Mol Biol Rep DOI 10.1007/s11033-014-3022-z
Association between type 2 diabetes mellitus-related SNP variants and obesity traits in a Saudi population Nasser M. Al-Daghri • Khalid M. Alkharfy • Omar S. Al-Attas • Soundararajan Krishnaswamy Abdul Khader Mohammed • Omar M. Albagha • Amal M. Alenad • George P. Chrousos • Majed S. Alokail
•
Received: 3 June 2013 / Accepted: 2 January 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract Obesity, commonly measured as body mass index (BMI), has been on a rapid rise around the world and is an underlying cause of several chronic non-communicable diseases, including type 2 diabetes mellitus (T2DM). In addition to the environmental factors, genetic factors may also contribute to the ongoing obesity epidemic in Saudi Arabia. This study investigated the association between variants of 36 previously established T2DM SNPs and obesity phenotypes in a population of Saudi subjects. Study subjects consisted of 975 obese (BMI: C30), 825 overweight (25–30) and 423 lean controls (18–25) and of these 927 had a history of T2DM. Subjects were genotyped for 36 SNPs, which have been previously proved to be T2DM linked, using the KASPar method and the means of BMI and waist circumference (WC) corresponding to each of the genotypes were compared by additive, recessive and dominant genetic models. Five and seven of 36 T2DMrelated SNPs were significantly associated with the BMI Electronic supplementary material The online version of this article (doi:10.1007/s11033-014-3022-z) contains supplementary material, which is available to authorized users. N. M. Al-Daghri (&) K. M. Alkharfy S. Krishnaswamy A. K. Mohammed M. S. Alokail Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia e-mail: [email protected]
and WC, respectively. Variants of SNPs rs7903146, rs1552224 and rs11642841 in the control group and rs7903146 in T2DM group showed significant association with both BMI and WC. Variant of SNP rs10440833 was significantly associated with BMI in T2DM group of both males [OR = 1.8 (1.0, 3.3); P = 0.04] and females [OR = 2.0 (1.0, 3.9); P = 0.04]. Genetic risk scores explained 19 and 14 % of WC and hip size variance in this population. Variants of a number of established T2DM related SNPs were associated with obesity phenotypes and may be significant hereditary factors in the pathogenesis of T2DM. Keywords Obesity SNP Type 2 diabetes BMI Waist circumference FTO GWA studies
Introduction In many Middle-Eastern countries rapidly rising incomes over the last few decades have dramatically affected the dietary habits and life-style, causing an obesity epidemic, which, in turn, is contributing to an epidemic of related O. S. Al-Attas Center of Excellence in Biotechnology Research, King Saud University, Riyadh, Kingdom of Saudi Arabia O. M. Albagha Molecular Medicine Centre, University of Edinburgh, Edinburgh, UK
N. M. Al-Daghri M. S. Alokail Prince Mutaib Chair for Biomarkers of Osteoporosis, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
A. M. Alenad School of Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
K. M. Alkharfy Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
G. P. Chrousos First Department of Pediatrics, Athens University Medical School, Athens, Greece
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diseases such as T2DM, heart disease, and hypertension and other metabolic syndrome manifestations [1]. Encouraging results from recent genome wide association studies (GWA studies) are expected to lead to the identification of underlying hereditary factors responsible for exacerbating the environmental effects on obesity phenotypes. Even though genetic factors have been shown to influence human body weight, the specific genetic and molecular mechanisms are yet to be completely defined [2, 3]. The identified obesity genotypes are quite common in several populations and o-occurrence of multiple obesogenic variants results in an additive effect on the phenotype [4]. Furthermore, the effects of genetic variance in obesity are exacerbated by sedentary behaviors and attenuated by physical activity, resulting in different phenotypes [4, 5]. Body mass index (BMI), is commonly used as a measure of obesity. For half of the population, variation in BMI is determined by inherited factors [2]. Variations in genes involved in leptin signaling alone were shown sufficient to cause obesity in animals [6]. Several GWA studies have revealed an association between variants of the ‘fat mass and obesity associated’ gene (FTO) and elevated BMI [7]. On the other hand the hereditary predisposition for the vast majority of obese individuals is probably polygenic since the monogenic effects of leptin or FTO polymorphisms, which are rare, cannot explain the present obesity epidemic. The Genetic Investigation of ANthropometric Traits study, a meta-analysis of data from various GWA studies, confirmed several genetic determinants of BMI, although these variants could account for only 1–2 % of the BMI variance [8]. Further studies are required to detect new variants that could explain a larger proportion of the heritability of obesity. Region/population-specific genetic studies are also expected to yield useful insights, as the distribution of single nucleotide polymorphisms is race and region-dependent. Such population-based differences in the association of adiponectin gene variants with metabolic phenotypes was also revealed in a previous study involving T2DM patients of Saudi Arabia [9]. Recent GWA studies involving European and South Asian populations have identified 36 genetic variants related to T2DM risk [10–17]. The present study investigated whether or not these SNPs may be associated with obesity, a critical T2DM precursor trait, and serum lipids in the Saudi population.
Methods Study subjects Two-thousand-two-hundred and twenty-three unrelated adult Saudi individuals [975 obese (BMI: C30), 825
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overweight (BMI:25–30) and 423 lean controls (BMI:18–25)] were randomly selected from the Biomarker Screening Project in Riyadh (RIYADH COHORT), a capital-wide epidemiological study of over 17,000 consenting Saudis coming from different Primary Health Care Centers (PHCCs) in Riyadh, Saudi Arabia. Out of participants 927 had T2DM (FBG C 7.0 mmol/L). Participating subjects and controls were given a self-administered questionnaire to collect medical history and demographic information. Those with co-morbidities that needed medical attention were excluded from the study. Written consent was obtained after orientation. Ethical approval was granted by the Ethics Committee of the College of Science Research Centre, King Saud University (Riyadh, Saudi Arabia). Participating subjects were requested to visit their assigned PHCCs after overnight fasting ([10 h) for anthropometry and blood withdrawal. Anthropometry included height (to the nearest 0.5 cm), weight (to the nearest 0.1 kg), waist and hip circumferences utilizing a standardized measuring tape in cm, systolic and diastolic arterial blood pressure, and BMI (calculated as kg/m2). Genomic DNA was isolated from whole blood using the blood genomic prep minispin kit (GE healthcare, USA). DNA concentration and purity (260/280) were checked using nano-drop spectrophotometer. Fasting serum samples were collected and stored at -20 °C freezer prior to analysis. Fasting glucose and lipid profile were measured using a chemical analyzer (Konelab, Vantaa, Finland) at the Biomarker Research Center (King Saud University, Riyadh, Saudi Arabia). Genotyping All DNA samples from cases and controls were genotyped for 36 SNPs using the KASPar method (KBioscience, Hoddesdon UK). The average genotype success rate for DNA samples was 99.1 %. Statistical analyses Data was analyzed using SPSS version 16.0. The data are presented as mean ± SD. The difference between the means of two groups was tested using Student’s t test. The means of 3 groups were compared using analysis of variance. Multinomial logistic regression was used to calculate odds ratios and 95 % confidence intervals. Level of significance was given at P B 0.05. Odds ratios and 95 % confidence intervals were calculated using logistic regression under additive, dominant and recessive genetic models. Risk models were constructed based on a logistic regression model from genetic and non-genetic predictors. The relative effects (or weights) of genetic variants come from beta coefficients of the risk model. All obesity related traits were linearly regressed against both un-weighted and weighted genetic
Mol Biol Rep Table 1 Clinical profile of the study population Lean controls (BMI:18–25) N
423
Overweight (BMI:25–30) 825
Obese (BMI: C30)
P value
975
Age (years)
43.6 ± 15.6
48.1 ± 14.6
49.3 ± 11.5
\0.001
BMI (kg/m2)
22.5 ± 2.0
27.4 ± 1.4
35.1 ± 4.1
\0.001
Waist (cm)
78.6 ± 16.3
88.6 ± 17.9
98.4 ± 21.6
\0.001
Hips (cm)
90.2 ± 17.9
97.8 ± 19.6
108.9 ± 23.6
\0.001
SAD (cm)
19.5 ± 71.0
22.7 ± 8.8
26.1 ± 10.7
\0.001
Systolic BP (mmHg)
116.8 ± 13.7
121.5 ± 14.3
125.7 ± 14.6
\0.001
Diastolic BP (mmHg)
74.1 ± 8.3
77.4 ± 8.2
79.1 ± 8.4
\0.001
Cholesterol (mmol/L)
5.0 ± 1.1
5.3 ± 1.2
5.3 ± 1.1
\0.001
0.89 ± 0.37
0.87 ± 0.35
0.87 ± 0.34
0.31
1.6 ± 0.92 7.0 ± 4.5
1.9 ± 0.96 8.1 ± 4.8
1.9 ± 1.0 8.6 ± 4.6
\0.001 \0.001
HDL (mmol/L) Triglycerides (mmol/L) Glucose (mmol/l)
Note Data presented as mean ± standard deviation; P-value significant at \0.05
risk scores. Potential confounders such as age and BMI were included in the model as covariates. Un-weighted and weighted genetic scores were computed using R scripts. Unweighted risk scores were calculated from the sum of risk alleles for each individual. The weighted risk score is a sum of the number of risk alleles multiplied by their beta coefficients. We performed power calculations using Genetic Power Calculator (http://pngu.mgh.harvard.edu/*purcell/ gpc/). The study had 70–85 % power to detect associations between SNP variants and obesity traits with an effect size of OR 1.2–2.7, assuming 35 % obesity prevalence and a risk allele frequency of 0.05–0.61.
