol/L; Propionate= 5.40± .141 mmol/L; Effect of trial p=0.823) and insulin levels

(Control= 29.88 ± 2.69 μU/mL; Propionate= 30.27± 2.17 μU/mL; Effect of trial p=0.833) over

a period of 300 min. However, GLP-1 concentrations after the overnight fasted state were

significantly higher with propionate supplementation (iAUC0-180 min p= 0.004 and +iAUC0-180

p=0.016). Post-prandially, propionate supplementation had no effect on insulin sensitivity as

measured by the Matsuda Index (Control: 106.6± 12.0; Propionate: 94.6 ± 5.6; p= 0.388) or β-

cell function as measured by the ODI (Control: 3.15 ± 0.28; Propionate: 4.34 ± 0.78; p= 0.452).

Conclusion:

This was the first in human study to compare the acute effect of propionate supplementation

on glucose homeostasis in three separate energy states. The impact of acute sodium

propionate ingestion on glucose levels in the overnight fasted state was only observed in one

of the three separate studies and thus would demonstrate that acute propionate

supplementation has no effect on markers of insulin sensitivity and glucose tolerance in

healthy humans. However, the present trial only included healthy individuals with normal

glucose tolerance, therefore, well-controlled future studies are needed to determine if similar

effects are observed in individuals with impaired glucose homeostasis.

5.2 Glucose Regulation:

Glucose levels in blood are tightly regulated via a feedback loop between insulin secretion

and insulin action on insulin sensitive tissues such as the liver, muscles and adipose tissue

(Zheng et al., 2018). This relationship can be characterized by a hyperbola with the product

of these two variables, ‘disposition index’, being constant in subjects with similar glucose

tolerance (Faerch et al., 2010). Insulin secretion relies on pancreatic β-cell response to raised

glucose concentrations while glucose levels are mainly regulated via hepatic insulin-

stimulated glucose production and uptake into muscle. Thus, a disruption in β-cell function

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indicates a suppressed response to glucose stimulated insulin secretion, whereas insulin

resistance develops with diminished hepatic response to insulin action and insulin-stimulated

uptake into muscle (Gutch et al., 2015) . Indeed, peripheral insulin resistance and β-cell failure

are considered the hallmarks in the pathogenesis of T2D (Zheng et al., 2018, Choi et al., 2012)

.

5.3 Indices:

The assessment of insulin resistance/sensitivity and β cell function can be quite challenging

due to the complex interplay between the two measures. However, several methods exist

that can measure each, and the method of choice mainly relies on the validity, reproducibility,

cost and practicality of use. Direct measurements are often more complex to perform in vivo,

whereas indirect and simple surrogate indexes are also available and can be more practical in

some cases. In any case, all methods rely on analysing either steady-states of glucose and

insulin, or on dynamic testing (Patarrão et al., 2014, Choi et al., 2012).

5.3.1 Direct Measures:

5.3.1.1.1 Hyperinsulinemic Euglycemic Glucose Clamp (HIEC) (Muniyappa and Madan, 2018,

Patarrão et al., 2014, Borai et al., 2007):

The Hyperinsulinemic Euglycemic Clamp technique originally developed by Andres and

DeFronzo is considered the ‘gold standard’ for directly measuring insulin sensitivity in

humans. After an overnight fast, insulin is intravenously infused at a constant rate (5 to 120

mU/m2 /min dose per body surface area per minute) to achieve a steady state insulin level

above fasting levels (hyperinsulinemia). Thus, hepatic glucose production is suppressed while

glucose uptake in skeletal muscles and adipose tissue is enhanced. In order to maintain

normal glucose levels (euglycemia), a glucose analyser is used to frequently assess glucose

levels and a 20% dextrose solution is intravenously infused to ‘clamp’ glucose levels. Steady

states of plasma insulin, blood glucose and glucose infusion rate (GIR) are then achieved after

a couple of hours of constant insulin infusion. When no net change in blood glucose levels is

observed, the hyperinsulinemic state is assumed to suppress hepatic glucose production and

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thus GIR is equal to glucose disposal rate (M). Hence, at any given level of hyperinsulinemia,

whole body glucose disposal can be calculated. After normalizing M for body weight and fat-

free mass, insulin sensitivity can be determined.

Advantages:

The primary advantage of this technique is that under steady state conditions, it can directly

measure glucose disposal rate at any given level of hyperinsulinemia. Thus, this method can

be used in research settings where insulin sensitivity is a primary outcome.

Limitations:

There are a number of limitations with this approach including expense, time-consumption,

intense labour requirement and expert personnel. Moreover, the technique requires steady

state conditions and a fasting state and thus does not portray the dynamic insulin release that

occurs after meals. Moreover, since it is an intravenous measure that bypasses the GI tract,

it does not portray a normal physiological response.

5.3.1.1.2 Hyperglycaemic clamp (Borai et al., 2007):

This method is mainly used to assess β-cell function in response to glucose. Plasma glucose

levels are acutely raised via a priming glucose dose and maintained at 7 mmol/L for 2 hours.

Glucose is then administered at 5 min intervals for maintenance till the end of the test. The

principle behind this method is that hyperglycaemia would stimulate β-cell secretion and

glucose disposal and therefore, the volume of the maintenance dose would reflect insulin

secretion. Once glucose levels become stable, its infusion rate M (mg/kg/min) can be

calculated and can provide an index of glucose metabolism. Moreover, M/I index can be

obtained where I is the average insulin concentration during the study. This index provides a

measure of endogenous insulin secretion sensitivity.

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Advantages:

This method can provide a quantitative procedure to assess β-cell function and can be used

to study prediabetic states and efficacy of medications.

Limitations:

This method is expensive, time-consuming, laborious and unsuitable for large scale screening

populations. Moreover, as glucose is administrated intravenously, it does not provide a

physiological state as it bypasses the gastrointestinal tract and its effects on glucose

homeostasis.

5.3.1.1.3 Other examples:

Other examples of direct measures of insulin sensitivity/resistance include frequently

sampled intravenous glucose tolerance, insulin suppression test and insulin tolerance test.

5.3.2 Indirect Measures:

5.3.2.1.1 Oral Glucose Tolerance Test (OGTT) (Muniyappa and Madan, 2018, Patarrão et al.,

2014):

This is a simple test that is widely used to assess glucose intolerance and T2D in clinical

settings. An oral glucose load (75g) is given after an overnight fast and blood samples are

taken at 0,30,60 and 120 minutes to determine glucose and insulin concentrations. This test

mainly assesses glucose tolerance i.e. the efficiency of the body to handle glucose after a meal

or glucose load. An individual is diagnosed with diabetes if the two-hour plasma glucose after

an OGTT is ≥ 200 mg/dl (11 mmol/l) while diagnosis of impaired glucose tolerance is identified

if the two hour glucose levels after an OGTT is in the range of 140–199 mg/dl (7.7–11 mmol/l)

(Association, 2014).

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Advantages:

This test mimics glucose and insulin dynamics of physiological states more explicitly than

other measures such as HIEC. Moreover, this test is simple and easy to perform and is quite

low in cost.

Limitations:

The OGTT gives a crude measure of glucose tolerance but it does not measure insulin

sensitivity/resistance and insulin secretion directly. Glucose tolerance is affected by other

several factors such as insulin secretion, incretin effects and metabolic actions of insulin and

thus is not equivalent to insulin sensitivity/resistance. Moreover, OGTT may elicit poor

reproducibility (Ko et al., 1998) since the rate of gastric emptying can have intraindividual

variability and also cannot give an accurate picture regarding the dynamics of glucose and

insulin. Thus, other indices based on OGTT have been developed to better assess β cell

function and insulin sensitivity/resistance.

5.3.2.1.2 Meal Tolerance Test (MTT) (Patarrão et al., 2014):

This involves a mixed meal of carbohydrates, fat and proteins in order to study blood glucose

profile in a better physiological setting than an OGTT. This test can thus be considered a

variant of OGTT and is also measured after an overnight fast and blood samples for glucose

and insulin are taken over two hours.

Advantages:

The test is simple to perform and more appealing for subjects than the standard OGTT.

Moreover, it mimics a more physiological response to glucoregulation with regards to insulin

sensitivity and secretion. In addition, it can provide a better overview of β cell function since

β cell sensitivity after a meal is higher than after an OGTT despite similar carbohydrate

content, which may be due to the lower glycaemic index of the MTT and the slower rate of

gastric emptying in comparison to the OGTT. A liquid meal test has also been shown to be a

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practical method to evaluate insulin secretion and sensitivity in clinical and population studies

(Maki et al., 2009).

Limitations:

MTT measures glucose tolerance and thus does not measure insulin sensitivity/resistance per

se and is also not reproducible within the same individual due to variability in glucose

absorption, splanchnic glucose uptake, and additional incretin effects.

5.3.2.1.3 Other examples:

Other examples of indirect measures of insulin sensitivity/resistance include minimal model

analysis of frequently sampled intravenous glucose tolerance test and intravenous and oral

tracer studies.

5.4 Simple surrogate indexes:

Insulin sensitivity indices are divided into two main groups: a. Indices that are measured using

fasting measures of plasma insulin, glucose and triglycerides b. Indices that are measured

using post-prandial plasma concentrations of insulin and glucose during 120 min of an OGTT

or MTT (Gutch et al., 2015).

5.4.1 Indices obtained from fasting blood sample:

These indexes rely on the following principles. In healthy humans, the fasting state represents

the basal steady state where glucose levels are maintained within a normal range via a basal

insulin secretion and a hepatic glucose production rate that is maintained at a constant rate.

Thus, insulin secretion by pancreatic β cells maintains insulin levels that can range from low

to high depending on insulin sensitivity/resistance in order that hepatic glucose production

equates to whole body glucose disposal (Muniyappa and Madan, 2018). Although these

indexes mainly reflect hepatic insulin sensitivity/resistance, these measures can also be used

as a surrogate for skeletal muscle sensitivity/resistance since in most cases hepatic and

skeletal muscle sensitivity are somewhat equivalent in the fasting state (Faerch et al., 2010,

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Muniyappa and Madan, 2018). However, in T2D and even prior to overt T2D, insulin

resistance can manifest separately or at varying degrees in the liver in comparison to skeletal

muscles (Faerch et al., 2010). Thus, more useful surrogate indexes try to take this into

account. However, universal cut-off points to be used in surrogate indexes have not yet been

determined due to a lack of a standardized insulin assay (Muniyappa and Madan, 2018).

5.4.1.1.1 Homeostasis Model Assessment (HOMA)(Choi et al., 2012, Patarrão et al., 2014,

Gutch et al., 2015):

HOMA was first developed by Matthews et al. in 1985 to quantify insulin resistance and β cell

function from fasting insulin and glucose levels. The model is based on the dynamic

interaction between glucose and insulin and the feedback loop between the liver and

pancreatic β cells i.e. glucose concentrations are determined via hepatic insulin-mediated

glucose production, while insulin levels are regulated via pancreatic β cell response to glucose

concentrations. Thus, the model is able to predict fasting steady state glucose and insulin

concentrations for a wide range of combinations of insulin resistance (HOMA-IR) and

pancreatic β-cell function (HOMA- β-cell).

The model describes the glucose-insulin homeostasis via a set of empirically derived non-

linear equations. Most studies using this model employ a simple approximate equation for

insulin resistance that is derived from the use of fasting plasma insulin-fasting plasma glucose

product which is the index of hepatic insulin resistance divided by a constant. The formula is

as follows:

HOMA= Fasting Insulin (IU/ml) x Fasting Glucose(mmol/l) / 22.5

where 22.5 is a normalizing factor obtained from the product of normal fasting plasma insulin

of 5 IU/ml and normal fasting plasma glucose of 4.5mmol/l of an “ideal and normal”

individual.

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Advantages:

This model is quite simple to perform and is minimally invasive requiring only a single blood

withdrawal from a fasting individual. Gungor et al. showed that HOMA-IR and HOMA- β-cell

as markers of insulin sensitivity and secretion respectively could have very good correlations

with gold standard techniques such as euglycemic clamp and hyperglycaemic clamp methods

in both healthy children and adolescents (Gungor et al., 2004). It is also low in cost and does

not require extensive expertise rendering it quite practical to use in large scale clinical,

epidemiological and prospective studies where only fasting blood samples are available.

Indeed, Bonora et al., using glucose clamp and glucose tracer dilution technique,

demonstrated in 115 individuals with varying degrees of glucose tolerance and insulin

sensitivity that HOMA-IR had a strong correlation with glucose clamp measures across a wide

range of varying population characteristics, where no substantial differences were found

between these measures in men and women, young and old subjects, obese and non-obese,

diabetic and non-diabetic as well as hypertensive and normotensive individuals (BONERA,

2000).

Limitations:

One of the main disadvantages of this method is that it cannot provide information regarding

stimulated glucose and insulin concentrations as it reflects the homeostatic, fasting state and

hence, provides information on insulin stimulated hepatic glucose production but no

information regarding peripheral glucose uptake. It is also challenging to use on subjects with

poor glycaemic control or those with β cell dysfunction or lean individuals with higher rates

of insulin sensitivity (Kang et al., 2005, Thompson et al., 2014). Also, its use in longitudinal

assessment of insulin sensitivity changes over time may be weak (Xiang et al., 2014).

Moreover, its use in clinical practice may be limited due to lack of reference values for normal

and impaired insulin sensitivity as well as lack of standardization of the insulin assay.

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5.4.1.1.2 Quantitative insulin sensitivity check index (QUICKI)(Patarrão et al., 2014, Gutch et

al., 2015):

QUICKI can be viewed as a simple variation of HOMA equation except that a log transform of

the insulin glucose product is used instead. Using fasting plasma glucose and insulin

concentrations, QUICKI is able to precisely and consistently predict insulin sensitivity index

with great predictive power as per the following equation:

QUICKI = 1/log (fasting insulin, IU/ml) + log (fasting glucose, mg/dl)

Advantages:

This tool is one of the most thoroughly evaluated and validated surrogate index for insulin

sensitivity. It is also quite simple, inexpensive and minimally invasive method and can be used

in large epidemiological or clinical research trials and where evaluation of insulin sensitivity is

not of primary interest.

Limitations:

Similar to HOMA, QUICKI is unable to provide information on peripheral glucose uptake,

which is an important aspect regarding insulin resistance, and can only provide information

concerning homeostatic measures in the fasting state. Also, each laboratory has its unique

normal range due to significant interlaboratory variations in insulin assay.

5.4.2 Indices obtained after dynamic testing such as OGTT/MTT (Muniyappa and Madan,

2018):

These indices rely on measures obtained from dynamic tests such as OGTT and MTT that

utilize both fasting and post-prandial plasma glucose and insulin levels. After a glucose load,

hepatic glucose production is maximally suppressed for a period of an hour and remains

suppressed at a constant level for the subsequent hour. Thus, the decrease in plasma glucose

concentration from its peak to its lowest range reflects glucose uptake by peripheral tissues.

Hence, these indices reflect both hepatic and peripheral insulin sensitivity which can be

favourable in many instances. However, if insulin sensitivity/resistance information is solely

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required, then it may be more favourable to use fasting surrogates as they are simpler to

obtain.

5.4.2.1.1 Matsuda Index (Patarrão et al., 2014, Gutch et al., 2015):

A method developed by Matsuda and Defronzo that can assess whole body insulin (ISIMatsuda)

sensitivity and thus reflects both hepatic and peripheral insulin sensitivity. The method is

quite simple to calculate and is obtained from plasma glucose and insulin concentrations in

fasting states and during an OGTT/MTT.

ISIMatsuda= !","""

$(&'×)'×&*+,-×)*+,-)

10,000: Simplifying constant to get numbers from 0 to 12;

√ : Correction of the nonlinear values distribution;

G0 – fasting plasma glucose concentration (mg/dl);

I0 – fasting plasma insulin concentration (mIU/l);

Gmean – mean plasma glucose concentration during OGTT (mg/dl);

Imean – mean plasma insulin concentration during OGTT (mIU/l);

Advantages:

The tool is quite simple to calculate and can provide a measure of both hepatic and peripheral

insulin sensitivity in subjects with a wide range of glucose tolerance ranging from normal

glucose tolerance to overt diabetes (Matsuda and DeFronzo, 1999). Moreover, race and

gender appear to have little impact on the reliability of using this index and can thus prove to

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be superior to other insulin sensitivity indices such as HOMA-IR and QUICKI in mixed race and

gender populations (Pisprasert et al., 2013).

Limitations:

Its use in longitudinal assessment of changes in insulin sensitivity may be weak (Xiang et al.,

2014).

5.4.2.1.2 Oral Disposition Index (Choi et al., 2012, Utzschneider et al., 2009, Muniyappa and

Madan, 2018):

This is a simple surrogate marker that can be used to estimate β-cell function relative to

insulin sensitivity in subjects with varying levels of glucose tolerance. The principle relies on

the hyperbolic relationship between insulin sensitivity and insulin secretion i.e. the regulation

in glucose homeostasis that occurs through pancreatic β-cell compensation for changes in

whole body insulin sensitivity via changes in insulin secretion and can be calculated as follows:

Oral disposition index = (DI0 –120/D G0 –120) x (1/fasting insulin)

Advantages:

This index can provide an excellent measure of β-cell function adjusted for insulin sensitivity

and can reliably predict the development of diabetes over 2-10 years in subjects with various

degrees of glucose tolerance (Utzschneider et al., 2009, Ram et al., 2015). It can also serve as

a simple surrogate that can be applied to obese adolescents especially when feasibility and

cost of clamp studies are a major challenge (Sjaarda et al., 2012).

Limitations:

As with all dynamic testing surrogates, this measure incorporates both peripheral and hepatic

insulin sensitivity. Moreover, since it is obtained from an OGTT or MTT, differentiating the

direct metabolic effects of insulin following the glucose load can be challenging. Also, oral

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disposition index can be faulty in a diabetic range rendering its main usefulness in non-

diabetic patients.

5.5 Methods of Choice:

Since measurement of glucose homeostasis is a secondary outcome of this thesis and since

surrogate markers have shown to be inexpensive quantitative tools that can be applied in

many settings, we have chosen to use HOMA-IR in the overnight fasted and sub-maximal

exercise studies to assess insulin resistance, and we have chosen the Matsuda Index and Oral

disposition index in the post-prandial study to assess insulin resistance and β-cell function

respectively.

5.6 Dietary Fibres and Type 2 Diabetes:

Diabetes Mellitus is a global health concern inflicting 1 in 11 adults worldwide with the

majority (90%) suffering from type 2 diabetes mellitus (T2D) (Zheng et al., 2018). A large scale

series of systematic reviews and meta-analysis have identified high intakes of dietary fibres

(DF) to be associated with a decreased incidence and mortality from multiple non-

communicable diseases, amongst which is T2D (Reynolds et al., 2019). Indeed, striking dose

response curves verified a 15% risk reduction in T2D incidence for every 8g/d increase in DF

intake. The optimal daily intake for incurred metabolic health benefits was >25g/d (Reynolds

et al., 2019); however, the majority of the population fails to consume that amount where

the average global DF intake is approximately 20g/d (Stephen et al., 2017). Thus, identifying

the mechanisms behind DF’s reported health benefits on glucose homeostasis may allow us

to prevent or treat associated metabolic diseases.

