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Codon 54 in the Fatty Acid–Binding Protein-2 (A54T)

Polymorphism studies in Type 2 Diabetes Mellitus in Saudi

Population

Khalid K Alharbi1, Mohammad D. Bazzi2, Nasser M Al-Daghri3, Imran Ali Khan 1*, May Salem

Al-Nbaheen4, 5, Fawiziah K Alharbi6, Yazeed A. Al-Sheik1, Rabbani Syed1, Motasim Ahmad

Atieh2 and Mohammed Ghouse Ahmed Ansari3

1Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud

University, P.O. Box 10219, Riyadh 11433, Kingdom of Saudi Arabia.

2Genome Research Chair, Department of Biochemistry, College of Science, King Saud

University, Riyadh 11451, Kingdom of Saudi Arabia.

3Biomarkers Research Program, Department of Biochemistry, College of Science, King Saud

University, Riyadh 11451, Kingdom of Saudi Arabia.

4Stem Cell unit, Department of Anatomy, College of Medicine, King Khalid University Hospital,

Kingdom of Saudi Arabia.

5Prepratory Year-Saudi Electronic University, Riyadh, Saudi Arabia.

6Department of Biology Science, College of Science and Arts, Al-Qassim University, P.O. Box

1300, Buraidah 51431, Kingdom of Saudi Arabia.

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Corresponding Author:

*Dr. Imran Ali Khan Mohammed

Dept. of Clinical Laboratory Sciences,

College of Applied Medical Sciences,

King Saud University,

Riyadh-11433,

Kingdom of Saudi Arabia,

Tel.:+966-567288142,

Fax: +966-14693630,

Email id: imranpataan@gmail.com & imkhan@ksu.edu.sa

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Abstract:

Diabetes is a multi-factorial diseases induced by genetic variations at multiple loci,

environmental factors, and gene-environment interactions. The prevalence of this health problem

is rapidly increasing throughout the world and particularly in the Kingdom of Saudi Arabia. A

large number of genes have been implicated in Type 2 Diabetes Mellitus (T2DM), but in

population dependent manner. The FABP2 gene codes for a protein responsible for the

absorption of long chain fatty acids. We investigated association of FABP2 A54T gene

polymorphism with T2DM cases. Genotyping was carried out by real time-polymerase chain

reaction using purified DNA. The study included 460 healthy controls and 438 T2DM cases.

None of the allele or genotype of FABP2 A54T was associated with T2DM cases versus the

controls (OR=1.219, 95% CI=0.988-1.502; p=0.06). Clinical data and anthropometric

measurements of the patients were significantly different from those of the controls (p<0.05).

We concluded that A54T polymorphism was not associated with either T2DM in Saudi

population.

Keywords: Type 2 Diabetes Mellitus, FABP2 Gene, A54T Polymorphism and Saudi Population.

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INTRODUCTION:

Type 2 Diabetes Mellitus (T2DM) is a global pandemic with multifactorial etiology, where

genetic, behavioral and environmental factors contribute to its development (Daghestani et al.,

2012) .T2DM prevalence has increased throughout the world, mainly due to rapid lifestyle

changes (Penas-Steinhardt et al., 2011). Obesity, a major recognized risk factor for T2DM,

cardiovascular diseases (CVD), osteoarthritis, cancer and mental health problems (Binh et al.,

2011), is itself rapidly increasing in prevalence resulting in a ‘diabesity’ epidemic. The

prevalence of obesity is rising in European and Middle East countries as a consequence of recent

changes in modern societies that promote high intake of caloric rich food and low physical

activity. The increase in body fat is a major contributor to the development of hyperlipidemia,

insulin resistance, and hypertension, and is associated with chronic diseases such as T2DM,

coronary heart disease (CHD), and certain forms of cancer (Al-Daghri et al., 2012). The adipose

tissue is a secretory organ that secrets many bioactive molecules that regulate metabolism and

energy homeostasis including insulin resistance, glucose intolerance, and hypertriglyceridemia.

Epidemiological studies have shown that low levels of adiponectin predicted later development

of T2DM (Ohrvall et al., 1996). Several risk factors including common, easily measurable

phenotypic features (such as obesity, hypertension, undesirable lipid profile and impaired fasting

glucose) as well as parental history of diabetes are already used efficiently to predict the

development of T2DM (Xi et al., 2012).

