OF ADULTS IN TRINIDAD & TOBAGO. - TSpace

DEVELOPMENT AND VALIDATION

OF A FOOD FREQUENCY QUESTIONNAIRE

TO ASSESS THE DIET GLYCEMIC INDEX

OF ADULTS IN TRINIDAD & TOBAGO.

Vasanti Malik

A thesis submitted in conformity with the requirements

for the Degree of Master of Science

Graduate Department of Nutritional Sciences

University of Toronto

© Copyright by Vasanti Malik 2003

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To my remarkable family and friends,

and Keith as these toils finally come to an end and a new chapter begins.

DEVELOPMENT AND VALIDATION OF A FOOD FREQUENCY

QUESTIONNAIRE TO ASSESS THE DIET GLYCEMIC INDEX OF ADULTS IN

TRINIDAD AND TOBAGO.

Vasanti Malik, Master of Science, 2003

Graduate Department of Nutritional Sciences, University of Toronto.

ABSTRACT

The objectives of this thesis were to develop a Food Frequency Questionnaire (FFQ)

that is able to assess diet glycemic index (GI), and compare estimates of nutrient intake with

those collected from 7-day Food records (FR) in Trinidad. 152 healthy adults completed the

FFQ either before or after the FR’s. Differences between means estimated by the FFQ and

FR’s are significant for all macronutrients except GI and cholesterol. Correlation coefficients

between the FFQ and FR data for fat, available carbohydrate, fibre and GI are; 0.50 (p<0.05),

0.40(p<0. 05), 0.25(p<0.05), and 0.25(p<0.05) respectively. The principal criterion for

validity for the FFQ is correlation coefficients > 0.5. These results show that intake assessed

by the FFQ is acceptable for measuring some nutrients but not particularly good for

measuring GI. Additional administrations of the FFQ and FR are needed to improve FFQ

precision and complete the validation process.

iT

ACKNOWLEDGEMENTS

To begin, I would like to express my utmost gratitude to Dr. Wolever for his support,

encouragement and generosity while providing me with an incredible learning experience,

which facilitated the pioneering of a body of work that is very important to me. I would like

to extend this gratitude to Dr. Ramdath, who made the field work in Trinidad possible.

I would also like to thank the members of my advisory committee; Dr.’s Eyssen and

Ward, for their valued input, guidance and support. Thank you to Dr. El Sohemy for

appraising the thesis and Dr Thompson for chairing my defence.

I am sincerely thankful to Vartouhi Jazmanji and Tamara Arenovich for their

irreplaceable counsel on biostatistics, and Keith for the outstanding technical support and

keen surveying skills.

To Curtis, Natasha, Yvonne Batson, June Holdip and the staff of the Central Statistics

Office in Trinidad, Port of Spain, I would like to thank you for your assistance in recruiting

subjects, and providing me with insight regarding the customs and culture in Trinidad. I

would also like to thank all participants of this study, without you, this work would not have

been possible.

Finally I would like to thank the departmental administrative staff for keeping me on

track and members of the Wolever lab (and Rana) for their kind words, and sound advice.

iil

TABLE OF CONTENTS

page

FN STO LON 0 Ma i

ACKNOWLEDGMENTS ............cccccec cece eee eee tence nent et eae eee Ease ee eee EE eee ene Ea aaES ii

LIST OF TABLES .......... ccc ccc cce cece cence een nen een ten E Eee ete eet e Ean EEE aE eed Vill

LIST OF FIGURES .......... cece cece cece cence ene nee n ene eee n nen ee nen e nA ene EEE bbe eee ena eee ES xiii

ABBREVIATIONS ..........cceccececece rene ee ene nee e ence tence eee ee nee ee eee eee HaEES LAER EEE GEES XIV

CHAPTER 1.

INTRODUCTION AND LITERATURE REVIEW

1.1 Introduction. ......... 0.2.0 cece eee ee cece ence eeee nest eee eneeeseeeneeennce ress 1

1.2 Literature Review ............ ccc cceccee ene eee ene eee ene eee tee ence nee naees 2

1.2.1 Diabetes Mellitus. ....... 0... cece ceee eee ee eee neee ee eeeeeenee teen ees 2

1.2.1.1 T2DM........ cece cece eee ne nee ee eee e nena eeeeeenseeeenaenees 4

1.2.2 Risk factors of T2DM.......... 00... cece cee eee eee ene eee ee eee nent eae 7

1.2.2.1 Gemnetics......... cece e cece cece erro eee neneeeenenernaees 7

1.2.2.2 ODESILY....... ice e cece eee eee ee eee e nee ee ence seat een een nae aas 9

1.2.2.3 EX€LrCiS€........ ccc cece eee ne eee e scene esee eens eeaenaeeeee ees 12

1.2.2.4 Diet... cee ce cence eee eeec ene eeeeteeeeeeeeneeneennenes 14

1.2.3 The Glycemic Index ............. ccc cece cece ence eee eeeeteeaeeeeaees 17

1.2.4 Epidemiology of T2DM in the Caribbean.......................0000 19

1.2.5 Dietary Assessment Methods.................:ccceceeeeeeeeeeeeeeeenes 22

1.2.5.1 24-hour dietary recall..............ecceeceec eee eee eee eneeeeeees 22

1.2.5.2 Diet Records.............ccc cece eecec eee ene esses eeseeeeeeee eens 24

iv

1.2.5.3 Biomarkers of diet........... 0c. ccc ccc ce ccc cceeeeeneeesseeeuns 25

1.2.5.4 Diet History............cccc cece cece eee cee ee eeteeeneeereeaees 27

1.2.5.5 FFQ oc ececccccceee cence cere ee eee eeeseneeneeeere ene eae reece 28

1.3 Study Objectives............ccccececeeeeeeeeeeeeee nena ee eneneneeeeneneneen ene eaae 36

CHAPTER 2.

MATERIALS AND METHODS

2.1 Development of FFQ........... cece cece cece eee ee eee e een ne ene e eee ene cates 36

2.1.1 Stage 1: Generation of food list.............. cc cece cece eee eee ee eee eee eeaee 36

2.1.1.1 Procedures........... cece cece cece eee eee ee eeea eee esas ene eee 36

2.1.2 Stage 2: Testing of FFQ....... ccc ce cence cee ne eee eneeneetaee ees 37

2.1.2.1 Procedures........ 0... ccececeeeee ee eee ee eee eeeeeea ceca eceeseees 37

2.1.2.2 Subjects....... cece ee eee ee cence ene eneea teeta eencas 38

2.1.2.3 Results... cece eee e eee e ee eee ee eee ene ene enn enenees 43

2.2 Validation Study. ......... cece cece ee ects ee eene eee nsec ne eeeeeene eens 43

2.2.1 PLOCEGUIES.... 0... eee ee cece tence cere nent sneer tree neta eneeeeneneeees 43

2.2.2 Subjects....... 0 cece cece eee ee eee ene eee ene eeeeneeegengeeneees 45

2.2.3 AMNALYSIS...... 0. cece ccc e cence cence nce e cence eee een ene teases eeaeeanee genase 46

2.2.3.1 Nutrient Analysis.............ccccccece cece eeeeeeeeeeeeeeeaeeeenea 46

2.2.3.2 Statistical Analysis...............cccceceeceneec eee eeeneeeeesanetees 47

CHAPTER 3.

RESULTS

3.1 Characteristics of Study Population. ................ceccceeceeeeeeeeee eens 49

3.1.1 Demographics of study participants.............. 6. ccc cc cece eee ee sea a ees 49

3.1.2 Comparison with National statistics..............ccce eee ee ee ee ese e eee nees 57

3.2. Normality of data... cece e cece eee eee renee enna 60

3.3 Nutrient Intake... cc eee eee ee ene e renee enone eee eee seneeeee 65

3.3.1 All study participants........... 0... cece cece cece eee e eee eee eenaeeeeeees 65

3.3.2 Males and Females............ ccc cece cece cece e sete eee beeen seen ones 66

3.3.3 African’s and South Asian’s...........cccseeeesceeeeeeeeeeeneneee eases ens 67

3.3.4 FFQ 1 and FFQ 2™ groups......0..ccccccceseeeccceeeseeesseeueeeeeenees 70

3.4 Association between FR’s and FFQ..............ccccceeeceeeeeeeenneneee 96

CHAPTER 4

DISCUSSION AND CONCLUSION

4.1 DISCUSSION. ...........c eee e eee ec cece eee eee e eee ee ene tees ene eeaeeeneeeaaee ener 109

4.1.1 All study participants... cece cece cee ee eee e eee ne scence ee na eas 109

4.1.2 Males and Females................cccccescee eee eeeeeeeeeneeeeteeetneeneeea 117

4.1.3 African’s and South Asian’s................ ccc cee ee cece cece nee e ence een ees 119

4.1.4 FFQ 1 and FFQ 2" groups....ccccccccccsscsscsstsssssssssesseeeeeseeeeen 122

4.2 Future directions.......... 0. cece ce cece e eee ne cece eee ee eee ene e ee eee eee e eaee 123

4.3 Conclusion..............0cc cee ece cece eee ee ence eee enna cence tee eeaeeeaeeesee eases 124

CHAPTER 5

REFERENCES.......... 0c ececeneeeeeceneeeeceneeeeeeeeeeaeeeeeeeeeeeaeneeneneeetneeneeaeeaeeaes 127

APPENDIX A __ Descriptions of Various Validation Studies and their findings............. 137

APPENDIX B = Consent forms......... 00... cece ccc e cence eee ee ene en eee nena eene ene eneenee nea enes 146

APPENDIX C FFQ and part of FR diary........... 0c cece cee ce ec enc eee ene eee eee eee een nen aeeees 149

APPENDIX D Portion sizes associated With FFQ.............ccccccceeeeeeneeeeeeeeeesseeeees 168

vi

APPENDIX E Map of an electoral district used for recruiting subjects................. seen 175

APPENDIX F Foods from the study that were added to the Nutrput database.............. 177

APPENDIX G_ All Chi-square tests.............c:eeceece eee e eee ee eee eeeeee tee e eee eneee ree tee eenes 181

APPENDIX H_ Shapiro Wilk scores, and Kurtosis and skewness values for FR and .......196

FFQ macronutrient and % macronutrient data.

APPENDIX I Frequency Distributions for macronutrients and % macronutrients ...... 211

from FR’s and FFQ’s

APPENDIX J Correlation coefficients using untransformed and transformed data........233

and Spearman rank correlations between FR’s and FFQ’s for

macronutrients and % macronutrients.

APPENDIX K_ Scatter-grams between FR and FFQ data for macronutrient and............ 248

macronutrient intake

vil

LIST OF TABLES

page

Table 2-1 oo. cece cee ce nee n ence eee eee ee ne ence een e nee eR EE EES SEE EE SDE E EEE DEE eE EEE E OEE SE EEE EH 40

FFQ testing phase: Recruitment for Administrative Divisions

0D 0) 29 C9 Al

Ethnic breakdown of Males in testing phase of FFQ

Table 2-2)... 0. ccccc ccc ce cere eee ne cee e ee eee eee ee ence eee ee eee e eens ee eee eee ne eens teers ene ene teeta 42

Ethnic Breakdown of Females in testing phase of FFQ

Table 3-1... cccc cece cece nee nen renee nee e nen Een EEE EEE EERE EEA; D EEA ED OEE EEE; E SOE CEE EEE ES 53

Gender, ethnicity and age of all subjects (counts and percentages)

Oe) 0) (RCAC: » apna 54

Completed Education Level and Occupation for Males and Females

8:15) (co (0) 55

Completed Education Level and Occupation for AF’s, SA’s and OTH’s for Female’s

Table 3-2(C).... 0... ccc ccc cece ence ence ence teen cence ence eens enna ener ene eennne sense ee eeee eens eeenneene es 56

Completed Education Level and Occupation for AF’s, SA’s and OTH’s for Male’s

Table 3-3(8)........ccceccecc enc ee ence nee ne een ene eee ne ene ee ne eee snes sees eee ea sees eeeeneeneeeeeeeennas 74

Means & Standard deviations, and Medians, & Interquartile ranges for

macronutrients from FR and FFQ data: All subjects

Table 3-3(D).... 0... cce ccc cc cece cence eee eee ne eee neces eee en ones neta ena eesenaeeaenaeeaenneeseeeneeeees 75


macronutrients expressed as a percentage of total energy from FR and FFQ data:

All subjects

viii

Wo) Con | 76


macronutrients from FR and FFQ data: Males

Table 3-4(D). 0... cc cecccc ccc ce cence nee ee erence cent nena eases esse eee neen ee eaeneeaeeaeaeeaeneeeeraeenes 77



Males

“Table 3-5(a).cccccccssccssscseseevesssvsesesesvevesssvsesesesessesesteveeassssestavevsreseeeateeetees 78


macronutrients from FR and FFQ data: Females

2) 0) (se to) (0) 79



Females

Table 3-6(8)...... 0... cece ccc ec ence ence eee ee eee ence nena ene eee cesses ene entenetneeeee eee teeeatenneeea 80


macronutrients from FR and FFQ data: AF’s

Table 3-6(b).... 2... cece cece cee ce ence e eee ence ne ene eee ne eee eee eae ee onan eeeeeeeeneenaenneegeenenenes 81



AF’s

1X

9010) (oa a1 €:) eee eeeS 82


macronutrients from FR and FFQ data: SA’s

=) 0) (on at] (2) ST 83


macronutrients expressed as a percentage of total energy from FR and FFQ data: SA’s

Table 3-8(8)..... 0. ccc ccece cece nee e rene eee cence eee nonce eee EE EERE EEE SEED EEE E DEED ERE SEER HERERO EEE EES 84

Anova with Duncan’s Multiple Range test for differences between SA’s and AF’s:

FR data.

8-10) Conic rs) ( 0) 85


FR data, (macronutrients expressed as a percentage of total energy).

010) Coc rk (0) ES 86


FFQ data

O10) Coane ero (9 87


FFQ data, (macronutrients expressed as a percentage of total energy) |

010) eee]: ) 88

Means & Standard deviations, and Medians, & Interquartile ranges for macronutrients

from FR and FFQ data: FFQ First group

01) 0) (cc ee) ( 0) eer 89


expressed as a percentage of total energy from FR and FFQ data: FFQ First group

01 0) oan 0) (Rn 90


from FR and FFQ data: FFQ Second group

8 O10) (oon 0) C0) 91


expressed as a percentage of total energy from FR and FFQ data: FFQ Second group

O10) Cones 00 ¢:) enn eee 92

Anova with Duncan’s Multiple Range test for differences between FFQ First and FFQ

Second groups: FR data

002) 9) (es 0 0) 93


Second groups: FR data, (macronutrients expressed as a percentage of total energy)

8 B10) (aR a On 0) nn 94

Anova with Duncan’s Multiple Range test for differences between FFQ First and

FFQ Second groups: FFQ data.

O10) Conc res (0) cece 95


Second groups: FFQ data, (macronutrients expressed as a percentage of total energy).

Table 3-12(a)..... 0.0... eee e cence eens tittitititititiitiiustistissassasistin 99

Pearson correlation coefficients for FR vs. FFQ data: All subject’s

Xi

010) CRs 072 (0) ene 100

Pearson correlation coefficients for FR vs. FFQ data for macronutrients expressed as

a percentage of total energy: All subject’s

0) 6) (oes Os: 101

Pearson correlation coefficients for FR vs. FFQ data and Fisher Z-test for differences

between males and females

0:10) (oan Re] (>) 102

Pearson correlation coefficients for FR vs. FFQ for macronutrients expressed as a

percentage of total energy and Fisher Z-test for differences between males and females

6010) (an as 02 103

Pearson correlation coefficients (r-value) for FR vs. FFQ data and Fisher Z-test for

differences between AF’s and SA’s

0) 6) (ons 1. (0) 104


percentage of total energy and Fisher Z-test for differences between AF’s and SA’s

Table 3-15(a). 0... cce cece cece ence eee tenet cette eens teeta eeneeeanenae eee es oreesetees beeeeees 105

Pearson correlation coefficients for FR vs. FFQ data and Fisher Z-test for differences

between FFQ First and FFQ Second groups

010) (nd Bs 0) 106


percentage of total energy and Fisher Z-test for differences between FFQ First and FFQ

Second groups

xil

LIST OF FIGURES

Figure 1-1... ccc cece een nen ne EEE E EEE EEE EEE EERE EERE ERE E EE EEE EEE 34

Prevalence of DM in Trinidad and Tobago in 1995 and estimated prevalence for the

years 2000 and 2025.

ASD FAV he) Se 52

Breakdown of recruitment and enrolment process for FFQ validation study

Figure 3-2()........ccceec cece eee ee eee eee eee e eee e eee ne eee e ee EEE REE R EEE EEE EEE EEE E EEE E EERE EE EEE EEE 58

Study data in relation to National Statistics: Gender and Ethnicity

ION Fo4 1 oN A (0) Re 59

Study data in relation to National Statistics: Completed Education Level

FIg“ure 3-3(a).......c ccc ecc eee e cece eee ee een need EEE cE A EEE EEE EEE EE LEED SEEDED OEE E ERED SHEER EEE EE OEE 62

Frequency Distributions for fat from 7-day Food Record and FFQ data: Example of

positively skewed data that required log transformation

15s F241 1 0] (0) 63

Frequency Distributions for GI from 7-day Food Record and FFQ data: Example of

negatively skewed data that required inversion

Figure 3-3(C).......cccccececeeenceceeeeeneeneneeeeseneeeeneneneee eens eae seen eee ee ere naeneea ene en ee aess 64

Frequency Distributions for PUFA expressed as a percentage of total energy from 7-

day Food Record and FFQ data: Example of data that did not require transformation

Figure 3-4(a)..... 0... cc ecece scene cne eee c scene ee eneeeeeeeeeeee ences eneneeeneea eee enseeneen neat eg sas 107

Scatter-gram between FR’s and FFQ for fat

Figure 3-4(b). 0.0.0... ccc ccc ce cece cence ence eee ence eee ee eens enced sees nee n deca en atte ete eenee nana ees 108

Scatter-gram between FR’s and FFQ for GI

xiii

T&T

AF

SA

MI

OTH

T2DM

TIDM

CEL

FFQ

FR

FFQ 1"

FFQ 2™

GI

AvCarb

Tcarb

SFA

MFA

PUFA

P:S

SD

ANOVA

ABBREVIATIONS

Trinidad and Tobago

African’s

South Asian’s

People whose background’s are a mixture of AF and SA

People from backgrounds other than AF, SA or MI

Type 2 Diabetes Mellitus

Type 1 Diabetes Mellitus

Highest education level completed

Food Frequency questionnaire

7-day food records

People that completed the FFQ before completing FR’s

People that completed the FFQ after completing the FR’s

Glycemic index

Available carbohydrate (does not include fiber)

Total carbohydrate (includes fiber)

Saturated Fatty Acid

Monounsaturated Fatty Acid

Polyunsaturated Fatty Acid

Ratio of polyunsaturated to saturated Fatty Acid

Standard deviation

Analysis of variance

Xiv

1. INTRODUCTION AND LITERATURE REVIEW

1.1 INTRODUCTION

Over the past decade, the prevalence of Diabetes Mellitus (DM) has increased

markedly in populations which have departed from their traditional lifestyles and undergone

“Westernization”.

In Trinidad and Tobago (T&T), mortality from diabetes is higher than elsewhere in

the Caribbean, and the prevalence of type 2 diabetes mellitus (T2DM) is six times that of

Canada and the US. T&T has a multi-ethnic population, comprised of individuals from

African (AF), South Asian (SA), and mixed (MI) backgrounds, making up approximately,

40%, 40%, and 20% of the population respectively. Epidemiological studies have found that

in T&T diabetes is twice as common in SA than AF men, with a smaller difference in

women. It has been shown elsewhere that a low diet glycemic index (GJ) is associated with

reduced risk of developing diabetes.

A pilot study was conducted in T&T to estimate the nutrient intakes and diet GI of

AF’s and SA’s. Nutrient intakes were assessed by 24-hour dietary recall and showed no

difference between the two ethnic groups. AF men were found to have a lower diet GI than

SA men, while no significant difference was observed in women. Although these results are

consistent with previous data suggesting that a high diet GI increases the risk of developing

T2DM (in men), a more rigorous nutrition survey tool is needed to further assess dietary

intake in T&T. Food Frequency Questionnaires, (FFQ) have become the principal dietary

survey tool in epidemiological studies of chronic disease. In addition, they have recently

been shown to be useful in assessing dietary intake with respect to diet GI. The first objective

of this study is to therefore, develop a FFQ, which can assess diet with respect to GI in T&T

using food intake data from previously collected 24-hour dietary recalls. The second

objective is to validate the FFQ using 7-day consecutive food records on a representative

sample of the population in Trinidad.

1.2 LITERATURE REVIEW

1.2.1 Diabetes Mellitus

Diabetes Mellitus (DM) is a chronic condition that contributes significantly to the

morbidity and mortality rates of many countries around the world. In Canada it is currently

ranked as the 7” leading cause of death (Tudor-Locke et al., 2000). Owing to an increased

life expectancy of the world’s population in conjunction with a rise in various

environmental/viral and lifestyle triggers, the prevalence of DM has risen considerably over

recent years and is now a global concern (World Health Organization; World Health Report,

1999). Concomitant with the rise of prevalence in DM is an increase in associated

complications such as retinopathy, neuropathy, cardiovascular disease, stroke and kidney

disease, which often lead to increased rates of morbidity and mortality (Tudor-Locke et al.,

2000). Complications of this nature are of grave importance not only because of the reduced

quality of life they bestow, but because of the substantial economic burden treatment places

on society (Tudor-Locke et al., 2000). This is of great concern particularly for countries of

the developing world whose economies are already in a state of economic stress. It has been

estimated that the annual treatment costs in 1992 were US $287 for individuals requiring

insulin and US$103 for individuals on oral treatment (Chale et al., 1992). These costs

represent 6-12 months’ wages for a labourer in some of the poorest non-industrialized

countries of the world such as Bangladesh and parts of rural India and Africa. Poverty and

lower levels of education will almost certainly translate into worse disease (Gulliford, 1995).

A large proportion of individuals becoming diabetic in adulthood will experience its chronic

complications during their working lives. Data from Africa and India show a high prevalence

of micro- and macro-albuminuria and a more rapid progression to end-stage renal failure than

in “Western” patients (Rahlenbeck et al., 1997). In the Caribbean, a large number of surgical

cases are patients with diabetic foot problems, and many lower-limb amputees remain

debilitated (Gulliford, 1995). There are few data from non-industrialized countries on

mortality from diabetes, however a report from a tertiary referral centre in Kashmir India,

suggests a 10- year reduction in life span. Infection and chronic renal failure were found to

be the most common causes of death, unlike coronary heart disease and stroke, which are the

leading causes of death among individuals with diabetes in more industrialized countries

(Fall, 2001).

DM is a heterogeneous group of disorders of varying aetiology and pathogenesis that

is characterized by hyperglycaemia and often associated with dyslipidemia (Pickup 1991, p.

155-60). Typically, a positive diagnosis for DM is made if fasting plasma glucose is greater

than or equal to 126 mg/dL and/or 2-hour post-load glucose level is greater than or equal to

200 mg/dL (Wei et al., 1999). Common symptoms include excessive thirst, polyuria,

unexplained weight-gain or loss, polyphagia, and fatigue (Pickup 1991 p. 155-60). The main

causes of this systematic syndrome are attributed to a relative or absolute deficiency of

insulin due to pancreatic beta cell failure and/or a resistance to the action of insulin at a

cellular level (insulin resistance). Many different forms of DM are known to exist and are

categorized with respect to how insulin deficiency or resistance is acquired. The most

common forms of DM include type 2 diabetes or non-insulin-dependent diabetes mellitus

(T2DM), type 1diabetes or insulin-dependent diabetes mellitus (T1DM), and gestational

diabetes mellitus (GDM) (Groff & Gropper, 1999 p. 242). Of these, T2DM is the most

common form of DM and will be the focus of this thesis.

1.2.1.1 Type 2 Diabetes Mellitus (T2DM)

Type 2 diabetes mellitus or non-insulin-dependent diabetes mellitus (T2DM)

currently accounts for 80%-90% of all reported cases of DM (Groff & Gropper, 1999, p.

242). Epidemiological studies have shown that T2DM has a global distribution and its

prevalence varies from country to country, in different ethnic groups in the same country, and

between the same ethnic group undergoing internal or external migration (Zimmet 1982).

Prevalence rates are highest among populations that have departed from their traditional way

of life and undergone rapid economic development and modernization (Fall, 2001). The

highest prevalence rates today are found in urbanized Pacific Island populations like the

Nauru, the Pima Indians of Arizona, the Oji-Cree of Northern Ontario, and Australian

Aborigines, all of who have adopted a more “modern” lifestyle in recent years (Zimmet,

1982; Ravussin et al., 1994). High prevalence rates have also been found in individuals that

have moved internally from rural to urban centres in the same country and migrants that have

moved externally from less developed to more developed countries. Among South Asians for

example, the age-adjusted prevalence rates of T2DM are less than 5 % in rural South India,

approximately 12% in urban South India, and 15-20% in migrants living in Trinidad and

Tobago, Mauritius, Fiji, Singapore, Tanzania, The Netherlands, and the UK (Gupta et al.,

1978; McKeigue et al 1988; Sinha, 1995; Ramachandron et al., 1996; Cappuccio et al., 1997;

Burden et al., 2000; Cruickshank et al., 2001). Among Chinese, the age-adjusted prevalence

rates of self-reported T2DM range from less than 3% in rural China to 15-20 % in urban

Taiwan and Mauritius, and among populations of the African Diaspora from less than 3% in

Cameroon, to 10% in individuals of West African descent living in Jamaica, and 15% in

Jamaicans living in the UK ( Mbanya et al., 1997; Cooper et al., 1997; Zimmet et al., 1997;

Unwin et al., 1998; Chen et al; 1999; Mbanya et al., 1999). In contrast, regions with low

levels of economic development or where people adhere to traditional ways of life such as

hunting and gathering or subsistence farming, the prevalence of T2DM is lowest. Examples

are the Mapuche Indians of Chile, rural Bantu of Tanzania, and the African nation of

Mauritania. In each case, prevalence of T2DM is between 1-3% for individuals aged 30-64

years (Swai et al., 1993; Ducorps et al., 1996; Cooper et al., 1997). These comparative data

demonstrate the determining influence of changes in living conditions on the population risk

of T2DM. Data for future predictions of T2DM prevalence are consistent with the trends

shown in current prevalence data. In 1998, the World Health Organization’s diabetes

database was used to predict global rates of T2DM for the years 2000 and 2025, based on

trends in population size, age structure and urbanization (King et al., 1998). According to this

analysis, the prevalence of T2DM is expected to rise by 30% globally, from 4.0% to 5.4%

(King et al., 1998). The number of adults with diabetes will increase from 135 million in

1995 to 300 million in 2025. Although prevalence rates will remain higher in industrialized

countries, the proportional rise will be greater in less-industrialized countries (48%), and

greatest in China (68%) and India (59%) (King et al., 1998). A particular concern regarding

future predictions of T2DM prevalence lies in the emerging problem among children and

adolescents.

Type 2 diabetes mellitus has a slow and insidious onset, and is often preceded by a

long period of impaired glucose tolerance (IGT), a reversible metabolic state associated with

increased prevalence of macrovascular complications. Clinical symptoms may not present

themselves for many years, and at the time of diagnosis long-term complications have

developed in almost one fourth of patients (Dagogo-Jack et al., 1997). Long-term

complications of T2DM include the metabolic syndrome, associated with insulin resistance

and characterized by an atherogenic lipoprotein profile; glucotoxicity; macrovascular disease

including cardiovascular, cerebrovascular, and peripheral vascular diseases; and

microvascular disease including retinopathy, nephropathy, and neuropathy (Dagogo-Jack et

al., 1997).

An actual cause of type 2 diabetes has yet to be determined, however there has been

some indication that susceptibility to T2DM may have a genetic component (most likely

polygenic) in addition to various acquired factors, and its pathogenesis involves an interplay

of progressive insulin resistance and beta-cell failure (Dagogo-Jack et al., 1997), Although

T2DM may occur at any age, it usually manifests itself in individuals over the age of fifty.

T2DM is more prevalent in obese individuals with 60-90% of all T2DM developing in obese

persons. The major risk factors for T2DM are age, and various environmental factors

including obesity, physical inactivity, and diet. The major cause of mortality in T2DM is

coronary artery disease (Manson et al., 1994; Mann, 1997; McLarty, 1997).

1.2.2 Risk Factors of T2DM

1.2.2.1 Genetics

Little is known of the genetic basis of type 2 diabetes mellitus. There appears to be a

varying genetic susceptibility to diabetes amongst different ethnic groups, which is expressed

under certain environmental conditions (Abate & Chandalia, 2001). The basis for the

susceptibility is unclear but could be a result of a thrifty gene. The “thrifty genotype”

hypothesis put forth by Neel in 1962 has been proposed to explain the high frequency of

T2DM in some populations. Neel postulated that individuals with the thrifty genotype

(hereditary tendency to be overweight or obese) had an exaggerated insulin response to food,

and were therefore better able to store energy efficiently, perhaps mediated by leptin

resistance, maximizing survival during alternating bouts of feast and famine, (Zimmet et al.,

1997). It has also been suggested that the tendency to store fat centrally, a feature of South

Asian Indian populations, may have a genetic basis. Central body fat, which is more

metabolically active than peripheral fat and less likely to impede locomotion may have

evolved as a site for quick storage and mobilization during times of need (McKeigue et al.,

1992). Genetic mechanisms associated with these phenomenon include an over responsive

beta-cell (a quick insulin trigger), and a genetically determined “down regulation” of insulin

receptors in response to repeatedly high levels of circulating insulin (Zimmet, 1982; Brand-

Miller et al., 1994; Zimmet, 1995). However, present-day social and cultural phenomenon of

the “West”, which favour dietary habits such as feasting with a preference for energy-dense

processed foods as well as a sedentary lifestyle, are thought to ameliorate the selective

advantage of the thrifty gene, resulting in increased rates of T2DM in susceptible populations

(Zimmet, 1982; Brand-Miller et al., 1994; Zimmet, 1995, Lindsay 2001). To date, the search

for T2DM gene(s) has not produced any major candidate for the thrifty gene.

Despite evidence from monozygote twin studies, which show high concordance rates

of T2DM, strong familial histories and ethnic differences in T2DM prevalence, candidate

genes have only shown weak associations (Cruickshank et al., 2001). One of the most

important loci found thus far is on chromosome 2 (T2DM1) of the insulin gene. However,

mutations in the coding region have not been consistently associated with T2DM

(Rosenbloom et al., 1999; Froguel, 2001). Several large genome-wide scans for linkage have

been conducted for T2DM in many populations, the largest in Pima Indian (Hanson et al.,

1998), Finnish (Mahtani et al., 1996; Ghosh et al. 2000; Watanabe et al., 2000), French

(Vionnet et al., 2000), British (Wiltshire et al., 2001), and Mexican-American (Hanis et al.,

1996; Duggirala et al., 1999) pedigrees. Although results from these studies have found

multiple loci that show an association or linkage to T2DM, no single locus across

populations has been identified (McCarthy, 2002). Recent data have demonstrated an

association between the VNTR (variable number of tandem repeats) region of the human

insulin gene and size at birth. This may explain the epidemiological finding that small size at

birth is associated with an increased risk for T2DM later in life (Rosenbloom et al., 1999;

Lindsay, 2001). Other genetic markers include the chlorpropamide alcohol flush

phenomenon, HLA-A2 in Xhosas and Pima Indians, and HLA-B61 in Fiji Indians (Zimmet,

1982). Although candidate genes for T2DM have been identified, definition of genes that

condition risk with respect to ethnicity are still unclear. A specific cause of T2DM remains to

be determined, however it appears to be a complex, poly and multi-genic interaction between

genetic susceptibility and environmental factors (Froguel, 2001).

1.2.2.2 Obesity

To date, a growing body of evidence exists, which suggests that obesity and central

adiposity are important and independent risk factors for the development of T2DM. Many

prevalence studies have shown that there is a strong positive association between the degree

of obesity in a population and the prevalence of T2DM. Some studies have also shown that

the greater the duration of obesity the higher the risk of T2DM (Everhart et al., 1992;

Wannamethee et al., 1999), and that obesity starting in childhood is an independent risk

factor (Vanhala et al., 1998). Although many prevalence studies exist, the majority of data

discussed here will pertain to populations of African and South Asian descent since they are

the focus of this thesis. Cooper et al., found that among persons of West African descent

living in Jamaica, age-adjusted diabetes prevalence was linearly related to the degree of

obesity. Jamaican women, whose body mass index (BMI) values are 17% higher than those

of Jamaican men, were found to have twice the prevalence of T2DM (Cooper et al., 1997).

Another prevalence study, conducted by Ramachandran et al., found that in an urban

population of SA’s living in Madras India, a 40 % increase in prevalence of T2DM had

occurred over a period of 6 years from 8.2% in 1988-1989 to 11.6 % in 1994-1995. Although

there was no concomitant increase in the rate of obesity, a strong association was found

between T2DM and BMI and central adiposity in both males and females (Ramachandran et

al., 1997). In a related cross-sectional study, epidemiological data from SA’s from Madras,

India and Mexican Americans (MA), and non-Hispanic Whites (NHW) from San Antonio,

Texas were compared to determine the possible contributions of various anthropometric

measurements to the varied prevalence of T2DM in these ethnic groups (Ramachandran et

al., 1997). Results showed that BMI was associated with T2DM within all ethnic groups.

10

MA’s were found to have the highest rate of obesity (mean BMI 28.9+5.9 km/m’) compared

to NHW’s (mean BMI 26.24+5.2 kg/m’), and SA’s (mean BMI 22.3+4.4kg/m7) and the

highest prevalence of T2DM (males 19.6%; females 11.8%, p<0.001 vs. other groups)

compared to NHW’s (males 4.4%; females 5.7%) and SA’s (males 9.9%; females 5.7%).

Presence of upper body adiposity even without being overweight seemed to be an indicator

of increased insulin resistance in SA’s increasing their risk for T2DM. Similarly, Karter et

al., found that increased abdominal obesity was related to lower insulin sensitivity

independent of overall obesity in African American’s, Hispanics, and NHW’s from Oakland

and Los Angeles, California, San Antonio Texas, and the San Luis Valley, Colorado (Karter

et al., 1996). Since prospective data exclusively pertaining to chronic disease in SA and AF

populations does not exist, prospective data collected from the US will be discussed. Among

43, 581 women enrolled in the Nurses’ Health Study who in 1986 were free of diabetes and

other major chronic diseases provided waist, hip, and weight information and were followed

from 1986 to 1994 for T2DM incidence. Results indicated that BMI, WHR and waist

circumference were powerful predictors of T2DM in US women (Carey et al., 1997).

Similarly, during an 8- year follow-up of 113, 861 US women aged 30-55 years in 1976,

Colditz et al., found 873 definite cases of T2DM among women initially free from diagnosed

diabetes or any other chronic disease. Relative risk of T2DM was found to increase

continuously with BMI, and weight gain after age 18, was found to be a major determinant of

risk (Colditz et al., 1990). Results from other prospective studies such as the San Antonio

Heart Study, which followed MA’s and NHW’s for 7-8 years to evaluate secular trends in

T2DM incidence, have also shown that rising BMI is a significant contributor to the

increasing prevalence of T2DM although other factors were also found to contribute to this

11

trend (Burke et al., 1999). Evidence from experimental studies have also shown that obesity

and overweight are associated with the development of T2DM in tandem with other risk

factors such as low physical activity, and poor diet. For example, in 1997, Pan conducted a

randomized, controlled trial of diet and/or exercise in men and women with impaired glucose

tolerance living in Da Qing, China. Subjects were randomized to receive advice on diet,

exercise, both or neither (control group). During 6 years of follow-up, the incidence of

T2DM was 68% in the control group, but significantly lower (40-50%) in all three

intervention groups. Despite the fact that no main effect on mean BMI was found per se, this

study provides evidence that weight reduction in conjunction with other lifestyle

management strategies is an important factor in the prevention of T2DM (Pan et al., 1997).

Although it has been known for many years that obesity is indeed related to the development

of T2DM, neither the exact nature of this relationship nor the underlying mechanisms is fully

understood.

Generally, obesity is considered to be an excess accumulation of body fat resulting

from positive energy balance (Mogenson et al., 2000). More specifically, obesity is defined

(conventionally) as BMI >30 kg/m’. As body weight increases, insulin resistance, glucose

intolerance and the propensity to develop diabetes increase (Haffner et al., 1997).

Development of obesity is insidious and associated with elevated free fatty acid (FFA) levels

as well as with enhanced availability of glucose (hyperphagia) and insulin (Belfiore, 2000

p.46). Increased utilization of FFA as fuel (as seen in obesity) results in an enhanced

"Definition of obesity for different ethnic groups is currently a matter of some debate. Seidell has argued that

traditional BMI cut-points (used for “White populations”) may in fact be of little value for identifying Asian

individuals at high risk who constitute more than half of the world’s population. It was shown among various

ethnic groups, particularly of Asian origin that the risk of T2DM starts to increase rapidly at levels of BMI or

waist circumference well in the acceptable range of BMI or waist circumference for Europeans (Kosaka et al., 1996).