Results The demographic, clinical and anthropometric clinical profiles of the subjects are shown in Table 1. The genotypes of the study subjects were determined for 36 T2DM associated SNPs. The mean of the BMI and waist circumference (WC) corresponding to each of the genotypes were statistically analyzed by the first logistic regression using additive, recessive and dominant genetic models (Table 2). The risk allelic variants of five T2DM-related SNPs were associated with BMI: rs10440833 SNP [recessive, OR = 1.4; P = 0.005], rs7578326 SNP [recessive, OR = 1.5; P = 0.003], rs7178572 SNP [dominant, OR = 1.3; P = 0.03], rs2028299 SNP [additive, OR = 1.9; P = 0.008] and rs11642841 SNP [additive, OR = 1.5; P = 0.01]. WC, an established determinant of central obesity, was associated with variants of seven of the 36 T2DM SNPs: rs7903146 [additive, OR = 1.4; P = 0.03], rs13081389 [recessive, OR = 1.5; P = 0.01], rs1552224 [recessive, OR = 1.5; P = 0.04], rs7957197 [additive, OR = 1.8; P = 0.03], rs8042680 [additive, OR = 1.3; P = 0.02], rs163184
[additive, OR = 1.3; P = 0.02] and rs11642841 [OR = 1.4; P = 0.01]. Since the previously established obesity association of the FTO gene variant [25] was replicated in the Saudi population, all the T2DM SNP variants were analyzed for relatedness to BMI in the Saudi population stratified for T2DM status. In the T2DM group, variants of the following SNPs were found to be significantly associated with BMI (Table 3): rs7903146 [recessive, OR = 2.0; P = 0.007], rs10440833 [recessive, OR = 1.8; P = 0.008], rs4812829 [additive, OR = 2.1; P = 0.04], rs16861329 [dominant, OR = 1.9; P = 0.02] and rs1801214 [recessive, OR = 0.58; P = 0.05]. The variant of FTO SNP rs11642841 was not associated with BMI in the T2DM group by any of the models used. Variants of the following SNPs were significantly associated with BMI in the control group (Table 3): rs7903146 [additive, OR = 1.6; P = 0.04], rs10440833 [recessive, OR = 1.3; P = 0.04], rs1470579 [additive, OR = 1.5; P = 0.05], rs2028299 [recessive, OR = 1.4; P = 0.01], rs2028299 [recessive, OR = 1.4; P = 0.01] and rs1801214 [dominant, OR = 1.3; P = 0.04]. The A allelic variant of FTO gene SNP rs11642841 was significantly associated with BMI in the nondiabetic control group with high ORs of 1.7 (P = 0.01), 1.5 (0.04) and 1.4 (0.01) as determined by additive, recessive and dominant models, respectively. Since gender is known to exert a significant influence on the obesity phenotypes [18], the association between T2DM-related SNP variants and BMI was analyzed in the population stratified for gender and results are presented in Table 4. Out of the 36 T2DM SNPs variants the following SNPs were significantly associated with BMI: rs10440833 [recessive; OR = 1.8 (1.0, 3.3); P = 0.04], rs4812829 [additive; 3.0 (1.1, 8.5); P = 0.03], rs1802295 [dominant; OR = 0.23 (0.06, 0.79); P = 0.02], rs7178572 [additive; 3.2 (1.1, 9.9); P = 0.04] and rs1801214 [recessive;
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Mol Biol Rep Table 2 Genotypic variants of T2DM-related SNPs associated with BMI and waist circumference
SNP
Risk allele
rs10440833
T
rs7578326
rs7178572
rs2028299
rs11642841
rs7903146
rs13081389
rs1552224
rs7957197
rs8042680
rs163184
A
A
A
A
C
A
T
T
C
T
Model
Odds ratio BMI (95 % CI)
P value
Odds ratio WC (95 % CI)
P value 0.39
Additive
1.05 (0.68, 1.6)
0.80
0.54 (0.58, 1.2)
Recessive
1.4 (1.1, 1.7)
0.005
1.1 (0.95, 1.4)
0.15
Dominant
0.91 (0.59, 1.4)
0.68
0.79 (0.54, 1.1)
0.21
Additive
1.3 (0.91, 1.8)
0.14
0.82 (0.63, 1.0)
0.14
Recessive
1.5 (1.1, 1.8)
0.003
0.94 (0.77, 1.1)
0.59
Dominant
0.98 (0.73, 1.3)
0.88
0.82 (0.65, 1.0)
0.09
Additive
1.3 (0.92, 1.9)
0.12
1.2 (0.92, 1.6)
0.15
Recessive
1.2 (0.84, 1.6)
0.33
1.2 (0.91, 1.6)
0.18
Dominant
1.3 (1.1, 1.6)
0.03
1.1 (0.91, 1.3)
0.34
Additive
1.9 (1.2, 3.1)
0.008
1.2 (0.79, 1.8)
0.39
Recessive Dominant
1.2 (0.98, 1.6) 1.8 (1.1, 2.8)
0.07 0.01
1.1 (0.94, 1.3) 1.1 (0.77, 1.7)
0.18 0.50
Additive
1.5 (1.1, 2.1)
0.01
1.4 (1.1, 1.9)
0.01
Recessive
1.4 (0.97, 1.8)
0.07
1.3 (0.96, 1.6)
0.08
Dominant
1.3 (1.1, 1.7)
0.01
1.3 (1.1, 1.6)
0.005
Additive
1.2 (0.86, 1.7)
0.26
1.4 (1.0, 1.8)
0.03
Recessive
1.1 (0.89, 1.4)
0.33
1.1 (0.91, 1.3)
0.30
Dominant
1.2 (0.84, 1.6)
0.35
1.3 (1.1, 1.7)
0.03 0.66
Additive
0.40 (0.03, 4.6)
0.46
0.71 (0.15, 3.2)
Recessive
0.61 (0.06, 5.4)
0.65
1.5 (1.1, 2.1)
0.01
Dominant
0.47 (0.05, 4.3)
0.51
0.68 (0.15, 3.1)
0.62 0.37
Additive
–
–
2.8 (0.29, 27.2)
Recessive
1.4 (0.83, 2.2)
0.21
1.5 (1.0, 2.3)
0.04
Dominant
–
–
2.8 (0.28, 26.6)
0.38
Additive
1.1 (0.55, 2.1)
0.81
1.8 (1.1, 3.1)
0.03
Recessive Dominant
0.95 (0.74, 1.2) 1.1 (0.56, 2.1)
0.74 0.77
1.1 (0.93, 1.4) 1.7 (1.0, 3.0)
0.20 0.04
Additive
1.0 (0.75, 1.4)
0.86
1.3 (1.0, 1.7)
0.02
Recessive
1.0 (0.79, 1.3)
0.82
1.1 (0.91, 1.3)
0.30
Dominant
1.0 (0.77, 1.3)
0.94
1.3 (1.1, 1.6)
0.009
Additive
1.1 (0.77, 1.4)
0.71
1.3 (1.0, 1.7)
0.02
Recessive
1.0 (0.81, 1.3)
0.75
1.2 (0.94, 1.4)
0.15
Dominant
1.0 (0.79, 1.4)
0.75
1.3 (1.0, 1.6)
0.03
OR = 0.47 (0.22, 0.99); P = 0.04]. In non-diabetic control subjects variants of SNPs rs10440833 [recessive; OR = 1.8 (1.2, 2.8); P = 0.011] and rs1470579 [additive; OR = 2.7 (1.4, 5.1); P = 0.002] were significantly associated with BMI. In T2DM females, variants of SNPs rs7903146 [recessive; OR = 2.9 (1.1, 7.1); P = 0.02], rs10440833 [recessive; OR = 2.0 (1.0, 3.9); P = 0.04], rs849134 [additive; OR = 0.30 (0.10, 0.89); P = 0.03], rs6795735 [additive; OR = 3.4 (1.3, 9.1); P = 0.01], rs243021 [recessive; OR = 0.44(0.20, 0.97); P = 0.04] and rs10965250 [dominant; OR = 2.2 (1.0, 5.1); P = 0.04] were significantly associated with BMI. In non-diabetic control females the following SNP variants were significantly associated with BMI: rs849134 [additive; OR = 1.9(1.2, 3.4); P = 0.001], rs1387153 [additive;
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OR = 0.33 (0.14, 0.75); P = 0.009], rs11634397 [dominant; OR = 1.5 (01.0, 2.3); P = 0.02], rs1802295 [recessive; OR = 1.5(1.0, 2.2); P = 0.04], rs2028299 [additive; OR = 3.0(1.4, 6.5); P = 0.006], rs1801214 [recessive; OR = 1.6(1.1, 2.4); P = 0.01] and rs11642841 [additive; OR = 2.1 (1.1, 3.7); P = 0.02]. However, when Bonferroni correction was applied, only variant of the SNP rs1470579 was significantly associated with BMI, and that too only in T2DM males (P = 0.03). Compared with single genetic markers, polygenic scores that evaluate the composite effects of multiple trait-associated variants have been shown to be more effective in explaining the variance of traits and risk of complex diseases like T2DM [19, 20]. We incorporated all the 36 T2DM associated allelic variants and calculated the
Mol Biol Rep Table 3 Association between T2DM-related SNP variants and BMI in T2DM status stratified subjects
SNP
rs7903146
rs10440833
Risk allele
C
T
rs1470579
A
rs4812829
G
rs2028299
rs16861329
rs1801214
rs11642841
A
T
C
A
Model
T2DM
Control
Odds ratio BMI (95 % CI)
P value
Odds ratio BMI (95 % CI)
P value
Additive
1.4 (0.68, 2.9)
0.34
1.6 (1.0, 2.4)
0.04
Recessive
2.0 (1.2, 3.4)
0.007
1.1 (0.86, 1.5)
0.35
Dominant
0.84 (0.44, 1.6)
0.59
1.5 (1.0, 2.3)
0.04
Additive
1.1 (0.48, 2.7)
0.75
1.1 (0.63, 1.8)
0.75
Recessive
1.8 (1.2, 2.8)
0.008
1.3 (1.0, 1.8)
0.04
Dominant
0.85 (0.37, 1.9)
0.71
0.96 (0.56, 1.6)
0.91
Additive Recessive
1.4 (0.79, 2.4) 1.0 (0.67, 1.6)
0.25 0.83
1.5 (1.0, 2.2) 1.4 (1.1, 1.8)
0.05 0.01
Dominant
1.5 (0.87, 2.4)
0.14
1.3 (0.88, 1.8)
0.19
Additive
2.1 (1.0, 4.3)
0.04
0.59 (0.26, 1.3)
0.18
Recessive
1.0 (0.67, 1.6)
0.85
1.1 (0.80, 1.9)
0.63
Dominant
2.2 (1.1, 4.5)
0.02
0.56 (0.25, 1.2)
0.14
Additive
1.4 (0.56, 3.2)
0.48
2.4 (1.3, 4.5)
0.005
Recessive
0.95 (0.61, 1.5)
0.82
1.4 (1.1, 1.9)
0.01
Dominant
1.4 (0.59, 3.3)
0.42
2.2 (1.2, 4.0)
0.01
Additive
3.7 (0.49, 28.8)
0.19
0.79 (0.33, 1.8)
0.59
Recessive
3.3 (0.44, 25.4)
0.24
0.77 (0.32, 1.8)
0.55
Dominant
1.9 (1.1, 3.5)
0.02
1.1 (0.78, 1.5)
0.62
Additive
0.66 (0.36, 1.2)
0.18
1.2 (0.84, 1.9)
0.25
Recessive
0.58 (0.34, 1.0)
0.05
1.0 (0.72, 1.5)
0.84
Dominant
1.0 (0.66, 1.6)
0.87
1.3 (1.0, 1.8)
0.04
Additive Recessive
1.3 (0.66, 2.6) 1.2 (0.63, 2.3)
0.41 0.54
1.7 (1.1, 2.6) 1.5 (1.0, 2.2)
0.01 0.04
Dominant
1.2 (0.77, 1.8)
0.39
1.4 (1.1, 1.8)
0.01
cumulative genetic risk score to explore the combined effect of 36 SNPs on obesity related phenotypes. The genetic risk scores (un-weighted, weighted and weighted after adjusting for age and BMI) of variants of 36 T2DM associated SNPs on obesity related phenotypic traits like WC, hip size, systolic and diastolic blood pressure, and plasma levels of total cholesterol, triglycerides and LDL were estimated for the Saudi population (Table 5). The unweighted and weighted genetic scores indicated only weak associations with obesity traits. The weighted genetic risk scores reached after adjusting for age and BMI showed significant associations and could explain 3–21 % of different obesity traits. The combined genetic risk scores accounted for 19, 14, 21, 13, 1.3, 4, 3 percentages of WC, hip size, systolic and diastolic blood pressure, total cholesterol, triglycerides and LDL phenotypes, respectively.
