Association and function analysis of genetic variants and the risk of gestational diabetes mellitus in a southern Chinese population.

BACKGROUND

Gestational diabetes mellitus (GDM) is a complex metabolic disease that has short-term and long-term adverse effects on mothers and infants. However, the specific pathogenic mechanism has not been elucidated.

OBJECTIVE

The aim of this study was to confirm the associations between candidate genetic variants (rs4134819, rs720918, rs2034410, rs11109509, and rs12524768) and GDM risk and prediction in a southern Chinese population.

METHODS

Candidate variants were genotyped in 538 GDM cases and 626 healthy controls. The odds ratio (OR) and its corresponding 95% confidence interval (CI) were calculated to assess the associations between genotypes and GDM risk. Then, the false-positive report probability (FPRP) analysis was adopted to confirm the significant associations, and bioinformatics tools were used to explore the potential biological function of studied variants. Finally, risk factors of genetic variants and clinical indicators identified by logistics regression were used to construct a nomogram model for GDM prediction.

RESULTS

It was shown that the XAB2 gene rs4134819 was significantly associated with GDM susceptibility (CT vs. CC: adjusted OR = 1.38, 95% CI: 1.01-1.87, p = 0.044; CT/TT vs. CC: crude OR = 1.42, 95% CI: 1.08-1.86, p = 0.013). Functional analysis suggested that rs4134819 can alter the specific transcription factors (CPE bind and GATE-1) binding to the promoter of the XAB2 gene, regulating the transcription of XAB2. The nomogram established with factors such as age, FPG, HbA1c, 1hPG, 2hPG, TG, and rs4134819 showed a good discriminated and calibrated ability with an area under the curve (AUC) = 0.931 and a Hosmer-Lemeshow test p-value > 0.05.

CONCLUSION

The variant rs4134819 can significantly alter the susceptibility of the Chinese population to GDM possibly by regulating the transcription of functional genes. The nomogram prediction model constructed with genetic variants and clinical factors can help distinguish high-risk GDM individuals.