12

production of long-chain CoA or acyl-CoA (LC-CoA) in the cytosol and of acetyl-CoA in the

mitochondria. This leads to an inhibition of glucose metabolism, thereby inducing insulin

resistance, which is often followed by hyperinsulinemia (Belfiore, 2000 p. 47). Body fat

distribution is also significant in determining glucose intolerance. It has been shown that

individuals with central or abdominal obesity tend to have higher blood glucose and insulin

levels, raised plasma triacylglycerides, and reduced plasma high-density-lipoprotein (HDL)

cholesterol than individuals with peripheral obesity. Enlarged visceral adipocytes are

resistant to insulin’s antilipolytic action, and more responsive to lipolytic hormones.

Resultant elevated levels of FFA may induce insulin resistance in the liver and peripheral

tissues as discussed above (Karter et al., 1996).

1.2.2.3 Exercise

Although it is difficult to isolate activity level and study it as a single factor in

relation to diabetes prevalence and incidence, there is a growing body of evidence, which

suggests that physical inactivity is an independent risk factor for the precipitation of T2DM.

Sedentary individuals often develop obesity owing to their lack of exercise, which places

them at a greater risk for diabetes. In a thirteen year prospective study of British men, Perry

et al., found that the risk of developing diabetes was reduced by 50% in men who engaged in

moderate to vigorous physical activity (adjusted for BMI) compared to less active men (Perry

et al. 1995). Hu et al found in an eight year prospective study of 70 102 female nurses from

the Boston area initially free from chronic disease that the relative risk for developing T2DM

(after adjusting for age, smoking, alcohol use, history of hypertension, history of high

cholesterol level and BMI) was inversely related to physical activity level. Their data

13

suggests that greater physical activity level is associated with substantial reduction in risk of

T2DM, including physical activity of moderate intensity and duration (Hu et al., 1999).

Rural-urban shift and migration are often accompanied by change in levels of physical

activity, almost invariably to a more sedentary pattern (Zimmett, 1982). Epidemiological

studies in Cameroon (Cruickshank et al., 2001; Mbanya et al., 1997) and India

(Ramachandran et al., 1997) have shown that diabetes is more common in urban dwellers

than rural dwellers of the same weight. Cooper et al., found that Nigerians who are lean and

physically active have a much lower incidence of diabetes compared to the largely sedentary

population of the US (Cooper et al., 1997).

In experimental studies, exercise has been shown to improve glucose tolerance, lower

glycaemia, and increase insulin sensitivity (which results in increased peripheral use of

glucose) (Belfiore et al., 2000 p.69; Wei et al., 2000; Hayashi et al., 1997). Muscle

contraction increases the number of GLUT-4 glucose transporters translocated to muscle

cells during exercise independent of the action of insulin as well as increase muscle glycogen

utilization. Perseghin et al., found that increased glucose transport-phosphorylation and

muscle glycogen synthesis occurs after exercise in normal and insulin resistant subjects.

These phenomenon in conjunction with increased delivery of insulin to active muscle caused

by increased blood flow during exercise may be part of the mechanism for improving insulin

sensitivity by physical activity (Wei et al., 2000). In addition, physical activity has also been

shown to be inversely associated with obesity and central fat distribution (Wei et al., 2000).

Therefore, exercise may also prevent or delay the onset of T2DM, at least in part through

decreasing overall fat and/or intra-abdominal fat.

14

1.2.2.4 Diet

Diet has been thought to contribute to the development of T2DM primarily in two

ways: Firstly, through the supply of calories and resultant obesity if the level of physical

activity is low; and secondly, via the effects of specific foods (Zimmet, 1982). Prior to

industrialization, the diet of traditional or subsistence societies consisted of foods high in

fibre, starch, and vegetable protein, and low in animal protein, fat and sodium (Jenkins et al.,

1997; Brand-Miller et al., 1994). Over the past 60 years the diet of the “West” has departed

from this “traditional” regime and become characterized by intakes low in fibre and starch,

and high in refined sugar, salt, saturated fat and animal protein (Zimmet, 1982; Jenkins et al.,

1997). The “Western” diet is richer in fats and protein and includes fewer fruits, vegetables

and cereals. In addition, the increased processing and refining of food has reduced its amount

of valuable roughage and nutrient content (Zimmet 1982; Jenkins et al., 1997). The high

calorie density (calorie/weight ratio) of manufactured food in conjunction with a low fibre

intake has been postulated to delay satiety and hence contribute to a high total caloric intake

and, in turn obesity (Zimmet, 1982). These phenomenon have been shown to increase —

susceptibility to chronic disease (Daniel, 1996).

The present goal of medical nutrition therapy is to develop diets, which can be used to

prevent the onset of these diseases as well as provide successful treatment strategies (Balch et

al., 1997; Holler et al., 1997 p.137). Current nutrition therapy for T2DM aims to optimize

blood glucose and lipid control, maintain optimal body weight, and minimize hypoglycaemia

in individuals treated with insulin (Wolever et al., 1995; Aitken, 1997).

In the late 1970’s and early 1980’s, the traditional low carbohydrate recommendation

for the diabetic was revised in many centres when it was discovered that a concomitant

15

increase in saturated fat and cholesterol intake was taking place (Wolever et al., 1995). A

high fat diet is an independent risk factor for the development of insulin resistance and is

more associated with increased body weight and hence obesity than dietary carbohydrate or

energy intake (Shah et al., 1996). Epidemiological studies have also shown that diets high in

fat tend to precede the conversion to T2DM from impaired glucose tolerance and increase the

risk of developing long-term macrovascular complications such as atherosclerosis (Marshall

et al., 1994). Many diabetes associations around the world reviewed their dietary

recommendations accordingly, and began to advise a decrease in fat and increase in

carbohydrate intake (Wolever et al., 1995). High carbohydrate, fat-restricted diets have been

shown to reduce cholesterol and triacylglyceride levels as well as improve glycemic control

in diabetic subjects (Jovanovic et al., 1985; Rifkin et al., 1990; Shah et al., 1996). Current

dietary recommendations are patient-focused, and contingent on the individuals own

metabolic profile (Holler et al., 1997, p. 137; Wolever et al., 1995). The most common

dietary guidelines for diabetics include consuming 50-60% total energy from carbohydrates

and less than 30% from fats (predominately poly- and monounsaturated), and a high fibre

content (20-35 g/day) (Wolever et al., 1997). The specific role of fibre in the diet was not

recognized until the mid-1970’s when Burkitt and Trowell found that a population in rural

Uganda who consumed large quantities of fibre (50g fibre/1000Kcal) had a relatively low

incidence of diabetes (Wolever et al., 1997; Jenkins et al., 1997). An extensive body of

literature predominantly from experimental studies has subsequently shown that diets high in

both fibre and carbohydrate are able to improve blood glucose levels; decrease insulin

demands; and reduce total and LDL cholesterol, apolipoprotein B and lipid levels (Salmeron

et al., 1997; Salmeron et al., 1997; Jenkins et al., 1994). The most widely accepted definition

16

for dietary fibre is that proposed by Trowel et al., which basically asserts that it is that

portion of plant material, which is resistant to hydrolysis by the digestive enzymes in the

human small intestine (Groff et al., 1999, p. 107). Dietary fibre is often classified according

to its solubility in water. Insoluble fibres such as cellulose and lignin pass through the body

largely unchanged and are known to decrease (speed up) intestinal transit time and increase

fecal bulk. In contrast, water-soluble fibres, which include some hemicelluloses, pectin, gums

and mucilages, tend to be degraded by the commensal micro flora in the colon (Jenkins et al.,

1988). They delay gastric emptying, increase satiety, increase transit time (slower movement)

through the intestine, and decrease nutrient (e.g. glucose) absorption (Groff et al., 1999, p.

111). With respect to therapeutic effects, soluble fibres have been shown to be the most

beneficial to the diabetic. Research to date, from experimental studies have shown that rises

in postprandial glucose and insulin levels can be reduced after consuming meals rich in

viscous soluble fibres, and that diets rich in soluble fibre can lower blood lipid levels

(Jenkins et al., 1993; Salmeron et al., 1997; Salmeron et al., 1997; Wursch et al., 1997). The

satiating effects of dietary fibre may also be a useful strategy for weight loss, a matter of

concern for many diabetics. Because, many foods contain both a dietary fibre and

carbohydrate component, their individual roles have been difficult to gauge. In addition,

some food composition tables lack information pertaining to content, and few dietary

assessment tools have been designed which are able to assess fiber intake accurately and

independent of carbohydrate intake.

Carbohydrates are naturally occurring compounds found in food, which supply nearly

half of the total caloric intake for most humans (Groff et al., 1999, p. 111). They are either

“simple” and include mono- and disaccharides such as glucose and lactose, or “complex” and

17

include polysaccharides such as starch and dextrin. Complex carbohydrates are formed from

a large number of glucose molecules in either branched (amlyopectin) or straight chains

(amylose). Previously, it was believed that molecular structure and chain length of the

carbohydrate (complex verses simple) determined the rate of digestion and predicted the

glycemic response (Wolever et al., 1995). However, Wahlqvist et al., in 1978 showed that the

number of glucose units in the polysaccharide molecule had no relation to the glycemic

effects of the carbohydrate. Shortly thereafter, Crapo et al., (1981) found wide variations in

glucose responses to various starchy foods of similar macronutrient content and Jenkins et

al., showed that the effects of starch on plasma glucose varied depending on food source

among other factors. The results from these as well as many subsequent studies confirmed

that the complex verses simple classification is not a suitable determinant of the rate of

carbohydrate metabolism and cannot be used to predict glycemic response (Wolever et al.,

1995; Groff et al., 1999 p. 112). In 1981, Jenkins et al., developed the concept of the

glycemic index (GI), which serves as a classification system for foods based on their

glycemic impact (Wolever et al., 1995).

1.2.3 The Glycemic Index

It has been well documented that different foods are able to produce markedly

different blood glucose responses independent of the amount of carbohydrate they contain.

Based on this premise, the Glycemic Index (GI) was developed in order to assess and classify

the blood glucose responses to food. GI values can be used to supplement data about food

composition for diabetes diet planning and determine the physiological effects of entire diets

(Wolever et al., 1992).

18

The GI is defined as the incremental blood glucose area following a 50g available

carbohydrate portion of the test food, expressed as the percentage of the corresponding area

following a carbohydrate equivalent load of a reference food, usually white bread (Bjorck et

al., 2000). The clinical efficacy of the GI has been questioned for a number of reasons

including, variability of glycemic responses in different subjects, difficulty in ascribing GI

values to mixed meals, and different methodologies used in GI calculations. Despite these

shortcomings, many studies have clearly shown that low-GI diets are beneficial in the

management of T2DM. The glycemic index depends largely on the rate of digestion or

absorption of the carbohydrate. Low-GI foods such as whole grain products and legumes

release glucose to the blood at a slower rate than high GI foods such as potato or white bread,

and have a reduced insulin demand (Bjorck et al., 2000; Willet, 1998, p. 447). Factors, which

reduce the rate of absorption include the nature of the starch, degree of food processing,

presence of viscous fibres and antinutrients, and altered food frequency (Jenkins et al., 1995).

Much of the evidence to date comes from experimental studies. Wolever et al., showed that

reducing the blood glucose raising potential of the diet without changing macronutrient

composition improved glycemic control, lowered total serum cholesterol, LDL cholesterol

and serum triglyceride levels in type 2 diabetics (Wolever et al., 1987, 1992; Jenkins et al.,

1987, 1988). Improvements in long term glycemic control were found in T2DM subjects on

low GI diets verses high GI diets (Brand et al., 1991) and insulin secretion as measured by

24-hour urinary C-peptide levels was found to be lower in subjects on low verses high GI

diets (Jenkins et al., 1987). Dietary fibre has recently been incorporated into the body of

research examining the glycemic effects of foods. Salmeron et al found that a low cereal fibre

diet and high glycemic load” (GL) increases the risk of developing T2DM in both men and

* Glycemic load is calculated as the product of a food’s carbohydrate content and its glycemic index value.

19

women (Salmeron et al., 1997). After, adjusting for activity level, family history of diabetes,

total energy intake, and cereal fibre in the diet, they concluded that dietary GI is positively

correlated with risk of T2DM (Salmeron et al., 1997). Many epidemiological studies have

shown that diets high in saturated fats, and refined sugars, are associated with an increased

risk of developing T2DM and CHD (Rosenbloom et al., 1999; Mbanya et al., 1997;

Tsunehara et al., 1990; Jenkins et al., 1997), however few have looked specifically and

independently at the role of GJ. Liu et al., recently demonstrated that a high glycemic load

may be considered a potential risk factor for coronary artery disease in free-living women,

particularly those prone to insulin resistance (Liu et al., 2001). Understanding the

relationship between dietary GI and T2DM will be beneficial to many people around the

world, particularly those in developing countries where incidence rates are nearing epidemic

in some regions.

1.2.4 Epidemiology of T2DM in the Caribbean

During the last 30 years there has been an unprecedented epidemiological transition

in disease prevalence in many countries of the world. Malnutrition and infectious disease,

once the major causes of morbidity and mortality have been superseded by conditions such as

obesity and chronic non-communicable diseases including cardiovascular disease, cancer,

hypertension and diabetes (Sinha, 1995; Gulliford, 1996). Dietary and lifestyle changes

associated with economic development or ““Westernization” are believed to have been pivotal

in conditioning the increase in these such diseases. The Caribbean was one of the primary

regions where the importance of diabetes in developing communities was first recognized

(Gulliford, 1996). Despite a lack of information on the incidence of T2DM, prevalence data

20

suggest that similar changes in diet and lifestyle are responsible at least in part for the rise in

T2DM prevalence seen in many Caribbean nations. In 1998, a report published by King et

al., estimated that T2DM prevalence for individuals >20 and <64 years of age in Trinidad

and Tobago will increase from 4.5 % in 1995 to 4.7% in 2000 and 6.7% in 2025 (King et al.,

1998) (Figure 1-1). Mortality data have also provided some knowledge about T2DM

epidemiology in the Caribbean, however these data are not always reported so accurate

information is difficult to obtain. In 1995, Sinha reported that there was a significant increase

in the percent contribution of diabetes to total mortality in the Caribbean over the last 30

years (Sinha 1995). Shortly thereafter Gulliford, declared that T&T had the highest total

death rate and death rate due to diabetes in the Caribbean. Mortality rates from diabetes were

found to be higher in women than men in T&T, and prevalence rates increased linearly with

respect to age (Gulliford 1996; Sinha 1995). Trends regarding ethnicity were difficult to

determine since mortality analyses in T&T are not published according to ethnic group

although death certificates usually include mention of ethnicity (Gulliford, 1996).

One of the most comprehensive surveys addressing diabetes in the Caribbean was

carried out in Trinidad in 1961-62 where the ethnic composition of the population is

relatively representative of the Caribbean at large. The island is comprised of persons from

South Asian (SA), African (AF), and mixed (MI) backgrounds making up 40%, 40%, and

20% of the population respectively. These surveys indicated that T2DM prevalence was

significantly higher in the SA (2.4%) than in the AF population (1.4%) in T&T. SA men

were found to have a slightly higher but non-significant prevalence of T2DM (2.5%)

compared to SA women (2.3%), and AF women showed an almost two-fold higher

prevalence (2.1%) than AF men (1.1%) (Sinha, 1995). About fifteen years later, the St. James

21

study found a significant increase of T2DM prevalence in both ethnic groups. The prevalence

of T2DM for SA’s was still higher at approximately 20% for SA males and females, 9.6% for

AF males and 16.5% for AF females. Although T2DM prevalence was higher in SA

compared to AF females, AF females had a slightly higher prevalence of impaired glucose

tolerance (IGT) than SA females. SA men had an almost two-fold higher IGT prevalence

than AF men. These ethnic differences in T2DM prevalence are difficult to explain using a

genetic predisposition model because even within ethnic groups, prevalence rates vary from

district to district. It is possible that ethnic differences in dietary intake may provide an

alternative explanation since dietary habits between AF’s and SA’s have been known to

differ while other factors such as activity level and age are similar between the two groups

(Sinha, 1995).

The apparent decrease in utilization of complex carbohydrates such as indigenous

root tubers and vegetables and concomitant increase in total calories, most of which are from

saturated fat have been thought by some to account for the increased prevalence rates of

T2DM in T&T (Sinha, 1995). Although there appears to be a strong positive correlation,

much of the available information comes from food disappearance data, a source of data in

which the actual amount of food consumed by each ethnic group and the population taken as

a whole is not reflected accurately. According to the 1962 West Indies Nutrition Survey, the

dietary habits of the South Asians and Africans differ in some respects. For example, the

main staple foods of the South Asians were shown to be rice, flour in the form of roti, and

Irish potatoes and those of the Africans included rice, white bread, and indigenous root tubers

such as dasheen, cassava, eddoes, and white yam. In an attempt to investigate these

phenomenon, a pilot study was conducted in T&T, to determine the dietary GIs of SA’s and

22

AF’s in T&T. It was found that AF men had a lower diet GI than SA men, while intakes of

energy, fat, protein, carbohydrate and fibre did not differ significantly (Wolever et al., 2000

abstract). Since, the prevalence of diabetes in T&T was shown to be twice as common in SA

than AF males with a smaller difference in females these results are consistent with previous

data suggesting that a low dietary GI may reduce the risk of developing T2DM. However a

more thorough assessment of dietary intake is needed in T&T to fully understand this

relationship since results from the pilot study were based on 24-hour dietary recalls, which

rely on the participants memory and are affected by day-to-day variability in food intake.

Food Frequency Questionnaires, (FFQ) have become the principal dietary survey tool in

epidemiological studies of chronic disease because diet-disease links develop over long

periods of time, and FFQs allow assessment of long-term or habitual dietary intake (Teufel,

1997; Dwyer, 1999).

1.2.5 Dietary Assessment Methods

1.2.5.1 24-hour Dietary Recalls

Twenty-four-hour dietary recalls require participants to remember and report all foods

and beverages consumed over the preceding 24 hours. Recall is typically conducted by

personal interview either by a well-trained interviewer or dietician as probing questions are

often required to gather detail or specifics about dietary intake. In 1967, Campbell and

Dodds, found among groups of older individuals that respondents with interviewer probing

reported 25% higher dietary intakes that did respondents without interviewer probing.

Probing is particularly useful for collecting information about how foods are prepared, and

recovering items that may have not originally been reported such as butter on toast or snacks

23

and coffee breaks. However, interviewers should use standardized neutral probing questions

when conducting interviews to avoid leading the participant (Thompson and Byers, 1994).

Interviewers should also be aware of available foods in the environment/marketplace, and

preparation practises, including prevalent regional or ethnic foods. In order to minimize error

and increase reliability of interviewing and coding, a detailed protocol for administration of

24-hour dietary recalls, and training/retraining sessions for interviewers should be conducted

(Thompson and Byers, 1994). Duplicate collection and coding of a sample of recalls

throughout the study period and use of a computerized data base system for nutrient analysis

should also be used (Thompson and Byers, 1994). There are many advantages to using 24-

hour dietary recalls. Given that an interviewer administers the tool and records responses,

literacy of the respondent is not necessary and because the recall period is short, respondents

are usually able to recall most of their dietary intake. The average 24-hour dietary recall takes

approximately 20 minutes to complete (Thompson and Byers, 1994). Since little burden is

placed on the respondent, those who agree to participate in 24-hour dietary recalls are more

likely to be representative of the population than those who agree to keep food records or

complete FFQ’s (Thompson and Byers, 1994). In contrast to the keeping of food records,

diet recalls occur after the food has been consumed, so there is less potential for the

assessment method to interfere with dietary behaviour (Thompson and Byers, 1994). An

inherent problem with the 24-hour dietary recall method is that individuals may not report

their food consumption accurately for various reasons related to memory and/or the interview

situation. In addition, since most individual’s diets vary from day to day, it is not appropriate

to use data from a single 24-hour dietary recall to characterize an individual’s usual diet.

24

Instead, a series of recalls should be used that are conducted at different times of the year

since seasonality can influence dietary habits.

1.2.5.2 Diet Records

With the diet record approach, respondents are required to record their food and

beverage intake along with the amounts consumed over a specified number of days.

Typically 3 or 4 consecutive days are included since more than 7 consecutive days can result

in respondent fatigue, however day of the week variation in dietary intake also needs to be

considered (Thompson and Byers, 1994). The most practical application of food records

would include multiple administrations of 3-day food records, conducted at intervals that

would take into account day of the week and seasonal variation in dietary intake.

Theoretically, reporting of food intake is done at the time of consumption, however, it need

not be done on paper, since dictaphones have been used and are particularly useful for low

literacy groups (Thompson and Byers, 1994). Participants must be trained in how to record

dietary intake including the name of the food, brand name, preparation methods, recipes for

mixed dishes, and portion sizes. Contact with an administrator during the reporting period is

useful for ensuring proper completion of the records. Upon completion of the diet records, a

trained interviewer should review all entries with the respondent to clarify any ambiguities

and probe for forgotten items.

Diet records have the potential of providing quantitatively accurate information about

food items consumed during a specified recording period. For this reason, diet records are

often regarded as a “gold standard” against which other dietary assessment tools are

compared (Thompson and Byers, 1994). Because foods are recorded as they are consumed,

' 25

the problem of recall is lessened and the foods are described in more detail. In addition, the

measurement of the amounts of food consumed at each eating occasion provides more

accurate information about portion sizes than if participants were to recall the amounts of

foods eaten previously (Thompson and Byers, 1994). However, since diet record keeping

requires that participants be both motivated and literate, the utility of the method is limited in

various populations such as children, the elderly and recent immigrants, resulting in sample

selection bias. Participants may also develop habits whereby they record food intake only

once per day or at one time during the recording period (Thompson and Byers, 1994). If this

is the case, the record method approaches the 24-hour recall in terms of relying on memory

rather than on concurrent recording (Thompson and Byers, 1994). Recording dietary intake

as items are consumed can affect both the types of food chosen and the quantities consumed.

Therefore, the diet record method may alter dietary behaviours that the tool was initially

intended to measure (Thompson and Byers, 1994).

1.2.5.3 Biomarkers of Diet

Biomarkers have considerable potential in aiding the understanding of the

relationship between diet and disease or health since they can provide the link between the

consumption of specific foods and biological outcome. Laboratory analyses of blood, urine,

adipose tissue, stools, nails and hair have yielded a variety of biological markers that can be

useful for assessing diet. Biomarkers provide an accurate and objective measure of dietary

intake because they are not reliant on a subject’s memory or on the accuracy of recording in a

food record. There are two categories of biomarkers of diet: (1) those, which provide an

absolute quantitative measure of dietary intakes, such as 24-hr urinary nitrogen excretion as a

26

measure of 24-hr protein intake. Currently, only a small number of markers fall into this

group; (2) those which measure the concentration of a given factor, such as plasma vitamins,

but for which there is no time dimension to the measurement. The measure therefore

correlates with intake but does not provide an absolute measure of it. The majority of

biomarkers are of this type and are useful in categorizing individuals into relative levels of

intake (Wild et al., 2001). Other biomarkers in this category include: carotenoids,

tocopherols, ascorbic acid, vitamins Bs and B)2 and folate from blood; marine omega-3 fatty

acids, linoleic and linolenic acids from blood or adipose; and sodium, potassium, calcium and

magnesium from urine (Willett, p. 127, 1998). Biomarkers of diet are most often used in

validation studies of dietary assessment tools. The advantage of using biochemical markers

in dietary assessment is that their random errors are independent of those inherent in

questionnaire measurements and food-consumption records. The inclusion of biomarkers in

dietary validity studies increases the likelihood that the criteria of independent errors, crucial

in validity studies, are met (Kaaks, 1997). A major problem with most biomarkers studied is

that in well-fed populations there are many determinants of nutrient concentrations in

biological tissues apart from dietary intake (Thompson & Byers, 1994). Other issues of

concem include: proper analytic technique; how metabolism of a compound may alter the

validity of a marker; and difficulties that may arise when several biomarkers are available to

assess exposure to one nutrient (Crews et al., 2001). The use of biomarkers are costly and

require that samples be collected using a suitable protocol and stored under conditions that

will not result in any alteration of sample composition, or deterioration of sample quality

(Crews et al., 2001).

27

1.2.5.4 Diet History

The term “diet history” is used in many ways. Originally, as coined by Burke in 1947, the

term diet history referred to the collection of information about the frequency of intake of

various foods as well as the typical composition of meals. The diet history method,

characterizes foods in much more detail than is permissible in food frequency lists such as

preparation methods, and foods eaten in combination. The Burke diet history originally

included three elements: a detailed interview about the usual pattern of eating, a food list

asking for amount and frequency usually eaten, and a 3-day diet record (Burke, 1947). The

detailed interview (which sometimes includes a 24-hour diet recall) is the central feature of

the Burke method, with the food frequency list and diet records used as cross-checks of the

history. The original Burke diet history has rarely been reproduced since it requires a lot of

effort and expertise in capturing and coding the information collected in the interview.

However, many variations of the Burke method have been developed and are designed to

ascertain usual eating patterns over an extended period of time, including type, frequency,

and amount of foods consumed; many include a cross-check feature (Van Beresteyn et al.,

1987). More recently, the method has been automated, eliminating the need for an interview.

The major strength of the diet history method is its assessment of usual meal patterns and

details of dietary intake rather than intakes for a short period of time as in records or recalls,

or only frequency of food consumption. Details about food preparation can be helpful in

characterizing nutrient intake, as well as exposure to other factors in foods. More cognitive

support is provided for the recall process compared to other dietary intake assessment

methods, which may result in more accurate measures. However, participants are asked to

make many judgements about usual dietary intake and the amounts of foods eaten. These

28

subjective tasks may be challenging (and burdensome) for the participant. Burke cautioned

that nutrient intakes estimated from these data should be interpreted as relative rather than

absolute. All of these limitations are shared with the food frequency method. The diet history

approach, when conducted by interviewers requires trained dieticians, which could be costly.

1.2.5.5 Food Frequency Questionnaires (FFQ)

During the 1950’s, Stephanik and Trulson (1962), Heady (1961), Wiehl and Reed

(1960) and Marr (1962) developed the Food-Frequency Questionnaire (FFQ), and evaluated

their role in dietary assessment (Willett, 1998, p. 74). Stephanik and Trulson (1962) found

that a FFQ could discriminate between groups of subjects defined by ethnicity, but did not

consider that such a questionnaire could be useful in computing nutrient intakes. Interest in

FFQ’s waned during the 1970’s but greatly increased during the 1980’s and 1990’s when

substantial refinement, modification, and evaluation of FFQ methodologies took place

(Willett, 1998, p. 75).

The FFQ approach asks respondents to report their usual frequency of consumption of

each item from a list of individual foods for a specific time period. Although many

instruments collect information pertaining only to frequency (and sometimes quantity), some

enquire into methods of food preparation, and combinations of foods in meals (Thompson et

al., 1994). Semi-quantitative FFQ’s (SQFFQ) incorporate portion size questions, or specify

portion sizes as part of each question in order to estimate relative or absolute nutrient intakes.

Overall nutrient intake estimates are derived by summing over all foods the products of the

reported frequency of each food by the amount of nutrient in a specified serving of that food

(Thompson et al., 1994).

29

SQFFQ’s are the best method of dietary assessment for representing an individual’s

actual or relative food and nutrient intake over a long period of time (Rim et al., 1992).

Where single diet records or 24-dietary recall fail due to day-to-day variability, FFQ’s can

estimate usual intake without being affected by recent or transient changes in the diet

(Thompson et al., 1994). This is useful with respect to epidemiology because it is habitual

intake in the past, not random daily intake that is considered to be a determinant of disease

risk and development. In addition, FFQs are most often and best used to rank subjects into

groups based on the types of foods they eat most frequently, or on the levels of specific

nutrients consumed (Thompson et al., 1994). These classifications are useful in associating

disease risk with levels of food or nutrient intake (Salvini et al., 1989; Rimm et al., 1992;

Willett et al., 1985; Thompson et al., 1994).

FFQ’s are the most economical method of long-term dietary assessment. They do not

require much of the subjects’ time, can be self-administered, and do not require professional

interviewers. Some are also scannable, thereby reducing cost and time for data entry (Salvini

et al., 1989; Rimm et al., 1992; Thompson et al., 1994). To assess diet over the same period

of time as the FFQ, multiple dietary records (DRs), or recalls would have to be collected

from each subject, and that would be time consuming and costly. The determination of actual

intake using a FFQ however, may not be as accurate as with DRs or recalls. This could be

due to the limited number of foods listed on the instrument or difficulties with portion size

and frequency estimation (Thompson et al., 1994). The major limitation of FFQ’s is that

many details of dietary intake are not measured. Inaccuracies result from incomplete listing

of food items, from errors in frequency estimation, and from errors in estimation of usual

serving sizes (Thompson et al., 1994). In general, longer food lists overestimate intake,

30

whereas shorter lists underestimate intake (Krebs-Smith et al., 1994). Although some FFQ’s

produce estimates of population average intakes that are reasonable, different FFQ’s will

often perform in unpredictable ways in different populations, therefore the levels of nutrient

intakes estimated by FFQ’s should best be regarded as approximations (Thompson et al.,

1994). Participants have trouble recalling the average intake of certain food items especially

seasonal ones, and also have difficulty estimating usual serving size (Thompson et al., 1994).

However, frequency is believed to be a greater contributor to the variance in intake of most

foods than typical serving size which is why some epidemiologists prefer to use FFQ’s

without the additional respondent burden of reporting serving sizes (Thompson et al., 1994),

The FFQs ability to measure true dietary intake is assessed by a variety of

approaches, which is pivotal in evaluating the strength and usefulness of the survey tool.

Validity refers to the degree to which the questionnaire actually measures the aspect of the

diet that it was designed to measure. Put another way, validity is the process of assessing the

ability to measure truth (Willett, 1998, p. 101). This implies that a comparison is made with a

superior, although possibly imperfect standard. Ideally, the definitive validity study for a

FFQ would require constant, and non-invasive monitoring of the respondents food intake

over a long period of time. Since this is not possible for practical reasons, a series of DRs or

24-hour dietary recalls are generally used as a reference or “gold standard” (Thompson et al.,

1994). These types of studies are more appropriately called calibration studies because

recalls and records themselves may not represent the time period of interest, may contain

error, and may underestimate nutrient intakes by nearly 20% (Willet, 1998, p 103). Because,

DRs and 24-hour recalls do not depend on long-term memory, their error is independent of

the error implicit in FFQs (Thompson et al., 1994). The correlations between the methods for

31

most foods and nutrients are usually in the range of 0.4 to 0.7. Correlation coefficient values

(r) of r>0.8, 0.5<r<0.8, v<0.5 represent strong, moderate, and weak correlations respectively

(Abramson, 1994). Weak correlations are usually taken to indicate inaccuracy of the methods

used, and higher correlation coefficients will be characteristic of foods with increased

between-person variation, which is an important factor in determining associations with

disease (Feskanich et al., 1993). Because errors in DR’s will have a lowering effect on r-

values, correlations can be assumed to be conservative estimates of FFQ validity (Willett et

al., 1987).

Although reproducibility? is not indicative of validity, a valid method used in

epidemiological studies must be reproducible. Reproducibility coefficients are used to

compare the agreement between two identical FFQs administered at different times in similar

populations (Willet, 1998 p. 101; Feskanich et al., 1992). Assessment of the reproducibility

of a FFQ relies on the assumption that diet does not change between consecutive

administrations, and that the administrations are separated by enough time so that subjects

are not influenced by previous responses (Thompson et al., 1994). The level of

reproducibility found for a FFQ is sometimes equated with that of certain biological markers

such as serum cholesterol, blood pressure, and uric acid, which are often used as reliable

predictors of disease (Willett, 1998, p 124).

Many studies to date have found that the FFQ method shows sufficient validity and

reproducibility to make it a useful instrument for epidemiological studies (Jain et al., 1980).

The Harvard dietary Assessment form developed by Willett et al., and the Health Habits and

History Questionnaire developed by Block and coworkers at the National Cancer Institute,

° Reproducibility refers to consistency of questionnaire measurements on more than one administration to the

same individuals at different times, realizing that conditions are never identical on repeated administrations (Willett, p. 101, 1998).

32

are two instruments currently in widespread use. Both questionnaires have been validated for

their ability to assess the intake of various predictors of chronic disease, including total

energy, cholesterol, dietary fat, and calcium at the individual level (Dwyer 1999; Block et al.,

1990; Willet, 1998, p. 105; Teufel, 1997). Other validated FFQs such as those developed by

the National Centre for Health Statistics (NHANES III), International Agency for Research

on Cancer (EPIC), and the University of Hawaii (Multiethnic-minority Cohort study of Diet

and Cancer) have also been used to assess similar dietary factors. (Appendix A) lists

descriptions of various validation studies and their findings. Although total energy, and

dietary fat intakes are important to consider for diabetes risk and management, an estimation

of dietary glycemic index (GI), would be of interest since it has recently been shown that

diets with a high glycemic load and low cereal fibre content increase the risk of developing

T2DM in both men and women (Salmeron et al., 1997) and that low GI starchy foods may

be beneficial in the treatment of T2DM (Wolever et al., 1992). Very few instruments at this

time have been validated which specifically assess dietary intake with respect to GI. A

recent study however, examining the physiologic relevance of the glycemic load as a

potential risk factor for coronary heart disease documented the ability of a FFQ to

successfully assess dietary GIs and GLs relative to 4 repeated series of 7-day food records

(Liu et al., 2001). The FFQ was administered twice to 173 female participants at an interval

of one year (Willett et al., 1985). The correlation coefficient for energy-adjusted

carbohydrate intake between the FFQ and a 7-day DR was 0.73 (Willett p. 1998). The

questionnaire was found to assess individual foods high in carbohydrate content quite well.

For example, correlation coefficients were 0.71 for white bread, 0.77 for dark bread, 0.66 for

potatoes and 0.94 for yoghurt (Liu et al., 2001).

33

The ability of a FFQ to estimate diet GI could be extremely useful for identifying

specific elements of the diet, which may be contributing to the increased risk of T2DM in

populations, which are particularly susceptible. Specific dietary interventions and

management programs could subsequently be developed in these areas to promote risk

reduction and ultimately reduce T2DM development. This is of particular importance to

developing countries such as Trinidad and Tobago (T&T) where T2DM prevalence is six

times that of Canada and the US (Gulliford, 1996). Many FFQs however, are not designed to

record the frequency of food consumption in culturally diverse or ethnic populations and the

validity of these instruments in epidemiological studies (in these populations) has not been

thoroughly tested (Teufel, 1997; Mayer-Davis et al., 1999). Therefore, in order to assess diet

GI in a culturally distinct population like T&T, a novel instrument must be developed by

either generating a list from DRs or 24-hour dietary recalls collected in the population under

investigation or substantially modifying a previously validated FFQ, followed by an

exclusive validation process.

In 1998 a pilot study was conducted in T&T to estimate nutrient intakes and diet GI

of African (AF) and South Asian (SA) inhabitants of the island. Nutrient intakes were

assessed by 24-hour dietary recall. This information was used for the identification of

commonly eaten foods in T&T so that a culturally sensitive FFQ may be developed. A

survey tool that can specifically assess dietary GI and discriminate between dietary intake

across various ethnic groups is needed to permit study of the relationship between GI and

T2DM risk in susceptible individuals in T&T. The development of such a tool is the

objective of this study.

Figure 1-1

Prevalence of DM in Trinidad and Tobago in 1995 and estimated prevalence for the

years 2000 and 2025.

75-

= Z B 50 : 5° me o 20 2 83x 3 254 8 g* 3 o @

a

0.04 1995 2000 2025

Year

Mg Percentage of DM (%)

Number of people (x1000)

Adapted from King et al., 1998.

34

35

1.3 STUDY OBJECTIVES

The objectives of this study are to:

Develop a food Frequency Questionnaire (FFQ) for use in Trinidad, which is

sensitive to dietary glycemic index (GI) and ethnic differences in diet by

using a food list generated by a pilot study.

Calibrate the FFQ by comparing estimates of dietary intake for total energy

and 11 macronutrients to those obtained from 7-day consecutive food records

in Trinidad.

2. MATERIALS AND METHODS

2.1 DEVELOPMENT OF FFQ

Objective

To develop a FFQ that can be used to assess dietary GI in T&T, by using a food

list generated from 24-hour dietary recalls conducted in T&T.