Discussion Earlier studies identified allelic variants of 36 SNPs to be strongly associated with T2DM in European and South Asian populations [10–17]. A previous study replicated
eight of these associations in a Saudi population (manuscript submitted). Since T2DM develops readily, progresses rapidly and is difficult to control in people who are obese, we attempted to identify whether or not a direct relation exists between the allelic variants of T2DM-related SNPs and obesity in a Saudi population. We genotyped 2,223 Saudi adults for 36 T2DM-associated SNPs, and assessed the effect of various allotypes on BMI and WC using various models. Out of 36, allelic variants of five and seven SNPs were significantly associated with BMI and WC, respectively. The A allelic variant of rs11642841 SNP, located in the FTO gene, was associated with both BMI and WC. Furthermore, the association of the FTO variant with BMI was persistent in the control group after stratifying the population for T2DM status, indicating its relation to obesity in the Saudi population. Variant of rs7903146, a SNP located in the intronic region of TCF7L2, was associated with BMI and WC in both the T2DM and control groups and with HDL only in the T2DM group. Variants of 3 SNPs were associated with both BMI and WC in the control group following T2DM status stratification and thus implicate their direct relation to obesity. Variant of SNP rs10440833 was associated with
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Mol Biol Rep Table 4 Relation between T2DM-related SNP variants and BMI following gender stratification SNP
Risk allele
Model
Male T2DM Odds ratio (95 % CI)
rs7903146
rs10440833
rs849134
rs1470579
rs6795735
rs1387153
rs243021
rs11634397
rs4812829
rs1802295
rs7178572
rs2028299
rs1801214
rs10965250
rs11642841
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C
T
C
A
T
C
C
A
G
C
A
A
C
A
A
Female Control P
Odds ratio (95 % CI)
T2DM P
Odds ratio (95 % CI)
Control P
Odds ratio (95 % CI)
P
Additive
0.61 (0.25, 1.4)
0.27
1.4 (0.74, 2.9)
0.26
0.59 (0.21, 1.6)
0.42
1.5 (0.81, 2.7)
0.19
Recessive
1.8 (0.92, 3.4)
0.08
1.3 (0.85, 2.0)
0.21
2.9 (1.1, 7.1)
0.02
0.99 (0.68, 1.4)
0.96
Dominant
0.80 (0.34, 1.8)
0.60
1.6 (0.85, 3.0)
0.13
0.83 (0.31, 2.2)
0.71
1.4 (0.80, 2.4)
0.23
Additive
1.6 (0.58, 4.7)
0.33
1.6 (0.67, 3.8)
0.28
0.42 (0.05, 3.3)
0.42
0.85 (0.42, 1.8)
0.68
Recessive
1.8 (1.0, 3.3)
0.04
1.8 (1.2, 2.8)
0.011
2.0 (1.0, 3.9)
0.04
1.1 (0.76, 1.6)
0.57
Dominant
1.3 (0.47, 3.5)
0.59
1.3 (0.55, 3.0)
0.53
0.28 (0.03, 2.1)
0.22
0.80 (0.39, 1.6)
0.56
Additive
2.1 (0.78, 5.8)
0.13
0.93 (0.48, 1.8)
0.84
0.30 (0.10, 0.89)
0.03
1.9 (1.2, 3.4)
0.00
Recessive
1.8 (0.69, 4.4)
0.23
0.79 (0.43, 1.4)
0.46
0.64 (0.29, 1.4)
0.28
1.5 (0.92, 2.6)
0.09
Dominant
1.5 (0.82, 2.8)
0.17
1.2 (0.77, 1.9)
0.38
0.31 (0.12, 0.78)
0.01
1.6 (1.1, 2.3)
0.01
Additive
1.2 (0.56, 2.6)
0.59
2.7 (1.4, 5.1)
0.002
1.5 (0.65, 3.6)
0.31
0.96 (0.56, 1.6)
0.89
Recessive
1.0 (0.56, 1.8)
0.92
2.0 (1.3, 3.1)
0.002
1.1 (0.54, 2.1)
0.83
1.1 (0.73, 1.5)
0.72
Dominant
1.3 (0.63, 2.5)
0.49
2.0 (1.1, 3.6)
0.02
1.6 (0.76, 3.5)
0.20
0.91 (0.55, 1.5)
0.73
Additive
0.37 (0.10, 1.3)
0.13
0.93 (0.41, 2.1)
0.87
3.4 (1.3, 9.1)
0.01
1.5 (0.69, 3.1)
0.32
Recessive Dominant
0.91 (0.50, 1.6) 0.35 (0.10, 1.2)
0.76 0.10
0.86 (0.55, 1.3) 1.0 (0.44, 2.2)
0.50 0.98
1.2 (0.60, 2.3) 3.7 (1.4, 9.4)
0.61 0.007
0.70 (0.48, 1.0) 1.1 (0.56, 2.3)
0.06 0.73
Additive
1.2 (0.39, 3.7)
0.74
1.1 (0.45, 2.6)
0.83
0.73 (0.15, 3.3)
0.68
0.33 (0.14, 0.75)
0.009
Recessive
0.84 (0.46, 1.5)
0.57
0.87 (0.56, 1.3)
0.98
1.0 (0.53, 2.0)
0.92
1.3 (0.87, 1.8)
0.21
Dominant
1.1 (0.36, 3.1)
0.89
1.0 (0.43, 2.3)
0.98
0.75 (0.17, 3.3)
0.71
0.42 (0.19, 0.93)
0.03
Additive
1.3 (0.69, 2.6)
0.37
0.93 (0.60, 1.4)
0.77
1.2 (0.53, 2.5)
0.70
1.0 (0.68, 1.5)
0.88
Recessive
0.70 (0.33, 1.4)
0.34
1.3 (0.75, 2.2)
0.34
0.44 (0.20, 0.97)
0.04
1.1 (0.63, 1.8)
0.78
Dominant
1.2 (0.63, 2.2)
0.61
1.0 (0.65, 1.6)
0.91
0.88 (0.44, 1.7)
0.72
1.0 (0.71, 1.5)
0.81
Additive
0.83 (0.36, 1.9)
0.66
0.70 (0.39, 1.2)
0.24
0.61 (0.22, 1.7)
0.34
1.5 (0.90, 2.6)
0.11
Recessive
1.0 (0.49, 2.1)
0.95
0.69 (0.41, 1.1)
0.16
1.0 (0.44, 2.3)
0.98
1.2 (073, 1.9)
0.47
Dominant
0.73 (0.39, 1.4)
0.34
0.89 (0.55, 1.4)
0.63
0.50 (0.22, 1.1)
0.10
1.5 (01.0, 2.3)
0.02
Additive
3.0 (1.1, 8.5)
0.03
0.45 (0.15, 1.3)
0.16
1.4 (0.45, 4.5)
0.53
0.65 (0.21, 2.0)
0.52
Recessive
1.4 (0.77, 2.5)
0.26
1.1 (0.67, 1.7)
0.79
0.74 (0.37, 1.5)
0.40
1.1 (0.72, 1.5)
0.72
Dominant
2.8 (1.0, 7.8)
0.03
0.43 (0.14, 1.3)
0.12
1.7 (0.55, 5.2)
0.35
0.62 (0.20, 1.9)
0.42
Additive
0.28 (0.085, 1.0)
0.058
1.1 (0.51, 2.5)
0.72
1.4 (0.37, 5.0)
0.62
1.4 (0.80, 2.6)
0.21
Recessive Dominant
1.1 (0.61, 2.0) 0.23 (0.06, 0.79)
0.71 0.02
0.98 (0.63, 1.5) 1.2 (0.54, 2.5)
0.95 0.68
1.2 (0.62, 2.3) 1.3 (0.36, 4.5)
0.57 0.69
1.5 (1.0, 2.2) 1.2 (0.68, 2.0)
0.04 0.55
Additive
3.2 (1.1, 9.9)
0.04
1.5 (0.70, 3.1)
0.29
1.3 (0.43, 4.1)
0.60
0.99 (0.57, 1.7)
0.79
Recessive
2.8 (0.97, 8.5)
0.05
1.4 (0.68, 2.7)
0.37
1.2 (0.40, 3.5)
0.73
0.82 (0.49, 1.4)
0.47
Dominant
1.5 (0.82, 2.7)
0.18
1.2 (0.78, 1.8)
0.37
1.3 (0.67, 2.5)
0.43
1.3 (0.89, 1.9)
0.16
Additive
1.1 (0.26, 4.3)
0.92
1.7 (0.63, 4.7)
0.28
2.0 (0.64, 6.6)
0.22
3.0 (1.4, 6.5)
0.006
Recessive
0.64 (0.34, 1.2)
0.16
1.5 (0.99, 2.4)
0.05
1.4 (0.74, 2.8)
0.28
1.2 (0.86, 1.8)
0.21
Dominant
1.3 (0.32, 5.1)
0.72
1.5 (0.54, 3.9)
0.44
1.9 (0.60, 5.8)
0.27
2.9 (1.4, 6.3)
0.006
Additive
0.43 (0.148, 1.0)
0.05
1.0 (0.54, 1.9)
0.95
0.99 (0.41, 2.4)
0.99
1.4 (0.82, 2.3)
0.21
Recessive
0.47 (0.22, 0.99)
0.04
0.99 (0.56, 1.7)
0.97
0.69 (0.30, 1.6)
0.39
1.0 (0.64, 1.6)
0.94
Dominant
0.71 (0.37, 1.4)
0.31
1.0 (0.85, 1.9)
0.22
1.6 (0.83, 3.2)
0.67
1.6 (1.1, 2.4)
0.01
Additive
–
–
1.1 (0.38, 3.2)
0.85
2.2 (0.28, 17.6)
0.43
0.96 (0.46, 2.0)
0.93
Recessive
–
–
1.1 (0.40, 3.2)
0.80
1.8 (0.23, 14.2)
0.56
0.98 (0.47, 2.0)
0.95
Dominant
1.3 (0.72, 2.4)
0.35
0.94 (0.60, 1.5)
0.78
2.2 (1.0, 5.1)
0.04
0.96 (0.66, 1.4)
0.87
Additive
1.4 (0.57, 3.5)
0.43
1.4 (0.76, 2.6)
0.26
1.4 (0.47, 4.6)
0.51
2.1 (1.1, 3.7)
0.02
Recessive
1.3 (0.58, 3.2)
0.46
1.3 (0.72, 2.3)
0.37
1.3 (0.44, 3.8)
0.62
1.8 (1.1, 3.1)
0.04
Dominant
1.2 (0.64, 2.1)
0.60
1.3 (0.82, 1.9)
0.26
1.3 (0.46, 2.3)
0.46
1.4 (0.95, 2.0)
0.08
Mol Biol Rep Table 5 Combined genetic risk scores based on 36 T2DM SNP variants on obesity traits
Trait
Score type
Covariate
Adj. R2
P
Waist circumference
Weighted
–
0.002
0.04
Weighted sum
Age, BMI
0.004
1.2 9 10-16
Hip size Systolic BP
Diastolic BP T. Cholesterol
Triglycerides
LDL
Weighted sum
–
0.005
0.009
weighted sum
Age, BMI
0.004
7.4 9 10-18
Un-weighted sum
–
0.002
0.04
Weighted sum
–
0.007
3.6 9 10-4
Weighted
Age, BMI
0.001
2.6 9 10-9
Weighted
–
0.003
0.01
Weighted
Age, BMI
0.002
6.3 9 10-10
Un-weighted
–
0.004
0.006
Weighted
–
0.003
0.02
Weighted
Age, BMI
0.003
2.6 9 10-5
Un-weighted
–
0.004
0.006
Weighted
–
0.004
0.003
Weighted Un-weighted
Age, BMI –
0.003 0.005
1.9 9 10-15 0.01
Weighted
–
0.006
0.005
Weighted
Age, BMI
0.004
4.2 9 10-8
