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Genetic Susceptibility to Gestational Diabetes Mellitus in a Chinese Population.

ABSTRACT: Introduction: New genetic variants associated with susceptibility to obesity and metabolic diseases have been discovered in recent genome-wide association (GWA) studies. The aim of this study was to investigate the association of theses risk variants with gestational diabetes mellitus (GDM). Methods: We performed a case-control study including 964 unrelated pregnant women with GDM and 1,021 pregnant women with normal glucose tolerance (as controls). A total of 33 genetic variants confirmed by GWA studies for obesity and metabolic diseases were selected and measured. Results: We observed that FTO rs1121980 and KCNQ1 rs163182 conferred a decreased GDM risk in the dominant and additive model [additive model: OR (95% CI) = 0.79 (0.67-0.94), P = 0.007 for rs1121980; OR(95%CI) = 0.84 (0.73-0.96), P = 0.009 for rs163182], whereas MC4R rs12970134 and PROX1 rs340841 conferred an increased GDM risk in the dominant, recessive, and additive model [additive model: OR(95%CI) = 1.25 (1.07-1.46), P = 0.006 for rs12970134; OR(95%CI) = 1.22 (1.07-1.39), P = 0.002 for rs340841). With the increasing number of risk alleles of the four significant SNPs, GDM risk was significantly increased in a dose-dependent manner (P trend < 0.001). And the significant positive associations between the weighted genetic risk score and risk of GDM persisted. Further function annotation indicated that these four SNPs may fall on the functional elements of human pancreatic islets. The genotype-phenotype associations indicated that these SNPs may contribute to GDM by affecting the expression levels of their nearby or distant genes. Conclusion: Our study suggests that FTO rs1121980, KCNQ1 rs163182, MC4R rs12970134, and PROX1 rs340841 may be markers for susceptibility to GDM in a Chinese population.

SUBMITTER: Cao M

PROVIDER: S-EPMC7188786 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Genetic Susceptibility to Gestational Diabetes Mellitus in a Chinese Population.

Cao Minkai M Zhang Le L Chen Ting T Shi Aiwu A Xie Kaipeng K Li Zhengying Z Xu Jianjuan J Chen Zhong Z Ji Chenbo C Wen Juan J

Frontiers in endocrinology 20200422

<b>Introduction:</b> New genetic variants associated with susceptibility to obesity and metabolic diseases have been discovered in recent genome-wide association (GWA) studies. The aim of this study was to investigate the association of theses risk variants with gestational diabetes mellitus (GDM). <b>Methods:</b> We performed a case-control study including 964 unrelated pregnant women with GDM and 1,021 pregnant women with normal glucose tolerance (as controls). A total of 33 genetic variants c ...[more]

PMID: 32390949

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Project description:BackgroundGlyoxalase 1 (GLO1) plays a crucial role in defending against glycation. Single nucleotide polymorphism (SNP) variants in the GLO1 gene may affect gene expression and alter enzyme activity. However, there have been limited studies evaluating the association between GLO1 and diabetes, especially gestational diabetes mellitus (GDM). Therefore, this study is the first to explore the association of GLO1 SNPs and GDM risk.MethodsThe study included a total of 500 GDM patients and 502 control subjects. The SNPscan™ genotyping assay was used to genotype rs1781735, rs4746 and rs1130534. To assess the disparities in genotype, allele, and haplotype distributions and their correlation with GDM risk, the independent sample t-test, logistic regression, and chi-square test were employed during the data processing phase. Furthermore, one-way ANOVA was conducted to determine the differences in genotype and blood glucose and methylglyoxal(MG) levels.ResultsSignificant differences were observed in prepregnancy body mass index (pre-BMI), age, systolic blood pressure (SBP), diastolic blood pressure (DBP), and parity between GDM and healthy subjects (P < 0.05). After adjusting for these factors, GLO1 rs1130534 TA remained associated with an increased risk of GDM (TA vs. TT + AA: OR = 1.320; 95% CI: 1.008-1.728; P = 0.044), especially in the pre-BMI ≥ 24 subgroup (TA vs. TT + AA: OR = 2.424; 95% CI: 1.048-5.607; P = 0.039), with fasting glucose levels being significantly elevated in the TA genotype compared to the TT genotype (P < 0.05). Conversely, the GLO1 rs4746 TG was associated with a decreased risk of GDM (TG vs. TT: OR = 0.740; 95% CI: 0.548-0.999; P = 0.049; TG vs. TT + GG: OR = 0.740; 95% CI: 0.548-0.998; P = 0.048). Additionally, the haplotype T-G-T of rs1781735, rs4746 and rs1130534 was associated with a decreased risk of GDM among individuals with a pre-BMI ≥ 24 (OR = 0.423; 95% CI: 0.188-0.955; P = 0.038). Furthermore, the rs1781735 GG genotype was found to be more closely related to maternal MG accumulation and neonatal weight gain (P < 0.05).ConclusionOur findings suggested that GLO1 rs1130534 was associated with an increased susceptibility to GDM and higher blood glucose levels, but GLO1 rs4746 was associated with a decreased risk of GDM. The rs1781735 has been associated with the accumulation of maternal MG and subsequent weight gain in neonates.

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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Genetic Susceptibility to Gestational Diabetes Mellitus in a Chinese Population.

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Genetic Susceptibility to Gestational Diabetes Mellitus in a Chinese Population.

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