2.1.1 Stage 1: Generation of food list and design of FFQ

2.1.1.1 Procedures

All methods were approved by the Human Subjects Review Committee of the

University of Toronto, and committee on ethics of the University of the West Indies,

Mount Hope, Trinidad (consent forms are shown in appendix B). A validated semi-

quantitative Food Frequency Questionnaire (FFQ) developed by the Epidemiology

Program of the Cancer Centre of Hawaii‘, University of Hawaii (Epidemiology Program;

Cancer Research Center of Hawaii: University of Hawaii, 1998) was modified using data

from a food list generated from 24-hour dietary recalls collected from 101 subjects in

T&T by Renee Issacs (Issacs, R. Masters thesis, 1999). For each food item collected from

the 24-hour dietary recalls, the percentage of energy contributed from each macronutrient

was determined using Nutriput, a computer program based on the USDA database

developed by Dr. Thomas Wolever. Foods with the highest percentage of total energy

* The University of Hawaii Questionnaire was chosen because it has been previously validated, and has a

diverse food list which contains many ethnic items as well as Western. Items specific to the Hawaiian diet

were easily removed and replaced with items specific to the Trinidadian diet as assessed by the Pilot study

and the Western items were kept since they are prevalent in the Trinidadian diet as well. Differences

between the Hawaiian FFQ and the prototype for Trinidad is the Trinidadian FFQ is interviewer

administered, longer, has different headings, and addresses portion sizes differently.

36

37

from dietary carbohydrates and items with the highest percentage of total energy were

selected for inclusion on the FFQ. Items specific to the Hawaiian diet such as jook and

ramen were removed from the University of Hawaii questionnaire and replaced with

those deemed specific to the Trinidadian diet under the headings of the 6 official food

groups of the Caribbean (Food from Animals & Alternatives; Ground Provisions;

Cereals; Legumes & Nuts; Fats & Oils; and Fruit & Vegetables). Other headings include,

“Fast Foods”, “Mixed Dishes”, “Beverages”, and “Supplements”. Sections addressing the

seasonal use of food items as well as use during religious holidays were included since it

is likely that food consumption and hence energy intake may be different at these times.

A section regarding cooking methods was also included to provide an understanding of

how food is commonly prepared.

Portion sizes were indicated on the FFQ as “S”’, “M”’, and “L” (equated to

standard portion sizes) (Pennington, J. 15" ed., 1989; Health and Welfare Canada 1992;

The American Dietetic Association, 1998; Caribbean Food and Nutrition Institute, 2001)

and corresponded to a particular calibrated photograph that was shown at the time of

questionnaire administration. For certain items on the FFQ such as milk or bread an

additional field was included for specifying the brand, form or type.

2.1.2 Stage 2: testing of FFQ

2.1.2.1 Procedures

In order to assess the appropriateness of the FFQ and identify frequently and

infrequently used items, testing of the FFQ took place from the beginning of March 2000

to the beginning of April 2000 on a convenience sample in 7 of the 12 administrative

38

divisions of T&T °. Individuals from each area were asked to assess their average use

over the past year of the items listed on the FFQ in an interviewer- administered manner.

This approach was chosen rather than a self-administered one since the rate of functional

literacy is low among some groups in the sample. Subjects also answered questions

pertaining to their age, ethnicity, health status, highest level of completed education,

occupation, and area of residence’. Ethnicity was determined by grandparental origin, and

subjects of less than 75% common genetic stock were deemed “Mixed””’.

2.1.2.2 Subjects

For each county 10 individuals were approached at their workplace or on the street,

giving a total of 50. Of these, 9 initially refused to participate due to lack of interest

(eligibility unknown). Inclusion criteria included individuals aged 18-63 years, absence

of chronic or developmental diseases, not following a special diet, sober, and a non-

pregnant or lactating status. A total of 41 individuals agreed to answer the questionnaire.

Of these, 39 were eligible, and all answered the FFQ. For each municipality 15

individuals were also approached at their workplace or on the street, giving a total of 30.

Of these, 7 initially refused to participate due to lack of time or interest (eligibility

unknown). A total of 23 individuals agreed to answer the questionnaire. Of these 22 were

eligible and all answered the FFQ. A total of 61 individuals participated in answering the

° Administrative divisions of T&T are comprised of 8 counties, 3 municipalities and 1 ward. Five counties and 2 municipalities were surveyed.

° These questions are found on the first page of the FFQ.

’ This definition of “Mixed” was used in order to keep consistent with the pilot study. “Mixed” ethnicity

refers to a combination of African and South Asian ancestry while “Other” refers to any ethnic group aside from African, South Asian or Mixed.

FFQ. Of these 32 were female, and 29 were male. Among the females, 16 were SA, 14

AF and 2 OTH’. Among the males, 17 were SA, 11 AF and 1 OTH

* Categories of Mixed and Other ethnicities were collapsed due to small number.

39

40

Table 2-1 FFQ testing phase: Recruitment for Administrative Divisions

Administrative Number Number Number Number Number Division Approached Refused Interested Eligible Participated

(“) (>) (>) (%) (>)

St. David 10 (12.5) 2 (12.5) 8 (12.5) 8 (13.11) 3 (13)

St. George 10 (12.5) 1 (6) 9 (14) 9 (15) 9 (15)

Caroni 10 (12.5) 2 (12.5) 8 (12.5) 711) 7(11)

Victoria 10 (12.5) 1 (6) 9 (14) 8 (13) 8 (13)

Mayaro 10 (12.5) 3 (19) 7(11) 7(11) 7 (11)

Port of Spain 15 (19) 3 (19) 12 (19) 12 (20) 12 (20)

San Fernando 15 (18) 4 (25) 11 (17) 10 (17) 10 (17)

Total 80 (100) 16 (100) 64 (100) 61 (100) 61 (100)

Table 2-2 (a) Ethnic breakdown of Males

Administrative SA(%) AF(%) OTH(%) Division

St. David 3 (17) 2 (18) 0

St. George 2 (12) 1 (9) 1 (100)

Caroni 3 (17) 1 (9) 0

Victoria 2 (12) 1 (9) 0

Mayaro 2 (12) 1 (9) 0

Port of Spain 2 (12) 3 (27) 0

San Fernando 3 (17) 2 (18) 0

Total 17 (100) 11 (100) 1 (100) N=29

Table 2.2 (b) Ethnic Breakdown of Females

N=32

Administrative SA(%) AF(%) OTH(%)

Division

St. David 2 (12.5) 1(7) 0

St. George 1 (6) 3 (22) 1 (50)

Caroni 2 (12.5) 1(7) 0

Victoria 3 (19) 2 (14) 0

Mayaro 2 (12.5) 2 (14) 0

Port of Spain 2 (12.5) 4 (29) 1 (50)

San Fernando 4 (25) 1 (7) 0

Total 16(100) 14(100) 2(100)

42

43

2.1.2.3 Results

In general, the FFQ was found to be comprehensive and contained a variety of food items

that represented dietary intakes of both South Asians and Africans in Trinidad.

Changes to the FFQ after testing were very few in number but did include some additions

and deletions specifically with respect to fruits and vegetables. If more than half of the

sample reported that they did not consume a particular item it was removed. In some

cases individuals reported voluntarily that certain items that they (and their community

members) consume regularly was not on the list and should be included. All changes

were noted and discussed with two local Dieticians at the University of the West Indies

Mount Hope Hospital, Trinidad’. Following the testing phase, the FFQ was ready for use

in the validation study.

2.2 VALIDATION STUDY

Objective

To validate the FFQ by comparing estimates of dietary intake, specifically dietary

glycemic index (GI) to those obtained from 7-day consecutive Food Records in T&T.

2.2.1 Procedures

Validation of the FFQ was assessed in relation to 7-day consecutive food records

(FR’s) taken as the “gold standard”. Each participant completed both an interviewer-

administered FFQ assessing diet over the past year and a 7-day food record after

” June Holdip and Yvonne Batson, Registered Dieticians at the University of the West Indies, Mount Hope Hospital.

44

receiving detailed instructions from the interviewer [FFQ and part of FR diary is shown

in (appendix C) and portion sizes associated with FFQ are shown in (appendix D)].

The FFQ was administered either the day before or the day after the food records

were completed, and the order was determined by coin-toss. Subjects were asked to

assess their use of the food items listed on the questionnaire by me the interviewer, as

well as answer questions regarding methods of food preparation, the seasonal use of food

items and dietary changes during religious holidays. Although I was given no specific

training in how to conduct an epidemiological study such as this, my Master’s training in

nutrition and independent research on how FFQ validation studies are designed (part of

my Master’s thesis) prepared me for conducting this study in part. Calibrated

photographs of various food items and common household utensils were shown to allow

for estimation of portion sizes for foods listed on the FFQ. Each interview lasted

approximately 45 minutes. Information regarding the subjects’ gender, ethnicity,

occupation, age, highest completed level of education, and health status were recorded

prior to administration of the FFQ (first page of questionnaire).

Subjects were also given a standardized booklet in which they recorded their food

and beverage intake over 7 consecutive days. All booklets came equipped with

instructions, a hand-written example and calibrated photographs of food items and

household utensils (developed by myself). Participants were given explicit instructions by

the interviewer prior to the recording period, and asked to confirm all information upon

completion of the reporting period. A phone-call or visit mid-week was provided by the

interviewer in order to answer any questions and improve compliance.

45

Households were selected from the 36 electoral districts of Tunapuna, a county

regarded as representative of the population of Trinidad with respect to ethnicity, age,

gender, and socio-economic status, by the Central Statistics Office (CSO), Port of Spain,

Trinidad'°. Maps of each district were provided by the CSO, and every 15" house on

each map was approached (see appendix E for an example of a map). If the selected

house could not be found, the next house in succession was approached. If nobody

answered the door, the day of the week and time of day was noted, and the house was re-

visited either later in the day, or on the weekend. If after three tries there was still no

response, the house was omitted and the next house was selected. Individuals were

informed about the study by myself and invited to participate.

Only one member from each household was invited to participate in the study.

Generally, the person who answered the door was recruited. In some cases, an alternate

household member was recommended to whom we either spoke at that time or who was

interviewed later if they were not home initially. All data were collected over a period of

3 months from the middle of April to the middle of June 2000.

2.2.2 Subjects

The same inclusion criteria were used as in the testing phase of the FFQ. Subjects

were included if they were 18 to 65 years of age, free of any chronic disease (to the

individual’s best knowledge), mental and/or physical handicaps, not observing a special

diet, not pregnant or lactating and not intoxicated. Two hundred and fifty-four (254)

males and females were approached to engage in the study. Of these 180 (71%) agreed to

'° Interviews with Mrs. Ali, Mrs. Mason, Mr. Chady (director), and Mr. Wall, were conducted separately by

the same investigator at the CSO in March 2000 and in each case, Tunapuna was regarded as the most

representative area of Trinidad with respect to gender, ethnicity, and socio-economic status.

46

participate, of which 152 (84%) were eligible. All eligible subjects completed the study.

Eligibility of individuals who initially refused participation is not known.

2.2.3 Analyses

2.2.3.1 Nutrient Analyses

Nutrient analyses of the data collected from the FFQ’s and 7-day Food Records

(FR) were based on the USDA database using Nutriput (computer program developed by

Dr. Thomas Wolever). Additional values were added from the Caribbean Food and

Nutrition Institutes’ (CFNI) analysis of the recipes from a popular local cookbook (Indar

et al., 1988) and other local food tables (CFNI, Food Composition Tables 2"? ed., 1998).

Novel recipes collected from the FRs were analyzed and also entered into Nutriput’s

database (appendix F). Results from the FFQ were coded using lotus 123 for Windows

spreadsheets, and a spreadsheet was made for each subject. Lotus script was used to write

macros that combined the coded results from the FFQ with the amounts of macronutrient

for 100g of each food item listed on the questionnaire (and additional items from the

Additional Seasonal Fruits and Vegetables” section), and the amount of each item in

grams according to portion size. Based on the reported frequency of use, portion size and

grams consumed per day of each food item, the average amounts of macronutrient

consumed by each individual were determined. For each subject, modifications for the

type of dairy product, sweetener, flour and fat used were made by compiling a list of all

possible answers for questions pertaining to those products (such as; skimmed milk,

whole milk, whole wheat bread, white bread, lard, shortening and butter) and the specific

items specified by each subject were fed into a corresponding spreadsheet for coding

47

using lotus script. Modifications were also made for methods of food preparation. A list

of 7 meat and poultry items, and 6 cooking methods’’ was provided at the end of the FFQ

to better assess the amounts and type of fats consumed by the subjects. An additional 5 g

of fat was factored into g/day calculation of fat if pan-frying was selected in the

“Cooking Methods” section of FFQ. If deep-frying was selected, an additional 10 g of fat

was factored into the g/day calculation of fat. The type of fat used for this calculation was

determined from the proportion of different types of fats used by a particular subject as

indicated in the “Fats & Oils” section of the FFQ. This process was repeated for each

item that was indicated as being either pan or deep-fried in the “Cooking Methods”

section of the FFQ.

2.2.3.2 Statistical Analysis

In order to validate the FFQ, the nutrient analysis of the data collected from the

FFQ, was compared with the nutrient analysis of the data collected from the 7-day food

records. Means, & standard deviations, medians & inter-quartile ranges, the Shapiro-Wilk

statistic and kurtosis & skewness values were calculated for the nutrients estimated by the

FFQ, and 7-day food records using SAS version 8.1. Normality of the data was initially

assessed by the Shapiro-Wilk statistic. If a distribution was found to be significantly

different from normal, indices of kurtosis, frequency distributions and scatter-plots were

looked at to assess the shape of data and skewness was looked at to determine if the data

were positively or negatively skewed. Kurtosis values greater than or less than 3

indicated that distributions are not normal. Kurtosis of a normal distribution is 3,

"' The 6 categories for cooking methods were collapsed into 4. Namely deep-fried, pan-fried, baked/grilled, & curried/stewed.

48

distributions that exceed that value are more peaked, and those below are flatter than a

normal distribution (Zar, 1984 p 82-83). A distribution was considered positively skewed

if the skewness value was greater than 0, and negatively skewed if less than 0. Skewness

of a perfectly symmetric distribution is 0 (Zar, 1984 p 93-95). Macronutrient distributions

that were not normally distributed were log transformed if positively skewed, and

inverted if negatively skewed. To compare macronutrient means from the FR data with

FFQ data within different gender and ethnic groups paired t-tests were done using

transformed and untransformed data. Because untransformed and transformed data were

essentially identical (except for alcohol and % alcohol, which were the only measures

that showed extreme positive skewness), analysis using untransformed data is reported

for all variables. For alcohol and % alcohol data the removal of two outliers improved the

normality of both distributions and analysis is reported with those outliers omitted. One-

way ANOVA’s with Duncan’s multiple range testing were used to compare

macronutrient means between ethnic groups and to compare nutrient intake between

individuals that completed the FFQ prior to or after completing the FR’s, stratified by

gender. Correlation coefficients were used to assess agreement between the two

instruments. Pearson correlation coefficients using untransformed as well as transformed

data, and Spearman rank correlation coefficients were calculated and compared. Pearson

correlation coefficients using untransformed data (two outliers removed from alcohol and

% alcohol data) are reported. Fisher Z-tests were conducted to compare correlation .

coefficients between males and females, AF’s and SA’s, and the FFQ 1" group and FFQ

2" group. All statistical procedures were done using SAS version 8.1 for Windows.

3. RESULTS

3.1 Characteristics of study participants

3.1.1 Demographics of participants

Two hundred and fifty-four persons were approached for recruitment in the study.

Six individuals were excluded due to age greater than 65 years and/or health status and

sixty-eight individuals choose not to participate because of lack of time and/or interest

and in two cases extreme intoxication. The majority of individuals that rejected

participation were African males, Mixed males and African females. One hundred and

eighty subjects (180) in total were recruited for participation in the study (71% response

rate). Of these, twenty-eight were excluded from analysis for incomplete food records or

failure to return them. A total of 152 subjects completed the study and provided sufficient

data for analysis'”. Fig. 3-1 shows a breakdown of the recruitment process for this study.

The study sample was composed of 92 females (60%), of which 29 (32%) were African

(AF), 48 (52%) were South Asian (SA), and 15 (16%) were of other backgrounds

(OTH)"*, and 60 men (40%), of which, 20 (33%) were AF, 30 (50%) were SA, and 10

(17%) were OTH. No statistically significant differences at the 5 % level were found

between males and females with respect to the ethnic breakdown of each group [appendix

G (G.1)]. Ethnicity, gender and age of all subjects are shown in table 3-1.

" Response rates by gender and ethnic group cannot be reported since demographic information was not taken for non-participants.

® Individuals from Mixed and Other backgrounds were pooled and included in the Other (OTH) category.

49

50

Occupation'* and highest level of completed education (CEL)’* served as

indicators of socio-economic status. No statistically significant differences at the 5 %

level were found between ethnic groups for CEL’® or occupation’ [appendix G (G.2,

G.3)]. Table 3-2(a) shows counts and percentages for CEL and occupation for the male

and female study participants exclusively. For both males and females, secondary

education was reported most often as the highest level of education completed. There

were no significant statistical differences observed between males and females with

respect to CEL [appendix G (G.4)]'*. However, it is interesting to note qualitatively that a

higher percentage of males had a tertiary education as their highest level of completed

education compared to females. Chi- square tests showed a significant difference between

males and females with respect to occupation (p<0.05) [appendix G (G.5)]. More males

identified themselves as professionals than did females, (27% and 4 % respectively),

although when the “professional” category was omitted from analysis, the difference

between males and females regarding occupation was still statistically significant at the 1

% level [appendix G (G.5.1)]. More females reported being housewives or stay-at-home

mothers (45%) compared to males that reported being househusbands or stay-at-home

fathers (3%). The difference between males and females regarding occupation was also

statistically significant when the category “housewife/househusband was omitted from

analyses but at the 5 % level rather than 1% level [appendix G (G.5.2)].

' Categories are divided into whether individuals are employed for money or not employed for money. The

former includes: unskilled labor, skilled labor, clerical work, professional work, and the latter includes

housewife/husband and other. Occupation category “other” individuals voluntarily employed by a

church/temple or mosque, including nuns, priests, sanyasins and monks; and unemployed. If retired, nature

of previous job dictated how occupation was assessed.

'S Categories include: primary, secondary and tertiary.

'® Chi square test computed with CEL category “other” omitted.

'’ Chi square test was computed with ethnic group “other” omitted.

'8 Chi-square test was computed with CEL category “other” omitted.

51

Among females, no statistically significant differences between ethnic groups

were observed for CEL” [appendix G (G.6)]. It was not possible to discern differences

between ethnic groups for CEL among males since expected counts generated by chi-

square tests were below 5, irrespective of how groups were pooled therefore results from

the chi-square analysis are not valid in this case. For occupation, no statistically

significant differences between ethnic groups were found among males or females”

[appendix G (G.7) (G.7.1)]. It is possible that differences did not reach statistical

significance because of small sample size. Despite that fact, observational differences in

CEL and occupation were shown between ethnic groups for both men and women.

Among the females, a higher proportion of SA’s had a tertiary education than AF’s and

OTH’s. It was also observed that more SA females had “other” occupations than did

females from OTH backgrounds. Among the males, more SA’s also had a tertiary

education than AF’s and OTH’s. With respect to occupation, more SA males were

employed in professional and skilled labor and fewer in unskilled labor compared to AF’s

or OTH’s [Tables 3-2 (b) and (c)].

" For females, chi-square test was computed with ethnic group “other” omitted.

°° For males and females, chi square tests were computed with “other” ethnic group omitted, occupation

categories “housewife/husband” and “other” combined, “unskilled” and “skilled” combined, and

professional and clerical combined.

52

Figure 3-1

Breakdown of recruitment and enrolment process for FFQ validation study.

254 individuals approached.

68 lack of time, interest or intoxicated

186 individuals recruited (71% response rate).

28 individuals excluded due to incomplete 7-day Food Records

6 excluded due to age/health status

152 individuals included in the study (60 % of approached).

53

Table 3-1 Gender, ethnicity and age of all subjects (counts and percentages)

| AF SA OTH Total

Males (%) 20 (13) 30(20) —-:10(7) 60 (40)

Females (%) | 29 (19) 48 (31) —-15 (10) 92 (60)

Mean age + SD | 39 + 13.0 37+13.8 43+ 13.0 39 + 13.3

n=152

Table 3-2 (a)

Completed Education Level and Occupation

for Males and Females (counts and percentages).

54

Males (%) Females (“%)

n=60 n=92

Completed Education Level

Tertiary 14 (23) 15 (16)

Secondary/Technical/Vocational 35 (58) 57 (62)

Primary/Elementary 9 (15) 20 (22)

None 0 0

Other 2 (3) 0

*Occupation

a p Professional 15 (25) 4 (4)

Clerical 12 (20) 16 (17)

Skilled Worker 17 (28) 11 (12)

| ss Unnskilled Worker 5 (8) 4 (4)

b [ Housewife/husband 2 (3) 41 (45)

Other 9 (15) 16 (17) *p<.0001 for difference between males and females with respect to occupation. See appendix G for chi-square tests. Note this difference is attenuated when housewife/husband category is removed.

“Employed for money.

> Not employed for money.

55

Table 3-2 (b) Completed Education Level and Occupation for AF’s, SA’s and

OTH?’s for Female’s (n=92) (counts and percentages).

AF (%) SA (%) OTH (%) n=29 n=48 n=15


Tertiary 4 (14) 10 (21) 1(7)

Secondary/Technical/Vocational | 20 (69) 25 (52) 12 (80)

Primary/Elementary 5 (17) 13 (27) 2 (13)

None 0 0 0

Other 0 0 0

Occupation

a pS Professional 1 (3) 3 (6) 0

Clerical 5 (17) 8 (17) 3 (20)

Skilled Worker 4 (14) 5 (10) 2 (13)

| Ss Unsskilled Worker 3 (10) 0 1 (7)

b | Housewife/husband 12 (41) 21 (44) 8 (53)

| Other 4 (14) 11 (23) 1 (7)


Not employed for money.

Table 3-2 (c)

Completed Education Level and Occupation for AF’s, SA’s and OTH’s for Male’s (n=60) (counts and percentages).

AF (%) SA (%) OTH (%) n=20 n=30 n=10


Tertiary 4 (20) 8 (27) 2 (20)

Secondary/Technical/Vocational 10 (50) 18 (60) 7 (70)

Primary/Elementary 4 (20) 4 (13) 1 (10)

None 0 0 0

Other 2 (10) 0 0

Occupation

a _ Professional 4 (20) 7 (23) 4 (40)

Clerical 5 (25) 6 (20) 1 (10)

Skilled Worker 4 (20) 11 (37) 2 (20)

| ___- Unskilled Worker 3 (15) 0 2 (20)

b [ Housewife/husband 1 (5) 1() 0

| Other 3 (15) 5 (17) 1 (10)


Not employed for money

56

57

3.1.2 Comparison with National Statistics

Figure 3-2 (a) compares the gender and ethnicity of study participants with data

from Tunapuna an area deemed representative of Trinidad with respect to ethnicity,

gender, and socio-economic status (SES) by the Central Statistics Office (CSO) in Port of

Spain, Trinidad. Although differences between study participants and reference data did

not reach statistical significance, most likely due to small sample size, the study

participants included more females then expected from the reference data, and fewer

AF’s and OTH’s and more SA’s than expected [appendix G (G.8) (G.9)].

Comparison of the study participants and the reference data from Tunapuna for

highest level of education completed (CEL) showed a statistically significant difference

between groups”’ (p<0.001) [appendix G (G.10) (G.10.1)] Figure 3-2 (b) compares study

participants and the reference data for CEL. Study participants were shown to include a

higher proportion of individuals having completed a tertiary education (19%) than the

reference data (6%). The greatest difference in CEL between the study participants and

reference data was seen in the percentage with tertiary education.

*! Chi-square tests were computed with CEL categories “primary”, “none” and “other” combined and again

with CEL categories “none” and “other” omitted.

58

Figure 3-2 (a)

Study data in relation to National Statistics: Gender and Ethnicity

70 5

60 +

50 - O Study Sample a 40 +

ercent

= 30 | m@ Reference data from Tunapuna

20 +

10 -

Values

in

M F AF SA Oth

Gender and Ethnicity

Figure 3-2 (b)

Study data in relation to National Statistics: Completed Education

Level Values

in pe

rcen

t

=>

NO

w

-

Oo oO

~

oO

o

Oo oO

oO

Oo Qo

2

! |

| !

! L

\

Primary Secondary Tertiary None/Other


59

O Study Sample

@ Tunapuna

60

3.2 Normality of data

Analyses of frequency distributions and measures of kurtosis, skewness and the

Shapiro-Wilk statistic revealed that most nutrients estimated from both the FR and FFQ

datasets were skewed and required log transformation or inversion prior to assessing

agreement in nutrient intake between the FFQ and FR’s (appendix H). Most distributions

had a statistically significant (p S$ 0.05) Shapiro-Wilk score, which indicated that most

data differed significantly from normality. The majority of these distributions were found

to have skewness values greater than zero indicating positive skewness. Negative

skewness was indicated by skewness values less than zero. In order to bring in the tail of

a positively skewed distribution the natural logarithm of each value was taken. To adjust

for negative skewness, the reciprocal of each value was taken, thereby bringing in the tail

on the left side of the distribution. Kurtosis values varied considerably between nutrient

distributions, some were greater than 3, indicating a peaked distribution and others were

less than 3 indicating flatness. It is interesting to note that kurtosis values from the FFQ

nutrient intake data are greater than those from the FR nutrient intake data for all

variables and that skewness values from the FFQ data are greater than those from the FR

for all variables except MFA. Frequency distributions for total fat intake from the FFQ

and FR data are shown in figure 3-3 (a) as an example of positively skewed data that

required log transformation”. Figure 3-3 (b) shows frequency distributions for diet GI

from the FFQ and FR datasets as an example of data, which was negatively skewed and

” Data was transformed as an exercise before analysis. Since transformed and untransformed data resulted

in similar results when paired t-tests, Pearson correlation coefficients and ANOVA’s were computed,

untransformed data is being reported for all variables. For alcohol and % alcohol data, 2 outliers were removed for analysis.

61

required inversion”’. Figure 3-3 (c), shows frequency distributions for polyunsaturated fat

expressed as a percentage of total energy (% PUFA)” from FR and FFQ data as an

example of normally distributed data that did not require transformation. Frequency

distributions for other nutrients for both FR and FFQ data are shown in (appendix J).

> Data was transformed as an exercise before analysis. Since transformed and untransformed data resulted

im similar results when paired t-tests, Pearson correlation coefficients and ANOVA’s were computed,

untransformed data is being reported for all variables. For alcohol and % alcohol data two outliers were

removed for analysis

*4 % PUFA is the only measure where both the FR and FFQ distributions were normal and did not require transformation.

Figure 3-3 (a)

Frequency Distributions for fat from 7-day Food Record and FFQ

data: Example of positively skewed data that required log

transformation.

—*-FR

—@— FFQ

Frequency

0 EAH

123 4 5 6 7 8 9 10 11 12

i

Fat

Intake ranges divided into 12 equal intervals”

62

Median values of intervals from intake

ranges for fat from FR’s and FFQ’s

Intervals of | g/day fat g/day fat

intake FR FFQ

ranges

1 16.2 22.9

2 29.1 40.6

3 42.1 58.2

4 55.1 75.9

5 68.1 93.5

6 81.0 111.2

7 93.9 128.8

8 106.9 146.5

9 119.8 164.2

10 132.8 181.9

11 145.8 199.5

12 158.7 217.1

?° Number of intervals determined automatically by Microsoft excel @freq function, Windows 2000.

63

Figure 3-3 (b) Frequency Distributions for GI from 7-day Food Record and FFQ

data: Example of negatively skewed data that required inversion. Frequency

No

on

i

0 T T T Tr T 1

123 45 67 8 9 101112 Dietary GI

6 Intake ranges divided into 12 equal ranges 2


ranges for dietary GI from FR’s and FFQ’s

Intervals of GI GI

intake FR FFQ

ranges

1 67.0 66.4

2 68.8 68.5

3 70.7 70.7

4 72.5 72.8

5 74.3 75.0

6 76.2 771

7 78.0 719.3

8 79.8 81.4

9 81.7 83.6 10 83.5 85.7

11 85.3 87.9

12 87.2 90.0

°6 Number of intervals determined automatically by Microsoft excel @freq function, Windows 2000.

64

Figure 3-3(c) Frequency Distributions for PUFA expressed as a percentage of total

energy from 7-day Food Record and FFQ data: Example of data that did not require transformation.

35 —e—FR 30 —#—FFQ

>, 25 o

5 20 3

3 19

10 5

0 T T T T T T T T T T TT

12 3 4 5 6 7 8 9 10 11 12

% PUFA

Intake ranges divided into 12 equal ranges ’


ranges for dietary GI from FR’s and FFQ’s

Intervals of %PUFA %PUFA

intake FR FFQ

ranges

1 3.1 2.5

2 3.9 3.3

3 4.7 42

4 5.5 5.0 5 6.3 5.9

6 71 6.7

7 7.9 7.6

8 8.7 8.4

9 9.6 9.3

10 10.3 10.1

11 11.8 11.0

12 12.0 11.8

*7 Number of intervals determined automatically by Microsoft excel @freq function, Windows 2000.

65

3.3 Nutrient Intake

3.3.1 All study participants

Means and standard deviations, and medians and inter-quartile ranges”*, for the 7-

day food record (FR) and FFQ macronutrient data are reported for the study sample in

tables 3-3 (a) and 3-3 (b). Average nutrient intake estimated by the FFQ was higher than

estimated by the FR’s for all macronutrients except cholesterol and GI [table 3-3 (a)].

With respect to data expressed as a percentage of total energy, means estimated by the

FFQ were higher for % carbohydrate, fiber g/100kcal”’, % MUFA and % alcohol, and

lower for % protein, % fat, cholesterol mg/ 1000kcal*°, and the P: S compared to means

estimated by the FR’s [table 3-3 (b)]. Paired t-tests showed that differences between

means estimated by the FFQ’s and FR’s were statistically significant at the 5 % level for

all variables except cholesterol (p=0.58), GI (p=0.14), %SFA (p=0.36), and % PUFA

(p=0.47) [tables 3-3 (a) and (b)].

Generally, if a mean is found to be larger than a corresponding median, the

distribution is most likely skewed to the right (Pagano, 1993 p. 42). For most

distributions, the mean was slightly larger than the median; however, the difference was

under 10% for most measures. Interestingly, macronutrients expressed as a percentage of

total energy exhibited different mean to median differences when compared to data

expressed in grams [tables 3-3 (a) and (b)].

The distance between the 25" and 75" percentiles of data, or the interquartile

range (IQR), is a measure of spread that gives the range covered by the middle half of the

*8 Interquartile range for each measure is represented by the first and third quartiles of the distribution. ” Fiber does not provide energy. Throughout this thesis g/fiber per 1000 kcal is reported in tables with % values of other macronutrients. .

*° Cholesterol does not provide energy. Throughout this thesis mg/cholesterol per 1000 kcal is reported in tables with % values of other macronutrients.

66

data (Moore, 1999, p. 46). IQR’s for FR and FFQ data were similar in magnitude for

most macronutrients and macronutrients expressed as a percentage of total energy [tables

3-3 (a) and (b)].

3.3.2 Males and Females

Among men (n=60), average nutrient intake estimated by the FFQ was greater

than estimated by the FR’s for all macronutrients except GI, and cholesterol [table 3-4

(a)]. For macronutrients expressed as a percentage of total energy, average nutrient intake

estimated by the FR’s was greater than estimated by the FFQ for % fat, cholesterol

mg/1000kcal, and the P:S ratio, and average nutrient intake estimated by the FFQ was

greater than estimated by the FR’s for fiber g/1000kcal, % MFA and % alcohol [table 3-

4 (b)]. Differences in means between the FR and FFQ were statistically significant at or

near the 5 % level for all variables except for cholesterol (p=0.78), alcohol (p=0.85), GI

(p=0.22), % protein (p=0.14), % carbohydrate (p=0.10), % SFA (p=0.53), and %PUFA

(p=0.94).

For women (n=92), average nutrient intake estimated by the FFQ was greater

than estimated by the FR’s for all nutrients except cholesterol and GI [table 3-5 (a)]. For

macronutrients expressed as a percentage of total energy, estimates of % protein, % fat,

cholesterol mg/1000kcal, and the P:S ratio from FR data were greater than estimated by

FFQ data, and estimates of % carbohydrate, fiber g/1000kcal, % MFA and % alcohol

from FFQ data were greater than estimated by FR data [table 3-5 9b)]. Statistically

significant differences at the 5 % level among the females for average nutrient intake

estimated by FR’s and FFQ’s were shown for all variables except for cholesterol

67

(p=0.25), GI (p=0.33), % SFA (p=0.97), and % PUFA (p=0.68). For both males and

females, means were slightly greater than corresponding medians and IQR values were

slightly higher for FFQ data compared to FR data for most measures. Macronutrients

expressed as a percentage of total energy exhibited smaller mean to median differences

than data expressed in grams for both males and females.

3.3.3 African’s and South Asian’s

With respect to the AF group, average nutrient intake estimated by the FFQ was

greater than estimated by the FR’s for all variables except cholesterol, SFA, alcohol and

GI [table 3-6 (a)]. For PUFA, nutrient intake estimated by the FFQ was slightly greater

than estimated by the FR’s, however this difference is marginal since the p-value is very

near to the 5 % level of significance. For macronutrients expressed as a percentage of

total energy, estimates of average nutrient intake for cholesterol mg/1000kcal, and the P:

S ratio from the FR’s were greater than estimated by FFQ’s. Average nutrient intake

estimated by the FFQ’s for fiber g/1000kcal and % MFA, were greater than estimated by

the FR’s [table 3-6 (b)]. Differences between means from FR and FFQ data reached

statistical significance at or near the 5 % level for all variables except for cholesterol

(p=0.68), SFA (p=0.17), PUFA (p=0.06), alcohol (p=0.12), GI (p=0.99), % protein

(p=0.58), % fat (p=0.39), % carbohydrate (p=0.36), % SFA (p=0.64), % PUFA (p=0.19),

and % alcohol (p=0.08) [tables 3-6 (a) and 3-6 (b)]. For most measures means were

slightly greater than associated median and IQR values from the FFQ data were greater

than FR data all measures except cholesterol, PUFA and GI. For macronutrients

expressed as a percentage of total energy, IQR values from FFQ data were greater than

68

IQR values from FR data for all measures except cholesterol, SFA, PUFA, and the P: S

ratio [tables 3-6 (a) and 3-6 (b)].

Among the SA’s, average nutrient intake estimated by the FFQ was greater than

estimated by the FR’s for all variables except cholesterol, and PUFA, and lower for GI

[table 3-7 (a)]. For macronutrients expressed as a percentage of total energy, estimates of

average nutrient intake for “protein, Yofat, cholesterol mg/1000kcal, %SFA, %PUFA and

the P:S ratio from FR data were greater than estimated by the FFQ. Average nutrient

intake estimated by the FFQ for % carbohydrate, fiber g/1000kcal, % MUFA and %

alcohol were greater than estimated by the FR’s [table 3-7 (b)]. Statistically significant

differences at or near the 5 % level were found for all variables except for cholesterol

(p=0.81), and PUFA (p=0.10), [tables 3-7 (a) and 3-7 (b)]. Similar to the AF’s, the SA’s

had estimates of mean nutrient intake that were greater than the corresponding median for

nearly all measures. IQR values from FFQ tended to be greater than FR data for measures

expressed in grams. For macronutrients expressed as a percentage of total energy, IQR

values from FR data are greater than values from FFQ data. Similar to the male’s and

female’s, both the AF’s and SA’s had smaller mean to median differences for

macronutrients expressed as a percentage of total energy compared to data expressed in

grams for both FR and FFQ data sets.

ANOVA’s with Duncan’s multiple range testing were conducted for SA males,

SA females, AF males and AF females in order to compare average macronutrient intake

between groups [tables 3-8 (a), (b), (c), and (d)]. For the FR data, statistically significant

differences at or near the 5 % level between groups were found for protein, fat, total

carbohydrate, available carbohydrate, energy (kcal), SFA, MFA, %MFA, %PUFA, and

69

the P:S. These differences as shown by Duncan’s multiple range testing did not appear to

follow a trend and were quite varied among different variables [tables 3-8 (a) and (b)].

Although for absolute data SA females were shown to have a lower intake of total energy

and most nutrients compared to SA males, AF females and AF males, and AF males had

a higher intake of alcohol compared to the other groups although this difference is not

statistically significant.

For the FFQ data, statistically significant differences at or near the 5 % level

between groups were found for SFA, %SFA, and the P: S ratio. For SFA, the mean for

SA males was found to be higher than the mean for SA females, but did not differ from

AF females or AF males. SFA intake for AF males, SA females, and AF females did not

differ from one another. For % SFA, no difference in gender was found between the SA’s

and AF’s, however, SA males were found to have a lower intake than AF males and AF

females, and AF males were found to have a higher intake than SA males and SA

females. The differences between groups for P: S show that SA males and females have

a higher P: S ratio than AF males and females, although the P: S ratio of SA males does

not differ from that of AF males and females [tables 3-8 (c) and (d)]. Statistically

significant differences at or near the 10 % level were found for total carbohydrate,

available carbohydrate, total energy (kcal), % protein, and % fat. SA females were found

to have lower intakes of total and available carbohydrate and energy compared to SA

males but did not differ from AF males and females. For % protein, AF males had a

higher intake than SA males, but neither differed from SA or AF females. For % fat, SA

males and females had lower intakes compared to AF females, but did not differ from AF

males.