BMI in the T2DM subjects of both males and females. In addition, the combined effect of all the variants of 36 SNPs determined from genetic risk scores, could explain 19 and 14 % of WC and hip size variance, respectively in this population. Obesity, being the leading cause of preventable deaths, is a serious health concern for Saudi Arabia. Increasing trends in obesity levels in Central region of Saudi Arabia indicate a close association with increasing prevalence of chronic non-communicable diseases, and it is estimated that 68 % of its citizens are overweight/obese (BMI [ 25) [1]. Rapid economic growth in the past few decades has brought dietary and life-style changes, and these, together with the hot weather and sedentary behaviors, provide a highly obesogenic environment for the Saudi population [21]. Recent GWA studies have identified several SNPs significantly associated with BMI and imply an important role for hereditary factors. The hereditary predisposition to obesity has been shown to exacerbate the effect of dietary and life-style choices and various socio-cultural issues like assortative mating, common between obese partners [22]. The present study, involving a large number of T2DM and control subjects, confirms the involvement of variants of FTO gene in obesity and T2DM in the Saudi population. In this study, the variant of FTO gene SNP rs11642841 showed strong association with both BMI and WC in the Saudi subjects overall and also in the control group, but not in the T2DM group after stratifying the subjects by T2DM status. The persistent association of the FTO variant with BMI and WC in the control group after stratifying the population for
T2DM status indicates its direct relation to obesity in the Saudi population. The association between obesity and several SNPs of fat mass obesity-associated FTO gene has been replicated in several populations [23–26]. In particular, the A allelic variant of rs11642841 SNP, associated with both BMI and WC in the present study, has been previously associated with obesity [27]. This previous study, involving 843 unrelated individuals from an island population in the eastern Adriatic coast of Croatia, showed the variant of rs11642841 to be significantly associated with various body fatness measures like waist-hip ratio, weight and WC, and while the BMI seemed to be related to this variant it didn’t reach the required statistical significance. Variant of SNP rs7903146, located in the intronic region of TCF7L2, showed significant association with WC in the entire study population and with BMI and WC in both control and T2DM groups following stratification of the population for T2DM status. Several previous studies involving T2DM subjects of African [28], Swedish, Finnish [29] and Taiwanese [30] cohorts confirmed that rs7903146 is a significant T2DM trait-defining SNP. However, according to some studies the different genotypes of this SNP were not associated with BMI phenotypes [31] or T2DM [32]. Analysis of variants of rs7903146 in Framingham heart study subjects confirmed its association with hyperglycaemia and a higher proinsulin/insulin ratio but not with adiposity phenotypes [33]. These discrepancies may be due to the differences in genetic or environmental modifiers affecting BMI heterogeneously across populations [34, 35].
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Mol Biol Rep
The present study showed that the variant of CDKAL1 gene SNP rs10440833 is significantly associated with BMI in the whole population and with both T2DM and control groups following T2DM status stratification. Results of previous studies suggested independent roles in T2DM as well as in obesity phenotypes for CDKAL1 locus [36, 37]. In a 2007 study, the rs10440833 SNP variant was associated with T2DM in obese individuals of European ancestry and individuals from Hong Kong of Han Chinese ancestry [38], and this was further confirmed in 2010 in a meta-analysis [13]. The high OR of this variant in homozygotes suggested that the effect is substantially stronger in homozygous than in heterozygous carriers since it is functionally associated with a 20 % lower insulin response in homozygotes compared to heterozygotes or non-carriers indicating that this variant may confer risk of T2DM through reduced insulin secretion [38]. Gender associated differences have been established in the occurrence of T2DM as well as various underlying phenotypes like insulin resistance and obesity [39]. In the present study, variant of SNP rs10440833 was associated with BMI in T2DM subjects of both genders and in the male non-diabetics which suggests SNP rs10440833 may be directly associated with BMI. The present study also found a strong association between variant of rs849134 and BMI in women, irrespective of T2DM status. Variant of SNP rs1470579, located in intronic region of IGF2BP2, is the only polymorphic variant in our study that has remained significantly associated with BMI following Bonferroni correction. IGF2BP2, an insulin-like growth factor 2 transcription regulatory protein and expressed at high levels in pancreatic islet cells, has received considerable attention due to its role in growth and insulin signaling [40]. A meta-analysis of 35 studies has identified rs1470579 to be associated with elevated T2DM risk by both dominant and recessive genetic models [41]. However, studies on several genetic variants of IGF2BP2 in different ethnicities have yielded contradicting results [41]. In this context, our results on rs1470579 based on a large population of Saudi ethnicity, is expected to aid in unraveling the molecular mechanism of T2DM. Combined risk scores have been used to assess genetic effects of multiple genetic loci on complex diseases such as T2DM [42]. Combined genetic scores of our study explain, quantitatively, the association between the individual obesity traits and T2DM related SNP variants. Low scores in weighted/un-weighted models indicate weak associations. The disappearance of strong correlations seen for FTO gene SNP variant and BMI and WC may be due to the differences in the genetic models used. The authors acknowledge certain limitations of this study. The strength of the associations conferred by each of these variants to increased BMI and WC were relatively small and could have been confounded by unknown
123
variables. Confirmation of functional outcomes of common variants in the human genome, even if real, needs very large sample sizes to overcome additional genetic and environmental modifiers. However, the limitation resulting from the need for huge sample sizes may be overcome by replication of associations in several independent studies with small sample sizes. In the present study, variants of majority of the SNPs were associated with either BMI or WC, but not with both, implying significant effect of methodology. Furthermore, scope for confirmation of the functional phenotypes of these SNPs by alternative methods is limited since all but one of the SNPs of this study are located in the intronic or intergenic regions. In conclusion, we investigated 36 previously confirmed T2DM-associated loci in a Saudi population and identified several SNP variants associated with obesity phenotypes. In particular, the A allelic variant of rs11642841 SNP, located in the intronic region of FTO gene, showed significant association with both BMI and WC. The association of this variant with BMI persisted in the non-diabetic control group after stratifying the data for T2DM status implying a direct role for this variant in increased obesity. Variant of SNP rs7903146 was associated with both BMI and WC in both control and T2DM groups of Saudi subjects. SNP rs10440833 was associated with BMI in both men and women subjects. Replication of these results in future studies or in other populations and identifying the molecular basis underlying these genetic predispositions may lead to development of preventive strategies against obesity and T2DM in the carriers. Acknowledgments This study was funded by the Biomarkers Research Program at King Saud University, Riyadh, Saudi Arabia. The authors are grateful to the primary care physicians and nurses of the PHCCs in Riyadh for patient recruitment and sample collection. The authors also thank Mr. Benjamin Vinodson for statistical analysis of the data. Conflict of interest
The authors have nothing to disclose.