Human studies have shown that raised intake of DF can improve both insulin resistance and

β-cell function. For example, Suntari et al. reported that supplementing 20 T2D patients with

32g of fibre rich snacks daily for 4 weeks significantly decreased fasting blood glucose and

insulin resistance and significantly increased C-peptide levels (a key marker of endogenous

insulin secretion in diabetic patients) (Sunarti et al., 2019). In a randomized controlled trial,

Weickert et al., showed that supplementing 17 normoglycemic overweight and obese women

with fibre enriched bread (31.2 g fibre/d) versus control white bread (state fibre /d) for 72

hours, can significantly enhance insulin sensitivity as assessed by a significant increase in

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whole body glucose disposal (Weickert et al., 2006). Additionally, Chen et al., demonstrated

in a randomized double blind trial including 117 diabetic subjects that the fibre supplemented

group (10 or 20g/d) versus the control group (0 g/d) after 4 weeks supplementation, had

significant improvements in fasting and 2 hour glucose concentrations, fasting C-peptide

levels and 2-hour insulin and other metabolic markers in comparison to control. Also, the

higher fibre group (20g/d) had a significantly improved fasting glucose and insulin resistance

index in comparison to the other groups (Chen et al., 2016).

5.7 Propionate and Glucose Homeostasis:

The beneficial effects of dietary fibres on glucose homeostasis may in part be attributed to

DF’s major metabolic end products of gut microbial fermentation, the short-chain fatty acids

(SCFA): acetate, propionate and butyrate (Canfora et al., 2015, Hu et al., 2018). Den Besten

et al. showed that mice on a high fat diet (HFD) supplemented with either of the SCFA (5%

wt/wt) for 12 weeks had enhanced peripheral insulin sensitivity, as seen by lower fasting

insulin levels and similar glucose levels, in comparison to control mice who were on an HFD

alone. Also, the SCFA supplemented mice prevented the HFD-induced insulin resistance

associated with an HFD as verified by similar insulin levels and peripheral insulin sensitivity in

comparison to mice on a normal chow diet. The authors revealed that the SCFA induced

reduction in insulin resistance is mediated via adipose specific Pparg (Peroxisome proliferator-

activated receptor gamma) regulated effect on glucose homeostasis since Pparg adipose

specific knock out (KO) mice no longer exhibited these effects (den Besten et al., 2015). Also,

De Vadder et al. showed that rats fed a SCFA (propionate or butyrate) supplemented diet or

a fermentable carbohydrate diet had significantly improved glucose and insulin tolerance and

a significant decrease in hepatic glucose production in comparison to rats on a standard diet.

These proposed benefits on glucose homeostasis were mainly attributed to the gut brain

neural circuit and intestinal gluconeogenesis (IGN) since KO mice of intestinal glucose-6-

phospahate, a principal enzyme involved in IGN, no longer displayed those positive effects on

glucose homeostasis (De Vadder et al., 2014). Additionally, in 60 overweight adults, Pingitore

et al. showed that chronic supplementation of propionate in the form of an inulin propionate

ester (IPE) for 24 weeks resulted in a significant improvement in β-cell function as measured

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by the oral disposition index, which the authors concluded that might be via the propionate

acting directly on islet β-cells (Pingitore et al., 2017).

In the liver, propionate acts as a gluconeogenic substrate where it enters the TCA

(tricarboxylic acid) cycle at the level of succinyl-CoA and is then converted to oxaloacetate

and then ultimately into glucose (den Besten et al., 2013b). A stable isotope study in humans

has shown that a small fraction (6 %) of gut derived propionate can be utilized for hepatic

gluconeogenesis rather than be directly oxidized. However, the post-prandial nature of the

study may have underestimated the amount of propionate that could be used for

gluconeogenesis after an overnight, fasted state (Boets et al., 2017). Conversely, daily

propionate production (29.5 mg/kg/d for an average 85 kg man) is unlikely to substantially

contribute to endogenous glucose production, which is estimated to be 2.2 mg/kg/min

andonly half of which is attributed to gluconeogenesis (Morrison and Preston, 2016). In any

case, propionate supplementation has shown to have an impact on glucose homeostasis in

both rodent and human studies via various mechanisms:

5.7.1 The impact of Propionate on glucose homeostasis in non-ruminants:


Oral:

A single study examined the acute effect of oral propionate supplementation on glucose

homeostasis in rodents. Tirosh et al. showed that supplementing mice acutely with

propionate (15 mmol/kg) via an oral gavage can induce hyperglycaemia and increase plasma

insulin concentrations in comparison to pyruvate (Tirosh et al., 2019).

Infusion:

Only one study examined the effect of acute propionate infusion on glucose homeostasis in

rodents. Tirosh et al. showed that mice acutely supplemented with propionate via

intraperitoneal injection at a rate of 15 mmol/kg can dose dependently induce

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hyperglycaemia and increase plasma insulin concentrations in comparison to the

gluconeogenic substrate pyruvate (Tirosh et al., 2019).


Oral:

Several animal studies have examined the chronic effect of oral propionate supplementation

on glucose and insulin profile. Cameron-Smith et al. showed that sodium propionate

supplementation to an HFD (0.5% or 5% wt/wt) for 4 weeks has no effect on glucose

metabolism in normal and streptozocin (STZ)-induced diabetic rats since at the end of the

supplementation period, fasting glucose, hepatic glucose production and rate of glucose

disposal were similar to that of rats fed an HFD alone (Cameron-Smith et al., 1994). In

contrast, Boillot et al. showed that rats fed a standard diet supplemented with 74.1 mmol/kg

of propionic acid for 3 weeks had significant decreases in fasting glucose concentrations with

no apparent effect on hepatic glucose production and glucose utilization in comparison to

mice fed a diet supplemented with a corresponding amount of poorly fermentable cellulose

(Boillot et al., 1995). In obese hyperinsulinemic rats with a metabolic profile similar to subjects

at risk of developing metabolic disease, Berggren et al. demonstrated that oral sodium

propionate ingestion can have favourable effects on glucose metabolism. Mice were fed an

HFD supplemented with 26mmol/kg BW/d of sodium propionate for 19 days. At the end of

the supplementation period, fasting plasma glucose and urinary glucose excretion (after 5

days of supplementation) were significantly decreased in the propionate group in comparison

to mice who were on an HFD only (Berggren et al., 1996). More recent studies also displayed

similar findings. De Vadder et al. showed that mice fed a standard diet supplemented with

sodium propionate (5% wt/wt) for 10 days displayed improved glucose and insulin tolerance

with no associated increase in insulin secretion (De Vadder et al., 2014). Also, Den Besten et

al. showed that mice on an HFD supplemented with sodium propionate at 5% wt/wt for 12

weeks had significantly lower fasting insulin than mice on HFD alone. Moreover, the

supplemented mice exhibited similar peripheral insulin sensitivity in comparison to mice on

a normal chow diet (den Besten et al., 2015). Similarly, Zhu et al. showed that supplementing

obese mice on an HFD with IPE or propionate mixtures for 4 weeks can decrease fasting

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glucose and insulin levels as well as insulin resistance as assed by HOMA-IR, and can also

enhance pancreatic b cell function as assessed by HOMA-b in comparison to obese mice

controls on an HFD only (Zhu et al., 2020).

Infusion:

Other studies have examined the effect chronic propionate supplementation via infusions.

McBurney et al. showed that pigs receiving a portal infusion of an isomolar propionic acid

solution at a rate of 0.01 kg/BW/min, which is similar to a typical SCFA post-prandial

absorption rate, for seven days had no significant effect on glucose and insulin concentrations

as well as on insulin sensitivity and glucose tolerance (McBurney et al., 1995). Similarly,

Berggren et al. showed in obese hyperinsulinemic rats with a metabolic profile similar to

subjects at risk of developing metabolic disease, that rectal infusion of sodium propionate at

a rate of 4 mmol/kg BW/d has no effect on glucose profile. Fasting plasma glucose and urinary

glucose excretion were measured after 19 days of the intervention and compared with mice

an HFD. At the end of the supplementation period, these metabolic factors remained

unaffected (Berggren et al., 1996). On the other hand, Den Besten et al. demonstrated in mice

that the beneficial effects of long-term guar gum consumption on decreasing markers of

metabolic syndrome such as disturbed glucose homeostasis are related to in vivo fluxes of

SCFA including propionate. A phosphate buffered solution including tracer amounts of [2-13C]

propionate (1.5 mM, 99 atom %, Sigma-Aldrich) was infused into mice via a cecum catheter

for a period of 6 hours which allowed the calculation of in vivo flux rate. Propionate flux

uptake resulted in a significant inverse correlation with glucose and insulin levels as well as

with HOMA-IR. Moreover, it significantly correlated with genes involved in glycolysis and

inversely correlated with genes involved in gluconeogenesis (den Besten et al., 2014). Further,

Yu et al. showed that infusing pigs with 2 mol/L of sodium propionate via a cecal fistula twice

per day for 28 days versus 25 ml of saline control, can accelerate hepatic gluconeogenesis and

can decrease glycolysis with no effect on serum insulin and GLP-1 levels. The propionate

infusion was able to upregulate PCK1, a key enzyme involved in hepatic gluconeogenesis, and

can downregulate HKDC1 and GCK and G6PC3 genes which result in reduced glycolysis (Yu et

al., 2019). However, no effect on glucose per se was reported. Thus, it can be assumed that

supplementation had no effect on glucose concentrations.

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In summary, the majority of evidence in animals highlights that long-term oral propionate

supplementation can lead to improvements in glucose homeostasis while acute studies are

presently scarce, and more studies are needed to confirm any possible effect.

5.7.2 The impact of Propionate on glucose homeostasis in humans:


Oral:

A small number of studies have examined the acute effect of oral propionate

supplementation on glucose homeostasis in humans and these can be divided according to

their effects on glucose profile to positive glucose lowering effects, no effect and impaired

glucose effects as follows.

Glucose Lowering Effects:

Most studies have displayed positive lowering glucose effects with propionate

supplementation. Todesco et al. demonstrated that blood glucose levels can acutely be

decreased by ~50% in six healthy volunteers after consumption of bread supplemented with

31.2 mmol of sodium propionate per 50g of available carbohydrate in comparison to control

white bread (Todesco et al., 1991). Similarly, Liljeberg et al. showed that acute ingestion of

barely bread baked with a high concentration of sodium propionate (1.9 mol) can significantly

decrease postprandial blood glucose and insulin concentrations in 11 healthy adults in

comparison to control bread (Liljeberg et al., 1995). Also, Darwiche et al. showed in 9 healthy

volunteers that acute supplementation of sodium propionate (1.9 mol) in bread can

significantly decrease post-prandial glucose and insulin responses in comparison to control

bread (Darwiche et al., 2001). Frost et al. showed in 10 healthy volunteers that the

consumption of pasta with tomato sauce supplemented with 30 g sunflower oil and 30 mmol

of sodium propionate can significantly increase post-prandial GLP-1 levels, delay gastric

emptying and decrease glucose and insulin concentrations in comparison to psyllium enriched

pasta (Frost et al., 2003). Darzi et al. , too, showed in a randomized cross-over trial involving

20 healthy subjects, that acute ingestion of propionate (6 mmol) in a palatable sourdough,

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may lower post-prandial insulinemia although this was not significant in comparison to a

control bread (Darzi et al., 2012).

No Effect:

In contrast, in a randomized cross-over trial, Chambers et. al have shown no apparent effect

on post-prandial glucose and insulin profile in overweight individuals supplemented acutely

with 36.2 mmol of IPE (Chambers et al., 2015). Similarly, Chambers et al. showed that acutely

supplementing 18 healthy volunteers with 71 mmol of sodium propionate over 180 min has

no effect on fasting circulating glucose and insulin concentrations (Chambers et al., 2018).

Impaired Effect:

While, Tirosh et al. demonstrated in 14 healthy individuals that supplementing 5.37 mmol of

calcium propionate to a mixed meal can cause a significant decrease in insulin sensitivity and

a compensatory increase in serum insulin concentrations in comparison to a mixed meal

propionate-free control (Tirosh et al., 2019).

Infusion:

Other human studies have examined the effect of acute propionate administration via

infusions on glucose homeostasis and these seem to show no significant impact.

No Effect:

Wolver et al. showed in six healthy subjects that acute rectal infusion of a solution containing

90 mmol acetate plus 30 mmol propionate (90 mM, isotonic) or 180 mmol acetate and 60

mmol propionate (180 mM, hypertonic) per 800 ml has no significant effect on fasting blood

glucose concentrations and serum insulin levels in comparison to a saline control as well as

no effect on insulin secretion as evidenced by lack of rise in serum C-peptide level in all 3

infusions (Wolever et al., 1989). A later study showed somewhat similar findings where in six

healthy individuals, Wolver et al. showed that a rectally infused solution of propionate (180

mmol) relative to a saline control, can significantly increase fasting glucose concentrations

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with no significant effect on insulin concentrations. However, a rectal infusion mixture of

acetate (180 mmol) and propionate (60 mmol) resulted in a very small but significant lower

insulin increments than the propionate solution alone (Wolever et al., 1991). In addition,

Laurent et al. showed in 6 healthy volunteers that 3 hour gastric infusions of a sodium

propionate solution (4 mmol/h) or sodium acetate and sodium propionate mixture (12

mmol/h and 4 mmol/h respectively) which is an administration rate typical of that of daily

fermentation of 30g of dietary fibre, has no effect on fasting glucose and insulin

concentrations as well as hepatic glucose production (Laurent et al., 1995). Similarly, Canfora

et al showed that acute colonic administration of a high propionate solution (18 mmol sodium

acetate (45%), 14 mmol sodium propionate (35%), 8 mmol sodium butyrate (20%) dissolved

in 200 mL sterile water) in 12 normoglycemic men had no effect on fasting and post-prandial

glucose and insulin concentrations (Canfora et al., 2017).

5.7.2.1.1.1 Chronic Propionate Supplementation:

Oral:

Other human studies have observed the effect of chronic oral propionate supplementation

on glucose and insulin profile and have mostly found favourable results.

Glucose Lowering Effects:

Venter et al. showed that propionate supplementation can decrease fasting glucose

concentrations where in a double-blind paired controlled trial, 78.0 mmol of sodium

propionate was supplemented to the diet of 10 healthy female volunteers (20-22 years) daily

for 7 weeks which resulted in a significant decrease in fasting serum glucose concentrations

during a glucose tolerance test in comparison to the control group who were supplemented

with dibasic calcium phosphate (Venter et al., 1990). Similarly,

Todesco et al. showed that incorporation of 9.9 g of sodium propionate to bread can

significantly decrease blood glucose levels by ~40% and peak rise in blood glucose by ~10% in

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six healthy adults after one week of intervention in comparison to control white bread

(Todesco et al., 1991). Also, Chambers et al. have shown in 12 overweight and obese non-

diabetic adults that colonic delivery of propionate via IPE (72.4 mmol) for 42 days can improve

insulin resistance and sensitivity in comparison to a low fermentable cellulose control and can

also significantly improved adipose tissue insulin resistance. These changes were mainly

driven by a significant decrease in fasting insulin values in comparison to cellulose control

(Chambers et al., 2019).

No Effect:

In a randomized controlled trial, Chambers et al. were able to demonstrate in 60 overweight

adults that chronic supplementation of propionate in the form of an IPE (36.2 mmol ) has no

apparent effect on post-prandial glucose homeostasis (Chambers et al., 2015).

As can be seen, findings from human trials are not yet conclusive and whether propionate

administered in different methods, or form and over varying time-frames has a

positive/negative/no effect is yet to be determined.

5.8 Propionate’s mechanism of action on glucose

A number of mechanisms have been proposed to explain how propionate administration can

regulate glucose homeostasis:

5.8.1 The Impact of Propionate on enzymatic activity:

Propionate has been shown to decrease amylase activity in vivo and can thus reduce the rate

of starch digestibility which subsequently results in reduced postprandial glucose response

(Todesco et al., 1991).

5.8.2 The Impact of Propionate on Free-fatty acid receptors and Gut hormones:

Propionate is a potent activator of free fatty acid receptor 2 (FFAR-2) and free fatty acid

receptor 3 (FFAR-3) which are expressed throughout the body such as in colonic L-cells and

pancreatic β-cells, and both receptors have been shown to modulate energy metabolism and

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glucose homeostasis in both rodents and humans (Byrne et al., 2015, Pingitore et al., 2017,

Psichas et al., 2015). Activation of FFAR-2, in particular, via propionate has shown to increase

GLP-1 (glucagon-like peptide 1) release which subsequently increases insulin secretion from

β-cells (Tolhurst et al., 2012). GLP-1 is an incretin hormone secreted from intestinal L-cells in

response to nutrient ingestion and has shown to have a distinct impact on β-cell function. For

instance, in β-cells, GLP-1 has shown to stimulate insulin gene expression and biosynthesis

and also to enhance glucose stimulated insulin secretion and to restore glucose competence

in glucose resistant β-cells. Moreover, it can act as a growth factor and can thus increase β-

cell mass (Buteau, 2008). Also, GLP-1 has been shown to delay gastric emptying and can slow

down the absorption of nutrients which can in turn reduce post-prandial glucose response

(Van Bloemendaal et al., 2014). In addition, independent of GLP-1 secretion, propionate has

also shown to modulate glucose homeostasis and insulin sensitivity via FFAR-2 (Han et al.,

2014).

Rodent Studies:

5.8.2.1.1 In vitro:

Psichas et al. demonstrated in a rodent study that propionate is able to stimulate GLP-1

release in vitro via FFAR-2. Using murine colonic crypt cultures, the authors showed that

propionate was able to stimulate GLP-1 release in a dose response relation where higher

concentrations of propionate stimulated greater gut hormone release. However, propionate

was no longer able to stimulate GLP-1 release in FFAR-2 KO colonic crypts (Psichas et al.,

2015).

5.8.2.1.2 In vivo:

Acute Propionate Supplementation:

Oral:

Darwiche et al. showed in 9 healthy volunteers that acute supplementation of sodium

propionate (1.9 mol) in bread can significantly decrease post-prandial glucose and insulin

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responses in comparison to control bread due to delayed gastric emptying as evidenced via

ultrasonography (Darwiche et al., 2001). Also, Frost et al. showed in 10 healthy volunteers

that the consumption of pasta with tomato sauce supplemented with 30 g sunflower oil and

30 mmol of sodium propionate can significantly increase post-prandial GLP-1 levels and

decrease post-prandial glucose and insulin concentrations in comparison to psyllium enriched

pasta. The authors suggested this may be due to GLP-1 release and the concomitant delay in

gastric emptying (Frost et al., 2003). Further, De Vadder et al. showed that rats on a standard

diet for 10 days supplemented with sodium propionate (5% wt/wt) had increased glucose

tolerance and insulin sensitivity in comparison to rats on a standard diet alone. The authors

explained that this may be since propionate, as gluconeogenic substrate, was shown to

stimulate IGN, and via FFAR-3 in the portal vein, it can activate a gut-brain neural circuit that

can sense the glucose released by IGN and in turn the brain can favourably modulate glucose

homeostasis (De Vadder et al., 2014). In contrast, however, a single study, however, has

demonstrated that acute propionate supplementation might impair insulin action in mice by

promoting glucagon and fatty acid–binding protein 4 (FABP4) secretion via sympathetic

nervous system activation (SNS) (Tirosh et al., 2019). The mice were acutely supplemented

with propionate via an oral gavage or intraperitoneal administration at a rate of 15 mmol/kg

which resulted in hyperglycaemia in comparison with pyruvate supplementation (Tirosh et

al., 2019).