70

3.3.4 FFQ administered before or after completing FR’s.

For the group that was given the FFQ prior to completing 7-day food records

(FFQ 1"), average nutrient intake estimated by the FFQ was greater than estimated by the

FR’s for all measures except cholesterol, and GI [(table 3-9 (a)]. With respect to percent

data, no consistent differences were observed between means estimated by the FFQ and

FR’s [table 3-9 (b)]. Statistically significant differences at or near the 5 % level between

FR and FFQ data were found for all variables except cholesterol (p=0.92), GI (p=0.35),

% protein (p=0.26), % SFA (p=0.48), and %PUFA (p=0.62) (p=0.21) [tables 3-9 (a) and

(b)]. For both FR and FFQ data sets measured in grams, means were slightly greater than

medians for essentially all macronutrients. For data expressed as a percentage of total

energy, no consistent differences were observed between means and medians for both FR

and FFQ data sets. IQR values for data in grams from the FFQ data set were greater than

IQR values from the FR data set for all measures. For data expressed as a percentage of

total energy, IQR values did not differ consistently between FFQ and FR data.

For the sample that was given the FFQ after having completed 7-day food

records (FFQ*"’), average nutrient intake estimated by the FFQ was greater than

estimated by the FR’s for all measures except cholesterol, and GI [table 3-10 (a)].

However, for macronutrients expressed as a percentage of total energy, like the FFQ’

group, there were no consistent differences and means were greater than estimated by the

FR’s for some measures, and smaller for others [table 3-10 (b). Statistically significant

differences at or near the 5 % level between macronutrient means from FR and FFQ data

were found for all measures except cholesterol (p=0.46), GI (p=0.25), %SFA (p=0.85),

“PUFA (p=0.28) and % alcohol (p= (p=0.07) [tables 3-10 (a) and (b)]. For both FR and

71

FFQ distributions means were generally greater than medians for data expressed in grams

and greater for some measures and smaller for others for data expressed as a percentage

of total energy. IQR values from FFQ data were slightly greater than IQR values from FR

data for data expressed in grams. For nutrients expressed as a percentage of total energy,

IQR values from FFQ data were larger than from FR data for some measures and smaller

for others [tables 3-10 (a) and (b)]. Both the FFQ 1 and FFQ 2"! groups were found to

have smaller mean to median differences for macronutrients expressed as a percentage of

total energy compared to data expressed in grams.

ANOVA’s with Duncan’s multiple range tests were conducted between males and

females who completed the FFQ before completing the FR’s (FFQ 1* group), and those

who completed the FFQ after completing the FR’s (FFQ Qn group) [tables 3-11 (a), (b),

(c) and (d)]. For the FR data [tables 3-11 (a) and (b)], statistically significant differences

at or near the 5 % level between groups were found for cholesterol mg/1000kcal

(p=0.01). For cholesterol mg/1000kcal, intake estimated from the FFQ1* group was

significantly greater then when estimated from the FFQ?™ group for males but not for

females. Statistically significant differences at or near the 10 % level were found for

protein (p=0.11), total carbohydrate (p=0.09), available carbohydrate (p=0.09), total

energy (p=0.09) and % alcohol (p=0.15). For protein, females from the FFQ 2™ group

were found to have a lower intake than males from the FFQ 1* group, but did not differ

significantly from any other group. For total and available carbohydrate and total energy,

females from the FFQ 2" group had lower intakes than males from the FFQ 2™ group,

but did not differ significantly from any other group. For % alcohol, intake of males from

72

the FFQ Qn group was higher than intake of females from the FFQ 1" group, but did not

differ from any other group [tables 3-11 (a) and (b)].

With respect to the FFQ data [tables 3-11 (c) and (d)], statistically significant

differences at or near the 5 % level between groups were found for protein (p=0.06),

cholesterol (p=0.02), % protein (p=0.04), and cholesterol mg/1000kcal (0.05). For

protein, males from the FFQ 1" group had a higher intake than females from the FFQ 1*

and FFQ 2" groups, but did not differ from males from the FFQ Qn group. Duncan’s

grouping showed that the cholesterol intake of males from the FFQ 1“ group was

significantly higher than all other groups. For % protein and cholesterol mg/1000kcal,

males from the FFQ’"? group had lower intakes than males from the FFQ 1* group, and

intake of females did not differ from each other or from the male groups [tables 3-11 (c)

and (d)]. It is interesting to note that the diet GI values of females from the FFQ 1“ and

FEQ 2" groups estimated from the FFQ [table 3-11 (c)] are identical. Statistically

significant differences at or near the 10 % level were found for SFA (p=0.16), alcohol

(p=0.10), % fat (p=0.18), % carbohydrate (p=0.12), and % alcohol (p=0.08). For SFA,

females from the FFQ 1“ group had a significantly lower intake than males from the FFQ

1* group, but neither differed from any other group. With respect to alcohol, females

from the FFQ 2™ group had a significantly lower intake than males from the FFQ 1

group, but neither differed from any other group. For % fat, males from the FFQ 1* group

had a higher intake than males from the FFQ 2" group, but neither differed significantly

from females from either group. For % carbohydrate, males from the FFQ 2™ group had

a higher intake than males from the FFQ 1* group, but again neither differed significantly

from females from either group. For % alcohol, males from the FFQ 1“ group had a

73

significantly higher intake than females from both the FFQ 1" and FFQ 2™ groups, but

did not differ from males from the FFQ and group.

74

Table 3-3 (a)


macronutrients from FR and FFQ data: All subjects (n=152).

Mean FR Mean FFQ Median FR Median FFQ “p-value

(+SD) (+SD) (IQR) (IQR)

Pro (g) 78 101 74 96 <0.01 (23) (38.0) (61 , 93) (77 , 122)

Fat (g) 71.9 87 69 81 <0.01

(28) (40) (53 , 85) (60 , 108)

Tearb (g) 230.5 342 219 321 <0.01 (81) (138) (176 , 271) (259 , 404)

AvCarb (g) 214.7 309 203 288 <0.01 (76.5) (125) (161 , 252) (230 , 359)

Energy (Kcal) 1820 2432 1778 2338 <0.01

(590) (946) (1378 , 2166) (1831 , 2837)

Fiber (g) 15.8 33 16 31 <0.01

(5.6) (14.6) (12 , 19) (25 , 40)

Chol (g) 256.7 251 244 234 0.6

(115) (121.0) (174 , 310) (170 , 314)

SFA (g) 24.6 28 23 26 <0.01 (10) (13) (18, 31) (19, 35)

MEA (g) 23.9 30 22 28 <0.01 (10.4) (14) (16, 30) (21 , 39)

PUFA (g) 16.8 19 17 17 <0.01

(7) (10) (12 , 22) (12 , 23)

"Ale (g) 0.2 0.77 0.2 0 <0.01 (0.9) (1.4) (0 , 0) (0,1)

GI 81.0 80.4 81.0 80.7 0.14 (4) (3.7) (79 , 83) (79 , 82)

"Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically

significance if p < 0.05. All data is untransformed.

‘ For t-tests two outliers were removed from alcohol data.

75

Table 3-3 (b)



All subjects (n=152).

Mean Mean FFQ Median FR Median FFQ “p-value

FR (+SD) (IQR) (IQR)

(+SD)

Pro% 18 17 17 17 <0.01

(3.6) (2.8) (15 , 20) (15 , 19)

Fat% 34 32 35 33 <0.01

(5.3) (5.7) (30, 38) (29 , 36)

Carb% 48 51 48 50 <0.01

(5.7) (7.3) (45 , 52) (46 , 55)

Fiber 9.2 13 9 13 <0.01

g/1000kcal (2.7) (4) (7.5 , 10) (11, 16)

Chol 145 105 144 108 <0.01

meg/1000kceal (55) (34) (111, 169) (86 , 125)

SFA% 12 11 12 11 0.36

(2.9) (3) (10, 14) (10, 13)

MFA% 11 12 11 12 <0.01

(2.3) (3) (9.6 , 12.7) (11.6, 14)

PUFA% 8 77 77 75 0.47

(2) (2) (6.5 , 9.3) (6.3 , 9.0)

P:S 0.9 0.7 0.2 0.7 <0.01

(0.4) (0.2) (0.6 , 1.0) (0.6 , 0.8)

TAle% 0.1 0.2 0 0 <0.01 (0.3) (0.4) (0 , 0) (0, 0)

" Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically significance if p < 0.05. All data is untransformed.

* For paired t-tests 2 outliers were removed from % alcohol data.

Table 3-4 (a)


macronutrients from FR and FFQ data: Males (n=60).

76

Mean FR Mean FFQ Median FR Median FFQ “p-value (+SD) (£SD) (IQR) (IQR)

Pro (g) 83 110 86 106 <0.01

(24) (46) (66 , 98) (78 , 128)

Fat (g) 76 93 75 90 <0.01

(30) (43) (54 , 92) (59 , 117)

Tearb (g) 247 372 253 351 <0.01 (66) (147) (197 , 289) (279 , 428)

AvCarb (g) 230 334 233 322 <0.01 (63) (141) (183 , 272 (250 , 391)

Energy (Kcal) 1944 2767 1935 2716 <0.01

(555) (1089) (1510 , 2327) (1866 , 3102)

Fiber (g) 16.3 34.5 16 31 <0.01

(5.2) (15) (13 , 20) (24 , 44)

Chol (g) 273 279 267 278 0.78 (126) (147) (185 , 344) (162 , 348)

SFA (g) 26.3 30 25.5 29 0.03

(11.2) (15) (18 , 33) (20 , 38)

MFA (g) 25.6 32.4 25 34 <0.01

(11.3) (15) (17, 32) (22 , 42)

PUFA (g) 17.2 20 17 17 0.03

(7.7) (11.4) (12 , 22) (12 , 26)

‘Ale (g) 0.4 1.1 0 0.4 0.01 (1.4) (1.7) (0, 0) (0, 1.6)

GI 81.6 80.9 81.0 81.5 0.22 (3.6) (3.4) (79 , 84) (79 , 83)

” Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically


' For paired t-tests 2 outliers were removed from alcohol data.

77

Table 3-4 (b) Means & Standard deviations, and Medians, & Interquartile ranges for


Males (n=60).


FR (SD) (IQR) (IQR) (+SD)

Pro™% 18 17 17 17 0.14

(3.8) (4) (15, 19) (15, 19)

Fat% 34 32 35 32 0.05

(5.9) (7) (29 , 38) (28 36)

Carb% 48 51 49 50 0.10

(5.4) (9.8) (44 , 54) (46 , 56)

Fiber 8.8 14 8.6 12.5 <0.01

g/1000kcal (2.4) (5.5) (7.3 , 10.7) (11, 15)

Chol 146 108 140 111 <0.01

mg/1000keal | (66) (41) (107 , 166) (84, 136)

SFA% 12 11.6 12 12 0.53 (3.3) (3) (10, 14) (10, 14)

MFA% 11 12 11.4 13 <0.01 (2.5) (3) (9.5, 12.6) (11, 14)

PUFA% 75 7.6 75 7A 0.95

(2.2) (2.4) (6.3 , 9.1) (6.3 , 8.6)

P:S 0.8 0.7 0.8 0.7 <0.01

(0.4) (0.2) (0.6, 1.0) (0.5 , 0.8)

FAIc% 0.2 0.3 0 0.10 0.05 (0.5) (0.7) (0, 0) (0, 0.5)


' For paired t-tests 2 outliers were removed from % alcohol data.

Table 3-5 (a)


macronutrients from FR and FFQ data: Females (n=92).

78


(SD) (+SD) (IQR) (QR)

Pro (g) 75 95 71 95 <0.01 (22) (31) (60 , 87) (71 , 112)

Fat (g) 70 83 66 80 <0.01

(27) (37) (53 , 83) (62 , 98)

Tcarb (g) 220 322 200 307 <0.01 (87) (128) (167, 264) (255 , 365)

AvCarb (g) 205 290 183 279 <0.01 (83) (115) (153 , 246) (229 , 334)

Energy (Kcal) 1751 2297 1669 2189 <0.01

(600) (848) (1330 , 2025) (1761 , 2689)

Fiber (g) 15.5 32 15 30.5 <0.01 (6) (14.5) (11.8, 18.6) (23 , 39)

Chol (g) 248 234 240 224 0.25

(105) (97) (173 , 295) (171 , 292)

SFA (g) 23 26 22 25 <0.01

(9.7) (11.8) (17.8 , 27.3) (18.7 , 32.5)

MFA (g) 230 29 22 28 <0.01 (9.8) (13) (16 , 28) (20 , 35)

PUFA (g) 16.6 19 15 16.8 0.02 (7) (9.7) (11.6, 20.7) (12.4 , 22.8)

Alc (g) 0.09 0.6 0 0 <0.01 (0.3) (1.1) (0, 0) (0 , 0.6)

GI 80.6 80.1 81.0 80.5 0.33 (4.1) (3.9) (78 , 83) (79 , 82)

* Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically


79



Females (n=92).



(+SD)

Pro% 18 17 17 17 <0.01 (3.5) (2.7) (16, 20) (15, 19)

Fat% 34 32 34 33 <0.01 (4.9) (5.6) (30 , 38) (30, 36)

Carb% 48 50 48 50 <0.01 (5.9) (6.4) (45 , 52) (46.5 , 54.6)

Fiber 9.5 14 9 13 <0.01

¢/1000kcal (2.8) (4) (7.6 , 10.9) (11, 16)

Chol 145 105 148 108 <0.01

mg/1000kcal (48) (33) (112 , 172) (87 , 121)

SFA% 12 11.6 11.3 11.3 0.97

(2.6) (3.0) (9.8 , 13) (9.7, 13.5)

MFA% 11 12.3 11 12.4 <0.01

(2) (2.7) (9.7 , 12.6) (10.9 , 14.2)

PUFA% 8 8 7.8 7.9 0.68

(2) (2) (6.6 , 9.4) (6.4, 9.3)

P:S 0.9 0.7 0.8 0.7 <0.01 (0.3) (0.2) (0.7 , 1.0) (0.6 , 0.8)

Ale% 0.02 0.2 0 0 <0.01

(0.08) (0.3) (0, 0) (0 , 0.2)


80

Table 3-6 (a) Means & Standard deviations, and Medians, & Interquartile ranges for

macronutrients from FR and FFQ data: AF group (n=49).

Mean FR Mean FFQ Median FR Median FFQ “p-value (+SD) (+SD) (IQR) (IQR)

Pro (g) 82 103 80 101 <0.01 (24) (32) (67 , 94) (82 , 122)

Fat (g) 77 90 74 82 <0.01

(24) (31) (60 , 86) (68 , 108)

Tcarb (g) 247 336 239 314 <0.01 (90) (141) (188 , 281) (250 390)

AvCarb (g) 230 299 227 278 <0.01 (86) (127) (176, 265) (223 , 335)

Energy (Kcal) 1948 2424 1916 2334 <0.01

(589) (848) (1534 , 2164) (1916 , 2747)

Fiber (g) 16.9 33.4 16 31 <0.01

(6.2) (16) (13 , 20) (27 , 37)

Chol (g) 267 260 244 249 0.68

(129) (92.5) (180 , 305) (203 , 324)

SFA (g) 27 30 26 28 0.17

(9.5) (9.8) (21 , 32) (23 , 36)

MEA (g) 26 31 25 29 <0.01 (8.9) (11) (19 , 32) (23 , 40)

PUFA (g) 16.7 18.8 15 17 0.06 (7) (8.4) (12 , 21) (13 , 22)

‘Ale (g) 0.3 0.6 0 0 0.12 (1.1) (0.9) (0, 0) (0, 1.1)

GI 81 81 82 81 0.99 (4.2) (3.4) (79 , 83) (80 , 83)

" Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically


' For paired t-tests one outlier was removed from alcohol data.

81



AF group (n=49).

Mean MeanFFQ MedianFR Median FFQ ‘p-value FR (+SD) (IQR) (IQR)

(+SD)

" Pro% 17.5 17.3 17 17 0.58 (3.5) (2.5) (15, 19) (15, 19)

Fat% 34 33.4 34 33 0.39 (4.4) (5.1) (31, 36) (30 , 37)

Carb% A8 49 49 48 0.36 (5.9) (6.3) (46 , 51) (46 , 52)

Fiber 9 13.6 8.9 13 <0.01 g/1000keal (2.2) (3.4) (7.6 , 10.6) (11, 15)

Chol 139 110 140 117 <0.01 mg/1000kceal | (51) (33) (111, 158) (90 , 133)

SFA% 12 12.4 12 12.6 0.64 (2.7) (2.4) (11, 14) (11, 14)

MFA% 11 13 11.6 13.4 <0.01 (1.9) (2.6) (10, 13) (10.8 , 15)

PUFA% 7.2 7.7 7 TA 0.19 (1.9) (1.9) (6,9) (6.7 , 8.3)

P:S 0.7 0.6 0.7 0.6 0.01 (0.3) (0.2) (0.5 , 0.9) (0.5 , 0.7)

TAle% 0.1 0.2 0 0 0.08 (0.3) (0.3) (0,0) (0, 0.3)

"Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically


* For paired t-tests one outlier was removed from % alcohol data.

Table 3-7 (a)


macronutrients from FR and FFQ data: SA group (n=78).

82

Mean FR Mean FFQ Median FR Median FFQ “p-value (+SD) (+SD) (IQR) (IQR)

Pro (g) 74 101 71 94 <0.01

(21) (43) (58 , 92) (74 , 122)

Fat (g) 69 84 64 78 <0.01

(26) (42) (49 , 85) (53 , 104)

Tcarb (g) 221 342 208 318 <0.01 (69) (137) (169 , 264) (269 , 413)

AvCarb (g) 206 309 193 285 <0.01 (66) (126) (158 , 248) (233 , 373)

Energy (Kcal) 1743 2398 1701 2241 <0.01 (521) (1004) (1296 , 2071) (1752 , 2743)

Fiber (g) 15 33 15 31 <0.01

(4.8) (14) (11.5 , 17.5) (25 , 40)

Chol (g) 247 250 242 229 0.81 (97) (142) (178 , 298) (152 , 310)

SFA (g) 23 26 22 24 0.02

(9.4) (15) (16.4 , 27.8) (16.3 , 33.6)

MFA (g) 22 29 22 27 <0.01

(10) (15) (14, 28) (19.2 , 37.4)

PUFA (g) 17 19 17 17.7 0.10 (7.2) (+10.6) (12, 21) (10.8 , 23.4)

Alc (g) 0.2 0.8 0 0 <0.01 (0.9) (1.5) (0, 0) (0 , 0.9)

GI 81 80 81 80 0.03

(3.8) (3.9) (79 , 83) (78 , 81)



83



SA group (n=78).

Mean Mean FFQ Median FR Median FFQ "p-value


(+SD)

Pro% 17.8 16.8 17.5 17.2 0.01

(3.5) (3.4) (17 , 20) (15.3 , 19.2)

Fat% 34 31 34 32 <0.01 (5.5) (5.8) (30 , 39) (28 , 35)

Carb% 48 52 48 51 <0.01

(6) (7) (43 , 53) (47 , 55)

Fiber 9.3 14.3 8.5 13 <0.01 ¢/1000kcal (3) (4.8) (7,11) (11,17)

Chol 149 104 148 106 <0.01

meg/1000kcal (61) (37) (109 , 172) (86 , 122)

SFA% 11.5 10.7 11 11 <0.01 (2.9) (2.8) (9, 13) (9.3 , 12.6)

MFA% 11 11.8 11 12 <0.01 (2.4) (2.7) (9 , 13) (10.6 , 13.7)

PUFA% 8.4 7.8 8.2 7.8 0.04 (1.9) (1.9) (7 , 9.5) (6.3 , 9.4)

P:S 0.9 0.76 0.9 0.75 <0.01 (0.3) (0.2) (0.7 , 1.2) (0.6 , 0.9)

Alce% 0.1 0.24 0 0 <0.01 (0.4) (0.44) (0, 0) (0 , 0.2)



Table 3-8 (a)

84


FR data.

SA Males AF Males SAFemales AF Females ‘p-value

n= 30 n= 19 n= 48 n= 29

Pro (g) *99.4 * 93.2 569.4 92.4 0.02

Fat (g) "78.2 *78 611 *78 <0.01

TCarb (g) 9241 *260 208 ab 939 0.05

AvCarb (g) #>925 243 >194 a> 922 0.05

Energy (Kcal) *1937 *1992 >1621 *1918 0.01

Fiber (g) 15.9 16.9 14 16.9 0.09

Chol (g) 275 272 229 264 0.23

SFA (g) 4595.8 997.4 21 *25.8 0.01

MFA (g) *26.5 494.8 °20 *26.5 <0.01

PUFA (g) 18.8 16.5 16 16.8 0.42

TAle (g) 0.42 0.59 0.12 0.05 0.15

GI 81.9 81 80.6 80.8 0.54

" Differences are statistically significance if p < 0.05. Means with the same letter are not significantly

different. All data is untransformed.

t For AF males, one outlier was removed from alcohol data.

Table 3-8 (b) Anova with Duncan’s Multiple Range test for differences between SA’s and AF’s:

FR data, (macronutrients expressed as a percentage of total energy).

SA AF SA Females AF Females “p-value

Males Males n= 48 n= 29

n= 30 n= 19

Pro% 17.7 17 17.8 17.8 0.87

Fat% 35 32.7 33.3 35.2 0.16

Carb% 47 50 48.9 47 0.22

Fiber g/1000kcal 8.6 9 9.7 9.2 0.40

Chol mg/1000keal | 154 137 145 141 0.74

SFA% 11.8 12 11.2 12.3 0.39

MFA% *>11.6 °10.6 °10.4 *11.8 0.02

PUFA% *33 °F 78.4 ar74 0.02

P:S 250.92 -°0.77 20.97 0.75 0.02

TAle% 0.16 0.15 0.03 0.01 0.22

” Differences are statistically significance if p < 0.05. Means with the same letter are not significantly different. All data is untransformed.

* For AF males, one outlier was removed from % alcohol data.

Table 3-8 (c)

Anova with Duncan’s Multiple Range test for differences between SA’s and AF’s: FFQ data.

SA Males AF Males SA Females AF Females ‘p-value n= 30 n= 20 n= 48 n= 29

Pro (g) 113 109 93 99 0.12

Fat (g) 97 89 75.5 90 0.09

TCarb (g) #393 #> 339 °310 26334 0.08

AvyCarb (g) *357 4> 999 >279 5300 0.06

Energy (Keal) | *2759 > 9441 2173 #>9412 0.06

Fiber (g) 36.2 33 31 31 0.48

Chol (g) 289.8 278 225 247 0.12

SFA (g) #31 a> 39 593.5 ab 39 0.05

MFA (g) 33.6 31 26.3 31 0.12

PUFA (g) 22 18.4 17.4 19 0.24

Alc (g) 0.98 1 0.69 0.36 0.19

GI 80.7 80.6 79.4 81.1 0.21

” Differences are statistically significance if p < 0.05. Means with the same letter are not significantly


87

Table 3-8 (d) Anova with Duncan’s Multiple Range test for differences between SA’s and AF’s:

FFQ data, (macronutrients expressed as a percentage of total energy).

SA AF SA Females AF Females “p-value

Males Males n= 48 n= 29

n=30 n= 20

Pro% 16 *18 *>17.4 *>16.8 0.07

Fat% 31 ab33 531 934 0.06

Carb% 53 49 51.6 49 0.12

Fiber g/1000kcal 14 13.6 14.7 13.6 0.65

Chol mg/1000kcal | 103 115 105 107 0.64

SFA% °10.8 712.3 10.6 12.5 <0.01

MFA% 11.8 12.8 11.9 12.9 0.22

PUFA% 17 7.4 7.8 7.9 0.80

P:S #5073 -°0.63 *0.78 0.64 <0.01

Ale% 0.28 0.31 0.21 0.10 0.21



Table 3-9 (a)


macronutrients from FR and FFQ data: FFQ First group (n=75).

88

Mean FR Mean FFQ Median FR Median FFQ “Dp value

(+SD) (SD) (IQR) (IQR)

Pro (g) 80 105 80 98 <0.01 (22) (42) (64 , 96) (87 , 122)

Fat (g) 75 89 69 82 <0.01

(27) (40) (56 , 93) (66 , 107)

Tcarb (g) 235 350 227 318 <0.01 (87) (153) (164 , 284) (277 , 400)

AvCarb (g) 219 312 209 286 <0.01 (83) (143) (159 , 264) (244 , 358)

Energy (Keal) 1870 2477 1824 2443 <0.01 (603) (1032) (1377 , 2210) (1923 , 2740) :

Fiber (g) 16 34 16 31 <0.01

(6) (16) (11.7 , 20) (26 , 39)

Chol (g) 266 264 244 248 0.92 (110) (128) (198 , 319) (199 , 322)

SFA (g) 25.5 28.7 24 28 0.04

(10.3) (13.8) (18.4, 32) (20 , 36)

MFA (g) 25 30.6 22 28 <0.01 (9.5) (13.8) (17.4 , 32) (22.4 , 37.4)

PUFA (g) 17 19.7 15 17 0.02 (7.4) (10.3) (12 , 20) (12 , 23)

Alc (g) 0.11 0.86 0 0 <0.01

(0.67) (1.6) (0, 0) (0 , 0.9)

GI 80.8 80.2 81 80.7 0.35 (4) (3.8) (79 , 83) (79 , 82)

* Paired t-test conducted between FR and F FQ data for macronutrient means. Differences are statistically significance if p < 0.05. All data is untransformed.

89

Table 3-9 (b)



FFQ First group (n=75).


FR (+SD) (IQR) (IQR) (+SD)

Pro% 18 17 17 17.2 0.27

(3.4) (3.2) (15, 20) (15, 19)

Fat% 34 33 34 33 0.02

(5) (6.5) (30 , 38) (29 , 35)

Carb% 48 50 48 49 0.06

(5.4) (8.9) (45 , 51) (47 , 54)

Fiber 90 14 8.7 12.6 <0.01

¢/1000kcal (2.5) (5.6) (7.3 , 10.4) (11, 16)

Chol 148 111 141 115 <0.01

mg/1000kceal (60) (42) (110, 169) (86 , 131)

SFA% 12 11.6 11.7 11.4 0.48

(3) (3) (10, 13.4) (9.9 , 14)

MFA% 11 12.5 11.6 12.5 <0.01

(2) (3) (10,13) (10.8 , 14)

PUFA% 7.8 8 7.5 7.6 0.62

(2) (2.2) (6.5 , 9.2) (6.4 , 9.3)

P:S 0.8 0.7 0.7 0.7 <0.01 (0.3) (0.2) (0.6 , 1.0) (0.6 , 0.8)

Ale% 0.04 0.30 0 0 <0.01 (0.25) (0.7) (0, 0) (0 , 0.3)



Table 3-10 (a) Means & Standard deviations, and Medians, & Interquartile ranges for

macronutrients from FR and FFQ data: FFQ Second group (n =77).

90


(+SD) (+SD) (IQR) (IQR)

Pro (g) 76 97 74 92 <0.01

(24) (35) (60, 88) (69 , 122)

Fat (g) 69 85 68 80 <0.01 (29) (39) (45 , 81) (54, 110)

Tearb (g) 227 335 214 327 <0.01 (74) (122) (178 , 267) (246 , 421)

AvCarb (g) 211 305 201 294 <0.01 (70) (115) (164 , 251) (223 , 385)

Energy (Kcal) 1776 2386 1730 2205 <0.01

(573) (887) (1408 , 2071) (1716 , 3023)

Fiber (g) 15.5 32 15 31 <0.01 (5) (13) (12, 18) (22 , 42)

Chol (g) 249 238 240 223 0.46 (119) (112) (170 , 298) (152 , 313)

SFA (g) 23.5 27 22 15 0.06 (10.4) (12.5) (18 , 27) (18.5 , 35)

MEA (g) 23 30 22 28 <0.01

(11.2) (14) (15 , 28) (19 , 40)

PUFA (g) 16.6 19 15 17 0.04

(7.1) (11) (11, 22) (12 , 23)

FAle (g) 0.3 0.7 0 0 0.03 (0.1) (0.1) (0 , 0.6) (0, 1.1)

GI 81.2 80.6 81 80.8 0.25

(4.0) (3.7) (78 , 84) (79 , 82)

* Paired t-test conducted between FR and FFQ data for macronutrient means. Differences are statistically significance if p < 0.05. All data is untransformed.

* For paired t-tests two outliers were removed from alcohol data.

91

Table 3-10 (b)


macronutrients expressed as a percentage of total energy from FR and FFQ data: FFQ Second group (n=77).

Mean Mean FFQ Median FR Median FFQ "p -value

FR (+SD) (IQR) (IQR) (+SD)

Pro% 17.7 16.5 17.4 16.7 <0.01 (3.8) (3.4) (15.6, 19.3) (14.9 , 18.7)

Fat% 33.5 31 34 31 <0.01

(5.6) (6) (29 , 37) (28 , 36)

Carb% 49 52 49 51 <0.01

(6) (7) (45 , 52) (46 , 56)

Fiber 9.3 14 9 13 <0.01

¢/1000kceal (2.8) (3.5) (7.6, 9) (11, 16)

Chol 143 101 147 104 <0.01

mg/1000kcal | (50.4) (31) (112 , 172) (86 , 121)

SFA% 11.5 11.4 11.5 11.3 0.85

(2.8) (3) (9.3 , 13.8) (9.7 , 13.2)

MFA% 10.9 12 11 12.3 <0.01 (2.6) (2.8) (9,11) (10.4, 14)

PUFA% 8 7.6 7.8 74 0.28 (2.1) (2.2) (6.4 , 9.3) (6.3 , 8.8)

P:S 0.9 0.70 0.8 0.68 <0.01

(0.3) (0.21) (0.7 , 1.0) (0.5 , 0.8)

TAle% 0.1 0.2 0 0 0.07 (0.4) (0.3) (0,0) (0 , 0.3)


significance if p < 0.05. All data is untransformed. ' For paired t-tests two outliers were removed from % alcohol data.

Table 3-11 (a)

Anova with Duncan’s Multiple Range test for differences between FFQ First and

FFQ Second groups: FR data.

FFQ 1" FFQ2™ FFQ1" FFQ2™ p- Males Males Females Females value

n=28 n= 31 n=47 n=45

Pro (g) *g44 874 977.8 °72 0.11

Fat (g) 75 75.6 74.3 65 0.25

TCarb (g) ab 937 2253 a> 934 >208 0.10

AvCarb (g) >920 9937 #>918 >193 0.09

Energy (Keal) | 7°1896 71960 = *°' 1855, 1646 ~=—(0.09

Fiber (g) 16.3 16.2 16 15 0.72

Chol (g) 299 246 246 250 0.19

SFA (g) 26 26.4 25 21.5 0.12

MFA (g) 25.8 25 24 21.5 0.30

PUFA (g) 16.5 17.6 17.4 15.8 0.66

TAle (g) 0.22 0.47 0.04 0.19 0.23

GI 81.2 81.9 80.6 80.7 0.43



* For males from the FFQ 2™ group, one outlier was removed from alcohol data.

Table 3-11 (b) Anova with Duncan’s Multiple Range test for differences between FFQ First and FFQ Second groups: FR data, (macronutrients expressed as a percentage of total

energy).

FFQ 1% FFQ2™ FFQ1* FFQ2™ ‘p-value Males Males Females Females

n=28 n= 31 n=47 n=45

Pro% 18.4 17 17.6 18.2 0.42

Fat% 34.2 33.2 34.5 33.7 0.73

Carb% 47.3 49.4 47.9 48 0.49

Fiber g/1000kcal 9 8.7 9.2 9.8 0.32

Chol mg/1000kcal *169 °126 5136 ab 155 <0.01

SFA% 11.9 11.9 11.9 11.2 0.66

MFA% 11.6 10.7 11.2 11 0.48

PUFA% 7.4 7.6 8 8.2 0.42

P:S 0.81 0.88 0.84 0.95 0.37

TAle% 9.08 *0.17 0.01 “0.04 0.15


different. All data is untransformed. ' For males from the FFQ 2™ group, one outlier was removed from % alcohol data.

Table 3-11 (c) Anova with Duncan’s Multiple Range test for differences between FFQ First and

FFQ Second groups: FFQ data.

FFQ1* FFQ2™ FFQ1* FFQ2™ “p-value Males Males Females Females

n=28 n= 31 n=47 n=45

Pro (g) “118 = #1024 97.2 °o4 0.06

Fat (g) 99 86 83 83.7 0.33

TCarb (g) 378 367 332 311 0.14

AvCarb (g) 334 339.6 299 280 0.15

Energy (Kceal) 2709 2572 2338 2254 0.17

Fiber (g) 35 34.2 33.2 31.2 0.70

Chol (g) #315 243 5934 235.2 0.02

SFA (g) 233 9097.6 °26 abo7 0.16

MFA (g) 34 30.5 29 28.8 0.44

PUFA (g) 22 18.4 18.3 19.2 0.49

TAle (g) #12 aby 2b 0.6 50.5 0.10

GI 80.5 81.3 80 80 0.45



t For males from the FFQ 2™¢ group, one outlier was removed from alcohol data.

95

Table 3-11 (d) Anova with Duncan’s Multiple Range test for differences between FFQ First and

FFQ Second groups: FFQ data, (macronutrients expressed as a percentage of total

energy).

FFQ 1“ FFQ2™ FFQ1" FFQ2™ “p-value Males Males Females Females

n=28 n= 31 n=47 n=45

Pro% “18 15.6 abi7 apy7 0.04

Fat% *33.2 29.9 20392 4532.4 0.18

Carb% > 48.3 253.2 #506 °°50.3 0.13

Fiber g/1000kcal 14.2 13.5 14.2 13.9 0.91

Chol mg/1000keal 7121 >94.9 #>104 #>105 0.05

SFA% 12.2 10.8 11.3 11.8 0.32

MFA% 12.6 11.6 12.4 12.3 0.57

PUFA% 8.1 7 7.9 8.1 0.17

P:S 0.69 0.67 0.73 0.72 0.60

TAIce% * 0.46 *® 0.30 © 0.19 0.15 0.08



* For males from the FFQ 2™ group, one outlier was removed from alcohol data.

96

3.4 Association between FR and FFQ

In general, the Pearson method for assessing correlation using untransformed data

yielded slightly stronger correlations between FR’s and the FFQ than the Spearman rank

method for nonparametric data, and the Pearson method using transformed data

(appendix J). Because the differences between these methods of assessment were so

marginal, Pearson correlation coefficients using untransformed data were used for all

measures for evaluating agreement between the FR’s and FFQ’s. For alcohol and %

alcohol data 2 outliers were omitted from analysis”. Correlation coefficients varied from

0.11 for alcohol to 0.51 for fat and MFA and can be seen in [table 3-12 (a)]. Fats

[(fat=0.51), (SFA=0.47), (MFA=0.51), (PUFA=0.44)] and (Kcal=0.43) tended to

correlate better than carbohydrate [(Tcarb=0.35), (AvCarb=0.36)], fiber (0.25) and GI

(0.25). Correlation coefficients for macronutrients expressed as a percentage of total

energy were weaker with respect to fats (“% fat, %SFA, %MFA, %PUFA) compared to

gram data, but were stronger for protein, carbohydrate and fiber [table 3-12 (b)].

Correlations between the FR’s and FFQ for average nutrient intake and % nutrient intake

were statistically significant at the 5 % level for all variables.

For males* and females [tables 3-13 (a) and (b)], SA’s and AF’s™ [tables 3-14 (a)

and (b)], and FFQ 1‘ and FFQ 2"* groups [tables 3-15 (a) and (b)] fats also tended to

*? Pearson correlation coefficients using untransformed data with no outliers removed, using transformed data and Spearman rank correlation coefficients can be seen in appendix Q.

>> For males, 2 outliers were omitted from alcohol and % alcohol data. See appendix Q for correlation

coefficients for males and females using untransformed data with no outliers removed, transformed data

and Spearman rank correlations.

* For AF’s, 1 outlier was omitted from alcohol and % alcohol data. See appendix Q for correlation

coefficients for males and females using untransformed data with no outliers removed, transformed data

and Spearman rank correlations.

°° For the FFQ 2™ group, 2 outliers were removed from alcohol and % alcohol data. See appendix Q for

correlation coefficients for males and females using untransformed data with no outliers removed,

transformed data and Spearman rank correlations

97

correlate better than carbohydrates and GI, showed the weakest association between FR’s

and FFQ’s. As seen for all subjects combined, each group had weaker correlation

coefficients for % fats compared to fats measured in grams and stronger correlation

coefficients for % carbohydrate and fiber g/1000kcal compared to gram values. Males

had stronger correlations than females for total and available carbohydrate, energy,

cholesterol, MFA, cholesterol mg/1000kcal, % PUFA and the P: S ratio, though none was

statistically significant. Females had stronger correlations than males for protein, fat,

fiber, SFA, PUFA, alcohol, GI, % protein, % fat, % carbohydrate, and % alcohol though

none was statistically significant except for % protein [tables 3-13 (a) and (b)].