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Association between type 2 diabetes mellitus-related SNP variants and obesity traits in a Saudi population Nasser M. Al-Daghri • Khalid M. Alkharfy • Omar S. Al-Attas • Soundararajan Krishnaswamy Abdul Khader Mohammed • Omar M. Albagha • Amal M. Alenad • George P. Chrousos • Majed S. Alokail
•
Received: 3 June 2013 / Accepted: 2 January 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract Obesity, commonly measured as body mass index (BMI), has been on a rapid rise around the world and is an underlying cause of several chronic non-communicable diseases, including type 2 diabetes mellitus (T2DM). In addition to the environmental factors, genetic factors may also contribute to the ongoing obesity epidemic in Saudi Arabia. This study investigated the association between variants of 36 previously established T2DM SNPs and obesity phenotypes in a population of Saudi subjects. Study subjects consisted of 975 obese (BMI: C30), 825 overweight (25–30) and 423 lean controls (18–25) and of these 927 had a history of T2DM. Subjects were genotyped for 36 SNPs, which have been previously proved to be T2DM linked, using the KASPar method and the means of BMI and waist circumference (WC) corresponding to each of the genotypes were compared by additive, recessive and dominant genetic models. Five and seven of 36 T2DMrelated SNPs were significantly associated with the BMI Electronic supplementary material The online version of this article (doi:10.1007/s11033-014-3022-z) contains supplementary material, which is available to authorized users. N. M. Al-Daghri (&) K. M. Alkharfy S. Krishnaswamy A. K. Mohammed M. S. Alokail Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia e-mail: [email protected]
and WC, respectively. Variants of SNPs rs7903146, rs1552224 and rs11642841 in the control group and rs7903146 in T2DM group showed significant association with both BMI and WC. Variant of SNP rs10440833 was significantly associated with BMI in T2DM group of both males [OR = 1.8 (1.0, 3.3); P = 0.04] and females [OR = 2.0 (1.0, 3.9); P = 0.04]. Genetic risk scores explained 19 and 14 % of WC and hip size variance in this population. Variants of a number of established T2DM related SNPs were associated with obesity phenotypes and may be significant hereditary factors in the pathogenesis of T2DM. Keywords Obesity SNP Type 2 diabetes BMI Waist circumference FTO GWA studies
Introduction In many Middle-Eastern countries rapidly rising incomes over the last few decades have dramatically affected the dietary habits and life-style, causing an obesity epidemic, which, in turn, is contributing to an epidemic of related O. S. Al-Attas Center of Excellence in Biotechnology Research, King Saud University, Riyadh, Kingdom of Saudi Arabia O. M. Albagha Molecular Medicine Centre, University of Edinburgh, Edinburgh, UK
N. M. Al-Daghri M. S. Alokail Prince Mutaib Chair for Biomarkers of Osteoporosis, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
A. M. Alenad School of Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
K. M. Alkharfy Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
G. P. Chrousos First Department of Pediatrics, Athens University Medical School, Athens, Greece
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diseases such as T2DM, heart disease, and hypertension and other metabolic syndrome manifestations [1]. Encouraging results from recent genome wide association studies (GWA studies) are expected to lead to the identification of underlying hereditary factors responsible for exacerbating the environmental effects on obesity phenotypes. Even though genetic factors have been shown to influence human body weight, the specific genetic and molecular mechanisms are yet to be completely defined [2, 3]. The identified obesity genotypes are quite common in several populations and o-occurrence of multiple obesogenic variants results in an additive effect on the phenotype [4]. Furthermore, the effects of genetic variance in obesity are exacerbated by sedentary behaviors and attenuated by physical activity, resulting in different phenotypes [4, 5]. Body mass index (BMI), is commonly used as a measure of obesity. For half of the population, variation in BMI is determined by inherited factors [2]. Variations in genes involved in leptin signaling alone were shown sufficient to cause obesity in animals [6]. Several GWA studies have revealed an association between variants of the ‘fat mass and obesity associated’ gene (FTO) and elevated BMI [7]. On the other hand the hereditary predisposition for the vast majority of obese individuals is probably polygenic since the monogenic effects of leptin or FTO polymorphisms, which are rare, cannot explain the present obesity epidemic. The Genetic Investigation of ANthropometric Traits study, a meta-analysis of data from various GWA studies, confirmed several genetic determinants of BMI, although these variants could account for only 1–2 % of the BMI variance [8]. Further studies are required to detect new variants that could explain a larger proportion of the heritability of obesity. Region/population-specific genetic studies are also expected to yield useful insights, as the distribution of single nucleotide polymorphisms is race and region-dependent. Such population-based differences in the association of adiponectin gene variants with metabolic phenotypes was also revealed in a previous study involving T2DM patients of Saudi Arabia [9]. Recent GWA studies involving European and South Asian populations have identified 36 genetic variants related to T2DM risk [10–17]. The present study investigated whether or not these SNPs may be associated with obesity, a critical T2DM precursor trait, and serum lipids in the Saudi population.
Methods Study subjects Two-thousand-two-hundred and twenty-three unrelated adult Saudi individuals [975 obese (BMI: C30), 825
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overweight (BMI:25–30) and 423 lean controls (BMI:18–25)] were randomly selected from the Biomarker Screening Project in Riyadh (RIYADH COHORT), a capital-wide epidemiological study of over 17,000 consenting Saudis coming from different Primary Health Care Centers (PHCCs) in Riyadh, Saudi Arabia. Out of participants 927 had T2DM (FBG C 7.0 mmol/L). Participating subjects and controls were given a self-administered questionnaire to collect medical history and demographic information. Those with co-morbidities that needed medical attention were excluded from the study. Written consent was obtained after orientation. Ethical approval was granted by the Ethics Committee of the College of Science Research Centre, King Saud University (Riyadh, Saudi Arabia). Participating subjects were requested to visit their assigned PHCCs after overnight fasting ([10 h) for anthropometry and blood withdrawal. Anthropometry included height (to the nearest 0.5 cm), weight (to the nearest 0.1 kg), waist and hip circumferences utilizing a standardized measuring tape in cm, systolic and diastolic arterial blood pressure, and BMI (calculated as kg/m2). Genomic DNA was isolated from whole blood using the blood genomic prep minispin kit (GE healthcare, USA). DNA concentration and purity (260/280) were checked using nano-drop spectrophotometer. Fasting serum samples were collected and stored at -20 °C freezer prior to analysis. Fasting glucose and lipid profile were measured using a chemical analyzer (Konelab, Vantaa, Finland) at the Biomarker Research Center (King Saud University, Riyadh, Saudi Arabia). Genotyping All DNA samples from cases and controls were genotyped for 36 SNPs using the KASPar method (KBioscience, Hoddesdon UK). The average genotype success rate for DNA samples was 99.1 %. Statistical analyses Data was analyzed using SPSS version 16.0. The data are presented as mean ± SD. The difference between the means of two groups was tested using Student’s t test. The means of 3 groups were compared using analysis of variance. Multinomial logistic regression was used to calculate odds ratios and 95 % confidence intervals. Level of significance was given at P B 0.05. Odds ratios and 95 % confidence intervals were calculated using logistic regression under additive, dominant and recessive genetic models. Risk models were constructed based on a logistic regression model from genetic and non-genetic predictors. The relative effects (or weights) of genetic variants come from beta coefficients of the risk model. All obesity related traits were linearly regressed against both un-weighted and weighted genetic
Mol Biol Rep Table 1 Clinical profile of the study population Lean controls (BMI:18–25) N
423
Overweight (BMI:25–30) 825
Obese (BMI: C30)
P value
975
Age (years)
43.6 ± 15.6
48.1 ± 14.6
49.3 ± 11.5
\0.001
BMI (kg/m2)
22.5 ± 2.0
27.4 ± 1.4
35.1 ± 4.1
\0.001
Waist (cm)
78.6 ± 16.3
88.6 ± 17.9
98.4 ± 21.6
\0.001
Hips (cm)
90.2 ± 17.9
97.8 ± 19.6
108.9 ± 23.6
\0.001
SAD (cm)
19.5 ± 71.0
22.7 ± 8.8
26.1 ± 10.7
\0.001
Systolic BP (mmHg)
116.8 ± 13.7
121.5 ± 14.3
125.7 ± 14.6
\0.001
Diastolic BP (mmHg)
74.1 ± 8.3
77.4 ± 8.2
79.1 ± 8.4
\0.001
Cholesterol (mmol/L)
5.0 ± 1.1
5.3 ± 1.2
5.3 ± 1.1
\0.001
0.89 ± 0.37
0.87 ± 0.35
0.87 ± 0.34
0.31
1.6 ± 0.92 7.0 ± 4.5
1.9 ± 0.96 8.1 ± 4.8
1.9 ± 1.0 8.6 ± 4.6
\0.001 \0.001
HDL (mmol/L) Triglycerides (mmol/L) Glucose (mmol/l)
Note Data presented as mean ± standard deviation; P-value significant at \0.05
risk scores. Potential confounders such as age and BMI were included in the model as covariates. Un-weighted and weighted genetic scores were computed using R scripts. Unweighted risk scores were calculated from the sum of risk alleles for each individual. The weighted risk score is a sum of the number of risk alleles multiplied by their beta coefficients. We performed power calculations using Genetic Power Calculator (http://pngu.mgh.harvard.edu/*purcell/ gpc/). The study had 70–85 % power to detect associations between SNP variants and obesity traits with an effect size of OR 1.2–2.7, assuming 35 % obesity prevalence and a risk allele frequency of 0.05–0.61.