Infusion:

In a rodent study, Psichas et al. was able to show that a single, acute intra-colonic

administration of propionate of 180 mmol/l vs saline control was able to significantly increase

portal vein plasma GLP-1 concentrations in wild type (WT) rats but failed to do so in FFAR-2

KO rats (Psichas et al., 2015). However, impact on glucose and insulin levels were not

measured.

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Human Studies:

5.8.2.1.3 In vitro:

Chambers et al has shown that propionate is able to stimulate GLP-1 secretion using human

colonic cells where increasing propionate concentrations (200 mmol/L and 400 mmol/L) was

able to significantly promote GLP-1 release by 1.6-fold and 2.4-fold respectively (Chambers et

al., 2015).

Using human islets in vitro, Pingitore et al. has shown that propionate can potentiate dynamic

glucose-stimulated insulin secretion by activating FFAR-2 that was dependent on protein

kinase C signalling. Moreover, propionate was able to protect human β-cells from apoptosis

induced by sodium palmitate and inflammatory cytokines (Pingitore et al., 2017).

5.8.2.1.4 In vivo:

Oral:

Acute Propionate Supplementation:

In a randomized controlled trial, Chambers et al. were able to demonstrate in 20 overweight

adults that acute supplementation of propionate in the form of an IPE (36.2 mmol), can

significantly increase GLP-1 levels, however, with no apparent effect on glucose homeostasis

(Chambers et al., 2015).

Chronic Propionate Supplementation:

One study in humans has shown that propionate supplementation might impair glucose

homeostasis. This was a randomized, cross-over trial where healthy, non-diabetic and

nonobese subjects after an 8-hour fast, were supplemented with a mixed meal with or

without 5.37 mmol of calcium propionate with one week apart between measurements. The

propionate supplementation resulted in a significant decrease in insulin sensitivity as per the

Matsuda Index and a postprandial increase in both glucagon and FABP4 via SNS activation

(Tirosh et al., 2019).

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5.8.3 The Impact of Propionate on gut microbiota, systemic inflammation and plasma

metabolome:

Human Studies:

5.8.3.1.1 In vitro:


Chambers et al. has also shown in 12 overweight and obese non-diabetic adults that colonic

delivery of propionate via IPE (72.4 mmol) for 42 days caused a small shift in gut microbial

community at the species level compared with cellulose, that favoured an enhanced glucose

profile. IPE supplementation significantly improved measures of insulin resistance as assessed

by HOMA (1.23 ± 0.17 IPE vs 1.59±0.17 cellulose, p=0.001) and the Matsuda Insulin Sensitivity

Index (4.0±0.6 IPE vs 3.2±0.6 cellulose, p=0.002) in comparison to a low fermentable cellulose

control and also significantly improved adipose tissue insulin resistance (IPE : 6.5±1.0 vs

cellulose: 8.3±1.3 mmol/L×μU/mL, p=0.042). These metabolic benefits were associated with

a significant decrease in the pro-inflammatory marker, interleukin-8, and also a decrease in

plasma glutamine concentration (Chambers et al., 2019). This corroborated with the findings

of El Hage et al. who showed through a supplementation of propionate-producing consortium

using a selection of commensal gut bacteria can decrease pro-inflammatory markers such as

interleukin-8 and have favourable effects on hepatic insulin resistance (El Hage et al., 2020).

Similarly, in in vitro, treating human omental adipose tissue with propionate has shown to

decrease the expression of several inflammatory cytokines and chemokines such as tumour

necrosis factor-a (TNF-a) and CCL5 (Al-Lahham et al., 2012).

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5.8.4 The Impact of Propionate on hepatic tissue:

Rodent Studies:

5.8.4.1.1 In vitro:

In isolated rat hepatocytes, Anderson et al. showed that propionate can be converted to

glucose to a limited extent, however the net effect is a decrease in glucose production where

glucose production from lactate was reduced by 67% and 76% at propionate concentrations

of 5 mM and 10 mM respectively and can increase net rates of glucose conversion to lactate

by 25% at concentrations of 5 mM. Thus, propionate was shown to significantly increase

hepatic glycolysis and can increase glucose use and decrease glucose production (Anderson

and Bridges, 1984).

Human Studies:

5.8.4.1.2 In vitro:


Chambers et al. showed that long term supplementation of propionate for 24 weeks in the

form of an IPE in 60 overweight adults can significantly reduce intrahepatic triglyceride

concentration and weight gain in comparison to the inulin supplemented control group and

this is likely to explain the prevention in post-prandial deterioration in glucose response

displayed by the IPE group in comparison to the control group (Chambers et al., 2015).

5.8.5 The Impact of Propionate on adipose tissue:

Animal studies:

5.8.5.1.1 In vitro:

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Den Besten et al. showed that mice on an HFD supplemented with physiological amount of

propionate (5% wt/wt) for 12 weeks were protected from diet-induced obesity and

associated insulin resistance as seen by the similar body weight, white adipose tissue mass,

insulin levels and peripheral insulin sensitivity with mice on a normal chow diet. The

supplemented mice also displayed significant reduction in hepatic steatosis; however, the

authors revealed that the observed beneficial effects on glucose homeostasis, white adipose

tissue mass and body weight gain were due to adipose specific Pparg activity rather than

hepatic Pparg activity since adipose specific Pparg KO out mice no longer displayed these

effects while this was preserved in hepatic specific KOs. Propionate, it appears, was able to

downregulate Pparg activity and activate UCP2-AMPK-ACC activated protein kinase cascade

that ultimately shifts metabolism in adipose tissue to promote lipid oxidation (den Besten et

al., 2015). Indeed, propionate in vitro was also shown to increase the expression of glucose

transporter (GLUT-4) in adipose tissue and thus glucose uptake which in vivo would have

favourable outcomes on glucose profile (Al-Lahham et al., 2012).

5.8.6 The Impact of Propionate on β-cell:

Human Studies:

5.8.6.1.1 In vivo:


In 60 overweight adults, Pingitore et al. showed that chronic supplementation of propionate

in the form of IPE for 24 weeks resulted in a significant improvement in β-cell function as

measured by the oral disposition index, which the authors concluded that might be via the

propionate acting directly on islet β-cells (Pingitore et al., 2017).

217

5.8.7 Conclusion:

It can thus be concluded that the effects of propionate supplementation on glucose

homeostasis are currently inconsistent. Oral chronic supplementation of propionate in both

animals and humans appear to have favourable outcomes, whereas a lot more discrepancy

in the literature exists regarding acute effects. Tirosh et al. (Tirosh et al., 2019) was the only

human study that demonstrated an impairment in insulin profile following acute ingestion of

5.37 mmol of calcium propionate. However, it must be noted that they only examined the

post-prandial effects of supplementation. All other acute human studies demonstrated

positive (Todesco et al., 1991, Liljeberg et al., 1995, Darwiche et al., 2001, Frost et al., 2003,

Darzi et al., 2012) or no effects (Chambers et al., 2015) on post-prandial glucose homeostasis.

Chambers et al (Chambers et al., 2018) on the other hand, measured effects of propionate on

glucose homeostasis in the overnight fasted state and over 180 min. In that study, propionate

levels only became significantly increased at the end of the trial at 180 min which may not

have been a sufficient timeframe to assess the impact on glucose and insulin profile. The aim

of this work thus would be to build on that work and clarify the acute effects of raising gut

derived propionate on glucose homeostasis in healthy volunteers over an extended time

while assessing that in all three different physiological states (overnight fasted, sub-maximal

exercise and post-prandial).

5.9 Hypothesis:

I hypothesised that acute oral intake of sodium propionate would improve insulin resistance

during overnight fasted and sub-maximal exercise states and would improve β-cell function

and increase insulin sensitivity and GLP-1 secretion in the post-prandial state.

5.10 Aims:

This chapter will aim to present and discuss findings related to determining the acute effect

of propionate bioavailability on glucose homeostasis in different energy states (overnight

fasted, sub-maximal exercise and post-prandial).

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Measuring the acute effects of sodium propionate on glucose homeostasis was a secondary

outcome of this trial. Measuring insulin resistance will be conducted via HOMA-IR while

measuring insulin sensitivity will be conducted via Matsuda Index. The oral disposition index

will be used to assess β-cell function.

5.12 Methods of Choice:

Since measurement of glucose homeostasis is a secondary outcome of this thesis and since

surrogate markers have shown to be inexpensive quantitative tools that can be applied in

many settings as outlines in the first section of this chapter, HOMA-IR was used in the

overnight fasted and sub-maximal exercise studies to assess insulin resistance, and the

Matsuda Index and Oral disposition index were chosen in the post-prandial study to assess

insulin resistance and β-cell function respectively.

5.13 Methods:


5.14 Results:


5.14.1.1.1 Glucose:

Mean glucose concentrations between 0-360 min was not significantly different between the

Propionate and Control trials (Control= 4.12± 0.08 mmol/L; Propionate= 4.14± 0.08 mmol/L;

Effect of trial p= 0.712) (Figure 5: 1: A). However, in the Control trial, iAUC0-360 min was

219

significantly lower in comparison to the Propionate trial (p= 0.012) (Figure 5:1: B) yet +iAUC0-

360 min was not significantly different between the two trials (p=0.191) (Figure 5:1: C).

iAUC0-180 min was significantly lower in the Control trial (p=0.011); However, +iAUC0-180 min was

not significantly different between trials (p=0.938) respectively (Figure 5:1: D and E). There

was no significant difference in iAUC180-360 min and +iAUC180-360 min between trials (p=0.11; p=

0.400) (Figure 5:1: F and G).

A

B

C

220

D

E

F

G

Figure 5-1: Overnight Fasted Study: Effect of oral sodium propionate supplementation on glucose levels:

The effect of oral sodium propionate supplementation on glucose concentrations A. Glucose levels (Time×Trial:

p=0.398; Trial: p=0.712; Time: p= 0.000) B. Glucose levels iAUC0-360 (p=0.012). C. Glucose levels +iAUC0-360

(p=0.191). D. Glucose levels iAUC0-180 min (p=0.011). E. Glucose levels +iAUC0-180 min (p=0.938). F. Resting glucose

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levels iAUC180-360 min (p=0.107). G. Glucose levels +iAUC180-360 min (p=0.400). All data expressed as mean ± SEM

(n=19).

5.14.1.1.2 Insulin:

Mean insulin concentrations between 0-360 min was not significantly different between the

Propionate and Control trials (Control= 7.70 ± 0.48 μU/mL; Propionate= 7.87± 0.59 μU/mL;

Effect of trial p= 0.759) (Figure 5:2: A). Similarly, iAUC0-360 min and +iAUC0-360 min were not

significantly different between trials (p= 0.121; p=0.137) respectively (Figure 5:2: B and C).

iAUC0-180 min and +iAUC0-180 min in the Control trial was not significantly different between trials


in iAUC180-360 min and +iAUC180-360 min between trials (p=0.320; p= 0.096) respectively (Figure

5:2: F and G).

A

222

B

C

D

E

223

F

G

Figure 5-2: Overnight Fasted Study: Effect of oral sodium propionate supplementation on insulin levels:

The effect of oral sodium propionate supplementation on insulin concentrations A. Insulin levels (Time×Trial:

p=0.104; Trial: p=0.759; Time: p= 0.000) B. Insulin levels iAUC0-360 (p=0.121). C. Insulin levels +iAUC0-360 (p=0.137).

D. Insulin levels iAUC0-180 min (p=0.208). E. Resting insulin levels +iAUC0-180 min (p=0.196). F. Insulin levels iAUC180-

360 min (p=0.320). G. Insulin levels +iAUC180-360 min (p=0.096). All data expressed as mean ± SEM (n=19).

5.14.1.1.3 HOMA-IR:

Insulin resistance was comparable between trials as assessed by HOMA-IR (Control: 1.42 ±

0.10; Propionate: 1.46 ± 0.12; p=0.698) (Figure 5:3: A).

Figure 5-3: Overnight Fasted Study: Effect of oral sodium propionate supplementation on HOMA-IR levels

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5.14.2.1.1 Glucose:

Propionate had no effect on circulating glucose levels (Control= 4.35 ± 0.09 mmol/L;

Propionate= 4.47 ± 0.07 mmol/L; Effect of trial p= 0.214) (Figure 5:4: A). There was also no

significant effect on iAUC0-180 min (p=0.460) and + iAUC0-180 min between trials (p=0.700) (Figure

5:4: B and C).

There was also no significant difference in circulating levels of glucose during exercise

(Control= 4.40 ± 0.10 mmol/L; Propionate= 4.37 ± 0.09 mmol/L; Effect of trial: p=0.654)

(Figure 5:4: A) and no significant effect on iAUC180-240 min (p= 0.367) and +iAUC180-240 min

between trials (p= 0.679) (Figure 5 D) (Figure 5:4: D and E).

A

225

B

C

D

E

Figure 5-4: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on glucose levels:

The effect of oral sodium propionate supplementation on glucose concentrations A. Resting glucose levels

(Time×Trial: p=0.548; Trial: p== 0.026; Time: p= 0.214) and glucose levels during exercise (Time×Trial: p=0.647;

Trial: p=0.645; Time: p= 0.002) B. Glucose levels iAUC0-180 (p=0.460). C. Glucose levels +iAUC0-180 (p=0.700). D.

Glucose levels iAUC180-240 min (p=0.367). E. Glucose levels +iAUC180-240 min (p=0.679). All data expressed as mean ±

SEM (n=19).

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5.14.2.1.2 Insulin:

Propionate had no significant effect on fasting insulin levels (Control= 5.53 ± 0.47 μU/mL;

Propionate= 6.11 ± 0.59 μU/mL; Effect of trial: p=0.255) (Figure 5: 5: A) or iAUC0-180 min (p=

0.714) (Figure 5:5: B)and +iAUC0-180 min (p= 0.978) (Figure 5:5: C).

Propionate similarly had no effect on circulating insulin levels during exercise (Control= 5.34

± 0.39 μU/mL; Propionate= 5.63 ± 0.55 μU/mL; Effect of trial: p=0.379) (Figure 5:5: A). iAUC180-

240min (p=0.451) (Figure 5:5: D) and +iAUC180-240min was also comparable between the two trials

(p=0.679) (Figure 5:5: E).

A

227

B

C

D

E

Figure 5-5: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on insulin levels:

The effect of oral sodium propionate supplementation on insulin concentrations A. Resting insulin levels

(Time×Trial: p=0.358; Trial: p=0.255; Time: p= 0.025) and insulin levels during exercise (Time×Trial: p=0.501;

Trial: p=0.379; Time: p= 0.000) B. Insulin levels iAUC0-180 (p=0.714). C. Insulin levels +iAUC0-180 (p=0.639). D. Insulin

levels iAUC180-240 min (p=0.451). E. Insulin levels +iAUC180-240 min (p=0.978). All data expressed as mean ± SEM

(n=19).

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5.14.2.1.3 HOMA-IR:

Insulin resistance between 0-180 min was comparable between trials, as assessed by HOMA-

IR (Control: 1.05± 0.09; Propionate: 1.13 ± 0.12; p= 0.374) (Figure: 5:6 A).

A

Figure 5-6: Sub-maximal Exercise Study: Effect of oral sodium propionate supplementation on HOMA-IR


5.14.3.1.1 Glucose:

Glucose between 0-300 min was comparable between the Control and Propionate trials

(Control= 5.38± .133 mmol/L; Propionate= 5.40± .141 mmol/L; Effect of trial p=0.823) (Figure

5:7: A). Glucose iAUC0-300 and +iAUC0-300 was not significantly different between trials

(p=0.452; p=0.674) (Figure 5:7: B and C).


between trials (p= 0.941; p=0.404) respectively (Figure 5:7: D and E). Glucose iAUC180-300 and


F and G).

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A

B

C

D

E

230

F

G

Figure 5-7: Post-prandial Study: Effect of oral sodium propionate supplementation on glucose levels:

The effect of oral sodium propionate supplementation on glucose concentrations A. glucose levels (Time×Trial:

p=0 .086; Trial: p=0.823; Time: p= 0.000) and B. Glucose levels iAUC0-300 (p=0.452). C. Glucose levels +iAUC0-300

(p=0.674). D. Overnight fasted glucose levels iAUC0-180 min (p=0.941). E. Glucose levels +iAUC0-180 min (p=0.404). F.

Post-prandial glucose levels iAUC180-300 min (p=0.165). G. Post-prandial glucose levels +iAUC180-300 min (p=0.369). All


5.14.3.1.2 Insulin:

Insulin between 0-300 min was comparable between the Control and Propionate trials

(Control= 29.88 ± 2.69 μU/mL; Propionate= 30.27± 2.17 μU/mL; Effect of trial p=0.833) (Figure

5:8: A). Insulin iAUC0-300 and +iAUC0-300 was not significantly different between trials (p=0.970;

p=0.998) (Figure 5:8: B and C).

iAUC0-180 min and +iAUC0-180 min for the 180 min fasted period were not significantly different

between trials (p= 0.430; p=0.123) respectively (Figure 5:8: D and E). Insulin iAUC180-300 and


F and G).

231

A

B

C

232

D

E

F

G

Figure 5-8: Post-prandial Study: Effect of oral sodium propionate supplementation on insulin levels:

The effect of oral sodium propionate supplementation on insulin concentrations A. Insulin levels (Time×Trial:

p=0.320; Trial: p=0.833; Time: p= 0.000) and B. Insulin levels iAUC0-300 (p=0.970). C. Insulin levels +iAUC0-300

(p=0.998). D. Insulin levels iAUC0-180 min (p=0.430). E. insulin levels +iAUC0-180 min (p=0.123). F. Post-prandial insulin

levels iAUC180-300 min (p=0.930). G. Post-prandial insulin levels +iAUC180-300 min (p=0.930). All data expressed as

mean ± SEM (n=20).

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5.14.3.1.3 GLP-1:

GLP-1 between 0-300 min was comparable between the Control and Propionate trials

(Control= 67.9± 7.1 mmol/L; Propionate= 70.5± 6.2 mmol/L; Effect of trial p=0.626) (Figure 9:

A). GLP-1 iAUC0-300 and +iAUC0-300 was not significantly different between trials (p=0.095;

p=0.114) (Figure 9: B and C).

iAUC0-180 min and +iAUC0-180 for the overnight fasted period was significantly higher in the

Propionate trial (p= 0.004; p=0.016) respectively (Figure 9: D and E). GLP-1 iAUC180-300 and

+iAUC180-300 were not significantly different between the trials (p= 0.081; p=0.312) (Figure 9:

F and G).

A

234

B

C

D

E

235

F

G

Figure 5-9:Post-prandial Study: Effect of oral sodium propionate supplementation on GLP-1 levels

5.14.3.1.4 Insulin Sensitivity:

Matsuda Index:

Insulin sensitivity as per the Matsuda Index was comparable between trials (Control: 106.6±

12.0; Propionate: 94.6 ± 5.6; p= 0.388) (Figure 5:10: A).

A

Figure 5-10:Post-prandial Study: Effect of oral sodium propionate supplementation on Matsuda Index

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5.14.3.1.5 Oral Disposition Index:

β-cell function was comparable between trials (Control: 3.15 ± 0.28; Propionate: 4.34 ± 0.78;

p= 0.452) (Figure 5:11: A).