T2DM and its associated diseases: Overweight, obesity, hypertension, and CVD are complex

health problems often are the results of interaction of environmental and multiple genetic factors

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influencing body mass index (BMI), with heritability estimated at 40–70% (Stephan et al.,

2012). Genome Wide Association Studies (GWAS) are powerful tools to identify genetic

variants that are associated with common diseases. So far, GWAS identified at least 50-52

genetic loci robustly associated with T2DM (Wilson et al., 2007) and obesity (Stephan et al.,

2012). Several genetic markers have now been implicated for T2DM development pathways

involved in the disease (Albuquerque et al., 2013; Perry et al., 2012), distinct or overlapped with

monogenic forms of the disease (Lyssenko et al., 2009) and correlated with phenotypes

(Albuquerque et al., 2013; Prokopenko et al., 2009). However, the GWAS of T2DM have not

been able to explain the clinical heterogeneity of the disease. T2DM subjects vary appreciably in

their clinical characteristics, age of diagnosis, BMI, and autoimmune component to their diabetes

(Voight et al., 2010). In contrast, the identification of the genetic component to monogenic forms

of diabetes has often explained the clinical heterogeneity observed (Albuquerque et al., 2013).

The intestinal fatty acid–binding protein-2 (FABP2) gene codes a protein expressed in

enterocytes and is responsible for the absorption of long-chain fatty acids (FA) which absorbs

across the intestinal mucosa membrane. A single nucleotide polymorphism (SNP) A54T in the

FABP2 gene at codon 54 causes an amino acid change from Alanine→Threonine increases the

affinity of intestinal FABP for long-chain FA. This change affects the ability of the protein to

transport dietary FA (Freathy et al., 2010), which may elevate saturated fatty acids level in the

serum which might induce endothelial dysfunction leading to increased cardiovascular mortality.

In subjects without diabetes, the presence of the A54T polymorphism has been associated with

increases in serum postprandial lipids (Tuomi et al., 1999) in obese (Stephan et al., 2012).

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We have examined FABP2 A54T polymorphism in about 900 Saudi subjects: 460 controls and

438 T2DM patients. The purpose of the present study was to identify whether FABP2 Gene is a

susceptibility gene patients with T2DM. Our study focused particularly on the A54T

polymorphism, since this polymorphism results in a functionally altered FABP2 protein which

confers susceptibility to metabolic disorders like T2DM, thereby to contribute to the

personalized prevention of this condition.

MATERIALS AND METHODS:

Subjects of the study:

The subjects of this study were healthy controls or T2DM patients. All samples were collected

from primary health care outpatient and poly clinics in Riyadh, Kingdom of Saudi Arabia.

Informed consent was obtained from each patient involved in the study, ethical approval for the

study was obtained from the Ethics Committee of the College of Medicine, King Saud

University, Riyadh and Kingdom of Saudi Arabia. All healthy controls (n=460) selected based

upon the age and gender, had a normal oral glucose tolerance test. The study excluded all

subjects having history of other metabolic disorders apart from T2DM.

T2DM samples were secured by senior physicians at King Khalid University hospitals. The

inclusion criteria for T2DM were fasting plasma glucose of >7.0mmol/L, and the patients had

developed the disease early and not <5 years. The exclusion criteria were those with history of

ketoacidosis or exocrine pancreatic disease, or with other metabolic disorders. Total of 438

T2DM samples were collected from the clinics.

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Sample Collection:

Five milliliter of venous blood was collected and 3ml of the serum sample was used for the

biochemical analysis and 2ml of the EDTA sample was used for the molecular analysis. The

blood samples of obese students were all obtained after diagnosis from the pathology laboratory

for genotyping using standard protocol.

Clinical and anthropometric measurements:

All clinical and anthropometric parameters were measured by trained personnel of health care

centers. Height and body weight were measured to the nearest 0.5 cm or 0.1 kg. Waist

circumference was measured to the nearest 0.5 cm at the levels between the midpoint of the

lowest rib, while the hip circumference was measured to the nearest 0.5 cm at maximum

extension of the buttocks. BMI was calculated as weight/height2 (kg/m2). Subjects with BMI

>30 kg/m2 were categorized as obese group. Blood pressure of the subjects was measured in a

sitting position taking the mean of the two reading 30 min apart. Hypertension was defined as

mean systolic blood pressure of 140 mm Hg and/or a diastolic blood pressure of 90 mm Hg.

