Dataset Information

Systematic Meta-analysis Revealed an Association of PGC-1? rs8192678 Polymorphism in Type 2 Diabetes Mellitus.

ABSTRACT: Background:Genome-wide association study (GWAS) provides an unprecedented opportunity to reveal substantial genetic contribution to type 2 diabetes mellitus (T2DM) and glycemic identification of allelic heterogeneity and population-specific genetic variants, yet it also faces difficulty due to the vast amount of potential confounding factors and limited availability of clinical data. To identify responsible susceptibility loci and genomic polymorphism for T2DM and glycemic traits, we have systematically investigated a genome-wide association study related to T2DM. Although GWAS has captured many common genetic variations, which are related to T2DM, each risk allele (RA) of single-nucleotide polymorphisms (SNPs) at these loci is not conclusive. Therefore, it is common to present a combination of several SNPs to infer T2DM risk, yet it is still insufficient to be deterministic. To streamline the identification of a deterministic genetic variation in T2DM, we developed this meta-analysis as a showcase to comprehensively identify the association between cumulative RAs and T2DM risk by combining different studies in reported literature and databases. After all, we identified that PGC-1? rs8192678 polymorphism can be considered as a potentially deterministic biomarker in T2DM risk. Previous studies have potentially linked PGC-1? rs8192678 polymorphism to type 2 diabetes mellitus (T2DM) risk, but the results remain inconsistent in different populations and are not conclusive. We developed a new meta-analysis approach to systematically identify the association between PGC-1? rs8192678 polymorphism and T2DM, and we have comprehensively assessed different ethnic groups to validate our findings. Methods:We performed comprehensive information retrieval and knowledge discovery meta-analysis by searching extensively published literature and different electronic databases to acquire eligible studies for the above association study. We developed a method to use pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) in five genetic models (allelic, dominant, recessive, homozygous, and heterozygous genetic models) to identify the relationship among ethnicity subgroup analyses comprehensively. Results:We identified 20 eligible studies consisting of 16,182 subjects (8,038 cases and 8,144 controls) in our meta-analysis. PGC-1? rs8192678 polymorphisms of all subjects showed a significant association with T2DM susceptibility under all genetic models: allelic (OR: 1.24, 95% CI: 1.13-1.35), dominant (OR: 1.27, 95% CI: 1.14-1.42), recessive (OR: 1.24, 95% CI: 1.14-1.36), homozygous (OR: 1.40, 95% CI: 1.20-1.64), and heterozygous (OR: 1.20, 95% CI: 1.06-1.35). In the subgroup analysis, we identified a significant association between PGC-1? rs8192678 polymorphism and T2DM in the Caucasian and Indian populations under all genetic models we investigated. This is the most comprehensive study of the subject to date. Conclusion:Our development of meta-analysis revealed that the minor allele (A) carriers, especially AA genotype carriers, can lead to risk of T2DM in the Caucasian and Indian populations. This is the first report that such risk has been confirmed. Our finding shed new light into the genetic alteration in T2DM.

SUBMITTER: Xia W

PROVIDER: S-EPMC6421808 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Systematic Meta-analysis Revealed an Association of PGC-1<i>α</i> rs8192678 Polymorphism in Type 2 Diabetes Mellitus.

Xia Wanning W Chen Nanxing N Peng Wenjia W Jia Xianjie X Yu Ying Y Wu Xuesen X Gao Huaiquan H

Disease markers 20190303

<h4>Background</h4>Genome-wide association study (GWAS) provides an unprecedented opportunity to reveal substantial genetic contribution to type 2 diabetes mellitus (T2DM) and glycemic identification of allelic heterogeneity and population-specific genetic variants, yet it also faces difficulty due to the vast amount of potential confounding factors and limited availability of clinical data. To identify responsible susceptibility loci and genomic polymorphism for T2DM and glycemic traits, we hav ...[more]