SA’s had stronger correlation coefficients than AF’s for every measure except

PUFA, % protein, cholesterol mg/1000kcal, and % PUFA. Fisher Z-tests showed

statistically significant differences at or near the 5 % level between SA and AF

correlation coefficients for SFA (p=0.02), fiber g/1000kcal (p=0.05) and % SFA

(p<0.01), [tables 3-14 (a) and (b)].

Correlation coefficients from subjects who completed the FFQ after the FR’s

(FFQ 2" group) were stronger than those who completed the FFQ before the FR’s (FFQ

1* group) for all measures except alcohol, fiber g/1000kcal, cholesterol mg/1000kcal, %

SFA, and % alcohol. Fisher Z-tests showed statistically significant differences between

FFQ 1“ and FFQ 2" correlation coefficients for % fat, % carbohydrate, and % MFA,

[tables 3-15 (a) and (b)].

Examples of how agreement between FR’s and FFQ’s can be demonstrated

schematically are shown by scatter plots for fat and GI in figures 3-4 (a) and (b)

respectively. Both relationships are positively associated and roughly linear, although the

plot for fat shows a stronger linear relationship and less scatter. GI has a flatter slope

than fat indicating a somewhat weaker association. Both plots have a small number of

positive and negative outliers. Scatter-plots for other macronutrient distributions can be

seen in (appendix K).

98

99

Table 3-12 (a)

Pearson correlation coefficients for

FR vs. FFQ data:


Correlation “p-value

coefficient

Pro (g) 0.44 <0.01

Fat (g) 0.51 <0.01

TCarb (g) 0.35 <0.01

AvCarb (g) 0.36 <0.01

Energy (Kcal) 0.43 <0.01

Fiber (g) 0.25 <0.01

Chol (g) 0.42 <0.01

SFA (g) 0.47 <0.01

MFA (g) 0.51 <0.01

PUFA (g) 0.44 <0.01

"Ale (g) 0.11 0.19

GI 0.25 <0.01

* Correlation coefficients are statistically significant if p < 0.05. All data is untransformed.

* Two outliers removed from alcohol data, see appendix R for untransformed data without outlier removed.

100

Table 3-12 (b) Pearson correlation coefficients for

FR vs. FFQ data for macronutrients expressed as a percentage of total energy:


Correlation “p-value

coefficient

% Pro 0.46 <0.01

% Fat 0.48 <0.01

% Carb 0.45 <0.01

Fiber g/1000kcal 0.34 <0.01

Chol mg/1000keal 0.37 <0.01

% SFA 0.39 <0.01

% MFA 0.38 <0.01

% PUFA 0.25 <0.01

P:S 0.30 <0.01

'% Ale 0.13 0.10

” Correlation coefficients are statistically significant if p < 0.05. All data is untransformed.

* Two outliers removed from % alcohol data, see appendix R for untransformed data without outlier removed.

101

Table 3-13 (a) Pearson correlation coefficients (r-value) for FR vs. FFQ data and Fisher Z-test for

differences between males (n=60) and females (n=92)

Male “p Female “p ' Fisher Z-test

r-values r-values p

Pro (g) 0.43 <0.01 0.44 <0.01 0.94

Fat (g) 0.50 <0.01 0.51 <0.01 0.94

TCarb (g) 0.37 <0.01 0.32 <0.01 0.74

AvCarb (g) 0.41 <0.01 0.33 <0.01 0.58

Energy (Keal) 0.45 <0.01 0.41 <0.01 0.77

Fiber (g) 0.25 0.06 0.26 <0.01 0.95

Chol (g) 0.47 <0.01 0.37 <0.01 0.47

SFA (g) 0.42 <0.01 0.51 <0.01 0.50

MFA (g) 0.55 <0.01 0.45 <0.01 0.43

PUFA (g) 0.41 | <0.01 0.46 <0.01 0.72

FAle (g) 0.08 0.57 0.15 0.15 0.86

GI 0.10 0.45 0.25 0.01 0.36

" Correlation coefficients for FR vs. FFQ macronutrient data are statistically significant if p < 0.05. All data is untransformed.

* Correlation coefficients between males and females for macronutrient data are significantly different from one another if p < 0.05

* For males two outliers were removed from alcohol data.

Table 3-13 (b)

102

Pearson correlation coefficients (r-value) for FR vs. FFQ for macronutrients

expressed as a percentage of total energy and Fisher Z-test for differences between

males (n=60) and females (n=92)

Male . p Female . p ' Fisher Z-test r-values r-values p

% Pro 0.19 0.14 0.59 <0.01 <0.01

% Fat 0.39 <0.01 0.45 <0.01 0.67

% Carb 0.31 0.02 0.42 <0.01 0.45

Fiber g/1000kcal 0.29 0.03 0.29 <0.01 1

Chol mg/1000kceal 0.40 <0.01 0.38 <0.01 0.89

% SFA 0.42 <0.01 0.37 <0.01 0.73

% MFA 0.42 <0.01 0.32 <0.01 0.50

% PUFA 0.15 0.25 0.14 0.1885 0.95

P:S 0.34 <0.01 0.27 <0.01 0.65

*% Ale 0.07 0.61 0.10 0.35 0.98

" Correlation coefficients for FR vs. FFQ macronutrient data are statistically significant if p < 0.05. All data

is untransformed.

* Correlation coefficients between males and females for macronutrient data are significantly different from one another if p < 0.05

? For males two outliers were removed from % alcohol data.

Table 3-14 (a)

103


differences between AF’s (n=49) and SA’s (n=78).

AF p SA p ' Fisher Z-test

r-values r-values p

Pro (g) 0.36 <0.01 0.48 <0.01 0.43

Fat (g) 0.39 <0.01 0.49 <0.01 0.51

TCarb (g) 0.26 0.07 0.44 <0.01 0.38

AvCarb (g) 0.27 0.06 0.44 <0.01 0.38

Energy (Kcal) 0.33 0.02 0.47 <0.01 0.37

Fiber (g) 0.12 0.42 0.39 <0.01 0.12

Chol (g) 0.39 <0.01 0.49 <0.01 0.51

SFA (g) 0.16 0.28 0.54 <0.01 0.02

MFA (g) 0.36 0.01 0.51 <0.01 0.32

PUFA (g) 0.52 <0.01 0.34 <0.01 0.23

*Ale (g) 0.03 0.84 0.19 0.1010 0.39

GI 0.06 0.70 0.30 <0.01 0.18

" Correlation coefficients for FR vs. FFQ macronutrient data are statistically significant if p <0.05. All

data is untransformed.

* Correlation coefficients between males and females for macronutrient data are significantly different from

one another if p < 0.05.

* For AF’s one outlier was removed from alcohol data.

104

Table 3-14 (b)

Pearson correlation coefficients (r-value) for FR vs. FFQ for macronutrients expressed as a percentage of total energy and Fisher Z-test for differences between AF’s (n=49) and SA’s (n=78).

AF “p SA "p ' Fisher Z-test T- r-values p

values

% Pro 0.50 <0.01 0.50 <0.01 1

% Fat 0.28 0.05 0.51 <0.01 0.13

% Carb 0.32 0.03 0.55 <0.01 0.12

Fiber g/1000kcal 0.12 0.40 0.45 <0.01 0.05

Chol mg/1000keal | 0.52 <0.01 0.35 <0.01 0.26

% SFA 0.12 0.47 0.64 <0.01 <0.01

% MFA 0.19 0.20 0.41 <0.01 0.19

% PUFA 0.23 0.11 0.12 0.29 0.54

P:S 0.21 0.15 0.39 <0.01 0.29

'% Ale 0.12 0.4 0.17 0.14 0.79


* Correlation coefficients between males and females for macronutrient data are significantly different from one another if p < 0.05.

* For AF’s one outlier was removed from % alcohol data.

105

Table 3-15 (a)


differences between FFQ First (n=75) and FFQ Second (n=77) groups.

FFQ 1" "p FFQ 2" “p ' Fisher Z-test r-values r-values p

Pro (g) 0.35 <0.01 0.55 <0.01 0.13

Fat (g) 0.49 <0.01 0.54 <0.01 0.68

TCarb (g) 0.29 0.01 0.45 <0.01 0.26

AvCarb (g) 0.30 <0.01 0.47 <0.01 0.22

Energy (Keal) | 0.39 <0.01 0.50 <0.01 0.40

Fiber (g) 0.25 0.03 0.27 <0.01 0.90

Chol (g) 0.38 <0.01 0.46 <0.01 0.55

SFA (g) 0.46 <0.01 0.51 <0.01 0.69

MEA (g) 0.49 <0.01 0.54 <0.01 0.68

PUFA (g) 0.43 <0.01 0.46 <0.01 0.82

FAle (g) 0.21 0.08 0.05 0.65 0.33

GI 0.13 0.25 0.35 <0.01 0.15

" Correlation coefficients for FR vs. FFQ macronutrient data are statistically significant if p < 0.05. All data is untransformed. * Correlation coefficients between males and females for macronutrient data are significantly different from one another if p < 0.05.

* For the FFQ 2™ group, 2 outliers were removed from alcohol data.

106

Table 3-15 (b)

Pearson correlation coefficients (r-value) for FR vs. FFQ for macronutrients

expressed as a percentage of total energy and Fisher Z-test for differences between FFQ First (n=75) and FFQ Second (n=77) and groups.

FR First “ _ FFQ First “p * Fisher Z-test r-values p r-values p

% Pro 0.48 <0.01 0.26 0.02 0.12

% Fat 0.55 <0.01 0.29 0.01 0.05

% Carb 0.51 <0.01 0.20 0.08 0.03

Fiber g/1000kcal 0.28 0.01 0.33 <0.01 0.74

Chol mg/1000kcal 0.37. <0.01 0.38 <0.01 0.94

% SFA 0.34 <0.01 0.46 <0.01 0.39

% MFA 0.54 <0.01 0.17 0.15 <0.01

% PUFA 0.27 0.02 0.03 0.79 0.13

P:S 0.23 0.04 0.39 <0.01 0.28

*% Ale 0.06 0.63 0.13 0.2583 0.67


* Correlation coefficients between males and females for macronutrient data are significantly different from one another if p < 0.05.

? For FFQ 2" group, 2 outliers were removed from % alcohol data.

107

Figure 3-4 (a)

FFQ versus 7-day Food Record: Fat (grams)

250 5

200 5

150 +

FFQ

100 -

50 +

0 50 100 150 200

108

Figure 3-4(b)

FFQ versus 7-day Food Record: GI

95 -

Sd 90 -

85 -

80 + FFQ

75 +

70 - °

o¢ 65 T T T T T 1

65 70 75 80 85 90 95

4. DISCUSSION AND CONCLUSIONS

4.1 Discussion

In this study, a nine-page, 149-item interviewer-administered food frequency

questionnaire (FFQ) was developed to assess dietary intake with respect to diet glycemic

index (GI) in healthy adults from various ethnic groups in Trinidad. The FFQ was

administered once and was calibrated against one series of 7-day food records (FR),

which were completed either one-day prior to or following the FFQ administration. The

primary criterion for validity for the FFQ was Pearson correlation coefficients for

macronutrients and macronutrients expressed as a percentage of total energy between the

FFQ and FR’s greater than 0.5 (p<0.05) (Willett, 1998 p. 120, 132; Dwyer, 1900; Cade et

at., 2002; Thompson & Byers, 1994). The secondary criterion for validity for the FFQ

was that means of macronutrient and percent macronutrient should not differ when

assessed from FFQ and FR by more than 5 percent of the time in a particular distribution

(Moore & McCabe, 1999, p.458).

4.1.1 All participants

The results from this study show statistically significant correlations (p<0.05)

between nutrient intakes derived from the FFQ and FR’s for all variables examined. The

agreement was strongest for fat and monounsaturated fat (MFA) intake (r=0.51) and

weakest for alcohol (r=0.11). The distributions for alcohol and alcohol expressed as a

percentage of total energy both showed extreme positive skewness, therefore two

outliers were omitted from alcohol and % alcohol data from all analyses. The Pearson

correlation coefficient for GI, (fiber and % PUFA) is 0.25, which is the weakest

109

110

correlation between FR’s and the FFQ, after alcohol and % alcohol despite the fact that

the FFQ was designed to assess diet GI in particular. A rather interesting and novel

finding shown in this study is that although agreement between the FR’s and FFQ is so

poor for GI as indicated by the weak correlation coefficient, the mean value for GI does

not differ between FR’s and the FFQ. Estimates of GI therefore do not meet the primary

criterion for validity but do meet the second criterion. It is difficult to understand why

this may be happening. It is possible that variability in the GI measure itself and how it

varies between food items is responsible for the poor correlation. It is also possible that

the poor correlation for GI may be a result of carbohydrates with different GI values

being pooled together on the FFQ compared to carbohydrates (with different GI’s) that

are actually consumed and indicated in the FR’s hence allowing actual GI values to be

captured in the FR’s.

Generally, estimates of fats from the FFQ and FR’s tended to correlate better than

carbohydrate for both absolute and percent data, which is consistent with findings of

other FFQ validation studies using a variety of calibration criterion such as FR’s, 24-hour

dietary recalls, or biochemical indicators (Mayer-Davis et al., 1999; MacIntyre et al.,

2001, Rimm et al., 1992; Block et al., 1990; Margetts et al., 1989; Jain et al., 1996). In

fact correlation coefficients for fat and MFA were the only variables that satisfied the

primary criterion for validity. In contrast, some FFQ validation studies have found that

carbohydrate correlate better than fats (Block et al., 1992; Hodge et al., 200; DeCarli et

al., 1996; Shaefer et al., 2000). The performance of various nutrients in FFQ validation

studies is most likely a function of many factors including the FFQ design, nature of the

food list, method of administration, time of year of administration, choice of calibration

111

criterion and demographics of the population being surveyed and therefore cannot be

generalized. For example, Willett et al., in 1985 found that total carbohydrate estimated

from a FFQ correlated better than total fat when correlations were done using the third

and fourth sets of diet records, however when correlations were done using the first and

second sets of diet records total fat correlated better than total carbohydrate. It is

interesting to note that in the present study, the correlation coefficients for %

carbohydrate and fiber g/1000kcal, although weaker than correlations for % fats are both

stronger than correlations for absolute carbohydrate and fiber intake respectively.

It is possible that agreement between the FFQ and FR’s was weaker than

expected, particularly for carbohydrate (and fiber and GI) because the food list on the

FFQ did not adequately represent the types of foods consumed in the population,

particularly those that comprise the carbohydrate component of the diet. In addition, more

carbohydrate- rich foods may have been reported in the FR’s compared to the foods listed

on the FFQ and as a result valuable nutrient information may have been lost when items

from the FR’s were collapsed when being coded so as to be comparable to corresponding

items on the FFQ (Willett, 1998, p. 103). It is also possible that the order in which foods

that are a large source of carbohydrate were listed on the FFQ, did not capture

carbohydrate intake effectively. The FFQ was organized according to the 6 official food

groups of the English-speaking Caribbean. Perhaps this is unrealistic for people, since

they likely do not think of their dietary intake with respect to food groups. It may be more

effective to group food items according to how they are commonly consumed, such as

rice and peas together rather than rice appearing in the “cereals” section and peas in the

“legumes & nuts” section.

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The FFQ was designed to assess average dietary intake over the past year, and

because only one set of 7-day food records were completed, the items reported in the

diaries do not reflect average dietary intake over the same reference period as the

questionnaire, which could result in poor agreement between the two methods. A more

suitable validation process would include completion of food records at equal intervals

throughout the one- year reference period. In doing so, influences of seasonal variation in

the diet would also be addressed (Willett, 1998, p. 113, 132; Dwyer). Carbohydrate

intake and hence fiber intake and diet GI are influenced by seasonal changes especially in

Trinidad, since a large part of the diet is composed of fruits and vegetables. Glycemic

index values in particular, are influenced by levels of ripeness and post harvest changes

(Trout et al., 1993). An open-ended section on the FFQ was included so respondents

could list other seasonal fruits and vegetables consumed. A small food list was generated

from this exercise, and added to the Nutriput database, however, it is likely that many

items went unreported due to respondent fatigue (this section was at the end of the FFQ)

and inability to remember diet out of season particularly in the absence of certain cues

such as the name of the item and common serving sizes. Although, I, the interviewer am

familiar with Trinidadian culture, I am not aware of all of the seasonal fruits and

vegetables, and more particularly their slang names, and was therefore not well equipped

to probe respondents about their seasonal intake in order to improve recall. Since I was

the only interviewer, there was not enough time to conduct repeat administrations of food

records in various seasons. The population has a low level of functional literacy and a

significant amount of time was needed to give instructions for completing both the FR’s

and the FFQ. In addition, the infrastructure of some of the electoral districts that were

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surveyed was quite poor and therefore required a great deal of travel time. Safety was

also a consideration, since it was not appropriate for a woman to be walking about in the

evening, alone or in company.

Given that the glycemic index is influenced by cooking methods (Trout et al.,

1993), it is possible that the FFQ did not adequately assess how carbohydrate- containing

foods are prepared. The FFQ did however ask the respondent to report how meat and fish

was prepared as well as the content of fat in dairy products, which may be a reason that

fats correlated better than carbohydrates with the FR’s. Another reason why agreement

between the FFQ and FR’s was weaker than anticipated, particularly for carbohydrates

may be respondent errors in frequency estimation on the FFQ. Willett reports that

frequency of food consumption is the most important determinant of between—person

variation in nutrient intakes (Willett, 1998, p. 121; Thompson & Byers, 1994). The

process of remembering how often an item is consumed is difficult especially if dietary

habits change according to the seasons, as it does in Trinidad. This may directly affect

how carbohydrate intake is reported since a large source of carbohydrates are seasonal

items. Moreover, people may simply have a better recall for fat and protein rich foods

compared to carbohydrate rich foods, since the carbohydrate component of the meal for

many are side dishes. Because FR’s do not rely on memory or estimations of frequency,

they are considered to be an appropriate tool for use in calibration studies of FFQ’s

(Willett 1998, p. 102, Thompson & Byers, 1994; Dwyer 1999). The process of

estimating portion sizes is also difficult (Willet p. 85; Thompson & Byers, 1994; Hunter

et al., 1988), but even more so in a population as socially diverse as Trinidad where

different ethnic groups may have different conventions for measuring and serving food,

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and may have different terminologies for utensils. Assessing portion sizes was

particularly difficult for males since culturally, they shy away from conventionally

female duties such as cooking or going to the market. Since a large part of the diet is

carbohydrate-based, either rice, roti, bread (hops), or ground provisions, and seasonal

fruits and vegetables, the incorrect estimation of portion sizes will have an impact on

reported carbohydrate intake from the FFQ. For the population in Trinidad it was much

easier to estimate portion sizes in FR’s because recipes were often provided and

individuals reported consuming a proportion of the item that was prepared. Wives often

filled out FR’s for their husbands, and I probed the husbands for snack items in the

follow-up interview. Since individuals have difficulty estimating the frequency and

amounts of foods consumed on a FFQ, a tendency for over and under reporting various

items occurs.

Average nutrient intake assessed by the FFQ was found to be significantly greater

than assessed by the FR’s (p<0.05) for all variables except cholesterol, and GI for

absolute values and saturated fatty acid intake expressed as a percentage of total energy

(“%SFA) and polyunsaturated fatty acid intake expressed as a percentage of total energy

(“%PUFA). These four variables were therefore the only measures that met the secondary

criterion of validity. These findings are not consistent with other validation studies and

are likely a result of the types of foods consumed in the particular population being

surveyed.

Various validation studies have reported greater estimates of nutrient intake or

total energy assessed by FFQ’s compared to diet records (DR’s) or other calibration

methods such as 24-hour dietary recalls or biochemical indicators (DeCarli et al., 1996;

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Jain et al., 1996; Block et al., 1992; Mayer-Davis et al., 1999°. Jackson et al., 2001; Xu

et al., 2000). The findings of the present study are for the most part consistent with these

findings. Average nutrient intake estimated by the FFQ was significantly greater than

estimated by the FR’s for all variables except cholesterol (p=0.60) and GI (p=0.14).

However, for variables expressed as a percentage of total energy, nutrient intake assessed

by the FR’s was significantly greater than estimated by the FFQ for all nutrients except %

carbohydrate (p<0.01), fiber g/1000kcal (p<0.01),% SFA (p<0.36), % MFA and % PUFA

(p=0.47) . In addition, data from nutrients expressed as a percentage of total energy

exhibited smaller mean to median differences when compared to absolute nutrient intake.

A reason for this may be because certain sources of energy are missing from the FFQ. If

the missing calories came equally from all food sources, a general underestimation of

total intake would not distort the percentage of energy from other nutrients. It is possible

that fat and protein containing food items are missing from the FFQ. People also tend to

report sources of fat selectively compared to carbohydrate sources. It is therefore

possible that sources of fat (and protein) are being underreported on the FFQ in relation

to the FR’s. In addition, it appears, that cholesterol, SFA and PUFA are being slightly

underreported on the FFQ compared to the FR’s, or that sources of these macronutrients

are missing from the FFQ, similar to total fat. It is difficult to compare these specific

findings to other validation studies since only one other study has attempted to assess GI

using a FFQ (Liu et al., 2001), and few validation studies report nutrients as a percentage

of total energy, rather they correct absolute intake for total energy using a regression

*° Nutrient estimates from FFQ greater than FR’s only for rural non-Hispanic Whites from Colorado and

Rural Hispanic Whites from Colorado. This difference was not consistent in urban non-Hispanic Whites

from Oakland or urban African American’s from Oakland.

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model to calculate residuals (Thompson & Byers, 1994; Cade et al., 2002)°”. Liu et al

(2001) examined glycemic load (rather than diet GI) and had very good agreement

between the FFQ and multiple FR’s (means and correlations) for carbohydrate. It is

possible that this strong agreement was a result of multiple FR’s completed over the

reference period of the FFQ, thereby addressing seasonality and day-of-the-week-

variation in intake. The subjects for the validation study (Nurses from the Boston area)

have also had a lot of practice participating in dietary assessment studies, and may

therefore have been more compliant with the study protocol. Since dietary intake in

Boston does not likely differ too much from season to season, carbohydrate intake may

be more consistent throughout the year compared to a region like Trinidad where diet is

based on seasonal food items, which are largely rich in carbohydrate content. Similar to

the present study, Liu et al (2001) found that the average intake of cholesterol was also

greater when assessed by FR’s compared to the FFQ. Rimm et al., (1992) also reported

average cholesterol intake to be higher in diet records compared to a FFQ. Mayer-Davis

et al (1999) in the validation study of the multi-ethnic Insulin Resistance Atherosclerosis

Study (IRAS) did report percent data and found that some variables were greater when

assessed by the FR’s and others by the FFQ's when looking at absolute data and reversed

when looking at percent data. The specific direction of the differences is not consistent

with the present study, however it is interesting to note that the same phenomenon exists

in other validation studies.

Additional reasons for differential reporting on a FFQ may be due to social

acceptability or cultural ideals of healthy eating. Vuckovic et al., (2000), reported that

37 . . . ps . The regression model to calculate residuals is used if micronutrient values are needed because they are

not dependent on energy as macronutrients are.

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there are two major factors that influence dietary reporting; honesty and social

acceptability. Laurence et al., in 1991 reported that items not consumed very often tend to

be over reported on FFQ’s and items consumed often tend to be underreported. Because,

the FFQ was interviewer-administered it is possible that participants felt the need to

report eating large quantities since an abundance of food is related to affluence and status

in the Trinidadian culture, although perceptions are slowly changing. Participants may

also have reported intake according to what they believed the investigator wanted to hear

which is an example of the interviewer leading the interviewee, perpetuating a sort of

desirability bias. (Cade et al., 2002; Kristal et al.). Studies by Salvini et al., (1989); Hu et

al., (1999); and Mennen et al., (2000) all report how responses to questionnaires tended to

over-represent socially desirable foods. Interestingly, Cade et al. (2002) reports that

correlation coefficients for repeatability between interviewer-administered and self-

administered FFQ’s were better for interviewer-administered for fat (0.65 vs. 0.60),

energy (0.67 vs. 0.63) and vitamin A (0.59 vs. 0.58), but worse for vitamin C (0.59 vs.

0.66), and Kristal et al., in 1997 concluded that interviewer-administered FFQ's are better

for assessing diet in minority or poorly educated groups. The choice of using an

interviewer-administered technique therefore requires careful consideration of the

benefits and drawbacks inherent in the procedure itself.

4.1.2 Males and Females

The FFQ was able to discriminate between the dietary intake of males and

females as anticipated. With respect to absolute data, similar to the study population,

significant differences between the FFQ and FR’s in mean nutrient intake were found for

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all variables except GI, and cholesterol in both males and females, indicating that the

secondary criterion for validity was met only for these variables. For percent data, a

larger number of variables showed agreement between means (p>0.05). Males had

higher levels of intake compared to females for all absolute variables, which is expected

since men are known to consume a greater amount of energy compared to women

(Delcourt et al., 1994). Similar to all study participants, both the male and female groups

had higher estimates of nutrient intake when assessed by the FFQ compared to FR’s for

all variables except cholesterol and GI. When percent data was looked at, this trend was

attenuated and some variables were higher when assessed by FR’s compared to the FFQ

and others were lower for both male and female groups, indicating that some variables

were under being differentially reported. For males (similar to all study participants), it

appears that fat, and to a lesser extent protein and cholesterol are being underreported on

the FFQ in relation to the FR’s or that sources of fat (protein and cholesterol) are missing

from the FFQ. Carbohydrate, SFA and PUFA appear to be very slightly underreported on

the FFQ compared to the FR’s or some of these sources may be missing on the FFQ also.

For females, like with all study participants and male subjects, fat appears to be

underreported on the FFQ in relation to the FR’s or sources of fat may be missing from

the FFQ. Cholesterol appears to be slightly underreported in the FFQ compared to the

FR’s or the FFQ may be missing sources of cholesterol. SFA and PUFA were also found

to be slightly underreported on the FFQ compared to the FR’s or sources of these

macronutrients may be missing from the FFQ. The fact that fat is being underreported on

the FFQ for both males and females in addition to all study participants indicates that

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sources of fat may be missing from the FFQ, or subjects may be apprehensive about

reporting their actual fat intakes.

As found with all study participants, correlation coefficients between FR’s and the

FFQ for both males and females were generally stronger for fats and weaker for

carbohydrate, fiber and GI. For males and females, alcohol and % alcohol had the

weakest correlations, and for females the strongest correlation was found for % protein

and for males the strongest correlation was found for fat. Correlation coefficients

between FR’s and the FFQ did not differ significantly between males and females for

absolute nutrient intake for any variable, although males showed stronger agreement for

total carbohydrate, available carbohydrate and cholesterol than did females. However, a

statistically significant difference in correlation coefficients (FR’s vs. FFQ) between

males and females was found for % protein and may be due to the fact that males had a

rather weak correlation between the FR’s and FFQ for % protein while females had a

rather strong correlation for % protein.

4.1.3 AF’s and SA’s

For both AF’s and SA’s, nutrient intake assessed by the FFQ was greater than

assessed by the FR’s for most variables, keeping consistent with findings in the study

population and males and females, indicating the differential reporting of various

nutrients in the FR’s and FFQ. For AF’s, protein and fat appeared to be slightly

underreported on the FFQ compared to FR’s or sources of protein and fat may be missing

from the FFQ. Carbohydrate, cholesterol and PUFA were found to be slightly

underreported on the FFQ compared to the FR’s, indicating that sources of these

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macronutrients may also be missing from the FFQ. For SA’s, protein, fat and SFA

appeared to be underreported on the FFQ compared to FR’s, or sources of these

macronutrients may be missing from the FFQ. Cholesterol and PUFA were found to be

slightly underreported on the FFQ compared to the FR’s indicating that sources of these

macronutrients may also be missing from the FFQ. Interestingly, GI was found to be

significantly lower when assessed by the FFQ compared with the FR’s for SA’s, but not

for AF’s. This may signify that the FFQ might be missing items specific to the SA diet

that may impact diet GI or SA’s may be underreporting carbohydrate intake on the FFQ

in relation to the FR’s.

Differences in dietary intake between African’s (AF) and South Asians (SA) were

examined by gender. Because of small sample size, it was thought that true differences in

intake may not be apparent, however this is not the case since differences in mean

nutrient intake were detected between ethnic groups. Significant differences between

AF’s and SA’s by gender were found for most nutrients. SA females had a significantly

lower intake of protein, fat and energy compared to AF females, SA males and AF males,

and a lower intake of total and available carbohydrate compared to AF males and SA

males (total carbohydrate only). This is not surprising since it would be expected that SA

females would underreport nutrient intake due to cultural ideals of conventional beauty.

For South Asian women, the most desirable body type is small or ectomorphic,

highlighting cultural expectations of femininity (linked to frailty and submission) and the

demure role of women in South Asian culture. In addition, many SA families are wealthy

in Trinidad and offspring are often sent abroad to study. In doing so, wealthy, urban SA’s

in Trinidad have adapted Western ideals of beauty, which also promote thinness. Many

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(SA women) have also become aware of health issues linked to being overweight, as a

result of higher education. In contrast, African women have a different perception of

beauty that may encourage the over reporting of energy. For African women in Trinidad,

a larger more robust or endomorphic body type is more acceptable. Among African’s in

Trinidad a larger body is associated with power and status. African women tend not to

study abroad as much as SA women, and have a lower rate of higher education Likewise,

males may over report dietary intake irrespective of ethnicity, since a larger body size is

associated with wealth, status, and power in Trinidad. This attitude is changing in certain

communities, particularly those with access to higher education as health issues

pertaining to lifestyle and weight are becoming global concerns. The differences in

nutrient intake between SA males, AF males, SA females and AF females, disappeared

when percent data was looked at, indicating that the different groups may be

differentially reporting various sources of energy on the FFQ or in the FR’s. It is also

possible that commonly eaten food sources for a particular group may have not been

included on the FFQ.

With respect to correlations between the FR’s and FFQ, SA’s had stronger

correlations than AF’s for all variables except PUFA, cholesterol mg/1000kcal, and %

PUFA, however the only statistically significant differences in correlations between AF’s

and SA’s were found for SFA, fiber g/1000kcal, and % SFA. For both AF’s and SA’s,

fats correlated better than carbohydrate, fiber and GI. AF’s had a particularly weak

correlation for GI (r=0.06) between the FR’s and FFQ compared to SA’s (r=0.30),

although this difference was not statistically significant. It is possible that the FFQ did

not contain relevant ethnic food sources, particularly sources of carbohydrate that would

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influence GI. It is also possible that seasonality affects carbohydrate intake in AF’s more

so than in SA’s. If this is the case, using multiple FR’s at different times of the year

should improve agreement between the FFQ and FR’s for AF’s.

4.1.4 FFQ 1“ group compared to FFQ 2™ group.

Administration of the FFQ before or after the FR’s may affect the quality of the

data collected. The process of recording diet may alter the participant’s awareness of food

intake and thus improve accuracy in completing the questionnaire. It may also alter food

choices thus leading to biased assessment relative to the true diet (Willett, 1998, p. 102).

In order for these effects to be balanced within the study, participants either completed

the FFQ one-day before completing the FR’s (FFQ 1* group) or one-day after completing

them (FFQ and group), determined by coin-toss. This method of randomization although

crude is acceptable (Lilienfeld, D.E & Stolley, P.D., Foundations of Epidemiology, 3n4

ed., 1994, p. 183) and was appropriate for this study since computer resources in Trinidad

are limited.

Dietary intake of the two groups was examined separately and it was found that

the order of administration did not influence the findings, since differences between

gender and ethnic groups were the same as were most differences between the FR’s and

FFQ.

Out of interest, nutrient intake from FR data was compared between males from

the FFQ 1“ group, males from the FFQ 2™ group, females from the FFQ 1 group and

females from the FFQ 2™ group. Statistically significant differences between groups were

found for protein, total and available carbohydrate, energy and cholesterol] mg/1000kcal.

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Nutrient intake from FFQ data was also compared between males from the FFQ 1*

group, males from the FFQ 2" group, females from the FFQ 1" group and females from

the FFQ 2™ group. Statistically significant differences between groups were found for

protein, cholesterol, alcohol, % protein, cholesterol mg/1000kcal and % alcohol. These

findings show that there are few and no consistent differences in nutrient intake that

result from whether the FFQ administered before or after completing FR’s.

Correlation coefficients between FR’s and the FFQ did not differ between the

FFQ 1° and FFQ 2™ groups except for % fat, % carbohydrate and % MFA indicating that

whether the FFQ was done before or after the FR’s did not matter with respect to

agreement between the two assessment methods. It is interesting to note that all

correlations for the FFQ 1 group are weaker than the FFQ an group for all variables

except for alcohol, fiber g/1000kcal, cholesterol mg/1000kcal, % SFA, the P:S ratio and

% alcohol. Similar to all study participants, and different gender and ethnic groups, fats

correlated better than carbohydrate, fiber and GI, further indicating that whether the FFQ

was administered before or after completing the FR’s did not effect the quality of the data

collected.

4.2 Future Directions

Although reproducibility is not indicative of validity, a valid method used in

epidemiological studies must be reproducible (Willett, W. Nutritional, Epidemiology, and

ed. 1998, p. 105). Reproducibility coefficients are used to compare the agreement

between two identical FFQ’s administered at different times in similar populations. Any

future work related to this study should seek to readminister the FFQ at a different time

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of year to address seasonality as well as reproducibility. Future work should also include

repeat administrations of FR’s at different times of the year over a similar reference

period of the FFQ being calibrated. Anthropometric measures should also be taken if

possible so BMI can be calculated and underreporting and over reporting of energy can

be done. Although the population may not be receptive to participating in assessment of

biochemical indicators, doubly labeled water measures would be useful in assessing total

energy intake (although expensive), urinary nitrogen would be useful for assessing

protein intake and HbA1c would be useful in assessing long-term glycemic control. A

- more comprehensive approach to assessing portion sizes would also have great utility.

Either local food models or a larger number of calibrated photographs should be

generated for further work on the validation of this FFQ.

4.3 Conclusion

Prompted by the high prevalence rates of T2DM in T&T, and the epidemiological

finding that in T&T diabetes is twice as common in SA than AF men, with a smaller

difference in women, in 1998 a pilot study was conducted in T&T to estimate the nutrient

intakes and diet GI of AF and SA adults. The impetus behind the study was the

observation that low GI diets are beneficial in achieving long-term metabolic control in

persons with T2DM, and are associated with a reduced risk of developing T2DM. They

hypothesized that the diet GI of SA’s in T&T would be higher than that of AF’s. Nutrient

intakes were assessed by 24-hour dietary recall and indicated that AF men have a lower

diet GI than SA men while no significant difference was observed in women.

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The primary objective of this thesis was to develop a FFQ using a food list

generated by the 24-hour dietary recalls collected in the pilot study that is able to assess

diet GI in T&T. The secondary objective was to validate the FFQ by comparing estimates

of nutrient intake for various macronutrients and diet GI to those obtained from 7-day

food records in T&T. The first criterion for validity for the FFQ was correlation

coefficients between FR’s and the FFQ for a given macronutrient or % macronutrient

over 0.5. The second criterion for validity for the FFQ was that macronutrient and %

macronutrient means should not differ when assessed from either the FFQ or FR’s more

than 5 % of the time for a particular distribution.

In general, agreement between the FFQ and FR’s was somewhat poor for most

macronutrients and % macronutrients since few variables met either sets of validity

criteria for the FFQ. However, it was found that the FFQ was more effective at assessing

fat intake compared to carbohydrate, and fiber intake and diet GI. Although this finding is

consistent with other validation studies, correlation coefficients between the FR’s and

FFQ were particularly low for diet GI despite the fact that the FFQ was developed to

assess diet GI. It is possible that weak correlations between the FR’s and FFQ for

various nutrients resulted because the FFQ was designed to assess average dietary intake

over the past year and the FR’s were completed only once, therefore, the food items

reported in the diaries do not reflect average dietary intake over the same reference period

as assessed by the FFQ. Since a large part of the diet in Trinidad is based on fruits and

vegetables, and ground provisions, it is likely that seasonal changes in sources of

carbohydrate and fiber intake and hence diet GI were not detected in the FR’s, thereby

generating poor correlations for these variables between the FR’s and FFQ. An

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interesting and novel finding in this study is that although diet GI does not correlate well

between the FR's and FFQ, estimates of mean diet GI do not differ between the two

methods. This discordance may have arisen because the FFQ might not be very effective

in assessing diet GI compared to the FR’s because carbohydrates with different GI’s may

be pooled together on the FFQ but are listed individually in the FR’s. In conclusion,

further validation of this FFQ is needed to fully address its ability to assess dietary intake

in Trinidad, particularly with respect to sources of carbohydrate and diet GI. Diet records

need to be re-administered at different times of year, and repeatability or precision of the

FFQ needs to be addressed. Since the FFQ was able to discriminate between dietary

intakes of males and females and different ethnic groups, the discriminate validity of the

questionnaire is satisfactory, indicating that the FFQ already has some utility. Once the

validation of the FFQ is complete, it may be used in Trinidad as part of a diabetes-

screening program, so that the relationship between diet and diabetes may be further

elucidated. This will foster the development of dietary intervention programs in Trinidad

to ultimately improve the quality of life of people living with T2DM, and help ameliorate

the risk factors for developing T2DM.

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APPENDIX A

Descriptions of Various Validation Studies and their findings

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APPENDIX B

Consent form from the Ethics Committee of the University of The West Indies and

consent form for study participants.

146

147

THE UNIVERSITY OF THE WEST INDIES

FACULTY OF MEDICAL SCIENCES - ST. AUGUSTINE

Eric Williams Medical Sciences Complex

Uriah Butler Highway

Champs Fleurs,

Trinidad and Tobago, W.I.

May 16, 2000

Dr, Dan Ramdath

Lecturer Biochemistry Unit

Dear Dr. Ramdath

Please be advised that approval has been granted by the Ethics Committee for you to

undertake the project/study entitled: Development and Validation of a Food Frequency

Questionnaire for use in Trinidad.

Sincerely,

Professor FE Feytmans

Chairman Ethics Committee

Cables: “STOMATA” PORT-OF-SPAIN Telephone: (1-868) 645-2640-9 Fax: (1-868) 663-9836

148

VOLUNTEER INFORMATION *

1. This study is being conducted by the University of the West Indies in collaboration with the University of Toronto, Department of Nutritional Sciences, Faculty of Medicine.

2. The study seeks to develop a FOOD FREQUENCY QUESTIONNAIRE (FFQ) for use in Trinidad, which may ultimately be used to further elucidate the relationship between diet and chronic disease.

3. The principal investigator for the local phase of this study is Dr. Dan Ramdath who is a Lecturer ~~ inthe Faculty of Medical Sciences, UWI, St.Augustine: eR ER - Ps

4. The main features of this study are: * To conduct interviews on a representative sample population of Trinidadians in order to

assess the suitability of the food items listed on a preliminary FFQ.

e To validate a preliminary FFQ by means of 7-day consecutive food records on a representative sampie population of Trinidadians.

In the event of any problems, queries or if further clarification is needed, please call Dr. Dan Ramdath or Vasanti Malik at: 645-2640 ext. 4641/4643; Pager. 662-3377, pager #502539

INFORMED CONSENT

1. I, the undersigned, voluntarily agree to take part in a study that aims to test and validate a preliminary FFO, in order to develop an official dietary survey tool which, may in tum be used for epidemiological studies conducted in Trinidad.

~ 2. | have been given a full explanation by the supervising investigator of the nature and purpose’ ~~ of the study, and of what | will be expected to do.

. | have been given the opportunity to question the supervising investigator on all aspects of the a. Study and 1 have understood the advice andinforma iven. ye ea

4. | agree that | will not seek to restrict the use study maybe put.

5. ! understand that | am free to withdraw from the Study at any time without the need to justify my decision.

VOLUNTEER INVESTIGATOR

WITNESS DATE

APPENDIX C

Food Frequency questionnaire and first few pages of &-day Food Record booklet.

149

150

FOOD FREQUENCY QUESTIONNAIRE

FOR USE IN

TRINIDAD

DRAFT

dd mm yy

Date: Day: 1=Sun; 2=Mon; 3=Tue;

4=Wed; 5=Thurs; 6=Fri;

7=Sat

Demographic information

Locality ED# Household # Respondent

1. Area: (1=City; 2=Urban/suburban; 3=Rural)

2.Agerange (yrs)

3. Gender (1=Male; 2=Female)

4. Race (1=AF: 2=SA; 3=MI; 4=OTH)

5. Occupation: (1=Professional; 2= Clerical; 3= Skilled worker;

4=unskilled worker; 5=Housewife; 6=Other)

6. Completed Education level: (1=Primary/Elementary; 2=Secondary/ Technical/Vocational; 3=Tertiary;

4=None; 5=Other)

7. Have you ever been told by a doctor that you have any of the following;

diabetes, heart disease, high blood pressure, cancer, high cholesterol?

8. Are you currently on any special diet?

If yes for how long?

9. Are you currently pregnant or breast feeding?

PROVISIONS | AVERAGE USE DURING THE PAST YEAR

Never or Month Week Day USUAL SERVING SIZE

hardly ever

Potato (aloo) Os OM OL

Dasheen, Os OM OL

Yam Os OM OL

Eddoes Os OM OL

Cassava/ Os OM OL

Foofoo/ meal.

Sweet Potato Os OM OL

Tannia Os OM OL

Breadfruit Os OM OL

Green Os OM OL Banana/fig

Plantain Os C1M OL

CEREALS AVERAGE USE DURING THE PAST YEAR

Never or Month Week Day USUAL SERVING SIZE hardly ever

White rice Os C] M OL

Brown rice Os OM OL

Parboiled rice Os OM OL

White bread/ Os OM OL

sandwich/french

Whole wheat Os OM CL bread/ rye

Hops/ rolls/ Os OM OL buns

Biscuits/bagels/ Os CIM OL English muffins

Other bread Os OM OL

raisin bread

Sada roti &roast Os OM OL bake Type of flour

151

CEREALS Never or Month Week Day USUAL SERVING SIZE CONT’D hardly ever

Busupshut & Os OM OL fried bake. Type of flour

Dhall purie. Os OM OL

Type of flour

Paratha roti. Os OM OL

Type of flour Pita bread Os OM OL

Type of flour

Cornmeal Os OM OL

dumplings/roti

Breakfast cereal Os OM OL Corn Flakes

Pancake, waffle, Os OM OL French toast

Cooked cereals/ OS OM OL Cream of Wheat

Pasta/macaroni Os OM OL

with out egg Noodles, with Os OM OL

egg, chow mein

CooCoo Os OM OL

Bran /high fiber Os OM OL cereals/ granola

Dumplings Os OM OL

Crackers such Os OM OL as Crix

FRUITS & AVERAGE USE DURING THE PAST YEAR

VEGETABLES _| Never or Month Week Day USUAL SERVING SIZE hardly ever

Banana/fig Os OM OL

Plum Os OM OL

Guava Os OM OL

Orange/Tangerine Os OM OL

Pawpaw (papaya) Os OM OL

152

FRUITS & Never or Month Week Day USUAL SERVING SIZE

VEGETABLES _| hardly ever CONT’D Pineapple Os OM OL

Pumpkin/squash Os OM OL

Eggplant Os OM OL Baigan/melangen Tomato OSs OM OL

Com Os OM OL

Mixed tossed Os OM OL

salad

Spinach (bhaji) Os OM OL

Okra Os OM OL

Cooked mixed Os OM OL vegetables Carrot Os OM OL

Cabbage Os OM OL

Carombola Os OM OL (five fingers)

Mushroom Os OM OL

Calaloo (plain) Os OM OL

Dried fruits Os OM OL

Cauliflower Os OM OL

Christophene Os OM OL

Sweet pepper Os OM OL

Cucumber Os OM OL

Lettuce/greens/ Os OM OL Watercress

153

FOOD FROM AVERAGE USE DURING THE PAST

ANIMALS & YEAR USUAL SERVING SIZE ALTERNATIVES | Never or Month Week Day

hardly ever

Milk alone or with Os OM OL cereal Type:

Yogurt (plain or Os OM OL fruit), (smoothie) Type:

Cheese Os OM OL Type:

Cream cheese/paste Os OM OL Type:

Chicken/turkey Os OM OL

Beef/roast & corned

beef

Os OM OL

Pork Os OM OL

Goat Os OM OL

Lamb/Mutton Os OM OL

Goat OS OM OL

Fish Os OM OL

Salted/Smoked Fish Os OM OL

Shrimp/crab or Os OM OL other shell fish Egg/egg salad Os OM OL

Luncheon meats Os OM OL

Bacon Os OM OL

Tofu/bean curd & OS OM OL meat substitutes

LEGUMES & AVERAGE USE DURING THE PAST NUTS YEAR USUAL SERVING SIZE

Never or Month Week Day

hardly ever

Pigeon peas Os OM OL

Chick peas Os OM OL (channa)

154

LEGUMES & | Never or Month Week Day USUAL SERVING SIZE NUTS hardly ever

CONT’D Red beans Os OM OL (kidney beans)

Black-eyed peas Os OM OL

Split peas /Dhall Os OM OL

Lentils Cs OM OL

Cow peas Os OM OL

Lima beans Os OM OL

Soya beans Os OM OL

Green peas Os OM OL

Bodi Os OM OL

String beans Os OM OL

Peanuts Os OM OL

Other nuts such OSs OM OL as almonds

FATS & OILS AVERAGE USE DURING THE PAST YEAR USUAL SERVING SIZE

Never or Month Week Day

hardly ever

Butter, stick Os OM OL

Type

Margarine stick Os OM OL Type

Margarine, tub Os OM OL Type

Butter , tub Os OM OL

Type

Mayonnaise, Os OM OL

salad dressing Type

Salad dressing Os OM OL Italian/ French Type

155

FATS & OILS | Never or Month Week Day USUAL SERVING SIZE CONT’D hardly ever

Oil such as Os OM CL vegetable, olive Type

Lard or ghee Os OM OL Type

Coconut OSs OM OL milk/grated

Green coconut Os C1M OL

Avocado Os OM OL (zaboca)

SNACKS, AVERAGE USE DURING THE PAST

DESSERTS & YEAR USUAL SERVING SIZE CONDIMENTS | Never or Month Week Day

hardly

ever

Sweet bread Os OM OL

Potato, corn or Os []M OL other chips

Kurma, & goolab OS OM OL

jamoon

Cake Os OM OL

Cookies, Os OM OL brownie/fruitbar

Ice Cream, or ice Os OM OL milk

Frozen yogurt or Os OM OL sherbet

Puddings or Os OM OL custard

Chocolate bars OSs OM OL

Cassava Pone Os OM OL

Barfi (Indian OSs OM OL milk sweets)

Pastry pie, Os OM OL turnovers, danish

Preserved fruits Os OM OL

156

SNACKS, Never or Month Week Day USUAL SERVING SIZE

DESSERTS & | hardly CONDIMENTS | °°" Popcorn Os OM OL

Caramel or other OSs OM OL candy

Sugar cake, Os OM OL fudge, toolum

Jam/jelly Os OM OL

Peanut butter Os OM OL

Condensed milk Os OM OL

Syrup, molasses Os OM OL

Ketchup Os OM OL

Mustard Os OM OL

Barbecue sauce OSs OM OL

Soy sauce Os OM OL

MIXED AVERAGE USE DURING THE PAST YEAR

FOODS Never or Month Week Day USUAL SERVING SIZE

hardly ever

Veg. Fried rice, Os OM OL or Spanish rice

Macaroni pie OS OM OL mac& cheese

Soup such as Os OM OL vegetable

Cream soup or Os OM OL chowder

Broth such as Os OM OL fish broth

Potato or Os OM OL macaroni salad

Pastel Os OM OL

(any type)

157

MIXED Never or Month Week Day USUAL SERVING SIZE FOODS hardly ever

CONT’D

Noodles, Chow Os C1M OL mein

Pachownie, or Os OM OL black pudding

Corn pie Os OM OL

Buljoil Os OM OL

Pelau Os OM OL

Calaloo Os OM OL (with meat)

Pasta sauce Os OM OL (with veg.)

Pasta sauce Os OiM OL (with meat)

FAST FOODS | AVERAGE USE DURING THE PAST YEAR Never or Month Week Day USUAL SERVING SIZE

hardly ever

Doubles Os OM OL

Shark & bake OSs OM OL

Sahiena/baigani/ Os OM OL Phulorie

Aloo pie Os OM OL

Cheese pie Os C1M OL

Veg pie or patty Os OM OL

Beef patty Os CM OL

Pizza Os OM OL

Hamburger Os OM OL

158

FAST FOODS Never or Month Week Day USUAL SERVING SIZE CONT’D hardly ever

Hot dog Os OM OL

Fried chicken Os OM OL

KFC

Fish or chicken Os OM OL sandwich KFC

Grilled chicken Os OM OL sandwich

French Fries Os OM OL

Onion rings Os OM OL

BEVERAGES AVERAGE USE DURING THE PAST SERVING SIZE &

YEAR PREPARATION Never or Month Week Day

hardly ever

Fruit juices Os ClM OL

Maubi Os OM OL

Tomato or Os OM OL vegetable juice

Sweet drink ° Os OM OL

Diet sweet drink Os OM OL

Light beer or Os OM OL Shandy

Regular or draft Os OM OL beer

Stout Os OM OL

White/pink Os OM OL

wine

Red wine Os OM OL

159

BEVERAGES Never or Month Week Day SERVING SIZE &

CONT’D hardly ever PREPARATION Hard liquor Os OM OL

Suppligen or Os OM OL Ensure

Coconut water Os OM OL

Peanut punch OSs OM OL

Regular tea Os OM OL

Regular coffee Os CM OL Instant/brewed

Choc milk, Os OM O milo, ovaltine,

cocoa

Decaffeinated Os OM OL coffee/tea

Cappuccino & Os OM OL other coffees

Whitener Os OM OL

Type

Sweetener Os OM OL

Type

SEASONAL AVERAGE USE DURING THE PAST USUAL SERVING SIZE FRUITS & YEARS

VEG. Never or Month Week Day

hardly ever

Mango Os OM OL

Pommecythe Os OM OL

Portugal Os OM OL

Cherries OS OM OL

Pomerac Os OM OL

Watermelon Os OM OL

160

161

SEASONAL Never or Month Week Day SERVING SIZE &

FRUITS / VEG | hardly PREPARATION CONT’D ever

Lemon/lime Os OM OL

Plum Os OM OL

Chaimet Os OM OL

Chennette Os OM OL

Sorrel Os OM OL

Sapodilla Os OM OL

Chaigtange Os [1M OL

VITAMINS & | AVERAGE USE DURING THE PAST YEAR

SUPPLEMENTS Never or Month Week Day hardly ever

Multi-vitamins

Vitamin E

Vitamin A

Vitamin C

Iron

Calcium

Folic acid

FOOD ITEM PROCESSING FREQUENCY Deep Pan- Baked/ | Grilled/ Curried Stewed Never | Month | Week | Day

fried fried Broiled | BBq’d

Chicken

Fish

Corned beef

Steak

Hamb

Hotdo

Bacon

S e

Luncheon meats

Pork

Ribs

Goat

Lamb/mutton

Tofu

162

Please list any food items, which you eat more or less or during religious holidays.

Holiday:

Time of year:

Duration:

EAT MORE Never or | Month Week Day USUAL SERVING SIZE

OF... hardly ever

OS OM OL

Os OM OL

Os OM OL

Os [1M OL

Os OM OL

EAT LESS OF... | Never or | Month Week Day USUAL SERVING SIZE hardly

ever

Os OM OL

Os OM OL

Os OM OL

Os OM OL

OS OM OL

carne

A COLLABORA TI ON BE TWEEN THE UNIVERSI TY OF THE “WES T. INDIES, - BIOCHEMISTRY ‘UNIT & THE UNIVERSITY OF TORONTO, DEPARTMENT OF NUTRI PL ONAL, SCIENCES , FACULTY OF MEDI Cr NE.

164

7 DAY FOOD RECORD

INSTRUCTIONS

Please complete this FOOD RECORD over the next 7 days.

Write down everything you eat or drink at home or away- iricluding snacks. Please write down what you eat or drink right after doing so, rather than at the end of the day or on another day.

DO NOT change what you would normally eat. We would like a complete record of exactly what you eat and drink during the next 7 days, regardless of how little or how much.

Food items, should be listed under the appropriate heading (breakfast, lunch, dinner, snack) of each day (1 through 7) in the space provided.

Be sure to include the amounts of food or drink consumed (S, M. L) during each meal using the attached photographs to help estimate portion sizes. Items in mixed dishes such as salad or sandwiches should be listed separately. You should also indicate if the items being consumed are made at home or take-out.

For items made at home please include recipes and method of food preparation.

Don’t forget to record: All beverages (coffee, tea, milk, beer, water, sweet drink) Sweets (candy, desserts) Condiments and dressings (jams, butter, gravy, ketchup, chutney, soy sauce) Vitamins

A follow ~ up telephone call or visit will be given mid-week.

Please see the following example.

165

DAY 1!- EXAMPLE

Breakfast: Eco é. ch dlom- sm call wore,

Sada cory -_ b, Cin ve, ‘el Crea \

s Cu Sorgoacd thingly en cot: —-

a C05 af ikea, with /& gar aA cl eva pata ted milk

Recipes: Fr vec eile Os About 5 pt tatee s Q onions, _

| Cbs Li 3 cloves agarchic + 1 Eiespe NAYS Cocr Aa LL

Aw XE. SE [+ a ise ope 7

Sade ondt 2 44 cis tho _L iain FE ) Ly tes Peru y <y “he vk a G ouicd es

| $e ose ots Salt i

Ye Coted e Leiba ca ¥

Lunch: Ham dad Cheese Sdoclwich A a Sites brown beend

Mocgecun &. edhe, sacs: Chey 2 alice s che clebes Wed ine”

, a ioe ha musntoclcd 4. ey cl Hint ly

L tan ut Fz ue |

Tiseck salock 2 le tdure pobiuisadn be ye lecl es 4. Pomictt::

Carrot Sucall prot Ae cies ne Ly Preach \ aa] Soucy Fisy 3 ke net

Recipes:

166

DAY 1- EXAMPLE

Dinner: Calalen Poecliaw cacao Stewed ai aon be CS | = op cA eck Eny

(whi tye cule 3 tec Le fal 7 coc lf tnt cs ch.

Broile ch plarta A. 3 pire ! Cisco ch Cine k by Sum Gil mcs Ao ct

Line chia es Gls S32 Cpe OD ack Latta to

Recipes:_( cilaloo About 12 dasheen leaves a COs OE Ce ANG st iy dik ree . 2

tables veo vy bea + re a

Ore evs pepper md VF

a be. 3!

Cidacae SS

=p CL = > thy DA?

S

2

| Oya CoN

ig Cn eS

i fun hie t € 7

Snacks: | piece Je men cake C Steve | cught | 3 Cus ea, with Se cig cd eke. sp anateel ant Ik

hg Dike, Casasd. Sas Si C Ane, nucle \ soot 2 meta, Cons 5 Hens L

Recipes: ae SS. Ca ae ae 2 aA i waned Seek ie ck } ao coe ces wt

2. tle. Ay Mev id cs bist te. , {ttc twe api dyaistueey bit Bs .

a hist 2 Cnaed — bye ie ON Sea Sena 1 fe? fe ‘¢ PM nen lig ld S il el Gat:

Ts Flu Se jt fe wed é a / ‘ 24

167

DAY 1

Breakfast:

Recipes:

TRA ORARRRRRERKORERTEREEENERAERORROME TERROR ROEM TERRA AAEM AON ERA A

Launch:

Recipes:

APPENDIX D

Portion Sizes Associated with FFQ.

168

169

PORTION SIZES FOR ITEMS LISTED ON THE FFQ

Small Medium _ Large

POT,BLD IN SKIN,W/SL >78g 136g <272g

Dasheen >75g 125g <250g

White yam >75g 125g <250g

Eddoe >75g 125g <250g

CASSAVA,RAW >50g 100g <200g

SWTPOT,BL,WO/SK,W/SL >82g 164g <328g

BREADFRUIT,RAW >55g 110g 220g

Green Banana 57g 114g <228g

PLANTAIN,CKD 38.5g 77g 154g

RICE,WHIT,CKD,ENR >51g 102g <205g

RICE,BROWN,MED,CKD >51g 102g <205g

WHTBREAD,ENR, 1-2NFDM >25g 100g 200g

WHOLEWHTBREAD,2%NFDM >25g 100g 200g

ROLLS+BUNS,PLAIN,ENR >40g 120g 240g

ROLLS,WHOLE WHEAT >40g 120g 240g

sada roti 32.59 130g 260g

Fried Bake >68g 136g <272g

Johnny bakes >32g 96g <256

paratha roti >50g 98g <196

dhal purie >73g 146g <292

CORN FLKS, KELLOGG'S >15g 30g <60g

CRM OF WHT, INST,DRY >759g 188g <376g

SPAG,ENR,CKD,W/NA >70g 140g <280g

NOODLE,EGG,ENR,CKD >80g 160g <320g

flour dumplings >70g 140g <280g

CRACKERS,SODA >14g 28g <56g

BANANA,RAW >57g 114g <228g

ORANGE,RAW,ALL VARIE >65g 131g <262g

PAPAYAS,RAW >76g 152g <304

PINEAPPLE,RAW >84g 155g <310g

SQSH,SMMR,BLD,W/SLT >45g 90g <180g

PUMPKIN,BLD,W/SALT >61g 122g <244g

EGGPLANT,BLD,W/SALT >48g 96g <192g

TOMATO,RED,RIPE,RAW 60g 120g 240g

SPINACH,BLD,W/SALT 45g 90g 180g

OKRA,BLD,W/SALT

CARROTS,RAW

CABBAGE,BLD,W/SLT

CAULIFLWR,BLD,W/SALT

CUCUMBER,RAW

LETTUCE,LOOSELEAF,RW

2% LOWFAT MILK,FLUID

YOGURTPLAIN8GPRO/80Z

CHEDDAR CHEESE

CHICK,BREASTWSK,RSTD

TURKYBRST,W/SKN,RSTD

BF,COMP,ALL,L+F,CK,Q

PORK,COMP.CUTS,LN,CK

GOAT, ROASTED

LAMB,DOM,CUBED,BRLD

Fish

boiled salt fish

SHRIMP,BLD/STMD

EGG,CHICK,WHL,HRD-CK

SALAMI,CKD,BEEF/PORK

TOFU,SLTD&FRMNT,NIGR

PIGEON PEA,CKD,W/SLT

CHICKPEA,CKD,W/SALT

BN,KDNY,RED,CKD,W/SL

COWPEA,CMN,CKD,W/SLT

PEA,SPLIT,CKD,W/SALT

LENTIL,CKD,W/SALT

PEAS,GREEN,BLD,W/SAL

BN,FRENCH,CKD,W/SALT

PNUT,ALL,DRY-RST,NO

ALMONDS, TOASTED

BUTTER UNSALTED

MARG,SFT,SFL,HYD SFL

MAYON TYPE SAL DRESS

SALAD DR.1000 ISLAND

OIL,SOYBEAN LECITHIN

FAT,LARD (PORK)

COCONUT MILK,RAW

AVOCADO,RAW,ALL VAR

sweet bread

40g

36g

37.5g

31g

>26g

28g

>122g

>113.5g

>28g

59.79

>50g

>50g

>50g

>42.5

>42.5

>43g

62.5g

>42.5

>25g

>42.5

>62g

49g

41g

44g

60g

53.5g

49.5g

40g

43g

>14g

>14g

>5g

>5g

>5g

>5g

>5g

>5g

>5g

>57.5

>45g

80g

72g

75g

62g

52g

56g

244g

227g

56g

124g

100g

100g

100g

85g

85g

85g

125g

85g

50g

85g

124g

98g

82g

88g

120g

107g

99g

80g

86g

28g

28g

15g

14.19

14g

15g

13.69

12.89

15g

115g

90g

160g

144g

<150g

<124

" <104g 112g

<488g

<454g

112g

229g

<200g

<200g

<200g

<170

<170

<170g

250g

<170

<100g

<170

<248g

196g

164g

177g

240g

214g

198g

160g

177g

<56g

<56g

<45g

<42g

<429g

<45g

<40g

<39qg

<45g

<230g

<180g

170

POTATO CHIPS

Kurma

CAKE,SPONGE,ENR

COOKIES,CHOCOLATE

16%FAT VANILA ICECRM

CANDY,MILKCHOC/PLAIN

Cas Pone

Barfi

ROLLS+BUNS,DAN.PSTRY

green mango

CARAMELS,CHOC/PLAIN

JAM,NOT CHERRY/STRWB

PNUT BUTTER,SMTH,NO

SIRUP,CORN,LIGHT+DK.

CATSUP

MUSTARD,PREP., YELLOW

BARBEQUE SAUCE,RTS

SOY SAUCE (TAMARI)

Veg fried rice

MAC+CHEES,HOME,MARG

Prvsn Sp

Fish Broth

POTATO SALAD

Veg Chowmein

Corn Pie

buljol

Pelau

Callaloo

TOM SAU,HRB+CHSE,CND

doubles

Fried Bake

SHARK,BATTRD & FRIED

sahiena

aloo pie

Cheese Pie

Chkn Pie

PIZZA W/CHEESE

HAMBURGER,REG,COND

CHICK,FRIED,LIGHTMT

CHICK FIL SAND,PLAIN

>50g

30g

>20g

>12g

>74g

>25g

>30g

>40g

>25g

>75g

>14g

>20g

>16g

>20g

>15g

>129g

>16g

>18g

>50g

>100g

>120g

>93g

>63g

>67g

>749g

>67.5g

>60g

>161g

>60g

>52.5g

>68g

>42.5g

>110g

>75g

>91g

>69g

>118.5g

>109g

>81.5g

>91g

100g

90g

45g

429

148g.

47g

60g

81g

429

150g

28g

40g

32g

40g

30g

25g

32g

36g

97g

200g

240g

187g

125g

134g

147g

135g

120g

322g

122g

105g

136g

85g

220g

150g

182g

138g

237g

218g

163g

182g

<200g

<180g

<90g

<85g

<296g

<94g

<120g

<162g

<100g

<300g

<84g

<80g

<64g

<80g

<60g

<50g

<64g

<72g

<200g

<400g

<480g

<374g

<250g

<268g

<294g

<270g

<240g

<644g

<244g

<315g

<272g

<170g

<330g

<225g

<275g

<276g

<474g

<436g

<326g

<364g

171

HOTDOG, PLAIN

POTATO,FRIED,VEG OIL

ORANGE JUICE,CHILLED

sorrel (dried)

Mauby

TOMATO JUICE

COLA

COLA,LO CAL,ASPARTAM

BEER, LIGHT

BEER, REGULAR

WHITE WINE

RED WINE

DIST SPIR 80 PROOF

INSTANT BREAKFAST, P

COCONUT WATER

Peanut Punch

TEA,BREWED

COFFEE,INSTANT,PREP.

CHOC.FLAV.BEV.W/MILK

COFFEE,DECAF,PREPARD

DRY WHOLE MILK

3.7% FAT,WOLE MILK

1% LOWFAT MILK FLUID

REGULAR NONFATDRYMLK

2% LOWFAT MILK,FLUID

SKIM MILK,W/O VIT A

SWEET CONDENSED MILK

EVAPORATED WHOLEMILK

EVAPORATED SKIM MILK

SUGARS,BROWN

SUGARS,GRANULATED

HONEY,STRND/EXTRCTD

MANGO,RAW

CARISSA,RAW

TANGERINES,RAW

ACEROLA,RAW

GUAVAS,RAW,COMMON

WATERMELON,RAW

>49q

>38g

>124.5g

>90g

>125g

>91g

>185g

>177g

>177g

>178g

>59qg

>59g

>21g

>199g

120g

>144g

>89g

>89.5g

>16g

>89.5g

>8g

>15.3g

>15.3g

>7.5g

>15.3g

>15.3g

>19g

>16g

>16g

>4.6g

>6g

>10.5g

>103.5g

>30g

>84g

>49g

>90g

>80g

16g

249g

182g

250g

182g

370g

354g

354g

356g

118g

118g

429

38g

240g

287g

178g

179g

32g

179g

16g

30.5g

30.5g

15g

30.5g

30.6g

38g

32g

32g

9.19

12g

21g

207g

60g

168g

98g

180g

160g

<196g

<115g

<498g

<364g

<500g

<364g

<740g

<708g

<1062g

<1068g

<354g

<354g

<126g

<76g

<720g

<574g

<534g

<537g

<64g

<537g

<32g

<61g

<61g

<30g

<61g

<61.2g

<76g

<64g

<64g

<18.1g

<24g

<42g

<621g

<120g

<336g

<196g

<270g

<320g

172

PLUMS,RAW

SAPODILLA,RAW

JAVA-PLUM,RAW

CHAYOTE,FR,BL,W/SALT

GRAPEFT,RD/WH/PK,ALL

CHERRY,SOUR,RED,RAW

APPLE,RAW,W/SKIN

GRAPES,RAW,AMER TYPE

PEARS,RAW

CARAMBOLA,RAW

CBBGE,PK-CH,BLD,W/SL

STRAWBERRIES,RAW

TAMARINDS,RAW

SUGAR-APPLE,RAW

CORIANDER,RAW

MAMMY-APPLE,RAW

MUSHROOMS,BLD,W/SLT

PEPPERS, SWEET, RAW

JACKFRUIT,RAW

RAISINS,SEEDLESS

SOURSOP,RAW

MELON,HONEYDEW,RAW

PEACH,RAW

CELERY, RAW

CORN,YEL,BOILD,W/SLT

BEETS,BLD,W/SALT

PRUNES,DRD,CKD W/SUG

BN,FRENCH,CKD,W/SALT

KIWIFRUIT,RAW,FRESH

BROCCOLI,BLD,W/SLT

SOYBEANS,SPROUTS,RAW

RADISHES,RAW

POMEGRANATES,RAW

Pommerac

Coconut jelly

Chennette

Lychee

Caimit

Pewah

>66g

>85g

>67.5g

>40g

>123g

>68g

>69g

>46g

>83g

>63.5g

>85g

>74.5g

>60g

>77.59g >2g

>50g

>39g

>25g

>50g

>50g

>112.5g

>50g

>43.5g

>40g

>419g

>42.5g

>42g

>88.5g

>76g

>39g

>18g

>22.5g

>77g

>82g

>23g

>50g

>50g

>53g

>34g

132g

170g

135g

80g

246g

136g

138g

92g

166g

127g

170g

149g

120g

155g

4g 100g

78g

50g

100g

100g

225g

100g

87g

80g

82g

85g

84g

177g

152g

78g

35g

45g

154g

164g

45g

100g

100g

105g

68g

<264g

<510g

<270g

<160g

<492g

<272g

<276g

<184g

<332g

<254g

<340g

<298g

<240g

<310g

<8g

<200g

<156g

<100g

<200g

<200g

<450g

<200g

<174g

<160g

<164g

<170g

<168g

<354g

<304g

<156g

<70g

<90g

<308g

<328g

<90g

<200g

<200g

<210g

<136g

173

Karela

Ackee

Sugar cane

kush kush

Veg. pie

Fish sand.

Decaf. tea

>30g

>60g

>23g

>45g

>79g

>79g

>89g

60g

120g

45g

90g

158g

158g

178g

<120g

<240g

<90g

<180g

<316g

<316g

<534g

174

APPENDIX E

An example of a map from an electoral district of Tunapuna: used for recruiting

subjects for validation study.

175

176

Figure E.1

Map of an electoral district from Tunapuna: used for recruiting subject’s in FFQ

validation study.

BYR verted TRU Rr

PAGE Pads BE Cato ED

Sy ta ta HA

a

| i

val

My td WA: at |

{eo PD +,

‘ i

. : , !

Sy i \ \, f Nr

. q ! \ de

i S to

| mE ! v i | : bead

{1 i | an 1 a on Eaten ate

eS sh wot t : 4 woh & Nats A ! ) “s oo

in vetted J Moon

a aca) a “ys ie j P th ‘ . '

; \ m \

Late “s weaet us

uA i v

a

=

71/00}

Eres

oa

INTY = ST GORGE

[E

rc 8

rn ne en Gt settee wc

__UNAPUNA Eo TT) Pe

eA d.. Lipide oF ‘foclenswe resem lwcrmisvenrsetanls

Moto

APPENDIX F

Food items from the validation study that were added to the Nutrput database.

177

178

List of Food items collected in the validation study and added to the Nutriput database.

Pro Fat Carb Kcal Chol SFA MFA Pro_ Gi

Fr King Fish 11.80 12.30 12.40 206.0 35.7 1.810 2.810 6940 94

Fr Fly Fish 12.00 12.60 12.40 209.0 23.5 1.850 2.870 7.020 94

Bkd Snapper 10.10 7.94 3.44 124.0 32.9 3.970 2.140 1.280 56

Cur Shrimp 18.90 6.74 1.97 142.0 156.0 1.090 1.660 3.580 57

Fr Shr Wanton 8.72 24.30 26.10 359.0 31.8 3.540 5610 13.900 93

Cur Goat 21.00 4.77 2.24 134.0 57.1 1.040 1.570 1.510 52

Cur King Fish 9.96 8.09 4.52 127.5 35.0 5.680 0.660 1.004 42

Veg Chowmein [3.05 5.04 11.56 966 0.0 0.720 1.190 2.830 63

Cur Duck 9.12 32.90 4.46 346.0 56.0 10.300 14.600 5.790 59

Std Salmon 9.45 7.21 555 126.0 244 1.370 1.830 3.440 71

Veg Callaloo 1.93 11.00 6.01 128.0 36 9.240 0.760 0.200 43

Callaloo 5.79 14.20 464 167.0 16.2 9.140 3.330 0.830 42

Fr Carite 19.30 1.53 12.10 133.0 27.8 0.300 0.280 0.580 83

Std Pork 15.50 21.30 1.94 261.0 66.0 7.590 9690 2.520 79

Kurma 4.12 10.20 60.10 345.0 19 1.800 2.720 5.140 90

Std lamb 11.50 5.61 2.90 103.0 343 1.410 1.760 1.870 56

Pig Tail Soup 10.20 16.00 8.37 218.0 57.0 5.540 7490 1.810 87

Swt Sr Pork 8.14 10.70 9.20 165.0 336 3840 4940 1.190 84

Cnut Ice Cr 5.35 17.50 20.60 252.0 32.2 12.900 3.130 0.460 47

Std Beef 11.10 13.00 5.73 189.0 39.2 4820 5.290 1.440 78

Swt Sr Chkn 6.28 0.41 13.40 91.5 146 0.100 0.090 0.110 83

Bkd King Fish 11.70 3.42 10.10 114.0 42.9 1.110 1.300 0.730 88

Cheese cake 5.39 17.70 27.90 291.0 51.2 11.000 5.130 0820 87

Garlic Shrimp 9.25 3.32 9.20 107.0 63.2 0.510 0.700 1.770 75

Shmp Fr Rice 4.02 5.11 14.00 114.0 41.8 0890 1.370 2.530 65

Orange Cake 4.93 0.31 59.90 259.0 0.0 0.050 0.030 0.120 91

Cur Beef 12.50 6.22 2.72 114.0 342 1.690 2.100 1.500 55

Cur Chataigne 2.90 19.90 14.50 243.6 0.0 13.775 1.749 2.967 28

Egplt Cass 4.13 8.30 13.50 139.0 9.1 2440 2.430 3.020 81

Otml Muf 5.27 8.50 5460 3040 3.2 1.740 2.120 4.330 89

Sawine 4.71 447 23.80 151.0 103 1.830 1.880 0.510 44

Cas Pone 2.45 8.96 31.70 210.0 55 7.240 0.930 0.190 90

Persaud 3.78 7.52 37.20 229.0 21.2 4520 2.070 0.280 82

Barfi 7.59 11.80 41.90 304.0 41.7 7.350 3.450 0.350 75

Kuchla 0.69 9.65 10.50 129.0 0.0 1.090 5.600 2.020 77

Sugar Cake 3.59 34.50 44.90 504.0 0.0 29.900 1.980 0.500 68

Mac Salad 447 10.30 9.45 143.0 97.9 1.300 5.290 3.020 74

Sea Moss 8.27 4.71 84.40 413.0 18.3 2.920 1.360 0.180 80

Crnml Dmplg 5.94 1.98 39.90 188.0 3.6 0.720 0.500 0.510 97

Cur Clfwr 1.24 211 5.70 410 00 0290 0.450 1.130 54

BBq Ribs 14.10 19.10 3.87 244.0 63.1 7.570 8640 1.810 79

Beef Patty 12.20 17.50 14.30 261.0 419 6.100 7.950 1.860 87

Lmn Chkn 11.40 7.67 8.57 145.0 23.5 1.170 2.170 3.870 80 Cur Bodi 7.46 3.67 27.90 171.0 0.0 0490 0.680 2.000 90

Cur Crab

Bnna Brd

Shep Pie

Cur Grn Fg

Cur Tannia

Std pig tail

Std Trky

Pastel

Tuna Fritter

Soursop Punch

Sancoche

Cur Peas

Cur Pork

Chnse Shrimp

Brdft Chips

Veg Striry

Cur Btrmin

Corn Pie

Std Pgn Peas

Cur Channa

Fig Punch

Souse

Cheese Puffs

Nut Balls

Mngo Chtny

Plntn Chips

Prvsn Sp

Fried Bake

Tmto Chka

Fish Broth

Fried Aloo

Bf Lasagne

Chkn Lasagne

Veg Lasagne

Chkn Pie

Cheese Pie

Veg Pie

Dhall Pie

Banana Punch

Peanut Punch

Whtgrm Muf

Std Soya

Soya Wanton

Orange Pie

Popcorn Candy

Chkn Ft Soup

Cowhd Soup

5.64

4.90

7.10

4.64

1.64

13.60

14.30

6.21

13.70

1.42

7.75

1.86

13.80

11.50

0.32

1.25

1.49

4.54

3.39

3.93

2.77

15.30

7.92

18.90

8.22

0.36

2.04

3.16

0.98

8.30

1.71

10.70

13.30

10.90

12.80

19.60

3.41

6.24

3.34

8.51

7.90

9.12

6.91

3.33

1.05

3.01

3.82

5.33

17.80

7.11

11.20

2.10

29.50

6.03

7.54

36.60

1.18

0.49

0.39

20.30

5.87

68.00

1.64

4.73

1.87

3.60

4.35

2.56

18.10

24.80

6.83

3.96

68.00

1.35

53.70

2.65

0.47

5.11

10.60

9.36

10.20

12.20

23.10

12.40

16.20

3.63

14.50

9.81

6.34

3.23

10.30

5.26

1.53

2.19

4.11

43.00

9.55

12.60

22.30

1.94

5.09

15.70

5.21

17.30

14.00

9.02

2.00

6.65

8.11

7.38

9.71

11.80

14.60

16.70

31.30

1.32

11.30

60.00

38.54

8.88

13.10

23.80

5.34

3.96

13.30

10.30

9.85

11.30

21.10

22.20

23.20

29.30

16.80

22.10

33.30

6.67

9.56

32.70

92.10

12.50

12.40

84.8

345.0

127.0

164.0

108.0

326.0

129.0

148.0

404.0

77.2

84.4

38.8

244.0

132.0

646.0

41.8

78.2

78.4

101.0

109.0

152.0

229.0

299.0

373.0

219.7

649.0

70.2

587.0

45.1

51.2

101.0

175.0

173.0

176.0

241.0

370.0

210.0

279.0

111.0

248.0

242.0

114.0

91.4

235.0

415.0

73.3

82.4

10.9

36.0

21.3

5.6

0.0

102.0

41.0

20.0

17.8

4.0

11.9

0.0

57.9

81.6

0.0

0.0

0.0

5.8

0.0

0.0

8.6

103.0

25.8

8.6

3.2

0.0

0.0

0.0

0.0

14.9

0.0

42.9

43.5

38.3

29.1

36.0

4.5

5.9

13.5

12.9

3.0

0.0

1.2

9.2

0.0

16.0

3.3

4.000

8.750

2.940

8.080

0.300

10.000

1.450

2.720

5.080

0.650

0.120

0.030

6.860

0.900

9.780

0.231

0.600

1.080

0.540

0.480

1.530

6.260

6.070

1.120

0.903

9.770

0.200

7.730

0.380

0.110

0.740

5.260

5.130

5.630

4.180

11.400

3.830

5.000

2.230

4.170

1.600

0.920

0.480

3.920

0.600

0.270

0.640

0.380

4.810

2.490

0.880

0.190

13.600

2.040

3.130

10.800

0.290

0.080

0.020

8.840

1.270

15.800

0.346

0.950

0.530

0.790

0.790

0.650

8.500

10.100

3.290

2.237

15.800

0.300

12.500

0.590

0.090

1.170

3.430

2.660

2.900

4.920

8.050

4.640

6.080

1.040

6.320

2.580

1.420

0.700

4.080

1.510

0.340

0.670

0.430

3.180

1.010

1.370

0.850

3.810

1.940

0.950

19.100

0.040

0.150

0.080

3.050

3.200

39.300

0.893

2.370

0.160

2.050

1.900

0.150

1.970

7.680

2.060

0.473

39.300

0.740

31.000

1.490

0.150

2.930

1.050

0.960

1.020

2.410

2.480

3.250

4.260

0.140

3.330

5.090

3.590

1.760

1.780

2.900

0.760

0.720

43

91

76

69

73

58

67

94

99

59

79

69

60

76

70

59

55

70

16

51

65

59

83

70

96

80

90

100

54

80

76

21

22

23

97

93

94

84

55

68

88

56

93

89

87

90

89

179

Geera Pork

Anapas

Tamarind Sce

Jalabi

Upma

Channa Pie

Pepper Shrimp

Frd Bodi

Cur Pgn Peas

Chex Mix

Std Tuna

Cur Carite

Cur Red Fish

Cur Branch Fish

Std King Fish

Std Red Fish

Std Carite

Frd Strpd Fish

Frd Herring

Frd Jashwar

Frd Branch Fish

Pommerac

Coconut jelly

Chennette

Lychee

Caimit

Pewah

Karela

Ackee

Sugar cane

kush kush

2.81

0.28

1.37

5.93

6.30

9.79

11.60

3.50

12.80

13.10

9.07

9.48

6.64

7.33

9.38

7.02

11.50

11.60

11.90

10.20

0.60

4.00

1.10

0.90

0.80

1.20

1.60

2.90

0.30 1.50

12.30 16.20

58.60

0.06

46.60

48.80

18.00

3.58

5.66

5.14

16.20

4.06

3.99

4.07

4.60

2.53

3.21

2.67

13.10

16.40

18.80

15.30

0.10

24.80

0.20

0.10

1.60

0.20

0.20

15.20

0.00

0.20

2.83 205.0 50.7 5.770 7.390

25.90

17.80

31.50

28.20

32.40

6.91

40.90

13.40

63.80

4.75

4.90

4.79

5.88

5.14

4.94

4.89

12.40

12.40

12.40

15.50

8.00

8.30

19.90

15.80

14.50

20.20

4.40

0.80

18.00

27.90

628.0

69.9

549.0

572.0

305.0

103.0

259.0

111.0

439.0

106.0

88.1

90.1

87.1

70.8

84.8

72.7

211.0

241.0

264.0

238.0

32.5

266.4

80.2

61.7

71.6

81.8

11.4

140.8

73.2

119.4

5.2

0.0

0.0

1.4

6.3

67.3

0.0

0.0

0.0

19.7

15.8

15.8

42.6

26.5

15.9

13.6

39.1

29.3

34.2

50.4

0.0

1.4

0.0

0.0

0.0

0.0

0.0

8.8

0.0

0.0

9.620

0.030

6.710

7.230

5.380

0.560

0.780

0.800

2.200

0.730

1.080

1.100

1.240

0.400

0.500

0.420

1.960

2.710

3.310

2.200

0.000

0.580

0.000

0.000

0.000

0.000

0.000

2.720

0.000

0.000

14.300

0.020

10.800

11.500

6.630

0.760

1.110

1.010

5.920

0.970

0.730

0.750

0.860

0.570

0.720

0.600

3.070

4.580

5.420

3.530

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

1.710

31.900

0.010

26.900

27.500

4.860

1.900

3.280

2.940

5.290

2.100

1.670

1.730

1.950

1.360

1.710

1.430

7.240

7.900

9.190

8.670

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

65

100

77

90

54

84

66

96

13

116

61

54

53

54

68

68

67

94

94

94

94

60

21

86

86

14

72

100

80

86

74

180

APPENDIX G

Chi square tests

G.1 Gender by ethnicity: All subjects

G.2 CEL by ethnicity: All subjects

G.3 Occupation by ethnicity: All subjects

G.4 CEL by gender: All subjects

G.5 Occupation by gender: All subjects

G.5.1 Occupation by gender (professional category omitted: All subjects

G.5.2: Occupation by gender (housewife/husband category omitted): All subjects

G.6 CEL by ethnicity: Females

G.7 Occupation by ethnicity: Males

G.7.1 Occupation by ethnicity: Females

G.8 Study data vs. reference data: Gender

G.9 Study data vs. reference data: Ethnicity

G.10 Study data vs. reference data (primary school, none and other combined): CEL

G.10.1 Study data vs. reference data (none and other omitted): CEL

181

G.l

GENDER BY ETHNICITY: ALL SUBJECTS

The FREQ Procedure

Table of SOURCE by ETHNICITY

SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct AF OT SA Total

FEMALE 29 15 48 92

19.08 9,87 31.58 60.53

31.52 16.30 52.17

59.18 60.00 61.54

MALE 20 10 30 60

13.16 6.58 19.74 39.47

33.33 16.67 50.00

40.82 40.00 38.46

Total 49 25 78 152

32.24 16.45 51.32 100.00

Statistics for Table of SOURCE by ETHNICITY

Statistic DF Value Prob

Chi-Square 0.0733 0.9640

Likelihood Ratio Chi-Square 0.0733 0.9640

Mantel-Haenszel Chi-Square 0.0717 0.7889

Phi Coefficient 0.0220

Contingency Coefficient 0.0220

Cramer's V 0.0220

Sample Size 152

182

G.2

CEL BY ETHNICITY ALL SUBJECTS'


The FREQ Procedure


SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct AF SA

PRIMARY 9 17

7.20 13.60

34.62 65.38

19.15 21.79

SECONDAR 30 43

24.00 34.40

41.10 58.90

63.83 55.13

TERTIARY 8 18

6.40 14.40

30.77 69.23

17.02 23.08

Total 47 78

37.60 62.40

1

Total

26

20.80

73

58.40

26

20.80

125

00.00


Chi-Square 2 0.9960 0.6077

Likelihood Ratio Chi-Square 2 1.0077 0.6042

Mantel-Haenszel Chi-Square 1 0.0813 0.7755



Cramer's V 0.0893

Sample Size = 125

' Ethnic group “other” ommitted

183

G.3 OCCUPATION BY ETHNICITY ALL SUBJECTS?


The FREQ Procedure


SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct |AF SA

CLERICAL 10 13

7.87 10.24

43.48 56.52

20.41 16.67

HOUSEWIF 14 25

11.02 19.69

35.90 64.10

28.57 32.05

OTHER 6 14

4.72 11.02

30.00 70.00

12.24 17.95

PROFESSI 5 10

3.94 7.87

33.33 66.67

10.20 12.82

SKILLED 8 16

6.30 12.60

33.33 66.67

16.33 20.51

UNSKILLE 6 0

4.72 0.00

100.00 0.00

12.24 0.00

Total 49 78

38.58 61.42

Total

23

18.11

39

30.71

20

15.75

15

11.81

24

18.90

127

100.00


Chi-Square 5 10.9775 0.0518





Cramer's V 0.2940

Sample Size = 127

* Ethnic group “other” omitted.

184

G.4

CEL BY GENDER ALL SUBJECTS 3

The FREQ Procedure

Table of SOURCE by GENDER

SOURCE GENDER

Frequency

Percent

Row Pct

Col Pct FEMALE MALE Total

PRIMARY 20 9 29

13.33 6.00 19.33

68.97 31.03

21.74 15.52

SECONDAR 57 35 92

38.00 23.33 61.33

61.96 38.04

61.96 60.34

TERTIARY 15 14 29

10.00 9.33 19.33

51.72 48.28

16.30 24.14

Total 92 58 150

61.33 38.67 100.00

Statistics for Table of SOURCE by GENDER


Chi-Square 2 1.8565 0.3952





Cramer's V 0.1112

Sample Size = 150

> CEL category “other” omitted.

185

G.5 OCCUPATION BY GENDER ALL SUBJECTS

The FREQ Procedure


SOURCE GENDER

Frequency

Percent

Row Pct

Col Pct FEMALE |MALE

CLERICAL 16 12

10.53 7.89

57.14 42.86

17.39 20.00

HOUSEWIF 41 2

26.97 1.32

95.35 4.65

44.57 3.33

OTHER 16 9

10.53 5.92

64.00 36.00

17.39 15.00

PROFESSI 4 15

2,63 9.87

21.05 78.95

4.35 25.00

SKILLED 14 17

7.24 11.18

39.29 60.71

11.96 28.33

UNSKILLE 4 5

2.63 3.29

44.44 55.56

4.35 8.33

Total 92 60

60.53 39.47


Total

28

18.42

43

28.29

25

16.45

19

12.50

28

18.42

152

100.00


Chi-Square 5 40.7375 <.0001

Likelihood Ratio Chi-Square 5 47.3951 <.0001

Mantel-Haenszel Chi-Square 1 5.3587 <.0001



Cramer's V 0.5177

Sample Size = 152

186

G.5.1

OCCUPATION BY GENDER ALL SUBJECTS*

The FREQ Procedure


SOURCE GENDER

Frequency

Percent

Row Pct

Col Pct FEMALE MALE

CLERICAL 16 12

12.03 9.02

57.14 42.86

18.18 26.67

HOUSEWIF 41 2

30.83 1.50

95.35 4.65

46.59 4.44

OTHER 16 9

12.03 6.77

64.00 36.00

18.18 20.00

SKILLED 11 17

8.27 12.78

39.29 60.71

12.50 37.78

UNSKILLE 4 5

3.01 3.76

44.44 55.56

4.55 11.11

Total 88 45

66.17 33.83


1

Total

28

21.05

43 32.33

25

18.80

28

21.05

133

00.00


Chi-Square 4 28.3628 <.0001





Cramer's V 0.4618

Sample Size = 133

“ Occupation category “professional” omitted.

187

G.5.2

OCCUPATION BY GENDER ALL SUBJECTS?

The FREQ Procedure


SOURCE GENDER

Frequency

Percent

Row Pct Col Pct FEMALE MALE Total

CLERICAL 16 12 28

14.68 11.01 25.69

57.14 42.86

31.37 20.69

OTHER 16 9 25

14.68 8.26 22.94

64.00 36.00

31.37 15.52

PROFESSI 4 15 19

21.05 78.95

SKILLED 11 17 28

10.09 15.60 25.69

39.29 60.71

21.57 29.31

UNSKILLE 4 5 9

3.67 4.59 8.26

44.44 55.56

7.84 8.62

Total 51 58 109

46.79 53.21 100.00



Chi-Square 4 9.8879 0.0424





Cramer's V 0.3012

Sample Size = 109

> Occupation category “housewife/husband omitted.

188

G.6 CEL BY ETHNICITY FEMALES®


The FREQ Procedure


SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct AF SA

PRIMARY 5 13

6.49 16.88

27.78 72.22

17.24 27.08

SECONDAR 20 25

25.97 32.47

44.44 55.56

68.97 52.08

TERTIARY 4 10

5.19 12.99

28.57 71.438

13.79 20.83

Total 29 48

37.66 62.34

{

Total

18

23.38

45

58.44

14

18.18

77

00.00


Chi-Square 2 2.1235 0.3458





Cramer's V 0.1661

° Ethnic group “other “ omitted.

Sample Size = 77

189

G.7

OCCUPATION BY ETHNICITY MALES’

The FREQ Procedure


SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct |AF SA Total

HOUSEOTH 4 6 10

8.00 12.00 20.00

40.00 60.00

20.00 20.00

PROFCLER 9 13 22

18.00 26.00 44,00

40.91 59.09

45.00 43.33

SKILUNSK 7 11 18

14.00 22.00 36.00

38.89 61.11

35.00 36.67

Total 20 30 50

40.00 60.00 100.00



Chi-Square 2 0.0168 0.9916





Cramer's V 0.0183

Sample Size = 50

’ Ethnic group “other” omitted; occupation categories “housewife/husband” and “other” combined,

“professional” and “clerical” combined and “skilled” and “unskilled” combined.

190

G.7.1

OCCUPATION BY ETHNICITY FEMALES®

The FREQ Procedure


SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct |AF SA

HOUSEOTH 16 32

20.78 41.56

33.33 66.67

55.17 66.67

PROFCLER 6 V1

7.79 14.29

35.29 64.71

s 20.69 22.92

SKILUNSK 7 5

9,09 6.49

58.33 41.67

24.14 10.42

Total 29 48

37.66 62.34


1

Total

48 62.34

17

22.08

12

15.58

77

00.00


Chi-Square 2 2.6077 0.2715





Cramer's V 0.1840

* Ethnic group “other” omitted; occupation categories “housewife/husband” and “other” combined, “professional” and “clerical” combined and “skilled” and “unskilled” combined.

Sample Size = 77

191

G.8

STUDY VS REF GENDER The FREQ Procedure


SOURCE GENDER

Frequency

Percent

Row Pct

Col Pct F M

REF 78 74

25.66 24.34

51.32 48.68

45.88 55.22

STUDY 92 60

30.26 19.74

60.53 39.47

54.12 44.78

Total 170 134

55.92 44.08


{

Total

152

50.00

152

50.00

304

00.00


Chi-Square 1 2.6156 0.1058


Continuity Adj. Chi-Square 1 2.2553 0.1332




Cramer's V 0.0928

Fisher's Exact Test

Cell (1,1) Frequency (F) 78 Left-sided Pr <= F 0.0665

Right-sided Pr >= F 0.9585

Table Probability (P) 0.0250

Two-sided Pr <= P 0.1330

Sample Size = 304

192

G9 STUDY VE REF ETHNICITY

The FREQ Procedure


SOURCE ETHNICITY

Frequency

Percent

Row Pct

Col Pct AF OT SA

REF 58 29 65

19.08 9.54 21.38

38.16 19.08 42.76

54.21 53.70 45.45

STUDY 49 25 78

16.12 8.22 25.66

32.24 16.45 51.32

45.79 46.30 54.55

Total 107 54 143

35.20 17.76 47.04

Statistics for Table of

1

193

Total

152

50.00

152

50.00

304

00.00

SOURCE by ETHNICITY


Chi- Square 2 2.2351 0.3271





Cramer's V 0.0857

Sample Size = 304

G.10

STUDY VS REF CEL® The FREQ Procedure

Table of SOURCE by CEL

SOURCE CEL

Frequency

Percent

Row Pct

Col Pct REF STUDY

PRINONEO 81 31

26.64 10.20

72,32 27.68

53.29 20.39

SECONDAR 65 92

21.38 30.26

41.40 58.60

42.76 60.53

TERTIARY 6 29

1.97 9.54

17.14 82.86

3.95 19.08

Total 152 152

50.00 50.00

Statistics for Table of SOURCE by CEL

1

Total

112

36.84

157

51.64

35

11.51

, 304

00.00


Chi-Square 2 42.0790 <.0001

Likelihood Ratio Chi-Square 2 44,2459 <.0001




Cramer's V 0.3720

9 : : . CEL categories “primary, “none” and “other” combined.

Sample Size = 304

194

G.11

STUDY VS REF CEL" The FREQ Procedure

Table of SOURCE by CEL

SOURCE CEL

Frequency

Percent

Row Pct

Col Pct REF STUDY

PRIMARY 75 29

25.34 9.80

72.12 27.88

51.37 19.33

SECONDAR 65 92

21.96 31.08

41.40 58.60

44.52 61.33

TERTIARY 6 29

2.03 9.80

17.14 82.86

4.11 19.33

Total 146 150

49.32 50.68

Statistics for Table of SOURCE by CEL

1

Total

104

35.14

157

53.04

35

11.82

296

00.00


Chi-Square 2 40.0570 <.0001





Cramer's V 0.3679

'° CEL categories “none” and “other” omitted.

Sample Size = 296

195

APPENDIX H

Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients estimated from FR’s and the FFQ

H.1 All subjects

H.2 Males

H.3 Females

H.4 African’s

H.5 South Asian’s

H.6 FFQ 1" group

H.7 FFQ 2™ group

196

197

Table H.1 Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients estimated

from FR’s and the FFQ for all subjects.

Shapiro-Wilk | Skewness Kurtosis Shapiro-Wilk Skewness Kurtosis (p-value) FR FR (p-value) FFQ FFQ

FR FFQ

Pro (g) 0.97 0.64 0.61 0.92 1.51 6.56 (0.0034) (<0.0001)

Fat (g) 0.96 0.80 0.77 0.93 1.11 2.11 (0.0003) (<0.0001)

Tcarb (g) 0.94 1.15 3.27 0.90 1.59 5.30 (<0.0001) (<0.0001)

AvCarb (g) 0.94 1.23 3.65 0.90 1.61 5.22 (<0.0001) (<0.0001)

Energy (Kceal) 0.95 0.96 1.81 0.92 1.37 3.87 (<0.0001) (<0.0001)

Fiber (g) 0.98 0.49 0.17 0.93 1.30 3.87 (0.0174) (<0.0001)

Chol (g) 0.93 1.23 2.99 0.92 1.42 5.75 (<0.0001) (<0.0001)

SFA (g) 0.96 0.80 0.77 0.93 1.14 2.41 (0.0002) (<0.0001)

MEA (g) 0.95 0.96 1.22 0.96 0.87 1.59 (<0.0001) (0.0002)

PUFA (g) 0.95 0.89 0.87 0.90 1.50 3.44 (<0.0001) (<0.0001)

Ale (g) 0.22 6.97 54.99 0.38 7.53 73.62 (<0.0001) (<0.0001)

GI 0.98 -0.42 0.46 0.91 -0.76 3.70 (0.0234) (<0.0001)

p-values <0.05 indicate that a distribution differs significantly from normal.

198

Table H.1.1 Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients expressed

as a percentage of total energy estimated from FR’s and the FFQ for all subjects.

Shapiro-Wilk | Skewness Kurtosis Shapiro-Wilk Skewness Kurtosis

(p-value) FR FR (p-value) FFQ FFQ

FR FFQ

Pro% 0.98 0.57 0.91 0.98 0.13 0.25 (0.0105) (0.1074)

Fat% 0.98 -0.32 1.01 0.97 -0.61 0.39 (0.0136) (0.0031)

Carb% 0.99 -0.11 0.26 0.98 0.56 0.47 (0.7637) (0.0156)

Fiber 0.95 1.06 2.42 0.93 1.09 1.57 ¢/1000kcal (<0.0001) (<0.0001)

Chol 0.94 1.17 4.42 0.97 0.18 1.82 mg/1000kcal (<0.0001) (0.0017)

SFA% 0.99 2.86 0.32 0.97 0.29 2.11

(0.4590) (0.0017)

MFA% 0.99 -0.24 0.46 0.98 -0.50 0.07 (0.7633) (0.0179)

PUFA% 0.99 0.18 -0.41 0.99 0.28 0.28 (0.3845) (0.37)

P:S 0.95 0.94 1.19 0.97 0.73 1.61

(<0.0001) (0.0015)

Ale% 0.19 7.38 61.72 0.48 5.42 42.59 (<0.0001) (<0.0001)

p-values <0.05 indicate that a distribution differs significantly from normal

199


from FR’s and the FFQ for Males.


FR FFQ

Pro (g) 0.99 0.26 0.29 0.89 1.6 6.4

(0.85) (<0.01)

Fat (g) 0.97 0.47 0.02 0.94 0.98 1.72

(0.26) (0.006)

Tcarb (g) 0.98 —— -0.003 -0.7 0.92 1.2 2.2 (0.44) (0.0008)

AvCarb (g) 0.98 0.04 -0.7 0.93 0.97 2.06

(0.59) (0.003)

Energy (Kceal) 0.99 0.18 -0.5 0.93 1.13 2.78

(0.81) (0.003)

Fiber (g) 0.98 0.14 -0.47 0.94 0.89 0.84 (0.53) (0.009)

Chol (g) 0.95 0.97 2.05 0.91 1.34 4.77

(0.01) (0.0004)

SFA (g) 0.98 0.46 -0.008 0.93 1.07 2.24

(0.42) (0.003)

MFA (g) 0.95 0.76 0.46 0.96 0.73 1.74

(0.02) (0.05)

PUFA (g) 0.97 0.66 0.43 0.90 1.34 2.11 (0.14) (0.0001)

Ale (g) 0.32 4.5 21.9 0.41 5.62 37.28 (<0.01) (<0.01)

GI 0.98 0.06 -0.62 0.93 -0.66 3.91

(0.45) (0.002) p-values <0.05 indicate that a distribution differs significantly from normal

200


as a percentage of total energy estimated from FR’s and the FFQ for Males.



FR FFQ

Pro% 0.94 0.88 1.76 0.84 -0.20 8.36

(0.009) (<0.01)

Fat% 0.95 -0.9 1.9 0.91 0.79 5.0

(0.02) (0.0005)

Carb% 0.98 -0.3 -0.13 0.83 -2.0 11.35

(0.69) (<0.01)

Fiber 0.97 0.56 0.54 0.78 2.59 10.04

g/1000kcal (0.24) (<0.01)

Chol 0.87 1.8 5.6 0.94 0.52 2.44

mg/1000kcal (<0.01) (0.009)

SFA% 0.99 -0.08 0.08 0.95 -0.12 0.93

(0.88) (0.02)

MFA% 0.98 -0.41 0.94 0.94 0.12 2.35

(0.55) (0.009)

PUFA% 0.98 0.04 -0.58 0.93 1.07 3.23

(0.57) (0.003)

P:S 0.94 0.99 1.23 0.96 0.68 0.86 (0.005) (0.05)

Alc% 0.30 4.6 23.6 0.49 3.95 17.34

(<0.01) (<0.01) p-values <0.05 indicate that a distribution differs significantly from normal

201

Table H.3 Shapiro- Wilk scores and measures of skewness and kurtosis for macronutrients estimated

from FR’s and the FFQ for Females.



FR FFQ

Pro (g) 0.96 0.87 1.32 0.96 0.74 1.90 (0.004) (0.009)

Fat (g) 0.94 1.03 1.74 0.93 1.15 2.53 (0.0004) (<0.01)

Tcarb (g) 0.89 1.65 4,82 0.87 1.98 9.76 (<0.01) (<0.01)

AvCarb (g) 0.88 1.74 5.36 0.87 1.96 9.61 (<0.01) (<0.01)

Energy (Keal) 0.91 1.45 3.78 0.92 1.34 4.82 (<0.01) (<0.01)

Fiber (g) 0.96 0.67 0.56 0.90 1.63 6.46 (0.01) (<0.01)

Chol (g) 0.92 1.38 4.17 0.96 0.80 2.70 (<0.01) (0.004)

SFA (g) 0.94 1.05 1.88 0.94 1.02 2.04 (0.0004) (0.0007)

MEA (g) 0.94 1.07 2.07 0.95 0.90 1.51 (0.0003) (0.002)

PUFA (g) 0.93 1.05 1.35 0.89 1.62 5.06 (0.0001) (<0.01)

Alc (g) 0.31 6.23 46.38 0.59 2.37 5.50 (<0.01) (<0.01)

GI 0.97 -0.59 0.61 0.89 -0.76 3.65

(0.04) (<0.01) p-values <0.05 indicate that a distribution differs significantly from normal

202


as a percentage of total energy estimated from FR’s and the FFQ for Females.


FR FFQ

Pro% 0.99 0.31 0.26 0.97 0.54 0.92

(0.56) (0.04)

Fat% 0.98 0.34 -0.51 0.97 -0.60 0.41 (0.11) (0.04)

Carb% 0.99 -0.01 0.47 0.98 0.45 0.29 (0.70) (0.30)

Fiber 0.93 1.18 2.69 0.93 1.02 1.09 g/1000kcal (0.0002) (0.0001)

Chol 0.99 0.07 0.07 0.94 0.66 3.82 mg/1000kcal (0.68) (0.0005)

SFA% 0.98 0.53 0.51 0.96 0.72 3.17 (0.14) (0.004)

MFA% 0.99 -0.07 -0.16 0.97 -0.43 -0.32 (0.72) (0.06)

PUFA% 0.97 0.42 -0.46 0.99 0.27 0.11 (0.06) (0.66)

P:S 0.95 0.96 1.17 0.96 0.76 1.98 (0.0009) (0.01)

Ale% 0.31 6.03 43.77 0.57 2.77 8.74 (<0.01) (<0.01)


203

Table H.4 Shapiro- Wilk scores and measures of skewness and kurtosis for macronutrients estimated

from FR’s and the FFQ for African’s.


FR FFO

Pro (g) 0.94 1.0 1.68 0.95 0.89 2.50 (0.01) (0.04)

Fat (g) 0.94 0.81 0.24 0.96 0.77 0.83 (0.01) (0.10)

Tearb (g) 0.89 1.67 5.41 0.77 2.73 11.97 (0.0002) (<0.01)

AvCarb (g) 0.88 1.77 5.94 0.77 2.77 11.9 (0.0001) (<0.01)

Energy (Keal) 0.90 1.45 3.96 0.85 2.04 8.20 (0.0006) (<0.01)

Fiber (g) 0.95 0.77 0.35 0.83 2.18 8.0 (0.05) (<0.01)

Chol (g) 0.84 1.86 4.96 0.98 0.08 0.11 (<0.01) (0.67)

SFA (g) 0.96 0.74 0.55 0.98 0.44 0.07

(0.09) (0.59)

MFA (g) 0.94 0.58 -0.63 0.97 0.51 -0.09

(0.16) (0.18)

PUFA (g) 0.89 1.17 0.93 0.92 0.95 0.33 (0.0003) (0.002)

Ale (g) 0.27 5.04 26.56 0.72 1.6 2.1 (<0.01) (<0.01)

GI 0.93 -1.06 1.86 0.86 -1.78 5.19


204

Table H.4.1

Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients expressed

as a percentage of total energy estimated from FR’s and the FFQ for African’s.


FR FFQ

Pro% 0.99 0.23 0.39 0.97 0.49 0.68 (0.85) (0.19)

Fat% 0.96 0.61 0.09 0.98 -0.31 -0.12 (0.08) (0.63)

Carb% 0.94 -0.79 0.58 0.98 0.21 0.16 (0.02) (0.58)

Fiber 0.97 0.66 0.77 0.96 0.70 0.55 ¢/1000kcal (0.21) (0.15)

Chol 0.97 0.49 0.35 0.97 -0.54 -0.21 mg/1000kcal (0.20) (0.22)

SFA% 0.96 0.48 - 0.87 0.98 -0.39 0.26 (0.14) (0.64)

MFA% 0.98 -0.29 0.06 0.98 -0.18 -0.52

(0.55) (0.62)

PUFA% 0.98 0.37 -0.35 0.96 0.65 1.20 (0.40) (0.07)

P:S 0.96 0.71 0.35 0.96 0.88 1.89

(0.07) (0.07)

Ale% 0.23 5.69 34.4 0.66 2.55 8.50 (<0.01) (<0.01)


205


from FR’s and the FFQ for South Asian’s.



FR FFQ

Pro (g) 0.97 0.32 -0.74 0.87 1.88 7.73 (0.05) (<0.01)

Fat (g) 0.95 0.70 0.26 0.92 1.22 2.21 (0.004) (<0.01)

Tcarb (g) 0.97 0.63 0.22 0.92 1.24 3.34 (0.06) (0.0002)

AvCarb (g) 0.97 0.66 0.33 0.92 1.30 3.41 (0.04) (<0.01)

Energy (Kcal) 0.96 0.51 -0.50 0.91 1.47 4.03 (0.03) (<0.01)

Fiber (g) 0.98 0.18 -0.21 0.97 0.73 1.18 (0.50) (0.04)

Chol (g) 0.98 0.32 -0.03 0.89 1.67 5.79

(0.45) (<0.01)

SFA (g) 0.96 0.54 -0.26 0.90 1.43 3.00 (0.03) (<0.01)

MFA (g) 0.92 1.1 1.6 0.94 1.02 1.8 (0.0001) (0.0009)

PUFA (g) 0.94 1.0 1.50 0.89 1.41 2.74 (0.0008) (<0.01)

Alc (g) 0.29 5.0 26.4 0.61 1.95 2.76

(<0.01) (<0.01)

GI 0.98 -0.20 -0.40 0.88 -0.61 4.35


206

Table H.5.1 Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients expressed as a percentage of total energy estimated from FR’s and the FFQ for South Asian’s.



FR FFQ

Pro% 0.99 0.20 0.23 0.90 -1.47 7.07 (0.85) (<0.01)

Fat% 0.98 -0.07 -0.45 0.94 -0.85 0.45 (0.21) (0.001)

Carb% 0.99 0.25 0.02 0.95 0.84 0.74 (0.56) (0.004)

Fiber 0.93 1.2 2.4 0.94 0.94 0.67 ¢/1000kcal (0.0006) (0.0009)

Chol 0.90 1.49 5.47 0.95 0.50 2.86 mg/1000kcal (<0.01) (0.003)

SFA% 0.98 0.39 -0.08 0.98 -0.24 -0.26 (0.47) (0.42)

MFA% 0.99 0.16 -0.22 0.95 -0.75 0.28 (0.91) (0.003)

PUFA% 0.98 0.18 -0.28 0.99 -0.01 -0.29 (0.42) (0.89)

P:S 0.95 0.67 0.16 0.95 0.92 1.44 (0.008) (0.002)

Ale% 0.23 5.67 32.23 0.62 2.03 3.51 (<0.01) (<0.01)


207


from FR’s and the FFQ for the FFQ 1“ group.


FR FFQ

Pro (g) 0.94 0.86 1.46 0.85 2.10 9.65 (0.002) (<0.01)

Fat (g) 0.97 0.54 -0.22 0.91 1.33 3.01 (0.05) (<0.01)

Tcarb (g) 0.90 1.42 4.49 0.83 2.08 0.87 (<0.01) (<0.01)

AvCarb (g) 0.90 1.54 5.00 0.84 1.78 6.02

(<0.01) (<0.01)

Energy (Kcal) 0.93 1.00 2.06 0.84 1.74 5.67 (0.0009) (<0.01)

Fiber (g) 0.98 0.33 -0.23 0.86 1.79 5.21 (0.34) (<0.01)

Chol (g) 0.91 1.40 3.60 0.88 1.82 8.29 (<0.01) (<0.01)

SFA (g) 0.97 0.53 -0.11 0.92 1.27 3.17 (0.09) (0.0001)

MFA (g) 0.95 0.62 -0.03 0.93 1.17 2.96 (0.009) (0.0005)

PUFA (g) 0.92 1.17 1.64 0.88 1.55 3.25 (0.0001) (<0.01)

Ale (g) 0.14 8.39 71.70 0.61 2.37 5.74 (<0.01) (<0.01)

GI 0.95 -0.71 1.65 0.88 -1.23 3.86 (0.005) (<0.01)


Table H.6.1

Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients expressed

208

as a percentage of total energy estimated from FR’s and the FFQ for the FFQ 1* group.



FR FFQ

Pro% 0.97 0.45 -0.34 0.88 1.83 7.23 (0.08) (<0.01)

Fat% 0.97 -0.05 0.33 0.91 0.96 5.62 (0.07) (<0.01)

Carb% 0.96 -0.63 0.29 0.82 -2.26 12.62 (0.03) (<0.01)

Fiber 0.97 0.66 0.65 0.83 2.10 6.65 g/1000kcal (0.05) (<0.01)

Chol 0.88 1.77 6.40 0.93 0.95 3.24 mg/1000kcal (<0.01) (0.0006)

SFA% 0.98 0.36 0.53 0.98 1.22 0.69 (0.45) (0.52)

MFA% 0.99 0.01 -0.10 0.97 0.32 2.20 (0.94) (0.05)

PUFA% 0.98 0.20 -0.44 0.95 1.01 2.78

(0.26) (0.005)

P:S 0.93 0.77 -0.17 0.94 1.05 2.84

(0.0009) (0.002)

Alce% 0.13 8.54 73.52 0.48 4.37 24.43 (<0.01) (<0.01)


209

Table H.7

Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients estimated

from FR’s and the FFQ for the FFQ 2" group.

Shapiro-Wilk | Skewness Kaurtosis Shapiro-Wilk Skewness Kurtosis FR FR FR FFQ FFQ FFQ

(p-value) (p-value)

Pro (g) 0.97 0.52 0.05 0.96 0.49 -0.45

(0.1) (0.02)

Fat (g) 0.93 1.09 1.93 0.95 0.92 1.32 (0.0004) (0.003)

Tcarb (g) 0.97 0.63 0.77 0.97 0.62 0.14

(0.08) (0.06)

AvCarb (g) 0.97 0.64 0.71 0.96 0.70 0.18

(0.07) (0.01)

Energy (Kcal) 0.96 0.92 1.74 0.96 0.55 -0.39

(0.01) (0.03)

Fiber (g) 0.97 0.65 0.85 0.97 0.22 -0.81 (0.04) (0.09)

Chol (g) 0.93 1.16 2.87 0.96 0.80 1.21 (0.0004) (0.01)

SFA (g) 0.93 1.14 2.15 0.94 0.96 1.37

(0.0003) (0.002)

MFA (g) 0.91 1.28 2.20 0.97 0.62 0.52 (<0.01) (0.05)

PUFA (g) 0.96 0.55 -0.11 0.90 1.49 3.93

(0.02) (<0.01)

Alc (g) 0.38 5.54 33.90 0.31 7.14 57.36 (<0.01) (<0.01)

GI 0.98 -0.12 -0.67 0.92 -0.33 3.72


210

Table H.7.1 Shapiro-Wilk scores and measures of skewness and kurtosis for macronutrients expressed as a percentage of total energy estimated from FR’s and the FFQ for the FFQ 2nd group.


FR FFQ

Pro% 0.95 0.67 1.86 0.90 -1.41 6.36 (0.003) (<0.01)

Fat% 0.97 -0.47 1.33 0.98 -0.54 0.22 (0.09) (0.16)

Carb% 0.99 0.22 0.04 0.97 0.66 0.45 (0.89) (0.06)

Fiber 0.92 1.33 3.52 0.96 0.70 0.13 ¢/1000kcal (0.0001) (0.01)

Chol 0.99 0.13 0.02 0.97 -0.45 -0.19 mg/1000keal (0.75) (0.09)

SFA% 0.99 0.07 0.11 0.93 0.84 4.35 (0.72) (0.0007)

MFA% 0.99 -0.18 0.14 0.96 -0.60 -0.03 (0.90) (0.03)

PUFA% 0.99 0.15 -0.34 0.98 0.34 0.44 (0.93) (0.46)

P:S 0.92 1.13 2.35 0.97 0.39 0.20 (0.0001) (0.04)

Ale% 0.25 5.9 38.9 0.40 6.07 44.47 (<0.01) (<0.01)


APPENDIX I

Frequency distributions for macronutrient and % macronutrient data from FR’s

and FFQ’s

211

Figure I.1

212

Frequency Distributions for Protein (g) from 7-day Food Record and FFQ data. oa

®

oO oO

i 4

Frequency ih

oO

f

>

N

wo

oO oO

So Qo

\ !

