Abstract
The pathogenesis of T2DM involves secretion of several pro-inflammatory molecules by the dramatically increased adipocytes, both by number and size, and associated macrophages of adipose tissue. Since T2DM is usually preceded by obesity and chronic systemic inflammation, the objective of this study was to explore for any association between genetic variants of previously established 36 T2DM-associated SNPs and altered serum adipocytokine levels and metabolic syndrome phenotypes. Study consisted of 566 subjects (284 males and 282 females) of whom 147 were T2DM patients and 419 healthy controls. Study subjects were genotyped for 36 T2DM-linked single nucleotide polymorphisms (SNPs) using the KASPar SNP Genotyping System and grouped into different genotypes for each SNP. Various anthropometric and biochemical parameters were measured following standard procedures. The mean values of serum levels of individual adipocytokines and the presence/absence of metabolic syndrome phenotypes corresponding to various genotypes were compared by determining the odds ratios. Genotypic variants of five and seven of the 36 T2DM-related SNPs were significantly associated with altered serum levels of adiponectin and aPAI, respectively. Six variants of the 36 SNPs were associated with metabolic syndrome manifestations. This study identified positive associations between genotypic variants of five and seven of the 36 T2DM related SNPs and altered serum levels of adiponectin and aPAI, respectively. Six of 36 SNPs were also associated with metabolic syndrome in the studied population. The relation between specific SNPs and individual phenotypic traits may be useful in explaining the causal mechanisms of hereditary component of T2DM.
Keywords: Adiponectin, resistin, aPAI, Type 2 Diabetes Mellitus, obesity, metabolic syndrome, insulin resistance, SNP, GWAS
Introduction
Obesity is an underlying cause of chronic non-communicable diseases like Type 2 Diabetes Mellitus (T2DM). In many parts of the world, rapidly economic changes over the last few decades have drastically affected the life-style and obesity levels have now reached alarming proportions, contributing to an epidemic of related diseases, such as metabolic syndrome and T2DM [1]. Several genetic factors predispose individuals to obesity and exaggerate the effects of environmental factors such as a sedentary life style and high intake of low quality foods [2].
In addition to storing lipids and serving as an energy reservoir, adipose tissue also serves as an endocrine organ by secreting many substances, known as ‘adipokines’ or ‘adipokines’, which are involved in the regulation of several physiologic metabolic processes [3]. Increased numbers and size of adipocytes in obese individuals lead to increased synthesis and secretion of adipokines, and hence, serum levels of adipokines are influenced primarily by obesity status. Due to their influence on insulin sensitivity and glucose metabolism of various tissues and pancreatic islet beta cell function, altered adipokine levels have been suggested to be responsible for several of the manifestations of T2DM, including systemic insulin resistance [3-7].
Decreases in adiponectin, the insulin sensitizing adipokine, have been associated with increased body fat [8], while increase in resistin, an inflammation-related adipokine has been linked to insulin resistance in humans [9]. Increased levels of activated plasminogen activator inhibitor 1 (aPAI-1) is an index of low-grade inflammatory state associated with obesity [10]. More than 75% of T2DM patients have metabolic syndrome (MetS). Combined occurrence of obesity and elevated blood pressure (BP), elevated serum triglyceride (TG) and glucose, and reduced HDL cholesterol mark MetS [25]. Also, elevated resistin and decreased adiponectin levels are risk factors for MetS [11].
Recent genome wide analysis (GWA) studies have identified reproducible associations between certain single nucleotide polymorphisms (SNPs) and incidence of T2DM [12]. Progression of T2DM parallels the increased systemic levels of several pro-inflammatory and decreased levels of anti-inflammatory cytokines and the worsening of MetS [3]. The indigenous Middle Eastern population may have an increased genetic predisposition to develop T2DM [13]. Earlier, we identified the highly heritable nature of adipokines and their pattern of co-variation with BMI from the pre-teen years in a Saudi population [14]. Following recent GWA studies in European and South Asian populations, which have revealed 36 genetic variants related to T2DM risk [15-23], we investigated whether these SNPs were also associated with altered serum levels of adipokines and presence of MetS manifestations in a T2DM Saudi population.