Results The demographic, clinical and anthropometric clinical profiles of the subjects are shown in Table 1. The genotypes of the study subjects were determined for 36 T2DM associated SNPs. The mean of the BMI and waist circumference (WC) corresponding to each of the genotypes were statistically analyzed by the first logistic regression using additive, recessive and dominant genetic models (Table 2). The risk allelic variants of five T2DM-related SNPs were associated with BMI: rs10440833 SNP [recessive, OR = 1.4; P = 0.005], rs7578326 SNP [recessive, OR = 1.5; P = 0.003], rs7178572 SNP [dominant, OR = 1.3; P = 0.03], rs2028299 SNP [additive, OR = 1.9; P = 0.008] and rs11642841 SNP [additive, OR = 1.5; P = 0.01]. WC, an established determinant of central obesity, was associated with variants of seven of the 36 T2DM SNPs: rs7903146 [additive, OR = 1.4; P = 0.03], rs13081389 [recessive, OR = 1.5; P = 0.01], rs1552224 [recessive, OR = 1.5; P = 0.04], rs7957197 [additive, OR = 1.8; P = 0.03], rs8042680 [additive, OR = 1.3; P = 0.02], rs163184
[additive, OR = 1.3; P = 0.02] and rs11642841 [OR = 1.4; P = 0.01]. Since the previously established obesity association of the FTO gene variant [25] was replicated in the Saudi population, all the T2DM SNP variants were analyzed for relatedness to BMI in the Saudi population stratified for T2DM status. In the T2DM group, variants of the following SNPs were found to be significantly associated with BMI (Table 3): rs7903146 [recessive, OR = 2.0; P = 0.007], rs10440833 [recessive, OR = 1.8; P = 0.008], rs4812829 [additive, OR = 2.1; P = 0.04], rs16861329 [dominant, OR = 1.9; P = 0.02] and rs1801214 [recessive, OR = 0.58; P = 0.05]. The variant of FTO SNP rs11642841 was not associated with BMI in the T2DM group by any of the models used. Variants of the following SNPs were significantly associated with BMI in the control group (Table 3): rs7903146 [additive, OR = 1.6; P = 0.04], rs10440833 [recessive, OR = 1.3; P = 0.04], rs1470579 [additive, OR = 1.5; P = 0.05], rs2028299 [recessive, OR = 1.4; P = 0.01], rs2028299 [recessive, OR = 1.4; P = 0.01] and rs1801214 [dominant, OR = 1.3; P = 0.04]. The A allelic variant of FTO gene SNP rs11642841 was significantly associated with BMI in the nondiabetic control group with high ORs of 1.7 (P = 0.01), 1.5 (0.04) and 1.4 (0.01) as determined by additive, recessive and dominant models, respectively. Since gender is known to exert a significant influence on the obesity phenotypes [18], the association between T2DM-related SNP variants and BMI was analyzed in the population stratified for gender and results are presented in Table 4. Out of the 36 T2DM SNPs variants the following SNPs were significantly associated with BMI: rs10440833 [recessive; OR = 1.8 (1.0, 3.3); P = 0.04], rs4812829 [additive; 3.0 (1.1, 8.5); P = 0.03], rs1802295 [dominant; OR = 0.23 (0.06, 0.79); P = 0.02], rs7178572 [additive; 3.2 (1.1, 9.9); P = 0.04] and rs1801214 [recessive;
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Mol Biol Rep Table 2 Genotypic variants of T2DM-related SNPs associated with BMI and waist circumference
SNP
Risk allele
rs10440833
T
rs7578326
rs7178572
rs2028299
rs11642841
rs7903146
rs13081389
rs1552224
rs7957197
rs8042680
rs163184
A
A
A
A
C
A
T
T
C
T
Model
Odds ratio BMI (95 % CI)
P value
Odds ratio WC (95 % CI)
P value 0.39
Additive
1.05 (0.68, 1.6)
0.80
0.54 (0.58, 1.2)
Recessive
1.4 (1.1, 1.7)
0.005
1.1 (0.95, 1.4)
0.15
Dominant
0.91 (0.59, 1.4)
0.68
0.79 (0.54, 1.1)
0.21
Additive
1.3 (0.91, 1.8)
0.14
0.82 (0.63, 1.0)
0.14
Recessive
1.5 (1.1, 1.8)
0.003
0.94 (0.77, 1.1)
0.59
Dominant
0.98 (0.73, 1.3)
0.88
0.82 (0.65, 1.0)
0.09
Additive
1.3 (0.92, 1.9)
0.12
1.2 (0.92, 1.6)
0.15
Recessive
1.2 (0.84, 1.6)
0.33
1.2 (0.91, 1.6)
0.18
Dominant
1.3 (1.1, 1.6)
0.03
1.1 (0.91, 1.3)
0.34
Additive
1.9 (1.2, 3.1)
0.008
1.2 (0.79, 1.8)
0.39
Recessive Dominant
1.2 (0.98, 1.6) 1.8 (1.1, 2.8)
0.07 0.01
1.1 (0.94, 1.3) 1.1 (0.77, 1.7)
0.18 0.50
Additive
1.5 (1.1, 2.1)
0.01
1.4 (1.1, 1.9)
0.01
Recessive
1.4 (0.97, 1.8)
0.07
1.3 (0.96, 1.6)
0.08
Dominant
1.3 (1.1, 1.7)
0.01
1.3 (1.1, 1.6)
0.005
Additive
1.2 (0.86, 1.7)
0.26
1.4 (1.0, 1.8)
0.03
Recessive
1.1 (0.89, 1.4)
0.33
1.1 (0.91, 1.3)
0.30
Dominant
1.2 (0.84, 1.6)
0.35
1.3 (1.1, 1.7)
0.03 0.66
Additive
0.40 (0.03, 4.6)
0.46
0.71 (0.15, 3.2)
Recessive
0.61 (0.06, 5.4)
0.65
1.5 (1.1, 2.1)
0.01
Dominant
0.47 (0.05, 4.3)
0.51
0.68 (0.15, 3.1)
0.62 0.37
Additive
–
–
2.8 (0.29, 27.2)
Recessive
1.4 (0.83, 2.2)
0.21
1.5 (1.0, 2.3)
0.04
Dominant
–
–
2.8 (0.28, 26.6)
0.38
Additive
1.1 (0.55, 2.1)
0.81
1.8 (1.1, 3.1)
0.03
Recessive Dominant
0.95 (0.74, 1.2) 1.1 (0.56, 2.1)
0.74 0.77
1.1 (0.93, 1.4) 1.7 (1.0, 3.0)
0.20 0.04
Additive
1.0 (0.75, 1.4)
0.86
1.3 (1.0, 1.7)
0.02
Recessive
1.0 (0.79, 1.3)
0.82
1.1 (0.91, 1.3)
0.30
Dominant
1.0 (0.77, 1.3)
0.94
1.3 (1.1, 1.6)
0.009
Additive
1.1 (0.77, 1.4)
0.71
1.3 (1.0, 1.7)
0.02
Recessive
1.0 (0.81, 1.3)
0.75
1.2 (0.94, 1.4)
0.15
Dominant
1.0 (0.79, 1.4)
0.75
1.3 (1.0, 1.6)
0.03
OR = 0.47 (0.22, 0.99); P = 0.04]. In non-diabetic control subjects variants of SNPs rs10440833 [recessive; OR = 1.8 (1.2, 2.8); P = 0.011] and rs1470579 [additive; OR = 2.7 (1.4, 5.1); P = 0.002] were significantly associated with BMI. In T2DM females, variants of SNPs rs7903146 [recessive; OR = 2.9 (1.1, 7.1); P = 0.02], rs10440833 [recessive; OR = 2.0 (1.0, 3.9); P = 0.04], rs849134 [additive; OR = 0.30 (0.10, 0.89); P = 0.03], rs6795735 [additive; OR = 3.4 (1.3, 9.1); P = 0.01], rs243021 [recessive; OR = 0.44(0.20, 0.97); P = 0.04] and rs10965250 [dominant; OR = 2.2 (1.0, 5.1); P = 0.04] were significantly associated with BMI. In non-diabetic control females the following SNP variants were significantly associated with BMI: rs849134 [additive; OR = 1.9(1.2, 3.4); P = 0.001], rs1387153 [additive;
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OR = 0.33 (0.14, 0.75); P = 0.009], rs11634397 [dominant; OR = 1.5 (01.0, 2.3); P = 0.02], rs1802295 [recessive; OR = 1.5(1.0, 2.2); P = 0.04], rs2028299 [additive; OR = 3.0(1.4, 6.5); P = 0.006], rs1801214 [recessive; OR = 1.6(1.1, 2.4); P = 0.01] and rs11642841 [additive; OR = 2.1 (1.1, 3.7); P = 0.02]. However, when Bonferroni correction was applied, only variant of the SNP rs1470579 was significantly associated with BMI, and that too only in T2DM males (P = 0.03). Compared with single genetic markers, polygenic scores that evaluate the composite effects of multiple trait-associated variants have been shown to be more effective in explaining the variance of traits and risk of complex diseases like T2DM [19, 20]. We incorporated all the 36 T2DM associated allelic variants and calculated the
Mol Biol Rep Table 3 Association between T2DM-related SNP variants and BMI in T2DM status stratified subjects
SNP
rs7903146
rs10440833
Risk allele
C
T
rs1470579
A
rs4812829
G
rs2028299
rs16861329
rs1801214
rs11642841
A
T
C
A
Model
T2DM
Control
Odds ratio BMI (95 % CI)
P value
Odds ratio BMI (95 % CI)
P value
Additive
1.4 (0.68, 2.9)
0.34
1.6 (1.0, 2.4)
0.04
Recessive
2.0 (1.2, 3.4)
0.007
1.1 (0.86, 1.5)
0.35
Dominant
0.84 (0.44, 1.6)
0.59
1.5 (1.0, 2.3)
0.04
Additive
1.1 (0.48, 2.7)
0.75
1.1 (0.63, 1.8)
0.75
Recessive
1.8 (1.2, 2.8)
0.008
1.3 (1.0, 1.8)
0.04
Dominant
0.85 (0.37, 1.9)
0.71
0.96 (0.56, 1.6)
0.91
Additive Recessive
1.4 (0.79, 2.4) 1.0 (0.67, 1.6)
0.25 0.83
1.5 (1.0, 2.2) 1.4 (1.1, 1.8)
0.05 0.01
Dominant
1.5 (0.87, 2.4)
0.14
1.3 (0.88, 1.8)
0.19
Additive
2.1 (1.0, 4.3)
0.04
0.59 (0.26, 1.3)
0.18
Recessive
1.0 (0.67, 1.6)
0.85
1.1 (0.80, 1.9)
0.63
Dominant
2.2 (1.1, 4.5)
0.02
0.56 (0.25, 1.2)
0.14
Additive
1.4 (0.56, 3.2)
0.48
2.4 (1.3, 4.5)
0.005
Recessive
0.95 (0.61, 1.5)
0.82
1.4 (1.1, 1.9)
0.01
Dominant
1.4 (0.59, 3.3)
0.42
2.2 (1.2, 4.0)
0.01
Additive
3.7 (0.49, 28.8)
0.19
0.79 (0.33, 1.8)
0.59
Recessive
3.3 (0.44, 25.4)
0.24
0.77 (0.32, 1.8)
0.55
Dominant
1.9 (1.1, 3.5)
0.02
1.1 (0.78, 1.5)
0.62
Additive
0.66 (0.36, 1.2)
0.18
1.2 (0.84, 1.9)
0.25
Recessive
0.58 (0.34, 1.0)
0.05
1.0 (0.72, 1.5)
0.84
Dominant
1.0 (0.66, 1.6)
0.87
1.3 (1.0, 1.8)
0.04
Additive Recessive
1.3 (0.66, 2.6) 1.2 (0.63, 2.3)
0.41 0.54
1.7 (1.1, 2.6) 1.5 (1.0, 2.2)
0.01 0.04
Dominant
1.2 (0.77, 1.8)
0.39
1.4 (1.1, 1.8)
0.01
cumulative genetic risk score to explore the combined effect of 36 SNPs on obesity related phenotypes. The genetic risk scores (un-weighted, weighted and weighted after adjusting for age and BMI) of variants of 36 T2DM associated SNPs on obesity related phenotypic traits like WC, hip size, systolic and diastolic blood pressure, and plasma levels of total cholesterol, triglycerides and LDL were estimated for the Saudi population (Table 5). The unweighted and weighted genetic scores indicated only weak associations with obesity traits. The weighted genetic risk scores reached after adjusting for age and BMI showed significant associations and could explain 3–21 % of different obesity traits. The combined genetic risk scores accounted for 19, 14, 21, 13, 1.3, 4, 3 percentages of WC, hip size, systolic and diastolic blood pressure, total cholesterol, triglycerides and LDL phenotypes, respectively.