A

Figure 5-11: Post-prandial Study: Effect of oral sodium propionate supplementation on Oral Disposition Index

5.15 Key Findings:


Overnight fasted state:

• Overnight fasted state: Acute Propionate ingestion attenuated the decrease in

glucose concentrations over a prolonged fasted period of 360 min. This is mainly

driven by the first half of the fasting period (0-180min). However, insulin resistance,

assessed by HOMA-IR, was unaffected.


• Overnight fasted state: Propionate had no effect on glucose homeostasis in the

overnight fasted state.

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• Sub-maximal exercise state: Propionate has no effect on glucose homeostasis during

exercise.


• Overnight fasted state: Propionate ingestion attenuated the decrease in GLP-1

concentrations over the initial 180 min fasted period. This was not associated with

differences in insulin-resistance.

• Post-prandial state: Propionate had no effect on β-cell function or peripheral insulin

resistance in the post-prandial state.

5.16 Summary:

Acute ingestion of oral sodium propionate in healthy human volunteers can maintain GLP-1

secretion in the overnight fasted state but has no effect on measures of insulin sensitivity or

β-cell function in healthy humans.

5.17 Discussion:

5.17.1 Impact of Propionate on glucose profile:

A single rodent study has examined the acute effect of propionate supplementation on

glucose homeostasis (Tirosh et al., 2019) while the bulk of evidence relies on findings from

human trials. Human studies have mainly focused on the post-prandial acute effects of oral

propionate supplementation on glucose homeostasis (Todesco et al., 1991, Liljeberg et al.,

1995, Darwiche et al., 2001, Frost et al., 2003, Darzi et al., 2012, Chambers et al., 2015, Tirosh

et al., 2019) while only a single study (Chambers et al., 2018) examined the acute effects of

propionate ingestion on fasting glucose and insulin profile. This current study examined the

effect of acute sodium propionate supplementation on glucose homeostasis in all three

energy states. Here, it is shown that acute sodium propionate ingestion (71 mmol) can

attenuate the decrease in glucose concentrations that is normally seen with a prolonged fast

but has no effect on glucose homeostasis during physical activity or in the acute post-prandial

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period. Todesco et al. demonstrated that post-prandial blood glucose levels can acutely be

decreased by ~50% in six healthy volunteers after consumption of bread supplemented with

31.2 mmol of sodium propionate per 50g of available carbohydrate in comparison to control

white bread (Todesco et al., 1991). Similarly, Liljeberg et al. and Darwiche et al. showed in

healthy volunteers that acute supplementation of 1.9 mmol of sodium propionate in bread

can significantly decrease post-prandial glucose and insulin responses in comparison to

control bread (Liljeberg et al., 1995, Darwiche et al., 2001). Also, Frost et al. showed in 10

healthy volunteers that the consumption of pasta with tomato sauce supplemented with 30

g sunflower oil and 30 mmol of sodium propionate can decrease glucose and insulin

concentrations in comparison to psyllium enriched pasta (Frost et al., 2003). However, Darzi

et al. showed that acute ingestion of propionate (6 mmol) in a palatable sourdough, may

lower post-prandial insulinemia in comparison to control bread although this effect was not

significant (Darzi et al., 2012). In addition, Chambers et. al have shown no apparent effect on

post-prandial glucose and insulin profile in overweight individuals supplemented acutely with

36.2 mmol of IPE (Chambers et al., 2015). A single study, however, has demonstrated that

propionate supplementation might impair post-prandial insulin action in humans by

promoting glucagon and fatty acid–binding protein 4 (FABP4) secretion via sympathetic

nervous system activation (SNS) (Tirosh et al., 2019). In that study, healthy, non-diabetic and

nonobese subjects after an 8-hour fast, were supplemented with a mixed meal with or

without 5.37 mmol of calcium propionate with one week apart between measurements. The

propionate supplementation resulted in a significant decrease in insulin sensitivity as per the

Matsuda Index and a postprandial increase in both glucagon and FABP4 via SNS activation.

Indeed, propionate supplementation has previously been shown to stimulate SNS activity in

rodents (Kimura et al., 2011), however, findings remain inconsistent with respect to the effect

of propionate on glucagon secretion(Wolever et al., 1991, McBurney et al., 1995, Ørgaard et

al., 2019).The present findings do reveal an increase in SNS stimulation following propionate

supplementation, as assessed by heart rate (Chapter 3, Figure 5 E) however with no

detrimental effect on glucose homeostasis, despite the much higher dose used in this study

in comparison to the study of Tirosh et al.. It is quite puzzling when comparing findings of

Tirosh et al. with findings of the current study since the only main difference between studies

is the supplementation of calcium propionate versus sodium propionate. However, altered

calcium serum levels are shown to be significantly correlated with abnormalities in glucose

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levels ,insulin resistance and β-cell function with high serum calcium concentrations being

associated with the highest concentration of glucose and insulin resistance (Sun et al., 2005)

which may explain the discrepancy in findings. Moreover, one of the limitations in the Tirosh

study, is that the placebo consisted of an empty tablet rather than matched for calcium, in

contrast to the present study where the placebo consisted of a molar matched amount of

sodium so that any effect that occurs in this study could be attributed to the propionate rather

than the sodium. In any case, future human studies are therefore needed to better assess the

acute effect of propionate on SNS activation and its relation to overall glucose homeostasis.

All in all, it appears that the effect of propionate supplementation on post-prandial glucose

homeostasis are yet to be conclusive with the majority of studies portraying a positive effect

on glucose profile. The inconsistent findings may be related to the different dosing and

supplementation strategies between studies as well as the intestinal site of propionate

absorption that could have manipulated the effects on blood glucose and insulin profile. Also,

all the mentioned studies, including this trial, have used MTT to assess effects of

supplementation on glucose profile. However, although MTTs mimic physiological response

to glucoregulation, they may not be reproducible within the same individual due to variability

in glucose absorption, splanchnic glucose uptake, and additional incretin effects on various

days within the same individual (Patarrão et al., 2014) all of which can lead to discrepancies

in outcomes.

As for the effect of propionate supplementation on fasting glucose and insulin profile,

comparable with the present findings, Chambers et al., showed that acute sodium propionate

supplementation (71 mmol) over a 180 min time period has no effect on fasting circulating

glucose and insulin levels (Chambers et al., 2018). However, the current findings do reveal

attenuated glucose levels during the first 180 min of supplementation in the overnight fasted

trial. It is noteworthy, however, that the effect observed in this study was only seen during

the fasted state, particularly during the early 0-180 min period (Figure 1 D). This ‘increase’ in

glucose levels was not observed in the other two studies (Figure 4 B and Figure 7 D), despite

the same applied methods from 0-180min. Moreover, this may have been an artefact of a

slightly higher baseline glucose value in the overnight fasted study in the control trial (4.41 ±

0.12 mmol/L) versus propionate trial (4.28 ± 0.09 mmol/L) (p=0.062) especially since there

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was no significant main effect of trial (Figure 1 A) or a significant difference in +iAUC (Figure

1 A,C and E) (p>0.05) which reflects a stimulatory effect.

Furthermore, the observed increase in glucose in the overnight fasted trial was not sufficient

to stimulate a compensatory insulin response or a change in insulin sensitivity, as measured

by HOMA-IR. Therefore, it may be safe to conclude that acute sodium propionate

supplementation ingestion in the current trial has no effect on glucose homeostasis

nevertheless further future studies are definitely needed to better assess the effects of

propionate supplementation on fasting glucose and insulin profile.

Gut-derived propionate is a known hepatic gluconeogenic precursor. However, this may not

have been sufficient to cause an increase above baseline glucose values since propionate has

been shown to reduce hepatic glycolysis (Anderson and Bridges, 1984) and to inhibit

gluconeogenesis from endogenous compounds such as lactate (Blair et al., 1973) and

pyruvate via increased propionyl CoA and methylmalonyl CoA which are known inhibitors of

pyruvate carboxylase as well as by decreasing acetyl CoA which potently activates this enzyme

(Chan and Freedland, 1972). Propionate has also been shown to decrease gluconeogenesis

from amino acids (Verbrugghe et al., 2012). Additionally, by acting as a substrate for IGN,

propionate can in turn inhibit hepatic glucose production via a gut-brain neural circuit (De

Vadder et al., 2014).

This trial was the only study that examined the effect of propionate supplementation on

glucose homeostasis during physical activity. And findings reveal no effect of acute sodium

propionate supplementation on glucose and insulin measures. However, this was examined

at a specific low to moderate exercise intensity (Control: 48 ± 2 %; Propionate: 47 ± 2%

VO2max). Therefore, whether these findings also apply at different exercise intensities where

substrate demand would differ, is yet to be determined. Future studies are therefore

warranted to further consolidate these findings.

In conclusion, in order to obtain a more accurate overview of the acute effects of propionate

on glucose homeostasis, future studies relying on more robust methodologies such as HIEC

are clearly needed.

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5.17.2 Impact of Propionate on GLP-1 levels:

The present trial shows that acute sodium propionate ingestion in healthy volunteers can

increase GLP-1 secretion in the fasted state, but not post-prandially. In contrast, Frost et al.

showed in 10 healthy volunteers that the consumption of pasta with tomato sauce

supplemented with 30g sunflower oil and 30 mmol of sodium propionate can significantly

increase post-prandial GLP-1 levels and delay gastric emptying in comparison to psyllium

enriched pasta (Frost et al., 2003). Also, Chambers et. al have shown an increase in post-

prandial GLP-1 secretion in overweight individuals supplemented acutely with 36.2 mmol of

propionate from IPE (Chambers et al., 2015). The varying response with regards to the effect

of propionate on GLP-1 stimulation may in part be related to the different mode of

supplementation and/or the different dose given. In the current trial, healthy normal weight

subjects were supplemented orally with 71 mmol of sodium propionate in the form of

capsules. Frost et al. study included healthy volunteers who consumed pasta with tomato

sauce supplemented with 30 g sunflower oil and 30 mmol of sodium propionate. Whereas,

Chambers et al. included overweight volunteers supplemented with 36.2 mmol of IPE that

was mixed with subjects’ standardized meals. Taking all these factors into prospective as well

as considering the unique rate, degree and site of intestinal absorption that varies between

the studies, may explain the disparity in the metabolic effects observed. It is also noteworthy

that the “increase” in GLP-1 concentration observed in the current study was relatively minor

and the difference observed from control appears to reflect the prevention of a further

decline in GLP-1 levels that normally occurs with continued fasting i.e. propionate appeared

to maintain baseline GLP-1 levels.

5.17.3 Impact of Propionate on Insulin Resistance and β-cell function:

In the present trial, acute propionate supplementation had no effect on insulin resistance and

β-cell function. In vitro findings nevertheless demonstrate that propionate can enhance

glucose stimulated glucose uptake and glucose stimulated insulin secretion, however, this

was observed with a large dose of propionate (2 to 20 mM) (Pingitore et al., 2017) (300 mM)

(Han et al., 2014) that is not physiologically relevant. Chronic in vivo studies in humans

display similar results. Pingitore et al. showed that long term (24 weeks) colonic delivery of

propionate in the form of an IPE (36.2 mmol) in 60 overweight adults resulted in improved β-

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cell function, as assessed by the oral disposition index (Pingitore et al., 2017). However,

similar to the present findings, the authors did not observe any effect on insulin resistance as

measured by the Matsuda Index. In contrast, Chambers et al. have shown in twelve

overweight or obese adults supplemented with 72.4 mmol/d of IPE for 42 days had significant

improvement in insulin resistance as measured by HOMA-IR and the Matsuda Index

(Chambers et al., 2019). Thus, the effect of propionate on insulin resistance and β-cell

function is relatively scarce and inconsistent in human studies. It also appears that

discrepancies occur depending on whether supplementation is acute or chronic and also on

the method of supplementation, the dose provided and BMI of the subjects.

5.18 Study Strengths:

This trial is the first to assess the impact of acute oral sodium propionate supplementation on

glucose homeostasis in all three energy states. It is particularly unique in that it examined the

effect of supplementation during exercise. Although there was no effect of acute sodium

propionate supplementation on glucose homeostasis during sub-maximal exercise, future

studies are needed to confirm these findings.


The acute effect of sodium propionate supplementation on glucose homeostasis was a

secondary outcome of the study. And thus, given the nature of the study, surrogate markers

were utilized to assess insulin resistance and β-cell function. Future intervention studies,

using more direct and reliable measures such as HIEC, would be able to give a better reflection

of the effect gut-derived on glucose and insulin profile. Also, carbon-13 (C-13) labelled

propionate may be utilized to investigate the role of gut-derived propionate on its

bioavailability, gluconeogenesis and overall glucose homeostasis. For instance, the present

trial could be repeated using C-13 labelled propionate and by measuring incorporation of C-

13 into expired CO2, an estimate of when propionate is being metabolized can be determined

and by measuring plasma C-13 glucose, propionate’s incorporation into glucose can be

measured in order to examine propionate’s impact on gluconeogenesis.

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Another limitation of the present trial is that it only included metabolically healthy individuals

and therefore whether the same effects can be replicated in individuals with impaired glucose

homeostasis or T2D is not yet apparent. If propionate is assumed to suppress gluconeogenesis

from other gluconeogenic substrate since no effect on glucose homeostasis was seen in

healthy individuals, this may not hold in compromised individuals since in T2D subjects,

gluconeogenesis is impaired and thus a rise in glucose levels may be seen with propionate

supplementation. Future studies therefore are needed to investigate that further.

5.20 Conclusion:

Acute ingestion of oral sodium propionate in healthy human volunteers can prevent

deterioration in GLP-1 levels observed with prolonged fasting but has no effect on insulin

resistance and β-cell function. Also, the impact of sodium propionate ingestion on glucose

levels in the fasted state (0-180 min) was only observed in one of the three studies and thus

would demonstrate that acute propionate ingestion has no effect on markers of insulin

sensitivity and glucose tolerance in healthy humans. It is noteworthy, however, that the

present trial only included normal weight, healthy individuals with normal glucose tolerance,

hence it may be challenging to enhance an already ‘normal’ parameter. Hence, well-

controlled future studies using more robust methodologies are needed to better investigate

the acute effect of gut derived propionate on glucose homeostasis in individuals with a range

of metabolic health and also in all three different energy states.

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Chapter 6: Effect of Sodium Propionate on Serum Metabolic

Phenotypes

6.1 Abstract:

Background:

Metabolomic techniques such as nuclear magnetic resonance (NMR) spectroscopy allow the

identification of changes to the metabolic profile of biofluids in response to changes in

biological activity and can thus allow reflection of an organism’s phenotypic state. No human

study up to date has employed metabolomic techniques to assess changes in serum

metabolome in response to raised propionate bioavailability. Thus, this study, was the first to

use NMR spectroscopy to investigate the acute effect of oral sodium propionate

supplementation I71 mmol) in healthy volunteers on serum metabolome in the overnight

fasted and post-prandial states.

Methodology:

This was a randomized controlled double-blind cross-over study. 19 volunteers (12 males and

7 females; age: 45.0 ± 3.5 years; BMI: 24.8 ± 0.8 kg/m2) completed two study visits. In each

visit, following an overnight fast, tablets containing either 6845mg sodium propionate or

4164mg sodium chloride (Control) were first administered over 180 min. At time-point 180



1-D (One-dimensional) 1H-NMR spectroscopy was used to analyse serum metabolite changes

associated with propionate supplementation at 0, 180 and 240 min.

Results:

Oral sodium propionate supplementation altered six serum metabolites. At baseline (0 min),

no significant differences between measured metabolites was present between Control and

Propionate (p>0.05). At the end of the overnight fasted period (180 min), 3-Hydroxybutyrate

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and lysine were downregulated with propionate supplementation, whereas LDL/VLDL, lactate

and methanol were upregulated. During the post-prandial period (240 min), LDL/VLDL,

lactate and alanine were upregulated with propionate supplementation.

Conclusion:

This study is the first to demonstrate that acute ingestion of oral sodium propionate (71

mmol) in healthy human volunteers can affect serum metabolite phenotype in both the

overnight fasted and post-prandial states. The increased methanol concentrations observed

during the overnight fasted period may highlight altered gut microbial activity associated with

propionate supplementation that support increases in methanol production and appearance

in systemic blood. The raised abundance of alanine and lactate could be the consequence of

propionate sparing other precursors for hepatic gluconeogenesis.

6.2 Metabolomics: 6.2.1 Definition:

Metabolomics can be defined as the study of metabolites in biological fluids where

metabolites refer to the small molecules present in biological samples such as blood or urine.

Metabolites, as the downstream products of metabolic pathways, can serve as direct

representatives of biological activity and allow reflection of an organism’s phenotypic state

(Patti et al., 2012). Thus, metabolomics can identify characteristic biomarkers of particular

phenotypes or disease states prior to even ‘symptoms’ occurrence (Wishart, 2016) and can

hence be considered a non-invasive, valuable tool method in many research settings. Indeed,

since its development, metabolomics has widely been employed in nutritional and medicinal

investigations for the identification of certain objective biomarkers related to disease and

health enabling the development of personalized nutrition and even applying that to

epidemiological studies in order to establish a link between diet and health (Astarita and

Langridge, 2013, Wishart, 2016).

6.2.2 Design:

When designing a metabolomic study, it is important to consider the purpose of the research

and the number of metabolites to be determined. For example, when a defined set of

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metabolites is needed, a targeted based approach is the preferred method. Targeted analysis

involves the prior identification and quantification of metabolites to be measured that are

often related to a particular metabolic pathway of interest. This technique is often used in

order to answer a specific research question or hypothesis. In contrast, the untargeted

analysis is a non-biased approach that measures as many metabolites as possible and allows

a fair comparison between samples under study. This technique analyses all detectable

metabolites, both unknown and known, and identifies which metabolites are significantly

perturbed. Thus, untargeted analysis could be described as a ‘discovery mode’ process and is

used to compare differences between groups such as interventions versus controls but

cannot be used for individual samples analysis (Patti et al., 2012, Gertsman and Barshop,

2018).

6.2.3 Analytical Tools:

The main analytical tools applied in metabolomics include nuclear magnetic resonance (NMR)

and mass spectrometry (MS) combined with chromatography, which are continuously

developing and allowing considerable progress in metabolomic research (González-Peña and

Brennan, 2019). Indeed, NMR and MS metabolomic database have recently expanded

considerably which has allowed significant improvement in query platforms and identification

of metabolites (Bingol et al., 2014, Bingol et al., 2015, Zhao et al., 2019).

6.2.3.1.1 Nuclear Magnetic Resonance Spectroscopy:

NMR is considered a robust and reproducible spectroscopic technique (Keun et al., 2002,

Barton et al., 2008) while also requiring minimal effort during sample preparation. In simple

terms, NMR relies on the behaviour of atoms when exposed to an external magnetic field.

With energy absorption and reemission, the atoms are able to generate radiofrequencies

which can be converted to a frequency spectrum. The resulting spectra can then give both

quantitative data of metabolite concentrations as well as information on their chemical

structure (Tognarelli et al., 2015). Hydrogen, as the most abundant element found in nature,

is often the most targeted nuclei during NMR analysis (1H-NMR). Nevertheless, other atoms

such as carbon (13 C-NMR) and phosphorus (31 P NMR) are also used in NMR (Marion, 2013,

Tognarelli et al., 2015). The various spectral peak areas generated by each molecule

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corresponds with the concentrations of the metabolites in the tested sample while the type

of metabolite is identified by the unique pattern of peaks that relay information on the

physical properties of each molecule. The spectral data generated can be expressed in two

main ways: a one-dimensional NMR (1D-NMR) or a two-dimensional NMR (2D-NMR). The 1

D-NMR, as the name implies, is based on a single frequency axis with each molecule peak

placed in its resonant frequency, while the 2D-NMR is based on two frequency axis that can

separate overlapping spectral peaks into a second dimension and is often only used when a

large number of overlapping peaks exists due to a large number of compounds present in the

tested sample. Otherwise, 2 D-NMR due to high resolution, can be used to determine

components of unfractionated or partially fractionated mixture such as tomato juice (Ward

et al., 2007).