Methods
Study subjects
Study subjects were 566 unrelated adult Saudi individuals consisting of 160 T2DM patients and 406 normal controls from the Biomarker Screening Project in Riyadh (RIYADH COHORT), a capital-wide epidemiological study involving over 17,000 consenting Saudis coming from different Primary Health Care Centres (PHCCs) of the city of Riyadh, Saudi Arabia. A generalized questionnaire aimed to seek demographic information and past medical history was given to all participating subjects. Those with morbidities that needed medical attention were excluded from the study. Written consent was obtained after orientation to the study. 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 attend their assigned PHCCs after an overnight fast (> 10 hours) for anthropometry and blood withdrawal. Anthropometry included height (to the nearest 0.5 cm), weight (to the nearest 0.1 kg), body mass index (BMI) (calculated as kg/m2), waist and hip circumference (measured utilizing a standardized measuring tape in cm), and systolic and diastolic blood pressure. Fasting serum samples were collected and stored at -20°C prior to analysis.
Genotyping
Genomic DNA was isolated from whole blood using the Blood GenomicPrep Mini Spin Kit (GE healthcare Life Sciences, Piscataway, NJ, USA). DNA concentration and purity (260/280) were checked using Nano-drop spectrophotometer. All DNA samples from T2DM patients and controls were genotyped for 36 SNPs using the KASPar SNP Genotyping System (KBioscience, Hoddesdon, UK).
Biochemical measurements
Fasting glucose and lipid profile were measured using a chemical autoanalyzer (Konelab, Vantaa, Finland) at the Biomarker Research Center (King Saud University, Riyadh, Saudi Arabia). Serum adiponectin, resistin and aPAI-1 were quantified using multiplex assay kits that utilize fluorescent microbead technology, allowing simultaneous quantification of several target proteins within a single serum sample of 50-100 μl. These included pre-mixed and fully customized panels that utilize the LuminexH xMAPH Technology platform (Luminex Corporation, Austin, TX, USA). Minimum detectable concentrations (MDC) were as follows: adiponectin, 145.4 pg/ml; resistin, 6.7 pg/ml and aPAI-1, 1.3 pg/ml.
Definition of MetS
The definition of MetS used was according to the National Cholesterol Education Program-Third Adult Treatment Panel (NCEP ATP III), according to which three or more of the following criteria must be fulfilled: fasting blood glucose level ≥ 5.6 mmol/l; blood pressure ≥ 130/85 mmHg; triglycerides ≥ 1.7 mmol/l; HDL-cholesterol < 1.03 mmol/l for men and < 1.29 mmol/l for women; and waist circumference > 102 cm for men and > 88 cm for women [24].
Statistical analysis
Data was analyzed using SPSS version 16.0 (Statistical package for Social Science, Inc., Chicago IL, USA). The data are presented as means ± SD. The difference between the means of 2 groups was tested using Student’s t test. Multiple group comparisons were performed using analysis of variance (ANOVA). Multinomial logistic regression was used to calculate odds ratios and 95% confidence intervals. Level of significance was given at P ≤ 0.05.
