Dataset Information

Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus.

ABSTRACT:

Background

A number of case-control studies were conducted to investigate the association of common type 2 diabetes (T2D) risk gene polymorphisms with gestational diabetes mellitus (GDM). However, these studies have yielded contradictory results. We therefore performed a meta-analysis to derive a more precise estimation of the association between these polymorphisms and GDM, hence achieve a better understanding to the relationship between T2D and GDM.

Methods

PubMed, EMBASE, ISI web of science and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. Data were abstracted independently by two reviewers. A meta-analysis was performed to examine the association between 9 polymorphisms from 8 genes and susceptibility to GDM. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. Heterogeneity among articles and their publication bias were also tested.

Results

We identified 22 eligible studies including a total of 10,336 GDM cases and 17,445 controls. We found 8 genetic polymorphisms were significantly associated with GDM in a random-effects meta-analysis. These polymorphisms were in or near the following genes: TCF7L2 (rs7903146), MTNR1B (rs10830963), IGF2BP2 (rs4402960), KCNJ11 (rs5219), CDKAL1 (rs7754840), KCNQ1 (rs2237892 and rs2237895) and GCK (rs4607517); while no association was found for PPARG with GDM risk. Similar results were also observed under dominant genetic model for these polymorphisms.

Conclusions

This meta-analysis found 8 genetic variants associated with GDM. The relative contribution and relevance of the identified genes in the pathogenesis of GDM should be the focus of future studies.

SUBMITTER: Mao H

PROVIDER: S-EPMC3454322 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus.

Mao Hongyan H Li Qin Q Gao Shujun S

PloS one 20120924 9

<h4>Background</h4>A number of case-control studies were conducted to investigate the association of common type 2 diabetes (T2D) risk gene polymorphisms with gestational diabetes mellitus (GDM). However, these studies have yielded contradictory results. We therefore performed a meta-analysis to derive a more precise estimation of the association between these polymorphisms and GDM, hence achieve a better understanding to the relationship between T2D and GDM.<h4>Methods</h4>PubMed, EMBASE, ISI w ...[more]

PMID: 23029294

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Project description:AimsSerum cystatin C (CysC) has recently been shown to be associated with the incidence of type 2 diabetes mellitus (T2DM) and progression to the pre-diabetic state. The aim of this study was to explore the relationship between serum CysC and the risk of gestational diabetes mellitus (GDM) in Chinese pregnant women.MethodsThis cross-sectional study consisted of 400 pregnant women including 111 with GDM and 289 with normal glucose tolerance at 24-28 weeks of gestation. The subjects were further divided into four groups according to the CysC quartiles, and their clinical characteristics were compared. The serum CysC concentration was measured using immunoturbidimetry and the degree of insulin resistance was assessed by the homeostasis model assessment of insulin resistance (HOMA-IR).ResultsSerum CysC levels were significantly higher in pregnant women with GDM than in the healthy pregnant women[1.0(0.8-1.8) vs 0.7(0.6-1.0), P<0.01). The Spearman's correlation analysis showed that serum CysC was positively associated with HOMA-IR(r = 0.118, P<0.05) and the occurrence of GDM(r = 0.348, P<0.01). The pregnant women were divided into quartiles according to their serum CysC concentrations. Compared to the first quartile, pregnant women in Q2 (OR, 2.441; P = 0.025), Q3 (OR, 3.383; P = 0.001) and Q4 (OR, 5.516; P<0.001) had higher risk of GDM after adjusted for age, BMI, HbA1c and HOMA-IR. Further, with a rise in the serum CysC, there was an increasing trend in the HOMA-IR levels (P<0.05). A binary logistic regression analysis after adjusting for other confounding variables revealed a significant and independent association between serum CysC and GDM [OR = 14.269; 95% confidence interval, 4.977-40.908, P<0.01].The receiver operating characteristic curve analysis revealed that the optimal cutoff point for serum CysC to indicate GDM was 0.95 mg/L.ConclusionsSerum CysC is significantly and independently associated with insulin resistance and GDM. It may be a helpful biomarker to identify the risk of GDM in Chinese pregnant women.

Project description:ObjectiveWe aimed to explore the associations between common genetic risk variants with gestational diabetes mellitus (GDM) risk in Russian women and to assess their utility in the identification of GDM cases.MethodsWe conducted a case-control study including 1,142 pregnant women (688 GDM cases and 454 controls) enrolled at Almazov National Medical Research Centre. The International Association of Diabetes and Pregnancy Study Groups criteria were used to diagnose GDM. A total of 11 single- nucleotide polymorphisms (SNPs), including those in HKDC1 (rs10762264), GCK (rs1799884), MTNR1B (rs10830963 and rs1387153), TCF7L2 (rs7903146 and rs12255372), KCNJ11 (rs5219), IGF2BP2 (rs4402960), IRS1 (rs1801278), FTO (rs9939609), and CDKAL1 (rs7754840) were genotyped using Taqman assays. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). A simple-count genetic risk score (GRS) was calculated using 6 SNPs. The area under the receiver operating characteristic curve (c-statistic) was calculated for the logistic regression model predicting the risk of GDM using clinical covariates, SNPs that had shown a significant association with GDM in our study, GRS, and their combinations.ResultsTwo variants in MTNR1B (rs1387153 and rs10830963) demonstrated a significant association with an increased risk of GDM. The association remained significant after adjustment for age, pre-gestational BMI, arterial hypertension, GDM in history, impaired glucose tolerance, polycystic ovary syndrome, family history of diabetes, and parity (P = 0.001 and P < 0.001, respectively). After being conditioned by each other, the effect of rs1387153 on GDM predisposition weakened while the effect of rs10830963 remained significant (P = 0.004). The risk of GDM was predicted by clinical variables (c-statistic 0.712, 95 % CI: 0.675 - 0.749), and the accuracy of prediction was modestly improved by adding GRS to the model (0.719, 95 % CI 0.682 - 0.755), and more by adding only rs10830963 (0.729, 95 % CI 0.693 - 0.764).ConclusionAmong 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in MTNR1B in Russian women. However, these variants showed limited value in the identification of GDM cases.

Dataset Information

Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus.

Background

Methods

Results

Conclusions

Publications

Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus.

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