Statistical analysis of 1 D-NMR spectra can indeed be challenging given the wide range of

metabolite concentrations found in biological fluids and even the variable composition that

could be found in the same biological fluid such as urine (Lindon et al., 2000) in addition that

each metabolite can have a large physiological concentration range (Psychogios et al., 2011).

Thus, this renders evaluation and interpretation of 1 D-NMR biased toward determining

changes in the more bountiful metabolites. Therefore, although 2 D-NMR is more laborious

in terms of time in comparison to 1 D-NMR, in studies where assessing changes in low-

abundance metabolites and overall metabolic profile is the main outcome, 2 D-NMR may

prove to be the preferred analysis method of choice (Van et al., 2008). Nevertheless, 1 D-

NMR, specifically 1D 1H NMR, is the most commonly used method in metabolomic profiling

research due to its highly automated, greatly reliable and speedy technique (Kruk et al., 2017,

Emwas et al., 2019) which allows for identification and quantification of around 50-100

metabolites at a time (Lindon et al., 2000). Indeed, 1D 1H NMR has been used extensively by

multiple research groups in order to assess the pathology and progression of various diseases

and health states such as in trauma and critical illness (Cohen et al., 2010, Serkova et al.,

2011), hepatic disease (Amathieu et al., 2011) as well as cancer (Odunsi et al., 2005).

Moreover, it seems to be the preferred technique for large scale population studies due to its

very high reproducibility (Dumas et al., 2006, Barton et al., 2008, Li-Gao et al., 2019).

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6.2.3.1.2 Mass Spectrometry:

MS is a highly sensitive analytical tool that could be used for the identification and

quantification of hundreds of metabolites with each use by determining their molecular

weight (Gowda and Djukovic, 2014). Indeed, MS-based metabolomics has been used vastly in

medical sciences including clinical biomarker discovery, drug development, nutritional and

forensic science (Zhang et al., 2020). For instance, in a cohort of 2,324 patients, Fan et al.

identified, using MS-based metabolomics, 89 different metabolites that were reflective of

metabolic disturbances characteristic of the occurrence or development of coronary artery

disease such as upregulation of specific amino acids and creatinine and downregulation of

certain phospholipids (Fan et al., 2016). The basis of MS relies on the development of

positively or negatively charged gas-phase ions that can be separated based on their mass-

to-charge ratio (m/z) via electrical or magnetic means. During the ionization process, some

molecules will break into charged fragments or remain intact and can then be separated and

analysed based on their mass to charge ratio (m/z). The MS spectrum acquired is a graphical

representation of (m/z) versus the total ion counts (the relative intensity of the measured

compounds) which could be used to identify the elements or isotopes present in the sample

and the masses of the molecules and their chemical structure. The detected ions can be

characterized by correlating known masses to the generated masses or via distinctive

fragmentation pattern. Thus, the MS process can be summarized in three main steps: 1)

ionization of molecules into the gaseous phase by an ionization source 2) separation of the

formed ions according to their m/z via magnetic or electric exposure though a mass analyser

3) interpretation of the different peak patterns generated from the electric charge of the

separated ions that are unique to each molecule and are proportional to the quantity of the

ionized compound. A range of equipment and technical variety exist for MS such as

electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) that can

transform molecules into ions and quadrupole and time of flight (ToF) mass analysers as

spectrometric instruments with each variant suited to specific compounds or applications (El-

Aneed et al., 2009, Girolamo et al., 2013, Urban, 2016). Since in metabolomics research, MS

is rarely performed on single compounds, a separation step is often needed prior to MS. This

is generally achieved through chromatographic techniques such as high-performance liquid

chromatography (HPLC) and gas chromatography (GC) which allow the differentiation or

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separation of metabolites based on their different absorbent properties inside the

chromatographic column. Thus, metabolites with different characteristics will have varying

time to pass through the column. Nevertheless, direct MS approaches include direct

injection/infusion (DIMS) technique which bypass the use of chromatographic separation and

prior sample preparation thus rendering them a rapid technique. However, high-resolution

mass spectrometers are often needed with more direct approaches in order to distinguish

between existing isomers (Dettmer et al., 2007, Gowda and Djukovic, 2014). Therefore, while

no consensus exists as in which MS analytical method is gold-standard use for measuring each

metabolite, the optimal method/tool to use depends on the purpose of the research and the

availability and cost of the instrument and the required resolution and sensitivity needed for

the study (Zhang et al., 2020).

6.2.3.1.3 NMR versus MS:

When comparing NMR and MS for metabolomic analysis, both technologies have their

strengths and limitations. NMR is typically used to trace metabolic pathways rather than

identify a whole metabolome matrix pathways (Fan and Lane, 2011). Indeed, the use of stable

isotope-enriched tracer in studies have allowed NMR to elucidate the dynamics of metabolite

transformations and biochemical pathways. For instance, Kalderon et al. developed using 13C-

labelled glucose and 1H NMR, a non-invasive and non-radioactive diagnostic test for

measuring hepatic glucose recycling to identify or differentiate between the different types

of glycogen storage disease (GSD) in children. In glycogen storage disease 1 (GSD I), glucose-

6-phosphatase activity is impaired causing inhibition of gluconeogenesis whereas in glycogen

storage disease III (GSD III) amylo-1,6-glucosdiase deficiency is prevalent thereby endogenous

glucose production can still stem from gluconeogenesis. A primed dose-constant infusion of

D-[U-13C] glucose or unlabelled glucose was administered to GSD-I and GSD- III patients after

fasting and glucose carbon recycling was determined by analysing 13C NMR resonances of

plasma glucose 13C-13C coupling of two adjacent glucose carbons C-1-C-C2. Results indicated

that plasma glucose of GSD-I subjects comprised of only a mixture of 99% 13C-enriched D-[U-

13C] glucose and unlabelled glucose but lacked any recycled glucose whereas endogenous

glucose production of non-13C-labeled and unrecycled glucose carbon was found in GSD-III

patients therefore highlighting the differing mechanism of glucose production between both

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diseases (Kalderon et al., 1989). NMR is indeed considered a fast, non-invasive, non-

destructive and far more reproducible method than MS. For example, in the same type of

fasting sample, with NMR analysis, coefficient of variation (CV) of metabolites, ranges from

0.9–42% in contrast to 1.1–86% with GC-MS technique (Karimpour et al., 2016). Moreover,

NMR allows for quantitative analysis where signal intensity is directly proportional to

metabolite concentrations and number of nuclei in the molecule in contrast to MS where

intensity of the MS line is not often associated with metabolite concentrations since

ionization efficiency is also a key factor. NMR is also the primary method for determination

of unknown structures where it can identify chemical structures and their stereochemistry

which are difficult to assess using MS technique. Among other advantages includes NMR’s

lack of need for a prior purification separation method and typically too, NMR is advantageous

over MS when detecting compounds that are difficult to ionize or require derivatization (Kruk

et al., 2017, Markley et al., 2017). Nevertheless, MS has a higher sensitivity than NMR and can

identify metabolites using much smaller samples and at much lower concentrations reaching

nanomolar and is also capable of detecting individual metabolites in complex mixtures with

simple extraction methods such as gas/liquid chromatography. In addition, the various MS

approaches using different ionization techniques and mass analysers enables a greater

number of metabolites to be detected (in urine sample: >500 different metabolites can be

detected using various MS techniques versus 40-200 using NMR with varying spectral

resolutions) (Bjerrum and Bjerrum, 2015, Emwas et al., 2019, Zhang et al., 2020). Moreover,

its superior targeted analysis over NMR makes it an ideal tool for precision metabolomics of

clinical purposes since in medical science, samples are often quite valuable therefore the

smaller sample size required for MS versus NMR makes MS-based technology widely used in

clinical research (Mittal, 2015). Indeed, although NMR can be utilized for targeted and

untargeted analysis, it is often reserved for untargeted analysis in contrast to MS where GC-

MS and (LC)-MS allow for superior targeted analyses (Emwas et al., 2019).

It must be noted, however, that NMR and MS techniques due to their distinct advantages and

limitations can both be combined to provide a wider coverage of the metabolome. In

combination, these methods can be used to avoid ambiguity when analysing analytes and

help identify individual metabolites in a mixture. Both NMR and MS convey valuable

structural data, however, in order to ensure that the data generated by both methods apply

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to the same analyte (Elipe, 2003) and in order to obtain more orthogonal data that can

facilitate the identification of metabolites (Li et al., 2019), together they could be used in an

LC (liquid chromatography)-MS-NMR technique. Applications of LC-MS-NMR technique have

expanded in different research settings. An example of this application is in the field of plant

research (Bhinderwala et al., 2018) and pharmakinetic studies of drug development (Rindgen

et al., 2001) to assess metabolic variations with varying treatments and for metabolite

detection and identification.

6.3 Fermentable Fibre and Metabolite Profiling:

Fermentable fibre (FerF) consumption has consistently shown to have positive effects on

energy balance and carbohydrate/lipid metabolism. Indeed, intake of this type of fibre seems

to have inverse associations with weight gain and body lipid accumulation (Wanders et al.,

2011, Adam et al., 2014) and can have favourable effects on glucose homeostasis (Schwartz

et al., 1988, Dehghan et al., 2013). However, in order to better understand the positive effects

of FerF on substrate metabolism and health status, metabolite profiling can be used to map

the relationship between the two by assessing related metabolite changes in biological fluids.

Liu et al. put forth an explorative study on FerF that aimed to examine the effects of sweet

potato (SF) or sweet potato residue (SR) supplementation, that mainly differ in fibre content,

on rat metabolism (Liu et al., 2014). SF typically contains more fibre than SR by an estimated

~70%. For 30 days, rats were given a basal diet containing either 15% SF or 15% SR, or no

supplemental fibre (control). Both SF and SR increased gut production of the short-chain fatty

acid (SCFA) acetate, which is marker of fermentability by the gut microbiota. Plasma 1H NMR

spectroscopy analysis, however, revealed differing metabolite change induced by SF and SR

interventions. In comparison to the control group, SF significantly increased plasma levels of

lipid, lactate, and myo-inositol while decreased plasma levels of glutamine,

glutamine/glutamate, lysine, phosphorylcholine/glycerolphosphocholine, tyrosine, alpha-

glucose, and ß -glucose. SR, on the other hand, also increased plasma levels of lipid and lactate

but differentially increased plasma acetone levels in comparison to control. Moreover, SR

significantly decreased plasma levels of citrate, glutamate, glutamine, isoleucine, lysine,

methionine, alpha-glucose, and ß -glucose. When comparing SF versus SR effects on plasma,

SF significantly decreased plasma levels of lysine, phosphorylcholine/glycerolphosphocholine,

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a-glucose, and ß-glucose, compared with the SR group. Establishing a link between those

metabolites changes and other biomarkers measured by various techniques allows better

understanding of the effect of SR and SF on host metabolism. For instance, SF decreased

plasma TG levels, and relating that with metabolites changes such as the decreased myo-

inositol, a lipid signalling molecule, indicates that SF can affect lipid metabolism. Myo-inositol

is a known structural backbone for numerous secondary messengers such as inositol

phosphates and phosphatidylinositol that are involved in the regulation of insulin signalling

and fatty acid oxidation. SR too seems to effect lipid metabolism, although differently. Plasma

levels of VLDL, LDL and acetone levels were increased with SR supplementation while plasma

TG levels were decreased. SF and SR also seemed to affect glucose and energy metabolism.

Both interventions increased plasma lactate levels. Lactate is an end-product of compound

energy metabolism and increased lactate levels may indicate increased anaerobic glycolysis,

inhibited gluconeogenesis and a variation in carbohydrate and energy metabolism. Moreover,

plasma glucose concentrations were significantly decreased with SF and SR administration in

comparison to the control which combined with the observed increase in lactate

concentrations can indicate increased glycolysis and decreased gluconeogenesis. Finally, SF

and SR supplementation also seemed to affect amino acid metabolism. Lowered amino acids

such as plasma lysine, glutamate, and glutamine/glutamate which are involved in protein

synthesis were observed with SF supplementation and combining this with the measured

decrease in blood urea nitrogen levels may indicate that total protein synthesis was

diminished with SF supplementation. Glutamine, which can activate signalling pathways that

promote protein synthesis was suppressed with SR supplementation which can also signify

altered amino acid metabolism with SR supplementation.

Wu et al. also examined the effect of a FerF, in this case inulin, on pigs’ metabolic health by

assessing plasma metabolite signatures and changes in metabolic pathways associated with

inulin supplementation using GC-MS (Wu et al., 2016). Pigs were fed with a corn-soybean

meal control diet or the same diet supplemented with 5% inulin for 60 days. Pigs

supplemented with inulin had significant increases in cecum propionate concentrations and

a subsequent lower acetate to propionate ratio in comparison to pigs on the control diet.

Among the metabolites assessed in plasma, the branched chain amino acid (BCAA) isoleucine

was greater (p< 0.05) with inulin supplementation in comparison to control. An increase in

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BCAA levels could be a marker for development of insulin resistance and elucidating BCAA

metabolism may be a target for therapeutic treatment of insulin resistance or type 2 diabetes

(Yoon, 2016). Proline and ornithine, key components of urea cycle, were also decreased with

inulin supplementation and the authors suggested this could be due to active amino acid

catabolism as there was an 80% increase in plasma urea. A decrease in proline concentration

is an encouraging outcome since increased proline levels are linked with coronary heart

disease risk (Liu et al., 2016). Plasma free fatty acids, saturated (SF) and mono- saturated (MF)

fatty acids were also found to be significantly lower and poly-saturated fatty acids (PUFA)

significantly higher with inulin supplementation in comparison to control. These effects are

quite favourable since higher PUFA and lower SF concentrations are associated with lower

cardiovascular disease risk (Siri-Tarino et al., 2015).

Samuelsson et al. also examined the effect of inulin supplementation on metabolic profile

using LC–MS with an emphasis on lipid metabolism (Samuelsson et al., 2016). Rats were fed

a basal diet supplemented with 5% inulin or a basal control diet for 14 days. Similar to the

findings of Wu et al., with inulin supplementation, elevated levels of propionate were found

in the small and large intestine. However, inulin supplementation seemed to induce

unfavourable effects on PUFA lipid metabolism. Serum eicosapentaenoic acid (EPA) (20:5)

and docosahexaenoic acid (DHA, 22:6) were decreased with inulin exposure i.e. n-3 pathway

was downregulated whereas n-6 pathway where linoleic acid is ultimately converted to

docosapentaenoic acid (DPA, 22:5) was upregulated. While it is often considered that

decreased EPA and DHA concentrations are associated with increased risk of cardiovascular

disease risk (Cottin et al., 2011) current evidence is yet to be conclusive (Abdelhamid et al.,

2020). In any case, rats in the inulin fed group experienced no weight gain in comparison to

controls.

Certainly, these findings highlight that interpretation of the effects of FerF on metabolic

health is quite complex and can involve numerous biochemical processes. Thus, the use of

metabolomic techniques to assess metabolite changes are vital to provide insight on

biochemical processes altered with FerF supplementation and their consequences on overall

metabolic health.

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6.4 Propionate and Metabolic Profiling:

Accumulating evidence has shown that propionate supplementation can protect against body

weight gain (De Vadder et al., 2014, den Besten et al., 2015, Chambers et al., 2015) and can

have differential effects on substrate metabolism. With regards to lipid metabolism,

propionate has been shown to have hypocholesterolemic effects (Chen et al., 1984, Demigné

et al., 1995, Berggren et al., 1996, Chambers et al., 2015) and can suppress de novo

lipogenesis (Demigné et al., 1995, Heimann et al., 2015) and decrease circulating free fatty

acid concentrations (Chen et al., 1984, den Besten et al., 2015) and triglyceride content in

both the liver (den Besten et al., 2015, Chambers et al., 2015) and white adipose tissue (De

Vadder et al., 2014, den Besten et al., 2015). Moreover, propionate has shown to have

favourable effects with regards to glucose homeostasis where studies have demonstrated

decreases in fasting (Venter et al., 1990, Boillot et al., 1995) and post-prandial (Liljeberg et al.,

1995, Darwiche et al., 2001) glucose and insulin concentrations as well as improvements in

insulin resistance (den Besten et al., 2015, Chambers et al., 2019) and β-cell function

(Pingitore et al., 2017). Stable isotope studies in rodent models have provided mechanistic

insight of how propionate is metabolized in the body. Propionate enters the TCA cycle at the

level of succinyl-CoA and can then be converted to oxaloacetate and ultimately into glucose

(Jones et al., 1997, Perry et al., 2016). However, very limited data actually exists with regards

to propionate’s effect on metabolite profile. A single, recent study by Yu et al. examined in

pigs the effects of cecal infused sodium propionate supplementation on biochemical

parameters in serum and hepatic tissues while using GC−MS for metabolomic analysis (Yu et

al., 2019). During a 28-day time period, pigs were infused with saline (n = 8) or sodium

propionate solution (n = 8) (25 mL, 2 mol/L, pH 5.8) twice per day. Hepatic metabolomic

profiling then revealed twelve metabolites that were altered with propionate

supplementation such as increases in amino acid metabolites (aspartic acid and serine),

tricarboxylic acid (TCA) cycle intermediates (malic acid, fructose-6-phosphate, succinic acid,

and aspartic acid) and decreases in lipid metabolism metabolites (stearic acid and glycerol-2-

phosphate) and in several long-chain fatty acids (LCFAs) (arachidonic acid, docosahexaenoic

acid and hexadecanoic acid) with a tendency for increased glycerin in the propionate group.

The decreases in hepatic lipid TG is suggestive of decreased LCFA synthesis since serum TG

levels were also similarly reduced. Moreover, hepatic transcriptome analysis then revealed

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numerous lipid−related metabolic pathways that were enriched with propionate

supplementation such as PPAR signalling, adipocytokine signalling, and fatty acid metabolism

that explained the molecular mechanisms of propionate-induced TG reduction. For instance,

CPT1 which transfers LCFAs into the mitochondria for fatty acid β-oxidation was upregulated.

Moreover, fatty acid synthesis genes such as SCD1 and FABP3 were downregulated after

propionate infusions. Therefore, it appears that propionate can affect lipid metabolism by

decreasing fatty acid synthesis and promoting lipid oxidation. Findings from this study

highlight the importance of linking metabolite changes such as lipid parameters detected with

metabolomic profiling with findings of alternative techniques that together provide a better

overview of how propionate can favourably affect host metabolism.

In summary, it appears that metabolomic profiling can help understand how propionate can

affect host metabolism by identifying metabolites responsive to increased propionate

bioavailability. However, a substantial gap remains in the literature, particularly in humans.

Given the limited research on this topic, the aim of this chapter is to examine how serum

metabolic phenotype is altered by acutely raising gut derived propionate in healthy humans.