Results
The means of anthropometric and clinical parameters of individuals enrolled in the study are given in Table 1. Various genetic characteristics of the 36 T2DM related SNPs are given in Supplementary Table 1. Results of analysis of associations between genotypic variants of 36 SNPs and serum adipokine levels are presented in Table 2. Genotypic variants of 5 and 7 of the 36 T2DM-related SNPs were significantly associated with altered blood levels of adiponectin and aPAI, respectively. Genetic variants of SNPs rs972283 [additive; OR 1.6 (1.0, 2.9)], rs896854 [additive; OR=2.7 (1.6, 4.6)], rs11634397 [recessive; OR=1.7 (1.1, 2.5)], rs5945326 [dominant; OR=0.56 (0.35,0.91)] and rs4812829 [additive; OR=0.37 (0.14, 0.98)] were associated with altered serum levels of adiponectin; rs7903146 [dominant; OR=0.61 (0.39, 0.96)], rs13081389 [dominant; OR=3.2 (1.3, 7.3)], rs1470579 [additive; OR=2.1 (1.1, 3.8)], rs243021 [additive; OR=0.48 (0.24, 0.96)], rs13292136 [additive; OR=0.10 (0.01, 0.93)], rs1552224 [recessive; OR=0.29 (0.09, 0.91)] and rs163184 [dominant; 0.59 (0.36, 0.96)] were associated with altered serum levels of aPAI. Serum level of resistin was not related to any of the 36 SNPs.
Table 1.
Clinical profile of study subjects
| General Characteristics | Mean ± SD |
|---|---|
| N | 566 |
| Gender (M/F) | 284/282 |
| Age | 42.4±9.0 |
| BMI (kg/m2) | 29.6±6.3 |
| Waist (cm) | 91.6±22.1 |
| Systolic BP (mmHg) | 119.1±12.7 |
| Diastolic BP (mmHg) | 76.7±7.7 |
| Cholesterol (mmol/l) | 5.3±1.1 |
| Glucose (mmol/l) | 6.9±3.1 |
| Triglycerides (mmol/l) | 1.8±0.99 |
| HDL (mmol/l) | 0.83±0.26 |
| LDL (mmol/l) | 4.1±1.0 |
| Adiponectin (ug/ml) | 2.7 (0.02, 8.5) |
| Resistin (ng/ml) | 17.2±4.2 |
| aPAI (ng/ml) | 11.6 (3.2, 67.5) |
| ANGII (ng/ml) | 1.2±0.29 |
| hsCRP (ug/ml) | 3.4 (1.2, 5.8) |
Table 2.
Relation between T2D-associated SNPs and altered serum adipocytokine levels
| Risk Allele | Model | Adiponectin Odds Ratio (95% CI) | P value | Resistin Odds Ratio (95% CI) | P value | aPAI Odds Ratio (95% CI) | P value | |
|---|---|---|---|---|---|---|---|---|
| rs7903146 | T | Additive | 1.5 (0.88, 2.7) | 0.11 | 1.1 (0.64, 1.9) | 0.65 | 1.0 (0.53, 1.9) | 0.95 |
| Recessive | 1.6 (0.96, 2.8) | 0.06 | 1.2 (0.70, 2.0) | 0.51 | 1.4 (0.76, 2.5) | 0.27 | ||
| Dominant | 1.0 (0.71, 1.5) | 0.85 | 0.95 (0.66, 1.3) | 0.81 | 0.61 (0.39, 0.96) | 0.03 | ||
| rs13081389 | A | Additive | - | - | - | - | 3.2 (1.3, 7.3) | 0.007 |
| Recessive | 1.2 (0.61, 2.1) | 0.64 | 1.6 (0.83, 3.0) | 0.15 | 3.2 (1.3, 7.3) | 0.007 | ||
| Dominant | - | - | - | - | - | - | ||
| rs1470579 | C | Additive | 0.71 (0.42, 1.1) | 0.18 | 1.5 (0.95, 2.5) | 0.07 | 2.1 (1.1, 3.8) | 0.02 |
