Nie Lijie, Sun Yan, Li Ruiqi, Liang Qiulian, Deng Guocun, He Xinyu, Zeng Yifei, Zheng Hui, Xiao Xinhe, Ding Xiaodong, Huang Jian, Yu Xiangyuan
The School of Public Health, Guilin Medical University, Guilin, China.
Institute of Biomedical Research, School of Intelligent Medicine and Biotechnology, Guilin Medical University, Guilin, China.
Front Endocrinol (Lausanne). 2025 Jul 23;16:1511561. doi: 10.3389/fendo.2025.1511561. eCollection 2025.
Gestational diabetes mellitus (GDM) is an endocrine disorder that occurs easily in women during pregnancy. genes play a crucial role in the regulation of the human immune and endocrine systems, potentially influencing the pathogenesis of GDM.
To explore the associations between single nucleotide polymorphisms (SNPs) in genes and the risk of GDM.
Seven functional SNPs of genes were selected and genotyped in 523 GDM patients and 638 normal pregnant women. The odds ratio (OR) and its corresponding 95% confidence interval (CI) were utilized to assess the association between candidate SNPs and the risk of GDM. And then, false positive report probability (FPRP), multifactor dimensionality reduction (MDR) and haplotype analysis were employed to validate the statistically significant associations between studied SNPs and GDM risk.
Compared to those with 0-1 risk genotypes, individuals with 2-7 unfavorable genotypes presented an increased risk of GDM (adjusted OR = 1.54, 95% CI=1.04-2.28, =0.033). A dose- accumulation effect was detected between the number of unfavorable genotypes and GDM risk ( =0.024). Furthermore, stratified analysis revealed that the increased GDM risk was more likely to occur in individuals with higher blood pressure and TG, and lower HDL-c levels (<0.05). Multifactor dimensionality reduction (MDR) analysis revealed that rs9274666 was the best single locus model, whereas the 7-loci model was the best multifactor interaction model for predicting GDM risk (χ²=134.28, <0.0001). Finally, haplotype analysis revealed that the ACGAGTA and ACGGATA haplotypes were significantly associated with the increased GDM risk. SNPs can significantly alter individuals' genetic susceptibility to GDM.
The genetic variations in the and genes may collectively contribute to the susceptibility to gestational diabetes mellitus. These findings suggest that these genetic markers could be useful for early prediction of GDM, and further validation in larger cohorts is warranted.
妊娠期糖尿病（GDM）是一种孕期女性易患的内分泌疾病。基因在人类免疫和内分泌系统的调节中起关键作用，可能影响GDM的发病机制。
探讨基因单核苷酸多态性（SNP）与GDM风险之间的关联。
选择基因的7个功能性SNP，对523例GDM患者和638例正常孕妇进行基因分型。采用优势比（OR）及其相应的95%置信区间（CI）评估候选SNP与GDM风险之间的关联。然后，采用假阳性报告概率（FPRP）、多因素降维法（MDR）和单倍型分析来验证所研究的SNP与GDM风险之间具有统计学意义的关联。
与具有0 - 1个风险基因型的个体相比，具有2 - 7个不利基因型的个体患GDM的风险增加（校正OR = 1.54，9 5% CI = 1.04 - 2.28，P = 0.033）。在不利基因型数量与GDM风险之间检测到剂量累积效应（P = 0.024）。此外，分层分析显示，GDM风险增加更可能发生在血压和甘油三酯较高、高密度脂蛋白胆固醇水平较低的个体中（P < 0.05）。多因素降维法（MDR）分析显示，rs9274666是预测GDM风险的最佳单基因座模型，而7基因座模型是最佳多因素相互作用模型（χ² = 134.28，P < 0.0001）。最后，单倍型分析显示，ACGAGTA和ACGGATA单倍型与GDM风险增加显著相关。这些SNP可显著改变个体对GDM的遗传易感性。
这些基因的遗传变异可能共同导致妊娠期糖尿病的易感性。这些发现表明，这些遗传标记物可能有助于GDM的早期预测，并且有必要在更大的队列中进行进一步验证。