Acute oral propionate supplementation consistently demonstrated an increase in lipid

oxidation (Chapter 3) and had no effect on serum glucose concentrations (Chapter 5) in the

overnight fasted state (0-180 min). Moreover, an increase in energy expenditure and no effect

on glucose profile was also seen post-prandially. Thus, in order to elucidate how those

metabolic changes came into effect, serum samples from the post-prandial trial that

encompassed all those three main outcomes were assessed using 1D 1H NMR analysis. 1D 1H

NMR technique was chosen since it is a non-destructive technique compared to MS and thus

allows for multiple analysis to be performed on the same sample. Untargeted analysis was

chosen to measure as many metabolites as possible and identify how raised propionate alters

the serum metabolic fingerprint.

6.5 Hypothesis:

I hypothesised that acute oral intake of sodium propionate would alter metabolite the serum

metabolic phenotype in both overnight fasted and postprandial state.

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6.6 Aims:

This chapter will aim to present and discuss findings related to identification of metabolites

altered with propionate supplementation and relate those changes with the observed

increases in energy expenditure and lipid oxidation (Chapter 3) as well as the lack of effect on

serum glucose levels (Chapter 5).


The primary outcome measure of this trial is the identification of metabolites altered by

propionate supplementation using untargeted analysis via 1D 1H NMR technique.

6.8 Methods:


6.9 Results:

6.9.1 Post-Prandial Trial:

RM-MCCV-PLS-DA score plot derived from 1D 1H-NMR CPMG spectra of serum samples,

indicating the comparison between propionate group and control group baselines (t = 0 min),

(t=180 min) and (t=240 min) can be found in Figures 6:1,6:2 and 6:3 respectively. At baseline

(0 min), no significant differences between measured metabolites was present between

Control and Propionate (p>0.05). At the end of the fasting period (timepoint 180 min), 3-

Hydroxybutyrate and lysine were downregulated with propionate supplementation whereas

LDL/VLDL, lactate and methanol were upregulated. During the post-prandial period

(timepoint 240 min), LDL/VLDL, lactate and alanine were upregulated with propionate

supplementation.

A summary of metabolites observed in plasma that significantly changed between the control

and propionate groups observed via RM-MCCV-PLS-DA models at different time points can

be found in Table 6.1.

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A List of 1H NMR peak assignments for significant metabolites derived from RM-MCCV-PLS-

DA models of serum data set can be found in Table 6.2.

Figure 6-1: RM-MCCV‐PLS‐DA score plot at baseline

RM-MCCV-PLS-DA score plot derived from 1D 1H-NMR CPMG spectra of plasma samples, indicating the

comparison between propionate group (red) and control group (blue) baselines (t = 0 min). The model is

comprised of Kernel Density Estimate (KDE) of the predicted scores (Tpred) for both groups. Dots represent

the metabolic profile of each volunteer from the study cohort when its corresponding NMR spectrum for both

interventions were available at this time point (n = 18). The fit and predictability of the model were obtained

and expressed as R2Y (explained variance) and Q2Y (capability of prediction) values. RM, Repeated Measures,

MCCV, Monte Carlo Cross-Validation; PLS-DA, partial least squares discriminant analysis.

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Figure 6-2: RM-MCCV‐PLS‐DA score plot at 180 min

A- RM-MCCV-PLS-DA score plot derived from 1D 1H-NMR CPMG spectra of plasma samples, indicating the

differentiation between propionate group (red) and control group (blue) at the end of fasting period (180

min). The model is comprised of Kernel Density Estimate (KDE) of the predicted scores (Tpred) for both

groups. Dots represent the metabolic profile of each volunteer from the study cohort when its corresponding

NMR spectrum for both interventions were available at this time point (n = 20). The fit and predictability of

259

the model were obtained and expressed as R2Y (explained variance) and Q2Y (capability of prediction) values.

B- The corresponding RM-MCCV-PLS-DA loading plot. Top depicts the average 1D 1H-NMR CPMG spectrum

of the 40 plasma samples. The bottom depicts the Manhattan plot showing − log10(q) × sign of regression

coefficient (β) of the RM-MCCV-PLS-DA model that in conjunction represent the contribution of each variable

on it. In blue, metabolites are shown that are significantly higher in the control group, and in red metabolites

significantly higher in the propionate group. Labels: 1, LDL/VLDL; 2, 3-hydroxybutyrate; 3, lactate; 5, lysine; 6,

methanol. RM, Repeated Measures, MCCV, Monte Carlo Cross-Validation; PLS-DA, partial least squares

discriminant analysis. For peak assignments refer to Table 6-2.

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Figure 6-3: RM-MCCV‐PLS‐DA score plot at 240 min

A- RM-MCCV-PLS-DA score plot derived from 1D 1H-NMR CPMG spectra of plasma samples, indicating the

differentiation between propionate group (red) and control group (blue) during the postprandial period (240

min). The model is comprised of Kernel Density Estimate (KDE) of the predicted scores (Tpred) for both groups.

Dots represent the metabolic profile of each volunteer from the study cohort when its corresponding NMR

spectrum for both interventions were available at this time point (n = 20). The fit and predictability of the model

were obtained and expressed as R2Y (explained variance) and Q2Y (capability of prediction) values. B- The

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corresponding RM-MCCV-PLS-DA loading plot. Top depicts the average 1D 1H-NMR CPMG spectrum of the 40

plasma samples. The bottom depicts the Manhattan plot showing − log10(q) × sign of regression coefficient (β)

of the RM-MCCV-PLS-DA model that in conjunction represent the contribution of each variable on it. In blue,

metabolites are shown that are significantly higher in the control group, and in red metabolites significantly

higher in the propionate group. Labels: 1, LDL/VLDL; 3, lactate; 4, alanine. RM, Repeated Measures, MCCV,

Monte Carlo Cross-Validation; PLS-DA, partial least squares discriminant analysis. For peak assignments refer to

Table 6-2.

Table 6-1: Serum Metabolite Changes:

Summary of metabolites observed in plasma that significantly changed between the control and propionate

groups observed via RM-MCCV-PLS-DA models at different time points.

Sampling Point R2Ya Q2Yb Metabolite Associationc Baseline (0 min)d 0.99 0.09 N/A N/A At the end of fasting period (180 min) 0.99 0.31 3-

Hydroxybutyrate ↑

Lysine ↑ LDL/VLDL ↓ Lactate ↓ Methanol ↓

During the postprandial period (240 min)

0.99 0.33 LDL/VLDL ↓ Lactate ↓ Alanine ↓

a,bValidation parameters of the corresponding PLS-DA models. cSign of association: ↑ indicates

upregulation in the control group, ↓ indicates upregulation in the group dosed with propionate.dNon-

significant model.

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Table 6-2: List of 1H NMR peak assignments

List of 1H NMR peak assignments for significant metabolites derived from RM-MCCV-PLS-DA models of serum

data set.

aNo. Metabolite Assignment

Confirmationc 1Hb

1 LDL/VLDL 0.88d 1D,STORM

1.29e

1.57f

2.02g

2.23h

5.31i

2 3-Hydroxybutyrate 1.19 (d) 1D

2.30 (dd) 1D, STORM

2.40 (dd)

4.14 (m)

3 Lactate 1.32 (d) 1D, STORM

4.10 (q)

4 Alanine 1.48 (d) 1D, STORM

3.79 (q)

5 Lysine 1.73 (m) 1D, STORM

1.90 (m)

3.03 (t)

6 Methanol 3.35 (s) 1D aNumber order is based on chemical shift. bThe chemical shifts and multiplicities are listed for

peaks from significantly associated metabolites. Multiplicity key is as follows: s – singlet, d –

doublet, t – triplet, q – quartet, dd – doublet of doublets, m – (other) multiplet. cCorrelation

spectroscopy using 1H 1D-NMR spectra data set via STORM.d1 CH3 group. e-CH2- groups. f -

CH2. gAllylic-CH2. h -CH2. iOlefinic hydrogens.

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6.10 Discussion:

In this study, the acute effect of oral sodium propionate supplementation (71 mmol) on serum

metabolome in healthy human volunteers was examined. Six metabolites were significantly

altered with propionate supplementation. Between 0-180 min, LDL/VLDL, lactate and

methanol were upregulated whereas 3-hydroxybutyrate and lysine were decreased. This

coincided with an increase in lipid oxidation and unchanged glucose profile. Post-prandially,

when propionate increased energy expenditure, LDL/VLDL, lactate and alanine were

increased with propionate supplementation.

6.10.1 3-hydroxybutyrate:

3-hydroxybutyrate (3OHB) is the most abundant and prominent ketone body found in

humans. Ketone bodies, which can be used as an energy source in most body tissues, are

mainly formed in liver cells from LCFAs derived from TG stored in adipose tissue. Although

the body continuously forms ketone bodies as energy fuels (22 ATP/ketone body), their

concentration in blood is normally low under fed conditions but levels rise significantly in

times of fasting or during prolonged exercise when carbohydrate stores are significantly

decreased or depleted. Several factors regulate ketogenesis and they mainly revolve around

the presence of hormones such as glucagon, cortisol, thyroid hormones, and catecholamines

that promote lipolysis which provide free fatty acids available for ketogenesis. However, the

primary hormone that regulates ketogenesis is insulin, which inhibits fatty acid release and

ketone body formation (McGarry and Foster, 1976, Dhillon and Gupta, 2020, Møller, 2020).

Insulin concentrations, however, were not significantly different (Insulin:). In that case, it

appears that with propionate supplementation, ketogenesis, which is typically active during

fasting, was suppressed and 3-hydroxybutyrate levels were decreased since an alternative

energy source was available in the form of propionate. Propionate oxidation can indeed yield

a maximum of 18 ATP/mol which is equivalent to 131.4 kcal/mol (Baldwin, 1995). And

assuming all the propionate provided (71 mmol) was oxidised, a maximal energy yield of 9.3

kcal would be released.

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6.10.2 Lysine:

In the fasting state, lysine concentrations were decreased with propionate supplementation.

Lysine is an essential amino acid and is one of two pure ketogenic amino acids along with

leucine (Litwack, 2018). After an overnight fast, hepatic glycogenolysis, gluconeogenesis and

ketogenesis provide half of the energy yielding fuels for the body and with prolonged fasting

ketogenesis from amino acids is typically increased (Lennarz and Lane, 2013) . Thus, when

introducing propionate as an energy source, providing 9.3 kcal if completely oxidized,

ketogenesis would be decreased and hence a decrease in serum ketogenic substrates, in this

case lysine, would be observed.

6.10.3 Methanol:

Methanol concentrations were upregulated with propionate supplementation. No study up

to knowledge has examined the association between propionate supplementation and

methanol concentrations. Therefore, the exact mechanism of how propionate can increase

fasting methanol concentrations in serum is yet to be determined. Nevertheless, dietary

sources rich in fibre such as fruits and vegetables are two main sources of exogenous

methanol in humans (Dorokhov et al., 2015). Particularly, ingestion of the fermentable fibre

pectin that is shown to increase propionate production (Tian et al., 2016), can increase

methanol concentrations in humans mainly via pectin demethylation by pectin

methylesterase (PME) (Dorokhov et al., 2012). However, this cannot explain the raised

methanol concentrations observed in this study since dietary intake was similar between

control and propionate trials and the primary difference in trials was propionate

adminstration. Anaerobic fermentation by human gut microbiota has however previously

shown to be a direct source of endogenous methanol production (Jensen and Canale-Parola,

1985, Siragusa et al., 1988) where gut microbiome knock out mice display significantly

decreased circulating methanol concentrations (Komarova et al., 2014) although the specific

bacterial strain responsible for methanol generation is yet to be identified. No study up to

current knowledge has examined the direct effect of increased propionate bioavailability on

methanol production. However, it may be that propionate can alter gut microbial activity to

promote methanol formation given that methanol was only increased in the fasting state

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where propionate was the only substrate available for microbial metabolism. Elucidate any

potential mechanism that propionate has on gut microbial activity related to methanol

production would be a very interesting area for future research.

As methanol is a toxic compound and unfit for human consumption, no human intervention

study up to current knowledge has examined the effect of methanol supplementation on

metabolic profile. However, methanol poisoning was found to increase lactate concentrations

(BENNETT Jr et al., 1953) and lipid peroxidation (Skrzydlewska, 2003) which may or may not

induce hyperglycaemia (BENNETT Jr et al., 1953).

Therefore, in the present trial, increased methanol concentrations may be an artefact of

propionate’s effect on gut microbial activity , however, circulating levels are unlikely to have

increased substantially to induce or explain the phenotypic changes observed in this study

especially since after the introduction of dietary substrates, serum methanol concentrations

were no longer changed . Nevertheless, follow up targeted analysis that quantify methanol

concentrations can clarify that further.

6.10.4 Lactate:

6.10.4.1.1 Fasting state:

Lactate is a gluconeogenic substrate that is mainly formed in a reversible reaction from

pyruvate via a fermentation process by lactate dehydrogenase during anaerobic metabolism,

and similarly under resting aerobic conditions although in lesser amounts (~25-50% of total

carbohydrate oxidized may pass through the lactate pool). In the post-absorptive state, stable

isotope studies in healthy humans indicate that ~67% of plasma lactate levels is derived from

glucose (Chochinov et al., 1978) versus 28% from alanine (Kalhan et al., 1988). Lactate and

glucose metabolism are highly interrelated as both compounds are transformed to each other

via the Cori Cycle where glucose is key source of lactate while lactate is a gluconeogenic

substrate able to synthesise glucose (Brooks, 1986, van Hall, 2010, Adeva-Andany et al.,

2014).. Nevertheless, although increasing a gluconeogenic substrate can stimulate

gluconeogenesis, increased lactate concentrations are not linked with increased glucose

production. Tappy et al. explained that this may be since lactate can regulate hepatic glucose

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production (Tappy et al., 1995). After an overnight fast, 5 healthy volunteers were infused

with a primed continuous glucose infusion over 360 min and subsequently with sodium

lactate after 3 hours. Endogenous glucose production was completely inhibited with glucose

infusion at timepoint 180 min and remained so for the remainder of the trial. Net

carbohydrate oxidation was significantly reduced by 42% with lactate and glucose infusions

in comparison to glucose alone and there was also a non-significant decrease in net lipid

oxidation with no effect on plasma glucose concentrations which indicates that lactate is

unable to stimulate endogenous glucose production but a portion of lactate can be converted

into glucose. The lack of effect of lactate on glucose levels despite increased gluconeogenesis

from lactate, also demonstrates that glucose produced from lactate is equivalent to glycogen

synthesis in the liver thereby highlighting the regulatory effect of lactate on hepatic glucose

production. Similarly, Jenssen et al. demonstrated in healthy human volunteers infused with

sodium lactate that lactate infusion can result in increased gluconeogenesis as evidenced by

a 50% increase in the incorporation of lactate into plasma glucose that was not associated

with an overall increase in glucose concentrations (Jenssen et al., 1990).These changes were

seen despite controlling of plasma hormones such as insulin and glucagon. Also, since lactate

disappearance from plasma was not fully recovered from lactate oxidation in plasma and

conversion into plasma glucose, the authors suggested that lactate could have contributed to

net glycogen synthesis which signifies an overall hepatic autoregulation of gluconeogenesis.

More recently, Lun et al. examined lactate metabolism in vitro to shed light on the underlying

cellular processes of lactate metabolism (Lund et al., 2018). The authors demonstrated that

acute exposure (4 hours) of human myotubes to varying concentrations of lactate can

significantly decrease glucose and oleic acid oxidation. Glucose oxidation was only

significantly decreased at 10 mM lactate whereas oleic acid oxidation was significantly

reduced with differing concentrations (2 mM, 4 mM, 6 mM and 10 mM) of lactate. However,

by replacing glucose with lactate (5mM) during the entire culturing period of the myotubes

significantly increased glucose and oleic acid oxidation by 3- and 1.4-fold respectively. The

underlying mechanism appeared to be related to increased expression of carnitine palmitoyl

transferase 1B (CPT1B) and cytochrome c1 (CYC1) which are involved in the transport of fatty

acids across the mitochondrial membrane and mitochondrial function, respectively as well as

increased protein expression of complex V (ATP synthase subunit α) of the mitochondrial

respiratory chain in lactate-cultured cells. The authors suggested that these finding illustrate

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that prolonged exposure of cells to lactate can drive cells towards an energy deprivation state

thereby enhancing the oxidative capacity of the cells. These findings also suggest that lactate

can impact the metabolism of glucose and fatty acids and this effect is dependent on the

duration of lactate exposure.

In this trial, it appears that the lack of effect of propionate supplementation on acute glucose

levels may be related to the increased serum lactate concentrations. In the fasting state, when

energy state is low, gluconeogenesis is typically active (Berg et al., 2002), and since propionate

has been shown to act as a gluconeogenic substrate in humans (Boets et al., 2017), it may

have substituted lactate during hepatic gluconeogenesis, which can hence explain the

increase in lactate concentrations observed with propionate supplementation. Indeed,

gluconeogenic substrate inhibition in favour of another gluconeogenic precursor has been

proposed before (Jahoor et al., 1990).

6.10.4.1.2 Post-prandial state:

Increased lactate concentrations were also observed post-prandially, and a likely explanation

is that propionate may have spared lactate from acting as a gluconeogenic substrate since

~6% of gut derived propionate is shown to contribute to endogenous glucose production

post-prandially (Boets et al., 2017). The increased energy expenditure observed post-

prandially may also be related to increased serum lactate concentrations. Ferrannini et al.

demonstrated that a small increase in blood lactate concentrations, similar to that observed

under physiological or pathological circumstances other than physical activity (2.9-2.4 mM),

can increase thermogenic capacity. 4 healthy subjects were infused with sodium lactate at a

rate of 25 µmol/min/kg for 3 hours after an overnight fast. Oxygen consumption and energy

expenditure significantly rose by 10% above baseline. In another experiment involving 8

healthy volunteers, similar lactate infusion rates and an insulin clamp resulted in a significant

increase in oxygen consumption in comparison to when volunteers were infused with a saline

control. Diet-induced thermogenesis also rose significantly by 16% and 10% above baseline

and saline control respectively. The authors suggest that the substantial rise in energy

expenditure observed with lactate supplementation may be an artefact of energy requiring

synthetic processes such as glycogen synthesis and de novo lipogenesis that are observed

with increased lactate levels (Ferrannini et al., 1993). Similarly, Tappy et al. showed in 5

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healthy volunteers infused with a primed continuous glucose infusion over 360 min and

subsequently with sodium lactate after 3 hours, significantly increased energy expenditure by

18.3% which is comparable with the speculative cost (18%) of lactate conversion into glucose

(Tappy et al., 1995). Also, Chioléro et al demonstrated in six healthy volunteers that infusion

of lactate at a rate of 20µmol/kg/min for 3 hours significantly increased energy expenditure

by 16.3%. The increase in thermogenesis was attributed to gluconeogenesis since the thermic

effect observed with lactate surpassed the hypothesised cost of oxidizing it (Chioléro et al.,

1993). Therefore, the observed increase in energy expenditure post-prandially in this trial

may be attributed to the increase in serum lactate concentrations.

What is quite striking is that all those three studies demonstrate that increased lactate

concentrations are associated with a decrease in lipid oxidation ((Tappy et al., 1995)(Tappy et

al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy

et al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et al.,

1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et al., 1995)(Tappy et

al., 1995)~15%, p>0.05 (Ferrannini et al., 1993); 27% p>0.05 (Tappy et al., 1995) ; 13% p<0.05

(Chioléro et al., 1993)) respectively. The primary difference between the present study and

those three trials is propionate supplementation. Thus, it may be proposed then, that

propionate that was associated with increased lipid oxidation in this study, was able to

counteract the decrease in lipid oxidation that is often associated with increased lactate

concentrations.