| Recessive | 0.77 (0.48, 1.2) | 0.28 | 1.4 (0.92, 2.3) | 0.10 | 1.9 (1.1, 3.4) | 0.02 | ||
| Dominant | 0.79 (0.54, 1.1) | 0.22 | 1.3 (0.89, 1.8) | 0.18 | 1.3 (0.85, 2.1) | 0.19 | ||
| rs243021 | T | Additive | 1.1 (0.59, 1.8) | 0.86 | 0.82 (0.47, 1.4) | 0.49 | 0.48 (0.24, 0.96) | 0.04 |
| Recessive | 0.90 (0.62, 1.3) | 0.59 | 1.0 (0.72, 1.4) | 0.83 | 0.59 (0.38, 0.94) | 0.02 | ||
| Dominant | 1.1 (0.67, 1.9) | 0.63 | 0.77 (0.46, 1.3) | 0.33 | 0.62 (0.32, 1.2) | 0.14 | ||
| rs972283 | G | Additive | 1.6 (1.0, 2.9) | 0.04 | 1.5 (0.87, 2.5) | 0.14 | 1.5 (0.77, 2.9) | 0.22 |
| Recessive | 1.2 (0.81, 1.7) | 0.38 | 1.3 (0.89, 1.8) | 0.17 | 1.1 (0.69, 1.7) | 0.71 | ||
| Dominant | 1.6 (1.0, 2.7) | 0.04 | 1.3 (0.81, 2.2) | 0.25 | 1.5 (0.82, 2.8) | 0.17 | ||
| rs896854 | A | Additive | 2.7 (1.6, 4.6) | 1.04*10-4 | 0.81 (0.49, 1.3) | 0.39 | 1.3 (0.74, 2.4) | 0.32 |
| Recessive | 1.68 (1.1, 2.5) | 0.01 | 0.81 (0.54, 1.2) | 0.31 | 1.1 (0.71, 1.8) | 0.56 | ||
| Dominant | 2.4 (1.5, 3.6) | 1.05*10-4 | 0.92 (0.61, 1.3) | 0.67 | 1.3 (0.79, 2.1) | 0.29 | ||
| rs13292136 | C | Additive | 0.65 (0.18, 2.3) | 0.51 | 0.36 (0.09, 1.4) | 0.13 | 0.10 (0.01, 0.93) | 0.03 |
| Recessive | 0.65 (0.18, 2.3) | 0.51 | 0.36 (0.09, 1.4) | 0.13 | 0.11 (0.01, 0.83) | 0.03 | ||
| Dominant | - | - | - | - | - | - | ||
| rs1552224 | T | Additive | - | - | - | - | - | - |
| Recessive | 1.3 (0.59, 2.8) | 0.52 | 0.59 (0.27, 1.2) | 0.18 | 0.29 (0.09, 0.91) | 0.03 | ||
| Dominant | - | - | - | - | - | - | ||
| rs11634397 | G | Additive | 1.4 (0.82, 2.3) | 0.22 | 1.0 (0.62, 1.7) | 0.87 | 0.76 (0.41, 1.4) | 0.38 |
| Recessive | 1.7 (1.1, 2.5) | 0.01 | 1.2 (0.83, 1.8) | 0.29 | 0.96 (0.59, 1.5) | 0.86 | ||
| Dominant | 0.94 (0.60, 1.4) | 0.94 | 0.87 (0.56, 1.3) | 0.54 | 0.73 (0.43, 1.2) | 0.25 | ||
| rs5945326 | G | Additive | 0.66 (0.29, 1.4) | 0.32 | 1.5 (0.69, 3.4) | 0.28 | 1.1 (0.48, 2.6) | 0.76 |
| Recessive | 0.72 (0.32, 1.6) | 0.43 | 1.5 (0.67, 3.3) | 0.32 | 1.1 (0.47, 2.5) | 0.81 | ||
| Dominant | 0.56 (0.35, 0.91) | 0.02 | 1.3 (0.85, 2.1) | 0.19 | 1.3 (0.69, 2.3) | 0.43 | ||
| rs163184 | G | Additive | 1.2 (0.71, 1.9) | 0.50 | 0.97 (0.59, 1.6) | 0.92 | 0.61 (0.33, 1.1) | 0.11 |
| Recessive | 1.3 (0.85, 2.0) | 0.20 | 1.1 (0.70, 1.6) | 0.74 | 0.85 (0.50, 1.4) | 0.55 | ||
| Dominant | 0.94 (0.63, 1.4) | 0.79 | 0.90 (0.60, 1.3) | 0.61 | 0.59 (0.36, 0.96) | 0.03 | ||
| rs4812829 | A | Additive | 0.37 (0.14, 0.98) | 0.04 | 0.98 (0.40, 2.3) | 0.96 | 1.2 (0.39, 3.7) | 0.72 |
| Recessive | 0.38 (0.14, 1.0) | 0.05 | 0.90 (0.37, 2.1) | 0.81 | 1.2 (0.38, 3.5) | 0.78 | ||