PMID: 30944665

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Project description:BackgroundDiabetes is one of the common chronic diseases in which susceptibility is determined by a combination of genetic and environmental factors, and more than 90% of diabetic patients are diabetes mellitus type 2 (T2DM). The existing studies on the association between CDKAL1 rs10946398 gene polymorphism and susceptibility to type 2 diabetes are inconsistent across populations.AimWe aim to explore the association between CDKAL1 rs10946398 gene polymorphism and susceptibility to type 2 diabetes in different populations.MethodsWe examined all studies before June 12, 2021, that associated CDKAL1 rs10946398 with T2DM. Heterogeneity was assessed by meta-analysis of allelic inheritance models (A vs. C), dominant inheritance models (AA vs. AC+CC), and recessive inheritance model (AA+AC vs. CC); I 2 was used to assess the heterogeneity (if I 2 < 50%, the fixed-effects model was used; if I 2 ≥ 50%, the random-effects model was used for data consolidation); correlation was judged by a forest map; potential publication bias was tested by the Egger test (p > 0.05 indicates that there is no publication bias).ResultsFourteen data totaling 30288 subjects, including 19272 controls and 11016 patients with T2DM, met our inclusion criteria. In the Asian population, the differences were statistically significant (p < 0.01) for dominant genetic model (OR = 0.75, 95%CI = 0.64-0.88, p = 0.0003). But the allelic effect model (OR = 0.87, 95%CI = 0.75-1.02, p = 0.08) and the recessive genetic model (OR = 0.85, 95%CI = 0.66-1.10, p = 0.23) were not statistically significant (p > 0.01). In the non-Asian population, the differences were statistically significant (p < 0.01) for the allelic effect model (OR = 0.83, 95%CI = 0.77-0.88, p < 0.00001), the dominant model (OR = 0.79, 95%CI = 0.72-0.87, p < 0.00001), and the recessive model (OR = 0.78, 95%CI = 0.70-0.87, p < 0.0001).ConclusionIn this study, CDKAL1 RS10946398 was positively associated with T2DM, but the association was different in Asian populations.

Project description:Background: Insulin-like growth factor-1 (IGF-1) has been demonstrated to increase fatty acid β oxidation during fasting, and play an important role in regulating lipid metabolism and type 2 diabetes mellitus (T2DM). The rs35767 (T > C) polymorphism, a functional SNP was found in IGF-1 promoter, which may directly affect IGF-1 expression. However, the inconsistent findings showed on the IGF-1 rs35767 polymorphism and T2DM risk. Methods: We performed a comprehensive meta-analysis to estimate the association between the IGF-1 rs35767 and T2DM risk among four genetic models (the allele, additive, recessive and dominant models). Results: A total 49,587 T2DM cases and 97,906 NDM controls were included in the allele model, a total 2256 T2DM cases and 2228 NDM controls were included in the other three genetic models (the additive; recessive and dominant models). In overall analysis, the IGF-1 rs35767 was shown to be significantly associated with increased T2DM risk for the allele model (T vs. C: OR = 1.251, 95% CI: 1.082-1.447, p = 0.002), additive model (homozygote comparisons: TT vs. CC: OR = 2.433, 95% CI: 1.095-5.405, p = 0.029; heterozygote comparisons: TC vs. CC: OR = 1.623, 95% CI: 1.055-2.495, p = 0.027) and dominant model (TT + CT vs. CC: OR = 1.934, 95% CI: 1.148-3.257, p = 0.013) with random effects model. After omitting Gouda's study could reduce the heterogeneity, especially in the recessive model (TT vs. CC + CT: I2 = 38.7%, p = 0.163), the fixed effects model for recessive effect of the T allele (TT vs. CC + CT) produce results that were of borderline statistical significance (OR = 1.206, 95% CI: 1.004-1.448, p = 0.045). And increasing the risk of T2DM in Uyghur population of subgroup for the allele model. Conclusion: The initial analyses that included all studies showed statistically significant associations between the rs35767 SNP and type 2 diabetes, but after removing the Gouda et al. study produced results that were mostly not statistically significant. Therefore, there is not enough evidence from the results of the meta-analysis to indicate that the rs35767 SNP has a statistically significant association with type 2 diabetes.

Dataset Information

Systematic Meta-analysis Revealed an Association of PGC-1? rs8192678 Polymorphism in Type 2 Diabetes Mellitus.

Publications

Systematic Meta-analysis Revealed an Association of PGC-1<i>α</i> rs8192678 Polymorphism in Type 2 Diabetes Mellitus.

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