Biochemical Parameters:

Fasting blood biochemical parameters: HDL-C, triglycerides, total cholesterol, and plasma

glucose were measured by automated clinical chemistry analyzer (Kit provided by KoneLab,

Espoo, Finland) using commercially available kits. Insulin was quantitated by immunoassay

(Medgenix INS-ELISA, Biosource, Belgium). Insulin resistance was measured by homeostasis

model assessment (HOMA-IR), using the formula: Insulin resistance = insulin (μU/ml) ×glucose

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(mmol/l)/ 22.5. Dyslipidemia (low levels of HDL-C) was defined as HDL-C levels <1.03 mmol/l

for men and <1.29 mmol/l for women (Almeida et al., 2010).

Molecular analysis:

Genomic DNA was extracted from peripheral blood leukocytes using Norgen DNA extraction kit

(Norgen Biotek corp, Canada). DNA samples were stored at -80°C.

TaqMan Genotyping:

The rs1799883 polymorphism was genotyped using a TaqMan® SNP genotyping assay (Assay

ID: C_2834835_10) on a 7300HT sequence detection system (Applied Biosystems). Primers and

probes were obtained from Applied Biosystems as Assays-by-Design™. Cases and controls were

ensured to have even treatment during the assay procedure, and each plate included negative

controls (with no DNA). Plates were read on the ABI Prism 7300 using the Sequence Detection

Software (Applied Biosystems) using 40 PCR cycles (92°C denaturation for 15 seconds, 60°C

annealing/extension for 60 seconds). Measurements were repeated for samples with failed

genotypes. Assays that did not show >95% concordance were discarded and replaced with

alternative assays with the same tagging properties. The distribution of the genotypes did not

deviate from Hardy-Weinberg equilibrium (HWE) (p >0.05) using Hap Stat program.

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Statistical methods:

The genotype distribution of rs1799883 among control versus T2DM patients were compared by

χ2 test (Case-Control studies). Statistical analysis was performed by Statistical Package for

Social Sciences (SPSS 16.0 SPSS Inc., Chicago, USA.). Genotype specific risks were estimated

as odds ratio (ORs) using unconditional logistic regression (95% confidence). We explored the

association for co-dominant model, dominant model, recessive model and allele versus allele,

respectively. All non-Gaussian variables were either log or square root transformed. Differences

between variables were computed using t test and ANOVA. Allele frequency difference between

controls and T2DM subjects was determined by Chi-square test, where p<0.05 was considered to

be statistically significant.

RESULTS:

Clinical Characteristics:

Clinical and anthropometric data are shown in Table 1 for control and T2DM patients. The

results show that T2DM subjects were significantly older than controls but Anthropometric

measurements including Weight, Height, BMI were not significant by different (p>0.05). Waist

and hip circumference were significantly higher in T2DM patients compared with control

subjects (p<0.05). T2DM subjects appear to have higher levels of fasting glucose, insulin and

HOMA-IR as well as triglycerides, LDL-C, systolic and diastolic blood pressures but not HDL-C

(p<0.05).

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Genotype frequencies:

The distribution of A54T polymorphisms of FABP2 gene are provided in Table 2. We observed

that the frequencies of CC, CT and TT genotypes of A54T SNP were not different to statistical

significance. FABP2 genotypic frequencies of A54A, A54T, and T54T were 56.6%, 37.1%, and

6.3% in the control group, 51.3%, 39.8% and 8.9% in the T2DM group; allelic frequencies of

Ala and Thr were 0.75 and 0.25 for the control group, 0.71 and 0.29 for the T2DM group. The

minor allelic frequencies of Controls and T2DM were 0.25 and 0.29 respectively (p>0.05).

Similarly, the frequency of CC, CT and TT genotypes of A54T SNP was comparable among the

control and T2DM (minor allelic frequency; 6.3 vs. 8.9, p>0.05). There were no significant

differences in the frequencies of the genotypic distributions of FABP2 A54T between the control

group and T2DM patients (p=0.43) (Table 2). The odds ratio for any genotype of A54T SNP was

not significantly related with the risk of developing T2DM or obesity in this studied population

(p=0.12) from the Saudi Population.

DISCUSSION:

In this study, we explored the effects of rs1799883of the FABP2 gene variants in the Saudi

population. FABP2 is an intracellular protein which is expressed only in the columnar absorptive

epithelial cells of the small intestine. It contains a single high affinity site for saturated and

unsaturated FA, and contributes to the absorption and intracellular transport of long-chain FA

(Canani et al., 2005). The product of A allele of FABP2 possesses a greater affinity for long-

chain FA than the G allele (Lowe et al., 1987). In addition, individuals with the A allele of this

polymorphism were more insulin resistant than were those with the G allele (Baier et al., 1995).