| Dominant | 0.81 (0.55, 1.2) | 0.28 | 1.2 (0.85, 1.8) | 0.27 | 1.1 (0.72, 1.7) | 0.59 |
Six variants of the 36 SNPs were associated with metabolic syndrome manifestations (Table 3). These were: rs10440833 [additive; OR=0.41 (0.20, 0.87)], rs11899863 [recessive; OR=0.51 (0.29, 0.90)]; rs5215 [recessive; OR=0.64 (0.44, 0.92)], rs1387153 [additive; OR=0.39 (0.18, 0.85)]; rs972283 [additive; OR=1.8 (1.1, 2.9)] and rs1801214 [additive; OR=1.9 (1.1, 3.0)].
Table 3.
Relation between T2D-associated SNPs and metabolic syndrome
| SNPs | Risk Allele | Model | Odds Ratio (95% CI) | P |
|---|---|---|---|---|
| rs10440833 | A | Additive | 0.41 (0.20, 0.87) | 0.02 |
| Recessive | 0.43 (0.20, 0.89) | 0.02 | ||
| Dominant | 0.79 (0.56, 1.1) | 0.20 | ||
| rs11899863 | G | Additive | 0.20 (0.02, 1.9) | 0.17 |
| Recessive | 0.51 (0.29, 0.90) | 0.02 | ||
| Dominant | 0.43 (0.07, 2.6) | 0.36 | ||
| rs5215 | T | Additive | 0.83 (0.31, 2.2) | 0.72 |
| Recessive | 0.64 (0.44, 0.92) | 0.01 | ||
| Dominant | 0.95 (0.35, 2.5) | 0.92 | ||
| rs1387153 | T | Additive | 0.39 (0.18, 0.85) | 0.01 |
| Recessive | 0.38 (0.18, 0.82) | 0.01 | ||
| Dominant | 0.89 (0.63, 1.2) | 0.51 | ||
| rs972283 | G | Additive | 1.8 (1.1, 2.9) | 0.02 |
| Recessive | 1.7 (1.2, 2.5) | 0.001 | ||
| Dominant | 1.3 (0.83, 2.1) | 0.22 | ||
| rs1801214 | T | Additive | 1.9 (1.1, 3.0) | 0.01 |
| Recessive | 1.6 (1.1, 2.3) | 0.01 | ||
| Dominant | 1.5 (0.97, 2.3) | 0.07 |
Discussion
Previous GWA studies have identified 36 SNPs strongly associated with T2DM in European and South Asian populations. Since T2DM develops and progresses readily, and is difficult to control in obese people adipokines secreted by adipocytes may be involved in the pathology of T2DM. Therefore, this study explored for any associations between genotypic variants of T2DM-related SNPs and serum adipokine levels and presence of metabolic syndrome phenotypes. Allelic variants of seven and five of the 36 SNPs were significantly associated with altered levels of adiponectin and aPAI, respectively. Allelic variants of six of the 36 SNPs showed significant association with metabolic syndrome.
Several traits like obesity, insulin resistance and increased levels of proinflammatory cytokines precede and accompany T2DM [25]. On the other hand, it is widely accepted that T2DM has a hereditary component. Hence, it is natural to expect patients suffering from hereditarily transferred predisposition to T2DM to have one or more SNPs responsible for accelerated progression of underlying traits. The indigenous Saudi population, having an increased genetic predisposition to develop T2DM [13], may be considered appropriate for this kind of study.
In a large-scale GWA study involving 34,840 individuals with T2DM and 114,981 controls, McCarthy and colleagues identified pathways associated with cell cycle regulation, adipokine protein signaling, and CREB-BP-related transcription involved in diabetes pathogenesis [26]. More studies are expected to detect new variants that will explain a larger proportion of the heritability of obesity, and hence, T2DM. Region/population-specific genetic studies are expected to yield useful insights, since the distribution of SNPs is a race- and region-dependent genetic variation. In this respect, risk allele frequencies of TCF7L2 SNPs showing the strongest effect on T2DM in European populations are very few in the Japanese (~5%) compared to populations of European descent (~40%) [12]. Resistin is a polypeptide hormone that has been associated with insulin resistance, inflammation and risk of T2DM [27]. The variant rs12779790, mapped to intergenic region between CDC123 and/CAMK1D, was associated with T2DM vulnerability, a finding that has been replicated [22]. However, in a German population, the rs12779790 mutation was not associated with T2DM or prediabetic phenotypes related to insulin secretion or sensitivity [28,29]. However, the rs11899863 variant, located in the intron region of THADA gene, was associated significantly with aPAI in our study. Moreover, the rs5945326 SNP, located near DUSP9 on the X-chromosome, was associated with increased resistin levels in our study. The latter SNP was only modestly related to T2DM in a Chinese population [30]. Another resistin-associated SNP, rs1801214, mapped to WFS1 gene coding region, was related to T2DM in an African American population [31]. The rs243021 mutation, an intergenic SNP located between EIF3FP3-BCL11A and reproducibly associated with T2DM [21] was also related to serum aPAI of T2DM patients in our study.
Adiponectin is negatively correlated with body mass index [32], and is decreased in the presence of insulin resistance and T2DM [33]. Meta-analyses have shown that hypoadiponectinemia and hyperresistinemia are strongly associated with increased risk of insulin resistance in T2DM [34]. One of the variants of the 36 SNPs examined in the current study was associated with decreased adiponectin levels. Previously we showed population-based differences in the association of adiponectin gene variants with metabolic phenotypes in a study involving T2DM Saudi patients [35].
The modest effect sizes of individual common susceptibility variants seen in our study resemble several GWA studies. For example, the largest allelic odds ratio of any established common variant for T2DM was ~1.35 (TCF7L2), with the nine other validated associations to common variants having allelic odds ratios between 1.1 and 1.2 [19]. The heterogeneous nature of pathogenesis of T2DM and the range of underlying traits may explain the modest contribution of individual genetic factors. These include environmental factors, dietary habits and levels of physical activity, which have a significant effect on T2DM and on the clinical, metabolic and inflammatory traits preceding T2DM, and hence, may be expected to affect adipokine levels.
The authors acknowledge certain limitations of this study. Confirmation of common variants in the human genome with modest effects on common disease risk, even if real, need large sample sizes to overcome the influence of many genetic and environmental modifiers. Also, our study subjects consisted of Saudi ethnicity, and thus, the generalizability to other ethnicities is unknown.
In conclusion, we investigated 36 previously confirmed T2DM-associated loci in a Saudi population and identified nine of them to be also significantly associated with alterations in the levels of serum adipokines and another five related to the presence of metabolic syndrome. Risk allele scores defined by the five loci were also associated with T2DM-related phenotypes, including systolic and diastolic blood pressure, cholesterol, triglycerides and fasting glucose levels. Replication of these results in additional studies/other populations may reveal the molecular mechanisms underlying specific T2DM precursor traits and may allow early detection and preventive measures.
Acknowledgements
The project was financially supported by Vice Deanship of Scientific Research Chairs, King Saud University, Riyadh, Saudi Arabia. The authors are especially thankful to Mr. Benjamin Vinodson for the statistical analysis of data.
Disclosure of conflict of interest
None.
Supporting Information
References
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