Dataset Information

Genome-wide analysis study of gestational diabetes mellitus and related pathogenic factors in a Chinese Han population.

ABSTRACT:

Background

Gestational diabetes mellitus (GDM) affects the metabolism of both the mother and fetus during and after pregnancy. Genetic factors are important in the pathogenesis of GDM, and associations vary by ethnicity. However, related studies about the relationship between the susceptibility genes and glucose traits remain limited in China. This study aimed to identify genes associated with GDM susceptibility in Chinese Han women and validate those findings using clinical data during pregnancy and postpartum period.

Methods

A genome-wide association study (GWAS) of 398 Chinese Han women (199 each with and without GDM) was conducted and associations between single nucleotide polymorphisms (SNPs) and glucose metabolism were identified by searching public databases. Relationships between filtered differential SNPs and glucose metabolism were verified using clinical data during pregnancy. The GDM group were followed up postpartum to evaluate the progression of glucose metabolism.

Results

We identified five novel SNPs with genome-wide significant associations with GDM: rs62069863 in TRPV3 gene and rs2232016 in PRMT6 gene were positive correlated with 1 h plasma glucose (1hPG) and 2 h plasma glucose (2hPG), rs1112718 in HHEX/EXOC6 gene and rs10460009 in LPIN2 gene were positive associated with fasting plasma glucose, 1hPG and 2hPG, rs927316 in GLIS3 gene was negative correlated with 2hPG. Of the 166 GDM women followed up postpartum, rs62069863 in TRPV3 gene was positively associated with fasting insulin, homoeostasis model assessment of insulin resistance.

Conclusions

The variants of rs62069863 in TRPV3 gene, rs2232016 in PRMT6 gene, rs1112718 in HHEX/EXOC6 gene, rs927316 in GLIS3 gene, and rs10460009 in LPIN2 gene were newly-identified susceptibility loci for GDM in the Chinese Han population. TRPV3 was associated with worse insulin resistance postpartum.

Trial registration

This study was registered in the Chinese Clinical Trial Registry.

Trial registration number

ChiCTR2100043762. Date of first registration: 28/02/2021.

SUBMITTER: Yue S

PROVIDER: S-EPMC10714520 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Genome-wide analysis study of gestational diabetes mellitus and related pathogenic factors in a Chinese Han population.

Yue Shufan S Pei Ling L Lai Fenghua F Xiao Huangmeng H Li Zeting Z Zeng Rui R Chen Li L Chen Wenzhan W Liu Huiling H Li Yanbing Y Xiao Haipeng H Cao Xiaopei X

BMC pregnancy and childbirth 20231212 1

<h4>Background</h4>Gestational diabetes mellitus (GDM) affects the metabolism of both the mother and fetus during and after pregnancy. Genetic factors are important in the pathogenesis of GDM, and associations vary by ethnicity. However, related studies about the relationship between the susceptibility genes and glucose traits remain limited in China. This study aimed to identify genes associated with GDM susceptibility in Chinese Han women and validate those findings using clinical data during ...[more]

PMID: 38087213

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Project description:BackgroundGestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.MethodsMetabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.ResultsThis study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.ConclusionsMetabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

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Dataset Information

Genome-wide analysis study of gestational diabetes mellitus and related pathogenic factors in a Chinese Han population.

Background

Methods

Results

Conclusions

Trial registration

Trial registration number

Publications

Genome-wide analysis study of gestational diabetes mellitus and related pathogenic factors in a Chinese Han population.

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