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The A allele was also shown to be associated with higher plasma levels of LDL-C (Yamada et

al., 1997) and dyslipidemia (high plasma concentration of triglycerides and low concentration of

HDL-C) (Galluzzi et al., 2001). In addition, the A allele of the 2445G➝A (A54T) polymorphism

has previously been associated with atherothrombotic cerebral infarction in individuals with

metabolic syndrome (Guettier et al., 2005) and a parental history of stroke in the Swedish

population (Yamada et al., 2008). Moreover, it was associated with a 2- to 3.5- fold increase in

cardiovascular risk in dyslipidemic men with diabetes compared with their dyslipidemic

nondiabetic counterparts (Carlsson et al., 2000). We have now shown that the 2445G➝A

(A54T) polymorphism was not significantly associated with T2DM, with the minor allele

representing the risk factor for this condition.

A54T polymorphism was identified via linkage disequilibrium map, as a haploblock spanning 50

kb that includes 22 SNPs. However, the T54 allele is present in only one of six possibilities

among the frequent haplotypes (>2%). It is interesting that in this haploblock, there are no other

known or putative genes except for FABP2 (Tuomi et al., 1999).

Perassolo MS, et al group have previously reported that patients with T2DM and micro

albuminuria had higher levels of saturated fatty acids, than normoalbuminuric patients

(Georgopoulos et al., 2007). Higher levels of saturated fatty acids as related to endothelial

dysfunction are risk factors for cardiovascular mortality (Perassolo et al., 2003) renal disease,

endothelial dysfunction and glomerular damage (Tuomi et al., 1999; Lowe et al.,

1987).Moreover, this polymorphism has also been associated with insulin resistance and higher

levels of triglycerides in the fasting state and after a fatty meal challenge (Perassolo et al., 2003)

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Previous studies of A54T polymorphisms investigated lipid related diseases such as coronary

artery disease (CAD) (Ohrvall et al., 1996). Canani LH et al., (1987) showed that A54T

polymorphism confers susceptibility to renal disease in T2DM patients. There are very few

reports on the association of this polymorphism with T2DM. To the best of our knowledge this is

the first study to investigate the association of FABP2 A54T polymorphism with T2DM in the

Saudi population. None of the alleles or genotypes examined was associated with T2DM. In the

demographic details, BMI, FBS, Triglycerides, HDL-C and LDL-C were associated significantly

with diabetes when (p<0.005). In our study, all T2DM patients had family history of T2DM

whereas only 52.3% of the control group had such a history.

Several studies from non-European ethnic backgrounds have reported a positive association

between FABP2 variants and T2DM. A54T genotyping was carried out in relation to multiple

diseases and the reports are summarize in Table 3, which shows positive association with T2DM

and a combination of other diseases like Chronic Kidney Disease, Microalbminuria, Postprandial

fatty acids and with the Glyburide therapy. In our study, we have examined only T2DM (n=438)

samples and that have not examined its association with other disease.

Many of the T2DM associations reported are spurious and not confirmed by replication studies.

In this study we found that this gene had a minor effect on T2DM in Saudis, since the frequency

of the risk genotype among case subjects was only ~12%. Therefore, other genes might be more

involved in the genetic predisposition for diabetes in Saudis. It has been proposed that genetic

association studies should include a large number of patients. Though our sample size is good

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(n=898), results showed no significant association between A54T of the FABP2 gene with

T2DM in the Saudi population. There might be correlation between genetic and certain

biochemical measurements such as (glucose blood level, insulin, TG, TC, LDL-C, HDL-C, etc).

The possibility that other SNPs in FABP2 may influence the disease risk in these populations

cannot be excluded. Studies with a larger sample size in different populations will help us

to understand relationship between FABP2 A54T polymorphism and Fatty acids parameters in

Cardiovascular diseases and other disease like diabetes mellitus and Obesity (Table 4).

Acknowlegements:

The authors extend their appreciation to the College of Applied Medical Sciences Research

Center and the Deanship of Scientific Research at King Saud University for funding this

research.

Funding:

This research received no precise grant from any of the funding agency in the public,

community, commercial, or not-for-profit sectors.

