Scholarlink

<p>Gestational diabetes mellitus, a heritable metabolic disorder and the most common pregnancy-related condition, remains understudied regarding its genetic architecture and its potential for early prediction using genetic data. Here we conducted genome-wide association studies on 116,144 Chinese pregnancies, leveraging their non-invasive prenatal test sequencing data and detailed prenatal records. We identified 13 novel loci for gestational diabetes mellitus and 111 for five glycemic traits, with minor allele frequencies of 0.01-0.5 and absolute effect sizes of 0.03-0.62. Approximately 50% of these loci were specific to gestational diabetes mellitus and gestational glycemic levels, distinct from type 2 diabetes and general glycemic levels in East Asians. A machine learning model integrating polygenic risk scores and prenatal records predicted gestational diabetes mellitus before 20 weeks of gestation, achieving an area under the receiver operating characteristic curve of 0.729 and an accuracy of 0.835. Shapley values highlighted polygenic risk scores as key contributors. This model offers a cost-effective strategy for early gestational diabetes mellitus prediction using clinical non-invasive prenatal test.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Genetic architecture and risk prediction of gestational diabetes mellitus in Chinese pregnancies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月5日

查看全文
  • Yuqin Gu,
  • Hao Zheng,
  • Piao Wang,
  • Yanhong Liu,
  • