Influence of KCNJ11 gene polymorphism in T2DM of south Indian population
Type-2 Diabetes mellitus (T2DM) is a complex metabolic disease. A case-control study was conducted with 218 T2DM and 214 controls to evaluate the T2DM risk of rs5219 polymorphism in the south Indian population. The analysis of allelic and genotype data showed a significant association of rs5219 polymorphism towards an increased risk of T2DM compared to controls with an odds ratio (OR) of 2.52, confidence interval (CI) (0.96-6.64) and p-value 0.046. The functional influence of rs5219 was tested which showed a significant correlation with HbA1c and serum uric acid levels. Although our results confirm rs5219 is a potential contributor to T2DM, several inconclusive results were noticed across the literature. Hence, the meta-analysis was performed by combining the results of case-control study with previous literature to confirm the rs5219 association with T2DM across various populations. Our meta-analysis revealed a significant risk association of rs5219 in T2DM under five genetic models. In summary, our analysis suggests, rs5219 polymorphism plays a significant role in T2DM susceptibility. Further, studies need to be conducted to determine the influence of rs5219 on the other characteristics of T2DM.
Type-2 Diabetes mellitus is a complex metabolic disorder caused due to the development of insulin resistance that leads to hyperglycemia (1). Globally, 347 million people are affected with diabetes, of which most from middle and low-income countries (2). In India, the prevalence of diabetes is expected to increase up to 10.1% by the year 2035 (3). The etiology of T2DM is well reported suggesting interplay of genes, environment, sedentary behavior, and obesity (4). Several genome-wide association studies (GWASs) have documented over 129 loci in genes such as TCF7L2, PPARG, FTO, PRC1, DUSP9, CDKAL1, NOTCH2, ABCC8, HNF1A, IGF2BP2, KCNQ1, and KCNJ11 were found to be related with T2DM (5, 6).
Of several genes, Potassium Voltage-Gated Channel Subfamily J Member-11 (KCNJ11) localized at chromosome 11 encode KATP channel protein, containing 390 amino acids considered as a susceptible gene for T2DM (7). In particular, a study from France analyzed variations in KCNJ11 and ABCC8 genes among 109 diazoxide-unresponsive patients having congenital hyperinsulinism, which revealed mutations in 82% of the probands (8). Also, several mutations in the KCNJ11 gene were noticed and considered as one of the causative factors for diseases like congenital hyperinsulinemia and neonatal diabetes (9). Functionally, mutations in the KCNJ11 gene causes diabetes by reducing the sensitivity of KATP to ATP (potassium channel-adenosine triphosphate), thus preventing the secretion of insulin (10). The earlier study suggests that polymorphic variants identified in KCNJ11-ABCC8 locus were found to be linked with T2DM due to high linkage disequilibrium (LD) (11).
Globally, several polymorphic variants were observed in the KCNJ11 gene which was positively associated with T2DM across various ethnic populations (12, 13). Among several polymorphisms, the rs5219 variant (Glu23Lys, results in a modification of glutamic acid to lysine) in the KCNJ11 gene was selected for the DNA genotyping. The prime interest for selection rs5219 is based on two fundamental backgrounds, (1) So far no study was conducted reporting the association of rs5219 polymorphism in T2DM in the South-Indian population. (2) The rs5219 polymorphism suggests altering the protein function that may cause T2DM (14). Hence this study is conducted to determine the genetic predisposition of rs5219 polymorphism with T2DM susceptibility in the south Indian population. Despite previous studies of the KCNJ11 gene (p.E23K) polymorphism, several inclusive results were obtained across ethnic origin on the association of T2DM. To bring the conclusive results, we also examined the relationship between rs5219 and T2DM risk by an extensive meta-analysis following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria (15).
3. Materials and methods
3.1. Association based on case-control study
3.1.1. Study sampling
The T2DM patients were recruited from the Department of General Medicine, Chettinad Health City, Kanchipuram district, Tamil Nadu, India, between January to June 2017. All recruited participants are belonging to South India, Asian ethnic backgrounds. The fasting blood glucose and Haemoglobin-A1c levels were determined based on WHO regulations (16) for the confirmation of T2DM. Similarly, the control group was screened for T2DM to confirm the participants are healthy control. The present study protocol was following the Helsinki Declaration and was approved by the Human Ethics Committee (205/IHEC/12-16) of the Chettinad Academy of Research and Education. The signed informed consent written in the local language was obtained from the study participant before sample collection. The general characteristics from each participant were obtained through a structured questionnaire. Besides the HbA1c levels, serum uric acid was measured in the participants were recorded and used while analysis.
3.1.2. Genotyping and statistical analysis of rs5219
Approximately 3 ml of venous blood was collected from T2DM subjects and controls; Genomic DNA was extracted from the collected samples using a standard protocol followed by ethanol precipitation (17). Genotyping of rs5219 polymorphism was executed by newly designed allele-specific primers using Amplification Refractory Mutation System-Polymerase Chain Reaction (ARMS-PCR) (Table 1) (18). The PCR mixture contained, a 20 μl reaction mix was used with 25 ng DNA, 10 mM dNTPs, 12 pmol/μl of forward primer and reverse primer and 1 Unit Taq polymerase. The ARMS-PCR reaction was performed in the Eppendorf Master Cycler Gradient (Hamburg, Germany). The cycling conditions for ARMS-PCR reaction were: initial denaturation at 92°C for 5 mins, 36 cycles of 92°C for 45 secs, 62°C for 45 secs, 72°C for 45 secs and 72°C for 7 mins. The PCR products were electrophoresed in agarose gel (1.6%) along with 100 bp DNA Ladder Dye Plus (Cat no: 3422A, Takara Bio). Further, the polymorphism was confirmed from the randomly selected samples (Controls = 10; T2DM =12) using DNA sequencing (ABI 3100, USA). To identify the chromosomal interactions between the SNPs, a 3DSNP software package was used for visualizing the genomic data by generating the Circos plots based on r2 values (19). The genotype distribution in controls was examined for Hardy-Weinberg equilibrium (HWE value >0.05) by Fisher’s exact test. The distribution of allelic and genotypic frequencies among T2DM subjects and the control group were determined by Pearson's chi-square test. Further, the effects were examined by calculating the odds ratio (OR), and confidence intervals (95% CIs) in dominant (F-major, f-minor allele: Ff + ff vs. FF) and recessive (ff vs. FF + Ff) genetic models. Both the allelic and genotype data were analyzed by SPSS software V-21 (IBM Analytics, USA). Further, the associations of rs5219 polymorphism with HbA1c and serum uric acid levels in T2DM were tested using the chi-square test.
|Primer-ID||Primer Sequence (5'-3')||Allele||No of base pairs||Tm (ºC)||Total Length (Bp)|
|SNP-1 OF -||CCACCAGCGTGGTGAACACGTCCTGCAG||28||68||300|
|SNP-1 OR -||CCCAGGGTGAGAAGGTGCCCACCGAGAG||28||68|
|SNP-1 IF -||CGCTGGCGGGCACGGTACCTGGGATT||T||26||68||200|
|SNP-1 IR -||CTGACACGCCTGGCAGAGGACCCTGACG||C||28||68||154|
|IF-inner forward, IR- inner reverse, OF-outer forward, OR- outer reverse|
3.2. Meta-analysis of rs5219
3.2.1. Analysis of rs5219 polymorphism
To determine the association between rs5219 polymorphism and T2DM susceptibility, a meta-analysis was performed by including the results of case-control study. The eligible studies for this meta-analysis were identified through a systematic electronic search from databases such as NCBI-PubMed, Google Scholar, Cochrane Library, EMBASE and MEDLINE up to December 2017, respectively. The Key Words used for literature mining were "Type-2 Diabetes mellitus", "T2DM", "Potassium Voltage-Gated Channel Subfamily J Member-11", "KCNJ11 gene", "rs5219", and "Polymorphism". The language selection for the article included in this meta-analysis was limited to the English language. A study was included in the meta-analysis based on the following criteria: first, it should be a case-control study, second, the association of rs5219 gene polymorphism with T2DM was determined and third it should provide sufficient genotype data to calculate OR and 95% confidence intervals. We excluded the few articles based on: first, if the studies containing overlapping data, second if the studies were from in vitro, cell lines, case reports, animal models and studies that lack genotype frequencies, respectively. The data for this meta-analysis were extracted by two independent researchers (PA and DV) and any disagreement was solved by a team (AH, SSJ and RK). The following study characteristics, including author name, publication year, country, ethnic background, sample size (T2DM cases and controls), the source of DNA isolation, Diagnostic criteria of T2DM, genotype frequency and genotyping method were extracted.
The quality assessment of all the included studies was verified using Hardy-Weinberg equilibrium (HWE) with P-value > 0.05 in controls (20) and by the Newcastle Ottawa Scale (NOS) (21). In this scale maximum, 9 points represent the high quality of studies, 6 points or above were considered in this analysis. All the statistics for meta-analyses were executed using RevMan V-5.0 (Cochrane Community, UK) and STATA V-12.0 (Stata Corp., USA). The significance of meta-analysis of pooled and subgroup (Caucasian, Asian and others) were confirmed using the odds ratios (OR) and 95% confidence interval (CI) with (P-value < 0.05) under allelic (j vs. J) (J-major, j-minor allele), homozygote (jj vs. JJ), heterozygote (Jj vs. JJ), dominant (Jj + jj vs. JJ) and recessive (jj vs. JJ +Jj) genetic models, respectively. The Q-test and I2 statistics (22) was used to assess the study heterogeneity in this meta-analysis. Based on the heterogeneity values (I2<50), a Mantel-Haenszel's (fixed effect) model was used else DerSimonian and Laird's (23) (random-effect) model was used. Further, the funnel plot and Egger's regression analysis were used to evaluate the publication bias in this meta-analysis. The findings of our meta-analysis were validated using a sensitivity test (Leave one out method) (24).
4.1. Case-control study
The demographic characteristics of T2DM subjects (N=218) and healthy controls (N=214) were represented in Table 2. The mean ± standard deviation (SD) for age in T2DM and control were 54.45±07.48 and 53.15±06.57 years. Further, the HbA1c levels and serum uric acid were determined in all the participants showed HbA1c: control (5.39±0.27) and T2M (7.34±0.63). Similarly, the average serum uric acid in control was 3.21±0.64 and in T2DM was 5.35±0.63 mg/dL.
|Characteristics||T2DM Cases (N = 218)||Controls (N = 214)|
|Men : women||144:74||128:86|
|Body mass index (kg/m2)||28.65±4.88||23.87±3.71|
|Age of disease onset||46.54±07.63||Nil|
|Duration of diabetes (years)||5.16±4.18||Nil|
|Family history of diabetes||102||35|
|T2DM-Type 2 Diabetes Mellitus, Data are presented as mean ±standard deviation (SD) for continuous variables|
The allelic and genotypic distributions of rs5219 polymorphism were illustrated in Table 3. An Agarose gel electrophoresis result of ARMS-PCR was represented in the fig1. The genotype distribution in control was not deviated from HWE (P = 0.183). The genotype frequencies of rs5219 polymorphism were 77.06% (CC), 16.51% (CT) and 06.41% (TT) in the T2DM. Whereas, in control, 70.64% (CC), 24.29% (CT) and 03.73% (TT), respectively. The distribution of rs5219 (TT genotype) was significantly increased in T2DM patients compared with control, OR=2.52 (95% CI (0.96-6.64)) P-value = 0.046. The results of dominant and recessive genetic models revealed no significant difference between T2DM and controls. The sequence electropherograms of KCNJ11 rs5219 polymorphism were presented in fig2. Alternatively, the results of the ARMS PCR were further confirmed with the DNA sequencing method which showed similar results. The KCNJ11 nucleotide sequences were deposited (MF109894, MF110273, and MF110298) in NCBI-Genbank. The Circos plot (outer to the inner circle) shows rs5219 variant associated other polymorphisms with r2 along with the annotated genes, chromatin states and 3D chromatin interactions (fig3). Further, the influence of polymorphism on clinical parameters showed a significant association of rs5219 with high HbA1c (Table 4) and serum uric acid (Table 5) concentration in T2DM patients.
|Polymorphism||Frequencies||Type 2 Diabetes Mellitus n =218 (%)||Controls n =214 (%)||HWE||OR||95% CI||χ2||P-value|
|C||372 (85.32)||360 (84.11)||-||Reference||0.24||0.344|
|T||64 (14.67)||68 (15.88)||-||0.91||(0.62-1.31)|
|CC||168 (77.06)||154 (70.64)||0.183||Reference||3.51||0.070|
|CT||36 (16.51)||52 (24.29)||0.63||(0.39-1.02)|
|TT||14 (06.41)||08 (03.73)||2.52||(0.96-6.64)||3.67||0.046*|
|Dominant||CT +TT vs CC||-||-||-||1.30||(0.84-2.02)||1.48||0.134|
|Recessive||TT vs CC+ CT||-||-||-||1.76||(0.72-4.30)||1.61||0.146|
4.2.1. General characteristics
Our initial literature search in the selected databases identified 946 papers published up to December 2017. The articles were screened for relevance which met the inclusion and exclusion criteria. Finally, 34 studies (12,13, 25-56) were finally selected for meta-analysis which include 26,991 T2DM cases and 35,899 controls. The characteristics of the included studies in the meta-analysis were illustrated in Table 6. Further, the genotype and allele frequencies were extracted from each study involved in the meta-analysis is represented in Table 7.
|Reference||Year||Country||Ethnicity||Source||Diagnostic criteria||Cases||Controls||NOS Score||Method|
|29||2008||Saudi Arabia||West-Asian||Blood||WHO||550||335||07||Real-time PCR|
|FP-TDI: Fluorescence polarization template-directed incorporation, SSCP: Single Stranded Conformational Polymorphism, AD PCR: Allelic Discrimination PCR, NA-Not available, ADA: American Diabetes Association, WHO: World Health Organization|
|Cases (CC/CT/TT)||Controls (CC/CT/TT)||Cases (C/T-Allele)||Controls (C/T-Allele)||HWE/ Chi-square|
|226/ 247 /59||148/169/57||699/365||465/283||0.446/0.580|
|535/ 656/ 193||158/204/52||1726/1042||520/308||0.266/1.236|
|169/ 232 /83||152/195/50||570/398||499/295||0.390/0.736|
|HWE, Hardy Weinberg equilibrium, OR-Odd’s ratio, χ2- Chi-square; P value-one tailed test; * - Results of current case-control study|
4.2.2. Meta-analysis of rs5219 polymorphism
The analysis of rs5219 SNP, revealed mild heterogeneity was observed in the heterozygote (I2=31%) and in allelic (I2=60%), homozygote (I2=53%) dominant (I2=54%) and recessive (I2=44%) genetic models moderate heterogeneity was observed. The fixed effects (Mantel-Haenszel's) model was used which showed significant (P< 0.05) association with T2DM risk in heterozygote (Jj vs. JJ), with OR = 0.86, (95% CI (0.82-0.91)), and recessive (jj vs. JJ +Jj) with OR = 1.19, (95% CI (1.14-1.25)), Random-effect (DerSimonian and Laird's) model was implemented which revealed a positive association with T2DM susceptibility in for allelic (j vs J) with OR = 1.13, (95% CI (1.08-1.18)), homozygote (jj vs. JJ), with OR = 1.30, (95% CI (1.19-1.41)), and dominant (Jj + jj vs. JJ) with OR = 1.14,(95% CI (1.08-1.21)) genetic models. The meta-analysis results were represented as allelic (Table 8), homozygote (Table 9), heterozygote (Table 10), dominant (Table 11) and recessive (Table 12) model. Further, the funnel plot for pooled (fig.4) and sub-group of Caucasian (fig.5) and Asian (fig.6) were performed. Similarly, Egger's linear regression analysis were performed which revealed no publication bias in the investigated five genetic models.
|Study or Subgroup||Cases Events||Total||Controls Events||Total||Weight||M-H, Fixed, 95% CI|
|33||85||287||161||778||0.5%||1.61 [1.19, 2.19]|
|44||196||619||641||2596||0.8%||1.41 [1.17, 1.71]|
|48||6||520||1||506||0.0%||5.89 [0.71, 49.14]|
|This study||14||182||8||162||0.1%||1.6[0.65, 3.93]|
|56||329||985||288||980||0.8%||1.21 [1.00, 1.46]|
|Subtotal (95% CI)||14683||19476||15.5%||1.30 [1.19,1.41]|
|Heterogeneity: Chi-Square = 76.71, df= 36 (P < 0.00001);12= 53%||Odds Ratio > 1; Increased Risk|
|Test for overall effect: Z= 5.93 (P < 0.00001)||Odds Ratio < 1; Decreased Risk|
|Study or Subgroup||Cases Events||Total||Controls Events||Total||Weight||M-H, Fixed, 95% CI|
|Subtotal (95% CI)||53982||71798||28.4%||1.13 [1.08,1.18]|
|Heterogeneity: Chi 2= 90.03, df= 36 (P < 0.00001);12= 60%||Odds Ratio > 1; Increased Risk|
|Test for overall effect: Z=5.53 (P < 0.00001)||Odds Ratio < 1; Decreased Risk|
|Study or Subgroup||Cases Events||Total||Controls Events||Total||Weight||M-H, Fixed, 95% CI|
|49||247||306||169||226||0.1%||1.41 [0.93,2.1 3]|
|Subtotal (95% CI)||16437||21261||12.0%||0.86[0.82. 0.91]|
|Heterogeneity: Chi-Square = 52.22, df=36 (P =0.04); 12= 31%||Odds Ratio > 1; Increased Risk|
|Test for overall effect: Z=5.73 (P < 0.00001)||Odds Ratio < 1; Decreased Risk|
|Study or Subgroup||Cases Events||Total||Controls Events||Total||Weight||M-H, Fixed, 95% CI|
|Subtotal (95% CI)||26991||35899||23%||1.14 [1.08,1.21]|
|Heterogeneity: Tau-= 0.01; Chi2= 78.53 ,df= 36 (P =0.0001); 12= 54%||Odds Ratio > 1; Increased Risk|
|Test for overall effect: Z=4.56 (P < 0.00001)||Odds Ratio < 1; Decreased Risk|
|Study or Subgroup||Cases Events||Total||Controls Events||Total||Weight||Odds Ratio|
|40||3||53||1||30||0.0%||1.74 [0.17, 17.51]|
|48||6||572||1||587||0.0%||6.21 [0.75, 51.76]|
|49||59||532||57||374||0.2%||0.69 [0.47, 1.03]|
|25||193||1384||52||414||0.3%||1.13 [0.81, 1.57]|
|50||83||484||50||397||0.2%||1.44 [0.98, 2.10]|
|This study||14||218||8||214||0.0%||1.77 [0.73, 4.30]|
|51||125||750||335||1879||0.6%||0.92 [0.74, 1.15]|
|52||49||287||403||2684||0.2%||1.17 [0.84, 1.61]|
|53||183||1165||193||1135||0.6%||0.91 [0.73, 1.13]|
|54||270||1114||181||953||0.5%||1.36 [1.10, 1.69]|
|55||246||1590||171||1244||0.6%||1.15 [0.93, 1.42]|
|56||329||1848||288||1910||0.8%||1.22 [1.03, 1.45]|
|Subtotal (95% CI)||26991||36899||11.4%||1.19 [1.14, 1.26]|
|Heterogeneity: Chi-Square = 64.47, df=36 (P = 0.002); 12= 44%||Odds Ratio > 1; Increased Risk|
|Test for overall effect: Z=7.38 (P < 0.00001)||Odds Ratio < 1; Decreased Risk|
4.2.3. Sub-group meta-analysis of rs5219
In a meta-analysis of sub-groups, the selected articles were stratified based on the ethnic background such as Caucasian (21 studies), others (04 studies) and Asian (12 studies), respectively. The results of sub-grouping Caucasian ethnicity revealed moderate heterogeneity in all the analyzed genetic models. Hence, the random-effects model was adopted to test the influence of polymorphism in the five genetic models. Similarly, the sub-group stratification results of the rs5219 variant in Asian ethnicity exhibited moderate heterogeneity in all the analyzed genetic models. Based on heterogeneity results, the fixed effects model was used which showed positive (p = 0.05) association with T2DM susceptibility in jj vs. JJ with OR = 1.21, (95% CI (1.05-1.40)), and Jj + jj vs. JJ with OR = 1.12, (95% CI (1.06-1.18)) respectively. Random-effect model was adopted which showed positive (p = 0.05) association with a risk of T2DM in j vs. J with OR = 1.10, (95% CI (1.02-1.20)) and jj vs. JJ +Jj with OR = 1.16, (95% CI (1.01-1.33)) genetic models respectively. Further, the Asian sub-group analysis was divided into (South-Asian=03, East-Asian=08, West-Asian =01) ethnic background. The results of subgroup analyses were illustrated in (Table 13).
|SNP-ID: rs5219||No of studies||Ethnicity||I2 (%)||Model||OR (95%CI)||Z-Test||P-value|
|C vs T Allelic Model||21||Caucasian||62||random||1.06 (1.09-1.23)||4.97||<0.00001|
|CC vs TT Homozygote Model||21||Caucasian||60||random||1.35 (1.20-1.52)||5.01||<0.00001|
|CT vs TT Heterozygote Model||21||Caucasian||20||fixed||0.84 (0.78-0.97)||4.39||<0.0001|
|CC + CT vs TT Dominant Model||21||Caucasian||61||random||1.14 (1.04-1.25)||2.91||0.004|
|CC vs CT + TT Recessive Model||21||Caucasian||66||random||1.21 (1.14-1.28)||6.32||<0.00001|
The current global prevalence of T2DM has been increased exponentially in recent years, which represents a major challenge to health care professionals and considered a global health concern with an impact on premature mortality, morbidity, and its related (Microvascular and Macrovascular) complications, especially in the elderly people (57). Previous studies suggest that T2DM is a multifactorial disorder caused because of complex genetic interactions and environmental factors (58, 59). The KCNJ11 gene based on its position in the chromosome, considered as a promising candidate gene for T2DM which functions in regulating glucose-induced insulin secretion (60). It has been documented that the rs5219 variant observed in the 11p15.1 region might play a significant role in T2DM development, hence making it a biomarker for assessing the KCNJ11 gene (25). In the association study, the relationship between KCNJ11 p.E23K polymorphism with T2DM susceptibility was identified, to the best of our understanding; this is the first study in South Indian population to determine the relationship between KCNJ11 gene rs5219 polymorphism and T2DM risk. The results of the case-control study showed a significant (P-value < 0.05) relationship with the genotype frequencies among T2DM subjects and controls revealing that the rs5219 variant may be a potential risk factor in the South Indians population. A study from UK diabetic subject’s revealed a significant association of rs5219 (TT genotype) compared with age-matched controls, OR=2.54 (95% CI (1.23-5.25)) P-value = 0.016, respectively (30). The results of the association study were in similarity with previously published studies from France (26), Sweden (27), Japanese (28) and Saudi-Arabian (29) T2DM subjects belonging to Caucasian and Asian ethnic populations. Our results from the case-control study confirm the involvement of the KCNJ11 gene rs5219 SNP in the T2DM etiology, despite the populations and also with the geographical locations, respectively. Besides, the rs5219 polymorphism showed an insight towards high HbA1c and serum uric acid, which confirms its functional importance in T2DM patients.
An extensive meta-analysis was executed to determine the relationship between KCNJ11 rs5219 SNP with T2DM among Asian and Caucasian ethnic populations. The research articles related to the KCNJ11 gene were identified through a systematic search followed by the quality assessment using HWE and NOS scores. The meta-analysis results for rs5219 SNP showed a significant association (P-value < 0.05) among the allelic (T vs C) homozygote (TT vs CC), heterozygote (CT vs CC), dominant (CT+TT vs CC) and recessive (TT vs CC+CT) genetic models. These results were in agreement with previously published studies from UK (30), USA (31), Chinese (32), Japanese (33) and T2DM cases belonging to Caucasian and Asian ethnic backgrounds. However, the insignificant association was observed in Finland (61), and Czech (36) T2DM subjects. The discrepancies in the outcomes might be because of the fewer sample size, bias and study heterogeneity. The stratification analysis based on Asian and Caucasian sub-groups revealed a significant association of rs5219 polymorphism with T2DM susceptibility among the studied genetic models. The cell line (in vitro) based studies on the p.E23K variant have suggested that it leads to a decrease in the sensitivity of Kir6.2 (subunit) towards the ATP, thus inhibiting the insulin secretion (62).
The potential strength of our KCNJ11 rs5219 meta-analysis includes a large sample size of 26,991 T2DM subjects and 35,899 controls there are few considerable limitations. First, we determined the association between rs5219 variant with T2DM risk, and the relationships with other confounding factors such as fasting insulin, fasting glucose concentrations, and lifestyle were not included in our case-control study. Second, stratification analysis based on gender, age, lifestyle factors were not performed, because of the lack of uniform background data. Third, articles published in the English language were only considered. Fourth, we could not explain the underlying mechanisms of gene-environmental interactions.
In conclusion, the rs5219 polymorphism in the KCNJ11 gene was found to be associated with T2DM susceptibility in south Indians. Our findings, together with previous reports from Asians and Caucasians, show that the KCNJ11 gene possesses a significant association with T2DM across multiple ethnicities. The results of meta-analysis, further add growing evidence of the positive effect of rs5219 SNP on T2DM susceptibility. However, T2DM confounding factors such as hyperlipidemia, obesity, environmental, gene-gene interactions are necessary for verifying this association.
Rajagopalan Aswathi, Dhasaiya Viji, Prathap seelan and Pricilla Charmine equally contributed this work. These three authors thank Chettinad Academy of Research and Education for funding this research. All the authors were thankful to the patients and controls for participating in the study. The author (Akram Husain) wishes to acknowledge Chettinad Academy of Research and Education (CARE) for providing chettinad research fellowship. All the authors declare that they have no conflict of interest.
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