L L

—@— ProFR

—t— Pro FFQ

12 3 4 5 6 7 8 9 10 11 12

Protein

Intake ranges divided into 12 equal intervals’


ranges for protein from FR’s and FFQ’s

Intervals of Protein Protein

intake FR FFQ

ranges

1 33.9 29.7

2 43.9 54.0

3 54.0 78.4

4 64.0 102.7

5 74.0 127.0

6 84.0 151.4

7 94.0 175.7

8 104.0 200.0

9 114.0 224.4

10 124.0 248.8 11 134.0 273.1

12 144.1 297.4

' Number of intervals determined automatically by Microsoft excel, Windows 2000.

213

Figure [.2

Frequency Distributions for Total Carbohydrate (g) from 7-day Food Record and FFQ

data.

60 -

50 + —@— Tcarb FR

fig Tcarb FFQ 40 +

30 5

Freq

uenc

y

20 5

10 - 12 3 4 5 6 7 8 9 10 11 12

Total carbohydrate



ranges for total carbohydrate from FR’s

and FFQ’s

Intervals of Tearb Tearb

intake FR FFQ

ranges

1 72.0 66.3

2 118.4 147.0

3 164.7 227.8

4 211.0 308.6

5 257.4 389.3

6 303.8 470.0 7 350.1 550.8

8 396.5 631.6

9 442.8 712.4

10 489.2 793.1

11 535.5 873.9

12 581.9 954.6

Number of intervals determined automatically by Microsoft excel, Windows 2000.

214

Figure [.3

Frequency Distributions for Available Carbohydrate (g) from 7-day Food Record and

FFQ data.

oa

Oo i

AA

oun

Lo

Ww

oun

14

—e— AvCarb FR

~—~t@-- AvCarb FFQ

Frequency

N

ND

om

L 1

=

= oao a

poop

T T T T T T T T

12 3 45 6 7 8 9 10 11 12

Available carbohydrate



ranges for total carbohydrate from FR’s

and FFQ’s

Intervals of Avecarb Avearb

intake FR FFQ

ranges

1 66.0 59.6

2 110.6 132.1

3 155.0 204.6

4 199.6 277.0

5 244.0 349.5

6 288.6 422.0

7 333.0 494.5

8 377.6 567.0 9 422.1 639.5

10 466.6 711.9

11 511.1 784.4

12 555.6 856.9


215

Figure I.4

Frequency Distributions for Total energy (kcal) from 7-day Food Record and FFQ data.

—e— Keal FR

tia Keal FFQ

Frequency

12 3 4 5 6 7 8 9 10 11 12

Total Energy (kcal)



ranges for total energy from FR’s and

FFO’s

Intervals of | Totalenergy Total energy

intake FR FFQ

ranges

1 693.0 595.9

2 993.0 1103.0

3 1293.1 1610.1

4 1594.0 2117.2

5 1893.0 2624.3

6 2193.2 3131.4

7 2493.3 3638.5

8 2793.3 4145.7 9 3093.4 4652.8

10 3393.5 5159.9

11 3693.5 5667.0

12 3993.6 6174.1


Figure 1.5

Frequency Distributions for Fiber (g) from 7-day Food Record and FFQ data.

Frequency

1

—¢e— Fiber FR

—@~— Fiber FFQ

T T T T m

23 4 5 6 7 8 9 10 11 12

Fiber


216


ranges for fiber from FR’s and FFQ’s

Intervals of | Fiber Fiber

intake FR FFQ

ranges

1 5.5 6.7

2 75 15.0

3 9.8 23.2

4 12.2 31.5

5 14.6 39.8

6 17.0 48.0

7 19.3 56.3

8 21.7 64.6

9 24.1 72.6 10 26.5 80.8

11 28.8 89.0

12 31.2 97.3


Figure 1.6

217

Frequency Distributions for Cholesterol (g) from 7-day Food Record and FFQ data.

—e— Chol FR

—i— Chol FFQ

Frequency

nN

a

12 3 4 5 6 7 8 9 10 11 12

Cholesterol

Intake ranges divided into 12 equal intervals


ranges for cholesterol from FR’s and

FFQ’s

Intervals of Cholesterol Cholesterol

intake FR FFQ

ranges

1 $4.7 48.0

2 110.8 120.8

3 167.0 193.6

4 223.2 266.4

5 279.3 339.2

6 335.4 412.0 7 391.6 484.8

8 447.7 557.7

9 503.9 630.5

10 560.0 703.3

11 616.2 776.1

12 672.3 848.9

‘Number of intervals determined automatically by Microsoft excel, Windows 2000.

218

Figure I.7

Frequency Distributions for Saturated Fatty Acid (SFA) (g) from 7-day Food Record and

FFQ data.

Frequency

wo #

ono

Oo =

NY

NY

W

oo

oO ©

| 1

| |

10

ze

—e— SFA FR

—@—- SFA FFQ T T T T T T T T T

12 3 45 6 7 8 9 10 11 12

SFA



ranges for SFA from FR’s and FFQ’s

Intervals of SFA SFA

intake FR FFQ

ranges

1 4.2 5.8

2 8.7 12.1

3 13.3 18.4

4 17.8 24.7

5 22.3 31.0

6 26.8 37.3 7 31.3 43.6

8 35.8 49.9

9 40.3 56.2

10 44.8 62.5

11 49.2 68.8

12 53.7 75.1


Figure 1.8

219

Frequency Distributions for Monounsaturated Fatty Acid (MFA) (g) from 7-day Food

Record and FFQ data.

Frequency

NO

oS 0 + TOT TE

12 3 45 6 7 8 9 10 11 12

MFA


—e— MFA FR

-—@—MFA FFQ


ranges for MFA from FR’s and FFQ’s

Intervals of MFA MFA

intake FR FFQ

ranges

1 40 5.4

2 9.0 12.2

3 13.9 19.0

4 18.8 25.7

5 23.7 32.6

6 28.7 39.3

7 33.6 46.1 8 38.5 52.9

9 43.5 59.7

10 48.4 66.5

11 53.3 73.3

12 58.2 80.1

Number of intervals determined automatically by Microsoft excel, Windows 2000.

Figure I.9

220

Frequency Distributions for Polyunsaturated Fatty Acid (PUFA) (g) from 7-day Food

Record and FFQ data.

oO

Bh

A

aoa

! 1

J

—e— PUFA FR

—@— PUFA FFQ NN

w

on

Oo

L !

L

Frequency

a

10 + T T T T T

45 6 7 8 9 10 11 12

PUFA

1 2 3

Intake ranges divided into 12 equal intervals!


ranges for PUFA from FR’s and FFQ’s

Intervals of | PUFA PUFA

intake FR FFQ

ranges

1 3.4 3.0

2 6.5 8.2

3 9.7 13.4

4 12.8 18.7

5 15.9 23.9

6 19.0 29.1 7 22.1 34.3

8 25.2 39.6

9 28.3 44.8

10 31.4 50.0

11 34.5 55.2

12 37.6 60.5


221

Figure I.10(a)

Frequency Distributions for Alcohol (g) from 7-day Food Record and FFQ data.

140 -

120 -

100 +

80 - —e—Alc FR 60 - —#—Alc FFQ

40

20 -

Frequency

12 3 4 5 6 7 8 Q9 10 11 12

Alcohol



ranges for alcohol from FR’s and FFQ’s

Intervals of | Alcohol Alcohol

intake FR FFQ

ranges

1 0 0

2 1.0 2.2

3 2.1 4.5

4 3.2 6.7

5 4.3 8.9 6 5.3 11.2

7 6.4 13.4

8 75 15.6

9 8.5 17.9

10 9.6 20.1

11 10.7 22.3

12 11.7 24.6


222

Figure I.10(b)

Frequency Distributions for Alcohol (g) from 7-day Food Record and FFQ data with two

outliers removed.

140 -

120 +

100 |

80 4 —e—Alc FR

60 - -—tig— Alc FFQ

Alc

FFQ

40

20 + 1 2 3 4 5 6 7 8 9 10 11 12

Aic FR



ranges for alcohol with two outliers

removed from FR’s and FFQ’s.

Intervals of | Alcohol Alcohol

intake FR FFQ

ranges

1 123 86

2 20 16

3 2 16

4 1 11

5 1 4

6 1 4

7 0 8 8 0 3

9 1 0

10 0 1

11 1 1

12 0 0


Figure I.11

223

Frequency Distributions for Protein expressed as a percentage of total energy from 7-day

Food Record and FFQ data.

Frequency

1)

oO 04 TO TT TT

123 45 67 8 9

% Protein

T T T T 1

10 11 12

—e—% ProFR

-i@-—- % Pro FFQ



ranges for % Protein from FR’s and

FFQ’s

Intervals of % Protein % Protein

intake FR FFQ

ranges

1 8.5 11.7

2 10.3 12.8

3 12.0 13.9

4 13.9 15.0

5 15.7 16.2

6 17.5 17.3

7 19.4 18.4 8 21.2 19.6

9 23.0 20.7

10 24.8 21.8

11 26.6 23.0

12 28.4 24.0 ' Number of intervals determined automatically by Microsoft excel, Windows 2000.

224

Figure I.12

Frequency Distributions for Fat expressed as a percentage of total energy from 7-day


—e— % Fat FR

mii Yo Fat FFQ

Frequency 0 + TT TFT TO TOT

12 3 45 6 7 8 9 10 11 12

% Fat

Intake ranges divided into 12 equal intervals'


ranges for % Fat from FR’s and FFQ’s

Intervals of | % Fat % Fat

intake FR FFQ

ranges

1 12.7 13.7

2 15.4 16.2

3 18.2 18.8

4 20.9 21.4

5 23.7 24.0

6 26.4 26.5

7 29.2 29.1

8 31.9 31.7 9 34.6 34.2

10 37.4 36.8

11 40.1 39.4

12 42.9 41.9


225

Figure 1.13

Frequency Distributions for Carbohydrate expressed as a percentage of total energy from

7-day Food Record and FFQ data.

—e— % Carb FR

—~ti— % Carb FFQ

Frequency

No

oO

}

O70, T T T T T T T T T 1

12 345 6 7 8 9 10 11 12

% Carbohydrate



ranges for % Carbohydrate from FR’s

and FFQ’s

Intervals of % Carb. % Carb.

intake FR FFQ

ranges

1 32.4 35.5

2 35.2 38.7

3 38.0 41.8

4 40.9 45.0

5 43.8 48.1

6 46.6 $1.3 7 49.5 54.4

8 52.3 57.6

9 55.2 60.7

10 58.0 63.9

11 60.8 67.0

12 70.2 70.2

’ Number of intervals determined automatically by Microsoft excel, Windows 2000.

Figure 1.14

226

Frequency Distributions for Fiber g/1000kcal from 7-day Food Record and FFQ data.

45 -

40 -

35 +

> 30 + o —e Fiber g/1000kcal 3 25 - FR = 20 + ~@— Fiber g/1000kcal

i FFQ 15 +

10 +

5 _

0 = T T t T T T T T q T

1234567 8 9 101112

Fiber g/1000kcal



ranges for Fiber g/1000kcal from FR’s

and FFQ’s.

Intervals of Fiber Fiber

intake 2/1000kcal 2/1000kcal

ranges FR FFQ

1 3.8 6.3

2 5.2 8.2

3 6.7 10.1

4 8.2 12.0

5 9.7 13.9

6 11.1 15.8 7 12.6 17.7

8 14.1 19.6

9 15.6 21.5

10 17.0 23.4

11 18.5 25.3

Figure L15 12 20.0 27.2


227

Frequency Distributions for Cholesterol mg/1000kcal from 7-day Food Record and FFQ

data. Frequency

123 45 6 7 8 9 101112

Cholesterol mg/1000kcal

FR

FFQ


—— Chol mg/1000kcal

~@-- Chol mg/1000kcal


ranges for Cholesterol mg/1000kcal


from FR’s and FFQ’s.

. Intervals of Chol. Chol.

intake mg/1000kcal mg/1000kcal

ranges FR FFQ

1 31.0 23.9

2 64.6 43.3

3 98.1 62.6

4 131.7 82.0

5 165.2 101.4

6 198.8 120.7

7 232.3 140.0

8 265.9 159.4

9 299.5 178.8 10 333.0 198.2

11 366.6 217.5

12 400.1 236.9

228

Figure 1.16

Frequency Distributions for Saturated Fatty Acid (SFA) expressed as a percentage of

total energy from 7-day Food Record and FFQ data. is

o ]

NO

@©

©

no

ao

L 1

!

—eo— % SFA FR

it Yo SFA FFQ

Frequency

= N

ao

I L

10 +

0-5 Tw T 7 T

123 45 67 8 9 10 11 12

% SFA



ranges for % SFA from FR’s and FFQ’s.

Intervals of % SFA % SFA

intake FR FFQ

ranges

1 3.4 3.7

2 4.8 5.2

3 6.1 6.8

4 75 8.3

5 8.9 9.9

6 10.2 11.5

7 11.6 13.0 8 13.0 14.6

9 14.4 16.2

10 15.7 17.8

11 17.1 19.3

12 18.5 20.9


229

Figure I.17

Frequency Distributions for Monounsaturated Fatty Acid (MFA) expressed as a percentage of total energy from 7-day Food Record and FFQ data.

35 -

30 -

25 |

5 20 —e—% MFA FR 5 15 4 tit Y MFA FFQ

49. 5.

0 | ee 12 345 6 7 8 9 10 11 12

% MFA



ranges for % MFA from FR’s and FFQ’s.

Intervals of % MFA % MFA

intake FR FFQ

ranges

1 3.1 4.0

2 4.3 5.0

3 5.4 6.0

4 6.6 7.0

5 7.8 8.0

6 8.9 9.0

7 10.1 10.0

8 11.3 11.0 9 12.4 12.0

10 13.6 13.0

11 14.8 14.0

12 15.9 15.0


Figure 1.18

Frequency Distributions for the P:S ratio from 7-day Food Record and FFQ data. NO

©

WwW nao

a

©

l L

! j

—e—P:S FR

—@—P:S FFQ

Frequency

=

N

a

©

| i

10 - 12 3 4 5 6 7 8 9 10 11 12

P:S


230


ranges for the P:S from FR’s and FFQ’s.

Intervals of | P:S P:S

intake FR FFQ

ranges

1 0.19 0.15

2 0.36 0.27

3 0.53 0.39

4 0.69 0.51

5 0.86 0.64

6 1.0 0.76

7 1.2 0.88 8 1.4 0.99

9 1.5 1.12

10 1.7 1.24

11 1.9 1.36

12 2.0 1.48

‘Number of intervals determined automatically by Microsoft excel, Windows 2000.

231

Figure I.19(a)

Frequency Distributions for Alcohol expressed as a percentage of total energy from 7-day


140 -

120 +

100 -

80 + —e—% Alc FR

60 + ttt % Alc FFQ

40 -

20 +

Frequency

12 3 45 67 8 9 10 11 12

% Alcohol



ranges for % Alcohol from FR’s and

FFQ’s.

Intervals of % Alcohol % Alcohol

intake FR FFQ

ranges

1 0 0

2 0.41 0.46

3 0.81 0.92

4 1.22 1.38

5 1.63 1.84

6 2.04 2.30

7 2.45 2.77 8 2.86 3.23

9 3.27 3.69

10 3.68 4.15

11 4.09 4.62

12 4.50 5.08

1 Number of intervals determined automatically by Microsoft excel, Windows 2000.

Figure I.19(b)

232

Frequency Distributions for Alcohol expressed as a percentage of total energy with two

outliers removed from 7-day Food Record and FFQ data .

140 5

120

100 -

% Alc

FFQ

40

20 + 12 3 4 5 6 7 8 9 10 11 12

% Alc FR


80 - —~e—% Alc FR

60 4 —@—% Alc FFQ


ranges for % Alcohol with two outliers

removed from FR’s and FFQ’s.

Intervals of

intake

ranges

Ceram

tk WN =

% Alcohol

FR

126 19

eocoococooorror


% Alcohol

FFQ

86 13 18

ma We

KS AN

BB ~]

APPENDIX J

Pearson correlation coefficients for transformed and untransformed nutrient intake

values, and Spearman rank correlation coefficients for nutrient intake values for FR

vs. FFQ data.

J.1 All subjects

J.2 Males

J.3 Females

J.4 African’s

J.5 South Asian’s

J.6 FFQ 1* group

J.7 FEQ 2™ group

233

234

Table J.1


values, and Spearman rank correlation coefficients for nutrient intake values for FR vs.

FFQ data: All subjects.

Pearson p- Pearson p- Spearman p-

untransfor value transformed value rank value

med

Pro (g) 0.44 <.0001 0.49 <.0001 0.49 <.0001

Fat (g) 0.51 <.0001 0.56 <.0001 0.52 <.0001

Tcarb (g) 0.35 <.0001 0.43 <.0001 0.38 <.0001

AvCarb (g) 0.36 <.0001 0.43 <.0001 0.38 <.0001

Energy (Keal) 0.43 <.0001 0.48 <,0001 0.46 <.0001

Fiber (g) | 0.25 <.0001 0.26 0.0011 0.25 0.0018

Chol (g) 0.42 <.0001 0.57 <.0001 0.45 <.0001

SFA (g) 0.47 <.0001 0.53 <.0001 0.53 <.0001

MFA (g) 0.51 <.0001 0.55 <.0001 0.49 <.0001

PUFA (g) 0.44 <.0001 0.49 <.0001 0.47 <.0001

Alc (g) 0.10 0.2335 0.65 0.2335 0.10 0.2379

GI 0.25 0.0021 0.23 0.0047 0.22 0.0058 p-values <0.05 indicate statistically significant correlation between FR’s and the FFQ.

235

Table J.1.1 Pearson correlation coefficients for transformed and untransformed nutrient intake values

expressed as a percentage of total energy, and Spearman rank correlation coefficients for

percent nutrient intake values for FR vs. FFQ data: All subjects.

Pearson p- Pearson p- Spearman p- untransformed value transformed value rank value

Pro% 0.46 <.0001 0.48 <.0001 0.45 <.0001

Fat% 0.48 <.0001 0.43 <.0001 0.47 <.0001

Carb% 0.45 <.0001 0.46 <.0001 0.45 <.0001

Fiber ¢/1000kcal 0.34 <.0001 0.34 <.0001 0.32 <.0001

Chol mg/1000kcal 0.37 <.0001 0.53 <.0001 0.36 <.0001

SFA% 0.39 <.0001 0.43 <.0001 0.45 <.0001

MFA% 0.38 <.0001 0.38 <.0001 0.35 <.0001

PUFA% 0.25 0.0019 0.25 0.0019 0.21 0.0096

P:S 0.30 0.0001 0.26 0.0011 0.27 0.0007

Ale% 0.19 0.0213 0.75 0.0018 0.10 0.2447

p-values <0.05 indicate statistically significant correlation between FR’s and the FFQ.

Table J.2


236


FFQ data: Males.


untransformed value transformed value rank value

Pro (g) 0.43 0.0007 0.49 <.0001 0.48 <,0001

Fat (g) 0.50 <.0001 0.54 <.0001 0.51 <.0001

Tcarb (g) 0.37 0.0035 0.40 0.0013 0.39 0.0021

AvCarb (g) 0.41 0.0012 0.41 0.0012 0.41 0.0010

Energy (Kceal) 0.45 0.0003 0.50 <.0001 0.46 0.0002

Fiber (g) 0.25 0.0565 0.25 0.0579 0.23 0.07

Chol (g) 0.47 0.0002 0.68 <.0001 0.56 <,0001

SFA (g) 0.42 0.0009 0.48 0.0001 0.48 0.0001

MFA (g) 0.55 <.0001 0.63 <.0001 0.55 <.0001

PUFA (g) 0.41 0.0011 0.50 <.0001 0.48 0.0001

Alc (g) 0.06 0.6391 0.83 0.0392 0.003 0.9801


237



percent nutrient intake values for FR vs. FFQ data: Males.


untransformed value transformed value _ rank value

Pro% 0.19 0.1366 0.17 0.1886 0.28 0.0280

Fat% 0.39 0.0018 0.42 0.0008 0.42 0.0008

Carb% 0.31 0.0154 0.45 0.0004 0.43 0.0007

Fiber ¢/1000kcal 0.29 0.0253 0.31 0.0165 0.26 0.0408

Chol mg/1000kcal 0.40 0.0016 0.56 <.0001 0.46 0.0002

SFA% 0.42 0.0008 0.45 0.0003 0.39 0.0019

MFA% 0.42 0.0007 0.42 0.0007 0.34 0.0079

PUFA% 0.15 0.2506 0.20 0.1291 0.22 0.0972

P:S 0.34 0.0082 0.34 0.0073 0.29 0.0223

Ale% 0.13 0.3195 0.87 0.0256 0.05 0.7076


238

Table J.3


values, and Spearman rank correlation coefficients for nutrient intake values for FR vs. FFQ data: Females.

Pearson p-value Pearson p-value Spearman p-

untransformed transformed rank value

Pro (g) 0.44 <.0001 0.45 <.0001 0.46 <.0001

Fat (g) 0.51 <.0001 0.53 <.0001 0.52 <.0001

Tcarb (g) 0.32 0.0018 0.42 <.0001 0.36 0.0004

AvCarb (g) 0.33 0.0014 0.43 <,0001 0.37 0.0003

Energy (Kcal) 0.41 <.0001 0.47 <.0001 0.44 <.0001

Fiber (g) 0.26 <.0001 0.31 0.0025 0.28 0.0068

Chol (g) 0.37 0.0003 0.49 <.0001 0.32 0.0019

SFA (g) 0.51 <.0001 0.53 <.0001 0.55 <.0001

MFA (g) 0.45 <.0001 0.48 <.0001 0.44 <.0001

PUFA (g) 0.46 <.0001 0.49 <.0001 0.46 <.0001

Ale (g) 0.15 0.1547 0.30 0.4816 0.15 0.1555


239

Table J.3.1 Pearson correlation coefficients for transformed and untransformed nutrient intake values expressed as a percentage of total energy, and Spearman rank correlation coefficients for

percent nutrient intake values for FR vs. FFQ data: Females.



Pro% 0.59 <.0001 0.60 <.0001 0.52 <.0001

Fat% 0.45 <.0001 0.40 <.0001 0.44 <.0001

Carb% 0.42 <.0001 0.43 <.0001 0.41 <.0001

Fiber g/1000kcal 0.29 0.0045 0.30 0.0033 0.32 0.0020

Chol mg/1000kcal 0.38 0.0002 0.45 <.0001 0.33 0.0014

SFA% 0.37 0.0003 0.42 <.0001 0.46 <.0001

MFA% 0.32 0.0018 0.31 0.0027 0.33 0.0016

PUFA% 0.14 0.1885 0.13 0.2299 0.12 0.2676

P:S 0.27 0.0090 0.22 0.0358 0.22 0.0369

Ale% 0.10 0.3498 0.53 0.1723 0.13 0.2045


Table J.4


240


FFQ data: African’s.

Pearson p- Pearson p- Spearman . p-


Pro (g) 0.36 0.0101 0.45 0.0013 0.39 0.0060

Fat (g) 0.39 0.0052 0.40 0.0046 0.29 0.0430

Tearb (g) 0.26 0.0746 0.27 0.0632 0.24 0.0989

AvCarb (g) 0.27 0.0627 0.27 0.0588 0.21 0.1487

Energy (Kceal) 0.33 0.0219 0.35 0.0140 0.31 0.0284

Fiber (g) 0.12 0.4240 0.133 0.3616 0.11 0.4311

Chol (g) 0.39 0.0060 0.47 0.0007 0.36 0.0122

SFA (g) 0.16 0.2854 0.16 0.2854 0.17 0.2433

MEA (g) 0.36 0.0105 0.37 0.0087 0.34 0.0160

PUFA (g) 0.52 0.0001 0.47 0.0006 0.38 0.0064

Ale (g) 0.41 0.0035 0.52 0.3692 0.19 0.1794


241


percent nutrient intake values for FR vs. FFQ data: African’s.



Pro% 0.50 0.0002 0.51 0.0002 0.49 0.0003

Fat% 0.28 0.0525 0.28 0.0525 0.32 0.0245

Carb% 0.32 0.0272 0.26 0.0723 0.36 0.0117

Fiber ¢/1000kcal 0.12 0.3979 0.15 0.3089 0.17 0.2520

Chol mg/1000kcal 0.52 0.0001 0.51 0.0002 0.59 <.0001

SFA% 0.12 0.4702 0.11 0.4475 0.09 0.5346

MFA% 0.19 0.2023 0.24 0.1000 0.21 0.1478

PUFA% 0.23 0.1149 0.23 0.1149 0.14 0.3519

P:S 0.21 0.1543 0.16 0.2747 0.18 0.2052

Ale% 0.64 <.0001 0.94 0.0566 0.19 0.1964 p-values <0.05 indicate statistically significant correlation between FR’s and the FFQ.

Table J.5


242


FFQ data: South Asian’s.



Pro (g) 0.48 <.0001 0.48 <.0001 0.49 <.0001

Fat (g) 0.49 <.0001 0.52 <.0001 0.54 <,0001

Tcarb (g) 0.44 <.0001 0.50 <.0001 0.49 <.0001

AvCarb (g) 0.44 <.0001 0.51 <.0001 0.49 <.0001

Energy (Keal) 0.47 <.0001 0.49 <,0001 0.50 <.0001

Fiber (g) 0.39 0.0005 0.35 0.0018 0.34 0.0020

Chol (g) 0.49 <.0001 0.56 <.0001 0.52 <.0001

SFA (g) 0.54 <.0001 0.56 <.0001 0.61 <.0001

MEA (g) 0.51 <.0001 0.51 <.0001 0.50 <,0001

PUFA (g) 0.34 0.0020 0.39 0.0004 0.40 0.0003

Alec (g) 0.19 0.1010 0.50 0.2501 0.05 0.6837


243


percent nutrient intake values for FR vs. FFQ data: South Asian’s.



Pro% 0.50 <.0001 0.53 <.0001 0.48 <.0001

Fat% 0.51 <.0001 0.42 0.0001 0.54 <.0001

Carb% 0.55 <.0001 0.57 <.0001 0.54 <.0001

Fiber g/1000kcal 0.45 <.0001 0.46 <.0001 0.45 <.0001

Chol mg/1000kcal 0.35 0.0019 0.55 <.0001 0.34 0.0020

SFA% 0.64 <.0001 0.64 <.0001 0.65 <.0001

MFA% 0.41 0.0002 0.38 0.0005 0.44 <.0001

PUFA% 0.12 0.29 0.12 0.2893 0.08 0.4812

P:S 0.39 0.0004 0.38 0.0006 0.37 0.0008

Alc% 0.17 0.1411 0.65 0.1114 0.05 0.6533 p-values <0.05 indicate statistically significant correlation between FR’s and the FFQ.

244

Table J.6


values, and Spearman rank correlation coefficients for nutrient intake values for FR vs. FFQ data: FFQ1* group.

Pearson p- Pearson p-value Spearman p- untransformed value _ transformed rank value

Pro (g) 0.35 0.0022 0.38 0.0007 0.38 0.0008

Fat (g) 0.49 <.0001 0.48 <,0001 0.48 <.0001

Tcarb (g) 0.29 0.0127 0.36 0.0014 0.32 0.0052

AvCarb (g) 0.30 0.0080 0.35 0.0021 0.32 0.0046

Energy (Kcal) 0.39 0.0005 0.44 <.0001 0.44 <.0001

Fiber (g) 0.25 0.0318 0.25 0.0309 0.21 0.0755

Chol (g) 0.38 0.0008 0.55 <.0001 0.38 0.0007

SFA (g) 0.46 <.0001 0.48 <.0001 0.50 <.0001

MFA (g) 0.49 <.0001 0.44 <.0001 0.38 0.0007

PUFA (g) 0.43 0.0001 0.44 <.0001 0.47 <.0001

Alc (g) 0.21 0.0768 0.92 0.0789 0.05 0.6641


245



percent nutrient intake values for FR vs. FFQ data: FFQ 1* group.



Pro% 0.26 0.0226 0.30 0.0081 0.33 0.0037

Fat% 0.29 0.0107 0.32 0.0048 0.37 0.0012

Carb% 0.20 0.0811 0.28 0.0139 0.35 0.0020

Fiber ¢/1000kcal 0.33 0.0033 0.38 0.0007 0.39 0.0006

Chol mg/1000kceal 0.38 0.0007 0.56 <.0001 0.45 <.0001

SFA% 0.46 <.0001 0.46 <.0001 0.46 <.0001

MFA% 0.17 0.1553 0.17 0.1553 0.14 0.2403

PUFA% 0.03 0.7932 0.07 0.5511 0.06 0.5841

P:S 0.39 0.0005 0.37 0.0011 0.35 0.0022

Alce% 0.13 0.2583 0.93 0.0669 0.03 0.7806 p-values <0.05 indicate statistically significant correlation between FR’s and the FFQ.

Table J.7


246


FFQ data: FFQ 2™ group.



Pro (g) 0.55 <.0001 0.56 <.0001 0.59 <.0001

Fat (g) 0.54 <.0001 0.62 <.0001 0.59 <.0001

Tcarb (g) 0.45 <.0001 0.47 <.0001 0.47 <.0001

AvCarb (g) 0.47 <.0001 0.48 <.0001 0.48 <,0001

Energy (Keal) 0.50 <.0001 0.54 <.0001 0.56 <.0001

Fiber (g) 0.27 0.0171 0.27 0.0171 0.33 0.0035

Chol (g) 0.46 <.0001 0.60 <.0001 0.52 <.0001

SFA (g) 0.51 <.0001 0.58 <.0001 0.59 <.0001

MFA (g) 0.54 <.0001 0.64 <.0001 0.62 <.0001

PUFA (g) 0.46 <.0001 0.55 <.0001 0.51 <.0001

Alec (g) 0.07 0.5347 0.60 0.0497 0.12 0.2908


247



percent nutrient intake values for FR vs. FFQ data: FFQ and group.



Pro% 0.48 <.0001 0.44 <.0001 0.52 <.0001

Fat% 0.55 <.0001 0.49 <.0001 0.49 <.0001

Carb% 0.51 <.0001 0.52 <.0001 0.48 <.0001

Fiber g/1000kcal 0.28 0.0139 0.28 0.0135 0.27 0.0180

Chol mg/1000kcal 0.37 0.0009 0.37 0.0009 0.28 0.0136

SFA% 0.34 0.0026 0.40 0.0003 0.44 <,0001

MFA% 0.54 <.0001 0.54 <.0001 0.50 <,0001

PUFA% 0.27 0.0176 0.27 0.0176 0.26 0.0204

P:S 0.23 0.0402 0.23 0.0410 0.24 0.0331

Alc% 0.18 0.1250 0.71 0.0215 0.14 0.2188 p-values <0.05 indicate statistically significant correlation between FR’s and the FFQ.

APPENDIX K

Scatter-grams between FR’s and FFQ’s for macronutrient and % macronutrient

data

248

Figure K.1 FFQ versus 7-day Food Record: Protein (g).

FFQ

Prot

ein

350 +

300 -

250 +

200 +

150 +

100 -

50 +

249

Figure K.2

FFQ versus 7-day Food Record: Total Carbohydrate (g)

FFQ

Total

Carboh

ydra

te

1200 -

1000 -

800 -

600 +

400 -

200 -

50 100 150 200

FR Protein

100 200 300 400 500 600 700

FR Total Carbohydrate

250

Figure K.3

FFQ versus 7-day Food Record: Available Carbohydrate (g).

1000 -

900 +

800 -

700 +

600 -

500 +

400 -

300 +

200 -

100 -

0 T 7 T T T T 1

0 100 200 300 400 500 600 700

FR Available Carbohydrate

FFQ Available

Carbohydrate

Figure K.4

FFQ versus 7-day Food Record: Total Energy (kcal).

8000 -

7000 +

y a

a eo

6 eo

6 5

6 |

I

4000 -

3000 +

FFQ

Tota

l En

erg

2000 -

1000 +

T 7 r

0 1000 2000 3000 4000 5000 6000

FR Total Energy

Figure K.5

FFQ versus 7-day Food Record: Fiber (g).

120 -

S

100 +

_ 805 .

2 %e iL 60 + °° ° S o eo ¢, o 3

° eo re AO - eee oeo*.? ?. ¢ o

4 ° “o wengee sf .

20 + of o% oe °S o °

oo *% ¢ i

0 T T T T T T T 7

0 5 10 15 20 25 30 35 40

FR Fiber

Figure K.6

FFQ versus 7-day Food Record: Cholesterol (g).

FFQ Ch

oles

tero

l

FR Cholesterol

251

252

Figure K.7

FFQ versus 7-day Food Record: Saturated Fatty Acid (SFA) (g).

FFQ SFA

th oO

0 10 20 30 40 50 60 70

Figure K.8

FFQ versus 7-day Food Record: Mono-unsaturated Fatty Acid (MFA) (g).

100 - 90 - 80 - ° 70 - 60 - 50 - 40 - 30 - 20 - 10 - 0 . |

0 10 20 30 40 50 60 70 FR MFA

FFQ

MFA

Figure K.9

FFQ versus 7-day Food Record: Poly-unsaturated Fatty Acid (PUFA) (g)

FFQ

PUFA

70 5

60 +

50 +

40 -

30 |

20 +

10 -

Figure K.10 (a)

FFQ versus 7-day Food Record: Alcohol (g)

FFQ Al

coho

l

30 5

25 +

20 +

10 20 30 40 50

yw rN wT T T 4 T T T 1

2 4 6 8 10 12 14

FR Alcohol

253

Figure K.10 (b)

254

FFQ versus 7-day Food Record: Alcohol (g) with two outliers removed.

FFQ

Figure K.11

FR Alcohol

FFQ versus 7-day Food Record: Protein expressed as a percentage of total energy.

30 -

25 +

20 +

15 +

FFQ

% Pr

otei

n

10 -

T T T T T

10 15 20 25 30 35

FR % Protein

255

Figure K.12

FFQ versus 7-day Food Record: Fat expressed as a percentage of total energy.

50 -

45 +

40 +

35 +

30 +

25 +

20 +

15 -

10 5

54

0 1

0 10 20 30 40 50

FR % Fat

FFQ

% Fa

t

Figure K.13

FFQ versus 7-day Food Record: Carbohydrate expressed as a percentage of total energy.

80 -

70 -

60 -

50 -

40 -

30 +

FFQ

% Carbohydrate

20 +

10 5 0 T T T T T T 1

0 10 20 30 40 50 60 70

FR % Carbohydrate

256

Figure K.14 FFQ versus 7-day Food Record: Fiber g/1000kcal

35 5

30 +

25 4

20 +

15 4

FFQ

% Fiber

L 10

FR Fiber g/1000kcal

Figure K.15

FFQ versus 7-day Food Record: Cholesterol mg/1000kcal

300 +

250 + °

200 |

150 +

100 +

FFQ %

Cholesterol

50 +

FR Cholesterol mg/1000kcal

Figure K.16

257

FFQ versus 7-day Food Record: Saturated Fatty Acid (SFA) expressed as a percentage of total energy.

FFQ

% SFA

Figure K.17

30 -

25 +

20 |

15 +

10 -

FR % SFA

FFQ versus 7-day Food Record: Mono-unsaturated Fatty Acid (MFA) expressed as a percentage of total energy.

FFQ

% MFA

5 10 15 20

FR %MFA

Figure K.18

258

FFQ versus 7-day Food Record: Poly-unsaturated Fatty Acid (PUFA) expressed as a percentage of total energy.

16 -

14 4 °

* 6 12 4 . *~6 oe

< o o ° u 10 - ° We ee ‘ °

oO 8. oh Fo 8%

3 eS be ON ° o wu 64 oe toy © oe °

4 - *o, 4 $ ° +

2 4

0 T 7 T T T 1

0 2 4 6 8 10 14

FR % PUFA

Figure K.19

FFQ versus 7-day Food Record: Ratio between Poly-unsaturated Fatty Acid (PUFA) and Saturated Fatty Acid (SFA).

FFQ

P:S

1.8 5

1.6 5

1.4 4

1.2 4

14

0.8 5

0.6 -

0.4 5

0.2 4

259

Figure K.20(a)

FFQ versus 7-day Food Record: Alcohol expressed as a percentage of total energy. FFQ

% Alcohol

FR % Alcohol

Figure K.20(b)

FFQ versus 7-day Food Record: Alcohol expressed as a percentage of total energy with

two outliers removed.

0 0.5 1 1.5 2 2.5

FR % Alcohol

oO

¢

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