Discussion Earlier studies identified allelic variants of 36 SNPs to be strongly associated with T2DM in European and South Asian populations [10–17]. A previous study replicated
eight of these associations in a Saudi population (manuscript submitted). Since T2DM develops readily, progresses rapidly and is difficult to control in people who are obese, we attempted to identify whether or not a direct relation exists between the allelic variants of T2DM-related SNPs and obesity in a Saudi population. We genotyped 2,223 Saudi adults for 36 T2DM-associated SNPs, and assessed the effect of various allotypes on BMI and WC using various models. Out of 36, allelic variants of five and seven SNPs were significantly associated with BMI and WC, respectively. The A allelic variant of rs11642841 SNP, located in the FTO gene, was associated with both BMI and WC. Furthermore, the association of the FTO variant with BMI was persistent in the control group after stratifying the population for T2DM status, indicating its relation to obesity in the Saudi population. Variant of rs7903146, a SNP located in the intronic region of TCF7L2, was associated with BMI and WC in both the T2DM and control groups and with HDL only in the T2DM group. Variants of 3 SNPs were associated with both BMI and WC in the control group following T2DM status stratification and thus implicate their direct relation to obesity. Variant of SNP rs10440833 was associated with
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Mol Biol Rep Table 4 Relation between T2DM-related SNP variants and BMI following gender stratification SNP
Risk allele
Model
Male T2DM Odds ratio (95 % CI)
rs7903146
rs10440833
rs849134
rs1470579
rs6795735
rs1387153
rs243021
rs11634397
rs4812829
rs1802295
rs7178572
rs2028299
rs1801214
rs10965250
rs11642841
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C
T
C
A
T
C
C
A
G
C
A
A
C
A
A
Female Control P
Odds ratio (95 % CI)
T2DM P
Odds ratio (95 % CI)
Control P
Odds ratio (95 % CI)
P
Additive
0.61 (0.25, 1.4)
0.27
1.4 (0.74, 2.9)
0.26
0.59 (0.21, 1.6)
0.42
1.5 (0.81, 2.7)
0.19
Recessive
1.8 (0.92, 3.4)
0.08
1.3 (0.85, 2.0)
0.21
2.9 (1.1, 7.1)
0.02
0.99 (0.68, 1.4)
0.96
Dominant
0.80 (0.34, 1.8)
0.60
1.6 (0.85, 3.0)
0.13
0.83 (0.31, 2.2)
0.71
1.4 (0.80, 2.4)
0.23
Additive
1.6 (0.58, 4.7)
0.33
1.6 (0.67, 3.8)
0.28
0.42 (0.05, 3.3)
0.42
0.85 (0.42, 1.8)
0.68
Recessive
1.8 (1.0, 3.3)
0.04
1.8 (1.2, 2.8)
0.011
2.0 (1.0, 3.9)
0.04
1.1 (0.76, 1.6)
0.57
Dominant
1.3 (0.47, 3.5)
0.59
1.3 (0.55, 3.0)
0.53
0.28 (0.03, 2.1)
0.22
0.80 (0.39, 1.6)
0.56
Additive
2.1 (0.78, 5.8)
0.13
0.93 (0.48, 1.8)
0.84
0.30 (0.10, 0.89)
0.03
1.9 (1.2, 3.4)
0.00
Recessive
1.8 (0.69, 4.4)
0.23
0.79 (0.43, 1.4)
0.46
0.64 (0.29, 1.4)
0.28
1.5 (0.92, 2.6)
0.09
Dominant
1.5 (0.82, 2.8)
0.17
1.2 (0.77, 1.9)
0.38
0.31 (0.12, 0.78)
0.01
1.6 (1.1, 2.3)
0.01
Additive
1.2 (0.56, 2.6)
0.59
2.7 (1.4, 5.1)
0.002
1.5 (0.65, 3.6)
0.31
0.96 (0.56, 1.6)
0.89
Recessive
1.0 (0.56, 1.8)
0.92
2.0 (1.3, 3.1)
0.002
1.1 (0.54, 2.1)
0.83
1.1 (0.73, 1.5)
0.72
Dominant
1.3 (0.63, 2.5)
0.49
2.0 (1.1, 3.6)
0.02
1.6 (0.76, 3.5)
0.20
0.91 (0.55, 1.5)
0.73
Additive
0.37 (0.10, 1.3)
0.13
0.93 (0.41, 2.1)
0.87
3.4 (1.3, 9.1)
0.01
1.5 (0.69, 3.1)
0.32
Recessive Dominant
0.91 (0.50, 1.6) 0.35 (0.10, 1.2)
0.76 0.10
0.86 (0.55, 1.3) 1.0 (0.44, 2.2)
0.50 0.98
1.2 (0.60, 2.3) 3.7 (1.4, 9.4)
0.61 0.007
0.70 (0.48, 1.0) 1.1 (0.56, 2.3)
0.06 0.73
Additive
1.2 (0.39, 3.7)
0.74
1.1 (0.45, 2.6)
0.83
0.73 (0.15, 3.3)
0.68
0.33 (0.14, 0.75)
0.009
Recessive
0.84 (0.46, 1.5)
0.57
0.87 (0.56, 1.3)
0.98
1.0 (0.53, 2.0)
0.92
1.3 (0.87, 1.8)
0.21
Dominant
1.1 (0.36, 3.1)
0.89
1.0 (0.43, 2.3)
0.98
0.75 (0.17, 3.3)
0.71
0.42 (0.19, 0.93)
0.03
Additive
1.3 (0.69, 2.6)
0.37
0.93 (0.60, 1.4)
0.77
1.2 (0.53, 2.5)
0.70
1.0 (0.68, 1.5)
0.88
Recessive
0.70 (0.33, 1.4)
0.34
1.3 (0.75, 2.2)
0.34
0.44 (0.20, 0.97)
0.04
1.1 (0.63, 1.8)
0.78
Dominant
1.2 (0.63, 2.2)
0.61
1.0 (0.65, 1.6)
0.91
0.88 (0.44, 1.7)
0.72
1.0 (0.71, 1.5)
0.81
Additive
0.83 (0.36, 1.9)
0.66
0.70 (0.39, 1.2)
0.24
0.61 (0.22, 1.7)
0.34
1.5 (0.90, 2.6)
0.11
Recessive
1.0 (0.49, 2.1)
0.95
0.69 (0.41, 1.1)
0.16
1.0 (0.44, 2.3)
0.98
1.2 (073, 1.9)
0.47
Dominant
0.73 (0.39, 1.4)
0.34
0.89 (0.55, 1.4)
0.63
0.50 (0.22, 1.1)
0.10
1.5 (01.0, 2.3)
0.02
Additive
3.0 (1.1, 8.5)
0.03
0.45 (0.15, 1.3)
0.16
1.4 (0.45, 4.5)
0.53
0.65 (0.21, 2.0)
0.52
Recessive
1.4 (0.77, 2.5)
0.26
1.1 (0.67, 1.7)
0.79
0.74 (0.37, 1.5)
0.40
1.1 (0.72, 1.5)
0.72
Dominant
2.8 (1.0, 7.8)
0.03
0.43 (0.14, 1.3)
0.12
1.7 (0.55, 5.2)
0.35
0.62 (0.20, 1.9)
0.42
Additive
0.28 (0.085, 1.0)
0.058
1.1 (0.51, 2.5)
0.72
1.4 (0.37, 5.0)
0.62
1.4 (0.80, 2.6)
0.21
Recessive Dominant
1.1 (0.61, 2.0) 0.23 (0.06, 0.79)
0.71 0.02
0.98 (0.63, 1.5) 1.2 (0.54, 2.5)
0.95 0.68
1.2 (0.62, 2.3) 1.3 (0.36, 4.5)
0.57 0.69
1.5 (1.0, 2.2) 1.2 (0.68, 2.0)
0.04 0.55
Additive
3.2 (1.1, 9.9)
0.04
1.5 (0.70, 3.1)
0.29
1.3 (0.43, 4.1)
0.60
0.99 (0.57, 1.7)
0.79
Recessive
2.8 (0.97, 8.5)
0.05
1.4 (0.68, 2.7)
0.37
1.2 (0.40, 3.5)
0.73
0.82 (0.49, 1.4)
0.47
Dominant
1.5 (0.82, 2.7)
0.18
1.2 (0.78, 1.8)
0.37
1.3 (0.67, 2.5)
0.43
1.3 (0.89, 1.9)
0.16
Additive
1.1 (0.26, 4.3)
0.92
1.7 (0.63, 4.7)
0.28
2.0 (0.64, 6.6)
0.22
3.0 (1.4, 6.5)
0.006
Recessive
0.64 (0.34, 1.2)
0.16
1.5 (0.99, 2.4)
0.05
1.4 (0.74, 2.8)
0.28
1.2 (0.86, 1.8)
0.21
Dominant
1.3 (0.32, 5.1)
0.72
1.5 (0.54, 3.9)
0.44
1.9 (0.60, 5.8)
0.27
2.9 (1.4, 6.3)
0.006
Additive
0.43 (0.148, 1.0)
0.05
1.0 (0.54, 1.9)
0.95
0.99 (0.41, 2.4)
0.99
1.4 (0.82, 2.3)
0.21
Recessive
0.47 (0.22, 0.99)
0.04
0.99 (0.56, 1.7)
0.97
0.69 (0.30, 1.6)
0.39
1.0 (0.64, 1.6)
0.94
Dominant
0.71 (0.37, 1.4)
0.31
1.0 (0.85, 1.9)
0.22
1.6 (0.83, 3.2)
0.67
1.6 (1.1, 2.4)
0.01
Additive
–
–
1.1 (0.38, 3.2)
0.85
2.2 (0.28, 17.6)
0.43
0.96 (0.46, 2.0)
0.93
Recessive
–
–
1.1 (0.40, 3.2)
0.80
1.8 (0.23, 14.2)
0.56
0.98 (0.47, 2.0)
0.95
Dominant
1.3 (0.72, 2.4)
0.35
0.94 (0.60, 1.5)
0.78
2.2 (1.0, 5.1)
0.04
0.96 (0.66, 1.4)
0.87
Additive
1.4 (0.57, 3.5)
0.43
1.4 (0.76, 2.6)
0.26
1.4 (0.47, 4.6)
0.51
2.1 (1.1, 3.7)
0.02
Recessive
1.3 (0.58, 3.2)
0.46
1.3 (0.72, 2.3)
0.37
1.3 (0.44, 3.8)
0.62
1.8 (1.1, 3.1)
0.04
Dominant
1.2 (0.64, 2.1)
0.60
1.3 (0.82, 1.9)
0.26
1.3 (0.46, 2.3)
0.46
1.4 (0.95, 2.0)
0.08
Mol Biol Rep Table 5 Combined genetic risk scores based on 36 T2DM SNP variants on obesity traits
Trait
Score type
Covariate
Adj. R2
P
Waist circumference
Weighted
–
0.002
0.04
Weighted sum
Age, BMI
0.004
1.2 9 10-16
Hip size Systolic BP
Diastolic BP T. Cholesterol
Triglycerides
LDL
Weighted sum
–
0.005
0.009
weighted sum
Age, BMI
0.004
7.4 9 10-18
Un-weighted sum
–
0.002
0.04
Weighted sum
–
0.007
3.6 9 10-4
Weighted
Age, BMI
0.001
2.6 9 10-9
Weighted
–
0.003
0.01
Weighted
Age, BMI
0.002
6.3 9 10-10
Un-weighted
–
0.004
0.006
Weighted
–
0.003
0.02
Weighted
Age, BMI
0.003
2.6 9 10-5
Un-weighted
–
0.004
0.006
Weighted
–
0.004
0.003
Weighted Un-weighted
Age, BMI –
0.003 0.005
1.9 9 10-15 0.01
Weighted
–
0.006
0.005
Weighted
Age, BMI
0.004
4.2 9 10-8