Competing interests:

No potential conflict of interest relevant to this article is reported.

Authors’ contributions:

All the authors have been equally contributed.

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polymorphism of BCHE with ischemic stroke in Japanese individuals with chronic

kidney disease. Mol Med Rep. 2,779-85.

32. Rios-Gonzalez, B.E. Luevano-Ortega, K.E. Saldana-Cruz, A.M. Gonzalez-Garcia,

J.R. Magana-Torres, M.T. 2011.

Polymorphisms of seven genes involved in lipid metabolism in an unselected

Mexican population. Genet. 16, 90:e114-9.

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G. Torres, F. Heras, M. Farres, X. Vaquer, J.V. Martinez, J.M. Almaraz, M.C. Anguera,

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hypercholesterolaemic patients: A randomized trail. Atherosclerosis. 211,630-637.

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Note: Data represented as mean±SD for continuous variables, pValues for independent T-test are given. p-value

significant at <0.05. NA= Not Analyzed/ Not Applicable.

Controls (n=460) T2DM (n=438) p

Age (Years) 45.99+7.77 53.5+10.78 <0.0001

Weight (kg) 76.61+14.52 77.37+13.55 0.41

Height (cm) 161.25+8.79 161.10+9.30 0.80

Body mass index (kg/m2) 29.22+5.58 29.9+5.89 0.95

Sex: Male/Female (52.6%)/ (47.4%) (57.3%)/ (42.7%) 0.0002

Hypertension (%) 8.7% 6.4% NA

SBP (mmHg) 114.80+8.04 125.83+9.96 <0.0001

DBP (mmHg) 75.81+6.20 81.25+4.82 0.0001

Waist 89.75+14.19 95.3+18.96 <0.0001

Hips 101.75+14.72 99.64+16.52 0.01

FBS (mmol/L) 5.23+0.61 12.92+4.60 <0.0001

Triglycerides (mmol/l) 1.62+0.86 2.24+1.62 <0.0001

Cholesterol (mmol/l) 5.04+0.96 5.61+1.26 <0.0001

HDLC (mmol/l) 0.64+0.23 0.84+0.37 <0.0001

LDLC (mmol/l) 3.66+0.85 3.76+1.05 0.0008

Total cholesterol/ LDLC 9.42+2.33 8.74+1.83 0.003

Glucose (mmol/l)

5.7±1.2 9.4±1.5 0.0002

Insulin(μU/ml) 12.5±1.8 16.2±2.2 0.0002

HOMA-IR 3.15±1.9 6.8±2.4 0.00008

Family History 233 (50.7%) 438 (100%) <0.0001

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Table 1: Demographic characteristics of the study population

Genotypes Controls (n=460) T2DM (n=438)

CC 260 (56.6%) 225 (51.3%)

CT 171 (37.1%) 174 (39.8%)

TT 29 (6.3%) 39 (8.9%)

C 691 (0.75) 624 (0.71)

T 229 (0.25) 252 (0.29)

Table 2: Distribution of FABP2 A54T genotypes and alleles of this study

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S.No Population Cases Controls Association Disease Reference

1 Germany - 68 No Obesity [7]

2 Japan 228 813 Yes MI + Chronic Kidney Disease [31]

3 Japan 636 1106 Yes Chronic Kidney Disease +T2DM [29]

4 Japan 313 971 Yes Atherothrombic cerebral infarction [23]

5 Brazil 72 37 Yes T2DM + Microalbminuria [26]

6 Mexico - 131 No Lipid metabolism [32]

7 Brazil 513 529 Yes Renal Disease +T2DM [17]

8 Brazil 26 529 Yes Postprandial fatty acids + T2DM [16]

9 Spain 108 101 Yes Hypercholesterolemic [33]

10 Present Study 438/115 469 No T2DM and Obesity

Table 3: Association Studies of FABP2 A54T gene polymorphism on different ethnic groups

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Genotypes SPSS Version 19.0 Odds Ratio

TT Vs CT+CC OR=1.452, 95%CI=0.8816-2.394; p=0.1411

CT+TT Vs CC OR=1.231, 95%CI=0.9462-1.601; p=0.1216

CT Vs CC+TT OR=1.114, 95%CI=0.8512-1.4582; p=0.4319

T Vs C OR=1.219, 95%CI=0.9886-1.502; p=0.063

Table 4: Genotypic and Allelic distribution of FABP2 gene in T2DM patients versus healthy

controls