6.10.5 Alanine:

Alanine is one of the major amino acids in proteins and is primarily produced by skeletal

muscles. In muscle cells, alanine is generated from pyruvate and an amino acid via glutamate-

pyruvate transaminase. Alanine can then be transported in the bloodstream to the liver and

used as a glucose precursor (Litwack, 2018).

Increased alanine concentrations may arise from propionate supplementation. Alanine is a

gluconeogenic amino acid (Litwack, 2018), and therefore it is plausible that propionate may

have spared alanine from contributing to glucose production and elevated alanine

concentrations since ~6% of gut derived propionate is shown to contribute to endogenous

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glucose production post-prandially (Boets et al., 2017). Indeed, competition between

gluconeogenic substrates has been proposed before. 15 healthy volunteers, following a 14

hour fast, were infused with glycerol (10%) and alanine (7%) solutions. Glucose production

rate, however, was not increased despite the presence of two prominent gluconeogenic

substrates. This suggests that one gluconeogenic substrate was inhibited in favour of the

other. Indeed, urea production rate was decreased, which imply that glycerol was primarily

used for gluconeogenesis (Jahoor et al., 1990). In line with that, stable isotope studies have

proposed that propionate enters the TCA cycle at the level of succinyl CoA (Jones et al., 1997,

Perry et al., 2016) and therefore unlike alanine, bypasses the rate limiting step of pyruvate

conversion into phosphoenolpyruvate before conversion into glucose. Thus, propionate

supplementation in the present trial may have been favoured over alanine with regards to

gluconeogenesis. This would actually be quite important since this suggests that amino acids

would hypothetically be spared and used for vital body processes such as tissue preservation

and immune function. Indeed, animal studies have shown that intervention studies that

increase systemic levels of propionate can increase the concentration of amino acid

metabolites that signify decreased gluconeogenesis originating from amino acids. For

instance, in a cross-over design, 3 normal weight and 3 obese cats were randomly

administered a colonic infusion of either a propionate solution (5.7 mmol/kg; 4 mmol/kg Ideal

body weight respectively) or a control saline solution (0.9% NaCl; 310 mOsmol/l) over 30 min.

30 and 60 min after the propionate infusion ensued a significantly lower plasma 3-hydroxy-3-

methylglutarylcarnitine, a specific metabolite of branched-chain amino acid catabolism that

hence portrays suppressed gluconeogenesis from amino acids (Verbrugghe et al., 2012). Also,

in a crossover study, involving 8 healthy dogs on a low protein diet supplemented with either

sugar beet pulp and guar gum mix (known to stimulate propionate production), or cellulose

for four weeks, resulted in an increase in amino acids (leucine, isoleucine, phenylalanine and

tyrosine) concentrations post-prandially which also indicate amino acid preservation for

other use in the body (Wambacq et al., 2016).

Nevertheless, increased alanine concentrations although has been shown to markedly

increase post-prandial gluconeogenesis, this was not shown to have any effect on glucose

levels in healthy individuals due to a counterbalance in insulin stimulated glucose rate of

disappearance (Krebs et al., 2003).

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Furthermore, the increased alanine concentrations observed post-prandially may be related

to the increased lactate concentrations also detected during that time-frame where it was

shown in 4 healthy adults that lactate infusions at a rate of 25 µmol/min/kg for 3 hours, can

result in a 37% rise in blood alanine concentrations (Ferrannini et al., 1993) although this

effect does not seem reversible since increased alanine concentrations are associated with

decreased lactate levels (Jahoor et al., 1990).

6.10.6 VLDL/LDL:

The present analysis was unable to differentiate between the different subclasses of

lipoproteins. A very broad class of metabolite exists under the detected VLDL/LDL peaks such

as subfractions of VLDL/LDL falling under cholesterol, free cholesterol, phospholipids, TG and

ApoB. Therefore, a wide gap exists for future research in order to determine which subclass

of LDL/VLDL could be influenced with propionate adminstration.

Given that, comparing the current findings with the literature which often referes to LDL/VLDL

cholesterol such as that obtained after a blood test, is limiting. However, the following section

highlights the possible mechanisms of how propionate can increase VLDL/LDL concnetrations

in different energy states.

6.10.6.1.1 Fasting state/Post-prandial States:

Increased serum VLDL/LDL concentrations with propionate supplementation during fasting

and post-prandial states is somewhat unexpected. Numerous research have highlighted that

propionate has hypercholesteraemic effects and can inhibit free fatty acid (FFA) synthesis

(Chen et al., 1984, Demigné et al., 1995, Berggren et al., 1996).On the other hand, lipid and

glucose metabolism are highly interrelated and a disturbance in one may lead to a disorder

in the other (Krauss, 2004, Parhofer, 2015).Increased lactate concentrations is shown to be

associated with type 2 diabetes and glucose disturbance (Crawford et al., 2010) which hence

may effect lipid homeostasis. Indeed, one study in humans demonstrated that increased

lactate concentrations is associated with disturbances in lipid profile. Sondermeijer et al.

undertook a cross-over study involving eight normolipidemic healthy men and examined the

effect of a 7-hour lactate clamp versus a saline control infusion on VLDL-TG homeostasis

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(Sondermeijer et al., 2013). The lactate infusion which generated a 5-fold increase in plasma

lactate levels, resulted in a significant increase in hepatic VLDL-TG secretion which may be

related to an increased stearoyl-CoA desaturase (SCD1) activity, a key mediator in hepatic

VLDL-TG excretion, as evidenced by a higher hepatic desaturation index i.e. the ratio of

plasma of palmitoleic acid (16:1) and palmitic acid (16:0) which reflects SCD1 activity. The

authors suggest that excess lactate may be converted into pyruvate and as a substrate of the

TCA cycle can lead to increased citrate synthesis which ultimately promotes FFA and TG

synthesis. Thus, an increase in VLDL/LDL concentrations post-prandially, may be related to

increased lactate concentrations. However, since only one study (Sondermeijer et al., 2013)

which involved a small number of volunteers and has infused a substantial amount of lactate

has highlighted this association, extrapolating these findings to the present outcomes must

be employed cautiously. Another plausible explanation is that other metabolites that could

have been increased with propionate supplementation but missed during NMR analysis may

have disrupted lipid profile and increased LDL/VLDL concentrations in fasting and post-

prandial states. However, future research can investigate that further using more sensitive

and comprehensive metabolomic techniques such as MS.


This study has several limitations. For instance, metabolomic analysis was only performed on

the post-prandial trial albeit the fasting and exercise trials. This was mainly due to time-

constraints and expense. However, the post-prandial trial was chosen since it encompasses

the three main outcomes in the study i.e. the consistent increase in lipid oxidation in the

overnight fasted state, the unaltered glucose profile and post-prandial increase in energy

expenditure. Also, the technique used ,1D 1H NMR, as do all techniques has its drawbacks.

For instance, NMR, although more reproducible and faster to perform than MS, is also less

sensitive and can detect far fewer metabolites than MS (Emwas et al., 2019). However, since

NMR is non-destructive, this method was chosen to allow for multiple sample analysis for

purpose of this research. Also due to time constraints, only an untargeted analysis was

employed. A follow up targeted analysis would have been quite useful in quantifying the

metabolites and comparing their concentrations with established references and other

research trials. Amino acid concentrations could have also been determined using HPLC

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technique. However, an untargeted analysis was ideal to be performed first since this

technique is inherently intended for ‘discovery’ and span of the whole metabolome rather

than restriction to a prior identified set of metabolites which would have been difficult to

determine given the paucity of research in that area. Another limitation is that the only

biological specimen available for analysis was serum. Urine samples, unfortunately, were only

analysed to determine urea concentration as an estimate of protein oxidation rates. Ideally,

metabolomic analysis on urine samples should have been done as well. Indeed, analysis of

both specimens can provide complementary information on metabolism where serum

metabolites reflect homeostatic regulatory concentrations versus urine metabolites which

highlight excretion after degradation processes (Playdon et al., 2016).

6.12 Conclusion:

This was the first in human trial that employed metabolomic techniques to examine the acute

effect of oral sodium propionate supplementation on serum metabolome. Given the present

findings, it appears that propionate can affect serum phenotype in both the overnight fasted

and post-prandial states. The increased methanol concentrations observed in the fasting

period may highlight altered gut microbial activity with propionate supplementation that

support increases in methanol production that could be an area for future research.

Moreover, future research should focus on differentiating the different subclasses of

lipoprotein subfractions that can be influenced with increased propionate bioavailability.

Perturbations in lactate and alanine metabolites appear to be the most closely related to the

observed effects on energy expenditure and glucose profile. It may be assumed that

propionate, as a gluconeogenic substrate, spared lactate and alanine from gluconeogenesis

for other bodily functions. None of the detected metabolites however, according to previous

research, seem to have an obvious association with the increases in lipid oxidation that was

prevalent in the overnight fasted states. Further research using more sensitive techniques

such as MS that can detect lower abundance molecules or a combination of both MS and

NMR techniques that provide a comprehensive analysis of the metabolome could be very

useful in providing a mechanistic insight of how propionate may affect serum metabolites to

support changes in energy expenditure, substrate oxidation and glucose homeostasis.

Moreover, a key area for future research is to employ an energy matched control in studies

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in order to characterize whether metabolite changes are influenced by the calories provided

or in fact due to the propionate itself.

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Chapter 7: General Discussion

7.1 Thesis aims:

The overall aim of this thesis was to investigate the impact of oral sodium propionate

supplementation on energy metabolism in humans. Three separate studies were therefore

conducted to investigate the impact of oral sodium propionate on energy expenditure,

substrate oxidation, appetite response, glucose homeostasis and serum metabolic phenotype

in different energy stats (overnight fasted, sub-maximal exercise and postprandial).

Chapter 3: Investigated the impact of oral sodium propionate supplementation on energy expenditure and substrate oxidation:

• Overnight fasted state: Propionate increased EE (energy expenditure) and lipid

oxidation and decreased CHO oxidation over a period of 360 min. Changes in EE and

lipid oxidation are mainly seen within the first 180 min following propionate ingestion.

HR was stimulated within the first 180 min of propionate ingestion. MAP also

increased with propionate supplementation after a prolonged fast.

• Sub-maximal exercise state: Propionate had no effect on EE and substrate oxidation

during sub-maximal exercise.

• Post-prandial state: Propionate increased EE in the post-prandial state with no

preferential use of a substrate.

Chapter 4 Evaluated the impact of oral sodium propionate supplementation on subjective appetite and nausea. The impact of oral sodium propionate on GLP-1 levels was also investigated:

• Overnight fasted state: Acute propionate ingestion stimulated subjective nausea over

a prolonged fasted period of 360 min. However, it had no effect on subjective hunger

and thirst.

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• Sub-maximal exercise state: Acute propionate supplementation stimulated

subjective thirst during exercise. No differences in appetite or nausea were observed.

• Post-prandial state: Acute propionate ingestion stimulated subjective nausea but has

no effect on GLP-1 secretion and subjective hunger

Chapter 5 Investigated the impact of propionate supplementation on glucose homeostasis:

• Overnight fasted state: Acute propionate ingestion attenuated the decrease in

glucose concentrations over a prolonged fasted period of 360 min. However, insulin

resistance, assessed by HOMA-IR, was unaffected.

• Sub-maximal exercise state: Propionate has no effect on glucose homeostasis during

sub-maximal exercise.

• Post-prandial state: Propionate ingestion attenuated the decrease in GLP-1

concentrations over the initial 180 min fasted period. This was not associated with

differences in insulin-resistance Propionate had no effect on β-cell function or

peripheral insulin resistance in the post-prandial state.

Chapter 6 Evaluated the impact of propionate Supplementation on serum metabolic phenotype in the overnight fasted and postprandial state:

• Overnight fasted state: LDL/VLDL, lactate and methanol were upregulated whereas

3-hydroxybutyrate and lysine were decreased with propionate supplementation.

• Post-prandial state: LDL/VLDL, lactate and alanine were increased with propionate

supplementation.

7.2 Summary of results:

These three studies observed that acute ingestion of oral sodium propionate (71 mmol) in

healthy human volunteers in different energy states (overnight fasted, sub-maximal exercise

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and post-prandial) had varying effects on energy expenditure, substrate oxidation, appetite

response, glucose homeostasis and serum metabolic phenotype.

An increase in energy expenditure was found in the overnight fasted state that was mainly

observed within the first 180 min of ingestion and in the post-prandial state. In the overnight

fasted state, heart rate (HR) and mean arterial pressure (MAP) were also stimulated. A

consistent increase in lipid oxidation was found in the overnight fasted state, however, these

effects were not observed during submaximal exercise nor in the post-prandial state. A

decrease in carbohydrate (CHO) oxidation was also found in the overnight, fasted state. With

regards to subjective measures of appetite, propionate ingestion increased subjective thirst

during sub-maximal exercise and subjective nausea in the overnight fasted and post-prandial

states. However, no effect on subjective hunger was found was found in different energy

states. GLP-1 (glucagon-like peptide 1) secretion was significantly increased in the overnight

fasted state, however, insulin sensitivity and β-cell function were unaffected with propionate

ingestion. After an overnight fasted state, LDL (low density lipoprotein)/VLDL (very low-

density lipoprotein), lactate and methanol were upregulated whereas 3-hydroxybutyrate and

lysine were decreased. This coincided with an increase in lipid oxidation and unchanged

glucose profile. Post-prandially, when energy expenditure was increased, LDL/VLDL, lactate

and alanine were upregulated.

7.3 Conclusion, Limitations and Future Work:

In the overnight fasted state, acute ingestion of oral sodium propionate (71 mmol) in healthy

human volunteers increased energy expenditure and lipid oxidation and decreased CHO

oxidation, which seems to be mediated via SNS activity stimulation. It also increased energy

expenditure post-prandially with no preferred use of a substrate. However, acute oral sodium

propionate ingestion had no effect on energy expenditure and substrate oxidation during sub-

maximal exercise. These findings support my hypothesis in the rested, fasted state and post-

prandially but not sub-maximal exercise. As discussed in Chapter 3:, there are multiple factors

that may have contributed to the discrepancy in findings. Nevertheless, this study is the first

in human study to provide direct evidence that acute oral ingestion of sodium propionate can

modulate energy expenditure and substrate metabolism in different energy states and, given

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these findings, future follow up studies could use a 24-h respiratory chamber and continuous

measurement of SNS activity to assess the full effect of oral sodium propionate

supplementation on energy expenditure and substrate oxidation. Moreover, more reliable

methodologies such as continuous heart rate variability or microneurography (Zygmunt and

Stanczyk, 2010, Seravalle et al., 2013) can also be used to better determine the effects of gut

absorbed propionate on SNS activity.

Acute ingestion of oral sodium propionate in healthy human volunteers influenced subjective

markers of appetite, mainly by increasing subjective feelings of nausea during fasted and

post-prandial states and increasing subjective thirst during exercise. Moreover, it can also

stimulate biological satiety markers such as GLP-1 concentrations in the overnight fasted

state. Of note, several human studies (Frost et al., 2003, Chambers et al., 2018) including this

trial have demonstrated an increase in subjective nausea with propionate supplementation.

Thus, future research aimed at developing therapeutic strategies to increase gut-derived

propionate bioavailability to target appetite would need to consider this limitation.

Noteworthy too, an increase in subjective feelings of nausea with propionate

supplementation may be an indicator that propionate can influence satiation rather than

satiety. Future studies can hence assess this by employing VAS continually during an ad

libitum test meal and observe if propionate promotes an early termination of meals due to

the raised feelings of nausea.

Furthermore, future intervention studies using a combination of subjective and objective

markers of appetite are clearly needed to give a better overview of the effect of sodium

propionate on appetite regulation. It would be interesting to assess the impact of sodium

propionate delivery on appetite in different areas in the gastrointestinal tract. One way of

achieving this is by using tablets similar to the ones used in the present trial, which can

disintegrate at varying pH along the tract. For instance, the current tablets are most likely to

disintegrate in the proximal intestine where pH is 6.6 (Evans et al., 1988) since these tablets

were previously shown to completely dissociate at pH 6.8 (Chapter 2 Section: Supplements)

whereas other tablets could be designed that disintegrate at pH 7.5 and 6.4 that would ideally

deliver propionate to the terminal ileum and cecum respectively.

278

Also, very importantly, future studies should quantify both energy intake and energy

expenditure in order to best determine the acute effect of oral sodium propionate

supplementation on overall energy balance.

This trial also indicates that acute ingestion of oral sodium propionate in healthy human

volunteers can prevent the decline in GLP-1 levels observed with prolonged fasting but has

no effect on insulin resistance and β-cell function. Also, the impact of sodium propionate

ingestion on glucose levels in the fasted state (0-180 min) was only observed in one of the

three studies and thus would demonstrate that acute propionate ingestion has minimal effect

on markers of insulin sensitivity and glucose tolerance in healthy humans. It is noteworthy,

however, that the present trial only included normal weight, healthy individuals with normal

glucose tolerance, hence it may be challenging to enhance an already ‘normal’ parameter.

Hence, well-controlled future studies using more robust methodologies are needed to better

investigate the acute effect of gut derived propionate on glucose homeostasis in individuals

with a range of metabolic health and also in all three different energy states.

Finally, this was the first in human trial that employed metabolomic techniques to examine

the acute effect of oral sodium propionate supplementation on serum metabolome profile.

Given the present findings, it appears that propionate can affect serum phenotype in both

the overnight fasted and post-prandial states. The increased methanol concentrations

observed in the overnight fasted period may highlight altered gut microbial activity with

propionate supplementation that support increases in methanol production. This is an area

that warrants future research. Moreover, future research should focus on differentiating the

different subclasses of lipoprotein subfractions that were raised with increased propionate

bioavailability. Perturbations in lactate and alanine appear to be the metabolites most closely

related to the observed effects on glucose profile. It may be assumed that propionate, as a

gluconeogenic substrate, “spared” lactate and alanine from hepatic gluconeogenesis for

other bodily functions. Moreover, increased lactate concentrations seem to be the most

closely related to the observed increase in post-prandial energy expenditure as a potential

link between raised lactate concentrations and increased energy expenditure has been

demonstrated previously (Chioléro et al., 1993, Ferrannini et al., 1993, Tappy et al., 1995).

However, none of the detected metabolites, according to previous research, seem to have an

279

obvious association with the increases in lipid oxidation that was prevalent in the overnight

fasted states. Thus, further research using more sensitive techniques such as MS that can

detect lower abundance molecules or a combination of both MS and NMR techniques that

provide a comprehensive analysis of the metabolome would be useful in providing a

mechanistic insight of how propionate may affect serum metabolites to support changes in

energy expenditure, substrate oxidation and glucose homeostasis.

A key area for future research is to employ an energy matched control. No study up to date

has included an energy-matched control such as palmitate (4 mmol), although this is vital in

order to distinguish whether the metabolic changes associated with propionate

supplementation is due the calories provided or in fact due to the propionate itself.