BMI in the T2DM subjects of both males and females. In addition, the combined effect of all the variants of 36 SNPs determined from genetic risk scores, could explain 19 and 14 % of WC and hip size variance, respectively in this population. Obesity, being the leading cause of preventable deaths, is a serious health concern for Saudi Arabia. Increasing trends in obesity levels in Central region of Saudi Arabia indicate a close association with increasing prevalence of chronic non-communicable diseases, and it is estimated that 68 % of its citizens are overweight/obese (BMI [ 25) [1]. Rapid economic growth in the past few decades has brought dietary and life-style changes, and these, together with the hot weather and sedentary behaviors, provide a highly obesogenic environment for the Saudi population [21]. Recent GWA studies have identified several SNPs significantly associated with BMI and imply an important role for hereditary factors. The hereditary predisposition to obesity has been shown to exacerbate the effect of dietary and life-style choices and various socio-cultural issues like assortative mating, common between obese partners [22]. The present study, involving a large number of T2DM and control subjects, confirms the involvement of variants of FTO gene in obesity and T2DM in the Saudi population. In this study, the variant of FTO gene SNP rs11642841 showed strong association with both BMI and WC in the Saudi subjects overall and also in the control group, but not in the T2DM group after stratifying the subjects by T2DM status. The persistent association of the FTO variant with BMI and WC in the control group after stratifying the population for
T2DM status indicates its direct relation to obesity in the Saudi population. The association between obesity and several SNPs of fat mass obesity-associated FTO gene has been replicated in several populations [23–26]. In particular, the A allelic variant of rs11642841 SNP, associated with both BMI and WC in the present study, has been previously associated with obesity [27]. This previous study, involving 843 unrelated individuals from an island population in the eastern Adriatic coast of Croatia, showed the variant of rs11642841 to be significantly associated with various body fatness measures like waist-hip ratio, weight and WC, and while the BMI seemed to be related to this variant it didn’t reach the required statistical significance. Variant of SNP rs7903146, located in the intronic region of TCF7L2, showed significant association with WC in the entire study population and with BMI and WC in both control and T2DM groups following stratification of the population for T2DM status. Several previous studies involving T2DM subjects of African [28], Swedish, Finnish [29] and Taiwanese [30] cohorts confirmed that rs7903146 is a significant T2DM trait-defining SNP. However, according to some studies the different genotypes of this SNP were not associated with BMI phenotypes [31] or T2DM [32]. Analysis of variants of rs7903146 in Framingham heart study subjects confirmed its association with hyperglycaemia and a higher proinsulin/insulin ratio but not with adiposity phenotypes [33]. These discrepancies may be due to the differences in genetic or environmental modifiers affecting BMI heterogeneously across populations [34, 35].
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Mol Biol Rep
The present study showed that the variant of CDKAL1 gene SNP rs10440833 is significantly associated with BMI in the whole population and with both T2DM and control groups following T2DM status stratification. Results of previous studies suggested independent roles in T2DM as well as in obesity phenotypes for CDKAL1 locus [36, 37]. In a 2007 study, the rs10440833 SNP variant was associated with T2DM in obese individuals of European ancestry and individuals from Hong Kong of Han Chinese ancestry [38], and this was further confirmed in 2010 in a meta-analysis [13]. The high OR of this variant in homozygotes suggested that the effect is substantially stronger in homozygous than in heterozygous carriers since it is functionally associated with a 20 % lower insulin response in homozygotes compared to heterozygotes or non-carriers indicating that this variant may confer risk of T2DM through reduced insulin secretion [38]. Gender associated differences have been established in the occurrence of T2DM as well as various underlying phenotypes like insulin resistance and obesity [39]. In the present study, variant of SNP rs10440833 was associated with BMI in T2DM subjects of both genders and in the male non-diabetics which suggests SNP rs10440833 may be directly associated with BMI. The present study also found a strong association between variant of rs849134 and BMI in women, irrespective of T2DM status. Variant of SNP rs1470579, located in intronic region of IGF2BP2, is the only polymorphic variant in our study that has remained significantly associated with BMI following Bonferroni correction. IGF2BP2, an insulin-like growth factor 2 transcription regulatory protein and expressed at high levels in pancreatic islet cells, has received considerable attention due to its role in growth and insulin signaling [40]. A meta-analysis of 35 studies has identified rs1470579 to be associated with elevated T2DM risk by both dominant and recessive genetic models [41]. However, studies on several genetic variants of IGF2BP2 in different ethnicities have yielded contradicting results [41]. In this context, our results on rs1470579 based on a large population of Saudi ethnicity, is expected to aid in unraveling the molecular mechanism of T2DM. Combined risk scores have been used to assess genetic effects of multiple genetic loci on complex diseases such as T2DM [42]. Combined genetic scores of our study explain, quantitatively, the association between the individual obesity traits and T2DM related SNP variants. Low scores in weighted/un-weighted models indicate weak associations. The disappearance of strong correlations seen for FTO gene SNP variant and BMI and WC may be due to the differences in the genetic models used. The authors acknowledge certain limitations of this study. The strength of the associations conferred by each of these variants to increased BMI and WC were relatively small and could have been confounded by unknown
123
variables. Confirmation of functional outcomes of common variants in the human genome, even if real, needs very large sample sizes to overcome additional genetic and environmental modifiers. However, the limitation resulting from the need for huge sample sizes may be overcome by replication of associations in several independent studies with small sample sizes. In the present study, variants of majority of the SNPs were associated with either BMI or WC, but not with both, implying significant effect of methodology. Furthermore, scope for confirmation of the functional phenotypes of these SNPs by alternative methods is limited since all but one of the SNPs of this study are located in the intronic or intergenic regions. In conclusion, we investigated 36 previously confirmed T2DM-associated loci in a Saudi population and identified several SNP variants associated with obesity phenotypes. In particular, the A allelic variant of rs11642841 SNP, located in the intronic region of FTO gene, showed significant association with both BMI and WC. The association of this variant with BMI persisted in the non-diabetic control group after stratifying the data for T2DM status implying a direct role for this variant in increased obesity. Variant of SNP rs7903146 was associated with both BMI and WC in both control and T2DM groups of Saudi subjects. SNP rs10440833 was associated with BMI in both men and women subjects. Replication of these results in future studies or in other populations and identifying the molecular basis underlying these genetic predispositions may lead to development of preventive strategies against obesity and T2DM in the carriers. Acknowledgments This study was funded by the Biomarkers Research Program at King Saud University, Riyadh, Saudi Arabia. The authors are grateful to the primary care physicians and nurses of the PHCCs in Riyadh for patient recruitment and sample collection. The authors also thank Mr. Benjamin Vinodson for statistical analysis of the data. Conflict of interest
The authors have nothing to disclose.
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