To conclude, this thesis is the first to demonstrate that acute oral sodium propionate

supplementation in healthy human volunteers can have favourable effects on energy

metabolism in different energy states. Future studies are warranted to determine if these

effects are sustained chronically. Should these effects be replicated over longer time periods

to improve energy balance, this suggest that increasing systemic levels of gut-derived

propionate in humans through dietary interventions such as the use of propiogenic dietary

fibres or targeted gut delivery methods (Frost et al., 2003, Chambers et al., 2018) would be a

promising strategy to improve long term energy balance and body weight management.

280

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Appendices

Appendix 1:

Version 3- 04/04/18

REC Reference number: 230710

305

Information Sheet for Research Participants

The Acute Effect of Propionate on Energy Homeostasis

Study 1: The acute effects of propionate on energy metabolism during fasting

You will be given a copy of this Information Sheet and a signed copy of your consent form to keep,

should you decide to participate in the study.

You are being invited to take part in a research study investigating the role of dietary supplements

called propionate on energy metabolism during fasting. This study is being conducted as part of an

educational qualification at Imperial College London. Before you decide if you would like to

participate, it is important for you to understand why the research is being done and what it will

involve. Please take time to read the following information carefully and discuss it with your friends,

relatives and your GP if you wish. Ask us if there is anything that is not clear or if you would like more

information. Take time to decide whether or not you wish to take part.

If you do decide to take part, please let us know beforehand if you have been involved in any other

study during the last year. You are free to withdraw at any time without explanation.

Thank you for reading this

What is the purpose of this study?

High fibre diets are known to have health benefits and have been widely advertised as a method of

helping people to lose weight. We are investigating the major breakdown products of fibre, short

chain fatty acids (SCFA), which are produced naturally by the body via fermentation of dietary fibre by

the gut microbiota. SCFA are believed to contribute to the beneficial effects of dietary fibre on human

health. One of these SCFA, propionate, may have a role in improving glucose, insulin and fat levels in

the blood as well as increasing energy expenditure which are important obesity risk factors.

306

The aim of the study is to determine the effects of capsules containing propionate on the body’s

response to fasting. The propionate and placebo capsules used in this study have been prepared by

Quay Pharma, whose manufacturing areas meet the standards set out in the Good Manufacturing

Practices criteria:

https://www.quaypharma.com/. The capsules have been used in previous research involving human

volunteers and are well-tolerated with no side-effects.

Who is suitable to participate?

• Male and female healthy adults (aged 18 to 65 years)

• Healthy volunteers (body mass index (BMI) of 18-35 kg/m2)

o (BMI is equal to body weight (kg) divided by height squared (m2))

You are NOT suitable to participate if you have or are:


• A current smoker

• Substance abuse


• Pregnancy

• Diabetes


• Cancer


• Kidney disease

• Liver disease

• Pancreatitis

• Started new medication within the last 3 months likely to interfere with energy metabolism,

appetite regulation and hormonal balance, including: anti-inflammatory drugs or steroids,

antibiotics, androgens, phenytoin, erythromycin or thyroid hormones.

• Involved in current research or have recently been involved in any research prior to

recruitment in the past 12 weeks.

307

It is entirely up to you whether or not to take part. If you do we will ask you to sign a consent form.

You are free to withdraw at any time and you do not have to give a reason. A decision either not to

take part or to withdraw from the study will not affect the standard of care you receive.

What will I have to do?

If you do decide to participate, the study will consist of 4 separate study visits. We request that you

do not start any new diets or intensive exercise regimes in-between the study visits as this may give

us conflicting results.

Visit 1- Health Screening

You will be asked to attend the NIHR Imperial Clinical Research Facility at Hammersmith Hospital

where you will be interviewed and examined by one of the research doctors. You will have a blood

test to check that you are not anaemic or diabetic and height and weight measurements and blood

pressure will be taken. You will also have an electrocardiogram (ECG). This is a non-invasive test to

look at the health of your heart. All women of child bearing age will have a pregnancy test. The health

screening should last about 1 hour.

Visits 2-4: Study Day

On the day before each study visit we will ask you to refrain from strenuous exercise, caffeine and

alcohol. You will then be requested to fast overnight (you are allowed to drink water).

On the study day, you will be asked to arrive at the NIHR/Wellcome Trust Imperial Clinical Research

Facility at Hammersmith Hospital at approximately 9:00am. A small plastic tube (cannula) will be

inserted into your arm. This will be in place for the duration of the study day and will be used to take

blood samples at regular intervals without causing you any further discomfort. A total of 110 ml of

blood (20 teaspoons) will be collected at each study visit.

308

Your resting energy expenditure will be collected throughout the study visit. This will involve a large,

clear Perspex calorimeter canopy being placed over your head and upper body to measure your

oxygen consumption for 20 min. We will demonstrate this to you during the initial health screening

visit.

Throughout the day, you will be asked to swallow capsules containing Propionate or a ‘placebo’. On

one of the visits the capsules will contain propionate and on the other two study visits the capsules

will contain the ‘placebo’. A ‘placebo’ is term used to describe a test or procedure that is used as a

control to measure the effectiveness of an intervention.

The study day should last approximately 6 hours and you will be able to go home at around 3:00 p.m.

The procedures collected during the study visit are summarised below:

We will also ask you to collect urine each time you go to the toilet during the study visits. You will be

provided with an appropriate measurement container to collect urine. During the course of the study

day you will be able to read or watch DVDs if you wish.

Will I get paid for participating?

309

You will be reimbursed for any inconvenience caused due to the study. You will receive £25 for

completing each of Study Visits 2-4. You would therefore receive payment of £75 for completing the

entire study.

What are the possible disadvantages and risks of taking part?

In the event that we discover something about your health that you were unaware of, for example if

your blood tests are abnormal, we would immediately inform you of this and inform your GP so that

you can be referred to an appropriate specialist. If you require more urgent assessment we would

arrange this for you immediately within the hospital.

Some of the procedures in this study, such as the recording of your weight, height and blood pressure

present no risk to you. Other procedures, such as taking blood samples, can cause mild discomfort.

The risks of taking a blood sample include: slight discomfort when the needle is inserted and possible

bruising and a localised infection. These procedures will only be carried out by experienced doctors

under aseptic conditions to minimise all these risks.

There are no major side effects associated with taking the propionate capsules. Propionate are

produced naturally in the gut following fermentation of foods which have high levels of non-digestible

fibre e.g. fruits and vegetables. Propionate are widely used in the food industry as a preservative and

as a natural component of food. The total dose of Propionate used in this study (5 grams) has been

used in previous research involving human volunteers and is well-tolerated with no side-effects.

Who has reviewed this study?

This study was reviewed and received favourable opinion by London - Bloomsbury Research Ethics

Committee.

What happens when the research study stops?

310

Once the study has finished, the results of the study can be made available to you and/or your GP. If

you have any problems immediately following the study, then you should contact one of the research

doctors on the numbers provided.

What if new information becomes available?

Sometimes during the course of a research study, new information becomes available about the

treatment that is being studied. If this happens, your research doctor will tell you about it and discuss

with you whether you want to continue in the study. If you decide to continue in the study you will be

asked to sign an updated consent form. Also, on receiving new information your research doctor might

consider it to be in your best interests to withdraw you from the study.

What will happen if I don’t want to carry on with the study?

You can withdraw from the study at any time and you do not need to give a reason. Any stored blood

or urine samples that can still be identified as yours will be destroyed.

Who is funding and organising the study?

This study is being funded and conducted as part of an educational qualification at Imperial College

London.

What if something goes wrong?

Imperial College London holds insurance policies which apply to this study. If you experience serious

and enduring harm or injury as a result of taking part in this study, you may be eligible to claim

compensation without having to prove that Imperial College is at fault. This does not affect your legal

rights to seek compensation.

If you are harmed due to someone’s negligence, then you may have grounds for a legal action.

Regardless of this, if you wish to complain, or have any concerns about any aspect of the way you have

311

been treated during the course of this study then you should immediately inform the Investigator

(Professor Gary Frost; [email protected]; 020 8383 3242). The normal National Health Service

complaint complaints mechanisms are also available to you. If you are still not satisfied with the

response, you may contact the Imperial AHSC Joint Research Compliance Office.

What steps would you take if you have given an informed consent but by an unlikely chance, you lose capacity to consent during the study?

All your identifiable data or tissue collected would be withdrawn from the study. Data or tissue

which is not identifiable to the research team may be retained.

What can I do if I have any complaints or concerns?

If you wish to complain, or have any concerns about any aspect of the way you have been treated

during the course of this study then you should immediately inform the Principal Investigator,

Professor Gary Frost, through his secretary on 020 8383 3242 or by email at [email protected]

Will my taking part in this study be kept confidential?

All information that is collected about you during the course of the research will be kept strictly

confidential. Any information about you that leaves the hospital will have your name and address

removed so that you cannot be recognised from it.

All electronic data about you will be stored on Imperial College London and Imperial College

Healthcare NHS Trust departmental database. This is a confidential computer system which requires

a specific password for access and can only be viewed by authorised persons. It is a requirement that

your GP is informed of your participation in this study.

What will happen to the results of the research study?

The results are likely to be published six months following the study. Your confidentiality will be

ensured at all times and you will not be identified in any publication. At the end of the study, the

results of the study can be made available to you and/or your GP.

312

What would happen to my samples once the study has been completed?

With your consent, your samples will be stored and may be used in future ethically approved studies

whilst keeping your samples unidentifiable to future researchers. If you would prefer, your samples

will be disposed at the end of the study in accordance with the Human Tissue Authority’s Code of

Practice.

Contact for Further Information

The researchers and doctors involved in the study, Professor Gary Frost and Dr. Ed Chambers, will be

available by telephone

During working hours through Professor Gary Frost’s secretary 020 8383 3242

At all other times through Hammersmith Hospital switchboard 020 8383 1000

Appendix 2:

Version 3- 04/04/18



313


STUDY 2: The acute effects of propionate on energy metabolism during

exercise




called propionate on how the human body responds to exercise. This study is being conducted as part

of an educational qualification at Imperial College London. Before you decide if you would like to

participate, it is important for you to understand why the research is being done and what it will

involve. Please take time to read the following information carefully and discuss it with your friends,

relatives and your GP if you wish. Ask us if there is anything that is not clear or if you would like more

information. Take time to decide whether or not you wish to take part.









health. One of these SCFA, propionate may have a role in improving glucose, insulin and fat levels in


314


response to exercising. We also want to see if these responses differ following rest and a period of

exercise. The propionate and placebo capsules used in this study have been prepared by Quay Pharma,

whose manufacturing areas meet the standards set out in the Good Manufacturing Practices criteria:

https://www.quaypharma.com/. The capsules have been used in previous research involving human

volunteers and are well-tolerated with no side-effects.








• Substance abuse


• Pregnancy

• Diabetes


• Cancer


• Kidney disease

• Liver disease

• Pancreatitis

315














You will be asked to attend the NIHR Imperial Clinical Research Facility at Hammersmith Hospital






Visit 2 - Maximal Exercise Test

This visit is to determine your maximal oxygen uptake (VO2 max) and power output. Following a five-

minute warm up you will exercise on an exercise bike until you reach your limit of endurance. The

workload increases every 3 minutes until you are unable to continue. Your breathing will be measured

316

through a mouthpiece during the test. Water will be freely available and the visit will last no longer

than 1 hour.












visit.

Throughout the day, you will be asked to swallow capsules containing propionate or a ‘placebo’. On

one of the visits, the capsules will contain propionate and on the other study visits the capsule will

contain the ‘placebo’. A ‘placebo’ is term used to describe a test or procedure that is used as a control

to measure the effectiveness of an intervention.

Three hours after ingesting the first capsule you will be asked to complete 60 min of cycling at a

workload equivalent to 40% of your maximum power output (determined from Visit 2). This is light-

intensity exercise (equivalent to a brisk walking) and shouldn’t feel too strenuous. Throughout the

exercise you will be asked to breathe through a mouthpiece connected to the calorimeter. Your heart

rate will also be recorded throughout the exercise period.



317







entire study.






318






During Visits 2-5 you will experience sensations associated with exercise but these will constitute no

greater a risk than your usual exercise activities.

There are no major side effects associated with taking the propionate capsules. Propionate is



as a natural component of food. The total dose of propionate used in this study (5 grams) has been




Committee.









319








London.













320




















Practice.





321


Appendix 3:

Version 3- 04/04/18


322



Study 3: The acute effects of propionate on postprandial metabolism




called propionate on how the human body responds to ingesting a sugary drink. This study is being

conducted as part of an educational qualification at Imperial College London. Before you decide if you

would like to participate, it is important for you to understand why the research is being done and

what it will involve. Please take time to read the following information carefully and discuss it with

your friends, relatives and your GP if you wish. Ask us if there is anything that is not clear or if you

would like more information. Take time to decide whether or not you wish to take part.









health. One of these SCFA, propionate, may have a role in improving glucose, insulin and fat levels in


323


response to ingesting a sugary drink. We also want to see if these responses differ following rest and

following a meal. The propionate and placebo capsules used in this study have been prepared by Quay

Pharma, whose manufacturing areas meet the standards set out in the Good Manufacturing Practices

criteria: https://www.quaypharma.com/. The capsules have been used in previous research involving

human volunteers and are well-tolerated with no side-effects.








• Substance abuse


• Pregnancy

• Diabetes


• Cancer


• Kidney disease

• Liver disease

• Pancreatitis






324









You will be asked to attend the NIHR Trust Imperial Clinical Research Facility at Hammersmith Hospital














325




visit.

Throughout the day, you will be asked to swallow capsules containing Propionate or a ‘placebo’. On

one of the visits the capsules will contain propionate and on the other two study visits the capsules

will contain the ‘placebo’. A ‘placebo’ is term used to describe a test or procedure that is used as a

control to measure the effectiveness of an intervention.

Three hours after ingesting the first capsule you will be asked to consume a mixed meal tolerance test

(500 kcal) will be given in the form of an “ Ensure” drink. This is known as an oral glucose tolerance

test (OGTT), and we will be measuring how quickly the sugar from the drink is cleared from your blood.






326




entire study.











There are no major side effects associated with taking the propionate capsules. Propionate are



as a natural component of food. The total dose of Propionate used in this study (5 grams) has been



327


Committee.
















London.


328











What steps would you take if you have given an informed consent but by an unlikely chance, you lose capacity to consent during the study?

All your identifiable data or tissue collected would be withdrawn from the study. Data or tissue

which is not identifiable to the research team may be retained.













329









Practice.






330

Appendix 4:

Propionate Study Pre-screening Questionnaire

Age

Weight

Height

BMI

Have you gained or lost any weight

in the last 3 months? If yes, approximately how much weight

did you gain/lose and in what length of

time? Eg: I lost 3kg in 2 months.

331

Are you a smoker or ex-smoker?

Do you take any regular

medication?

Do you currently suffer from any

particular illness?

Have you taken part in any research

studies in the last 3 months?

Have you donated any blood in the

last 3 months?

Database details:

Full name

Date of birth

NHS number

Contact address

Phone number

Email address

Appendix 5:

Version 1 – 14/10/17 REC Reference number: 230710

Department of Investigative Medicine, HammersmithHospital Campus,

ImperialCollegeLondon

6th Floor, CommonwealthBuilding, Ducane Road,

W120NN

Tel 020 838 33242 Fax 020 838 33142


STUDY 1: The acute effects of propionate on energy metabolism during fasting

Please initial the box if you agree with each statement.

332

1. I have been given the opportunity to ask questions and discuss the study

2. I have received satisfactory answers to all my questions

3. I have received enough information about the study

4. I confirm that I have read and understand the information sheet Version --------- date:------

“The acute effects of propionate on energy metabolism during fasting” for the above study.

5. I understand that my participation is voluntary and that I am free to withdraw at any time, without giving any reason, without my medical care or legal rights being affected.

6. I understand that sections of any of my research notes may be looked at by responsible

individuals from Imperial College, Imperial College NHS Healthcare Trust, and regulatory authorities

where it is relevant to my taking part in research. I give permission for these individuals to have access to my records.

7. I agree that that my Identifiable information can be stored on Imperial College London and Imperial College NHS Healthcare Trust computer systems.

8. I agree to have my GP informed about participating in this study and any incidental findings

9. I agree to have my blood taken.as detailed in the participant information sheet.

10. I agree to have my collected tissue samples stored and used in future ethically approved studies.

11. I agree to have my study data stored securely at Imperial College London and Imperial College NHS Healthcare Trust for 10 years following completion of the study.

12. I agree that in the event I lose capacity, my personal identifiable data/samples will not be retained and no

further data or samples will be collected. Data or tissue which is not identifiable to the research team may be

retained.

333

13. I agree to take part in this study.

________________________ ________________ ____________________ Name of Patient Date Signature

_________________________ ________________ ____________________

Name of Researcher Date Signature

1 for patient; 1 for researcher; 1 to be kept with hospital notes

Appendix 6:


Department of Investigative Medicine,

HammersmithHospital Campus,

ImperialCollegeLondon 6th Floor, CommonwealthBuilding,

Ducane Road,

W120NN Tel 020 838 33242

Fax 020 838 33142


STUDY 2: The acute effects of propionate on energy metabolism during exercise


334




4. I confirm that I have read and understand the information sheet Version --------- date:------ :

“The acute effects of propionate on energy metabolism during exercise” for the above study.

5. I understand that my participation is voluntary and that I am free to withdraw at any time, without giving any reason, without my medical care or legal rights being affected.

6. I understand that sections of any of my research notes may be looked at by responsible individuals from Imperial College, Imperial College NHS Healthcare Trust, and regulatory authorities

where it is relevant to my taking part in research. I give permission for these individuals to have

access to my records.

7. I agree that that my Identifiable information can be stored on Imperial College London and Imperial

College NHS Healthcare Trust computer systems.


9. I agree to have my blood taken as detailed in the participant information sheet.


11. I agree to have my study data stored securely at Imperial College and Imperial College NHS Healthcare Trust for

10 years following completion of the study.

12. I agree that in the event I lose capacity, my personal identifiable data/samples will not be retained and no

further data or samples will be collected. Data or tissue which is not identifiable to the research team may be

retained.


335

________________________ ________________ ____________________

Name of Patient Date Signature

_________________________ ________________ ____________________



Appendix 7:


Department of Investigative Medicine, HammersmithHospital Campus,

ImperialCollegeLondon

6th Floor, CommonwealthBuilding, Ducane Road,

W120NN

Tel 020 838 33242 Fax 020 838 33142


STUDY 3: The acute effects of propionate on energy metabolism on postprandial metabolism

336





4. I confirm that I have read and understand the information sheet : Version --------- date:------

“The acute effects of propionate on energy metabolism on postprandial metabolism”

for the above study.

5. I understand that my participation is voluntary and that I am free to withdraw at any time, without

giving any reason, without my medical care or legal rights being affected.

6. I understand that sections of any of my research notes may be looked at by responsible

individuals from Imperial College, Imperial College NHS Healthcare Trust, and regulatory authorities where it is relevant to my taking part in research. I give permission for these individuals to have

access to my records.

7. I agree that that my Identifiable information can be stored on Imperial College London and Imperial College NHS Healthcare Trust computer systems


9. I agree to have my blood taken as detailed in the participant information sheet.


11. I agree to have my study data stored securely at Imperial College and Imperial College NHS Healthcare Trust for 10 years following completion of the study.

12. I agree that in the event I lose capacity, my personal identifiable data/samples will not be retained but and no further data or samples will be collected. Data or tissue which is not identifiable to the research team may be retained.


337

________________________ ________________ ____________________ Name of Patient Date Signature

_________________________ ________________ ____________________



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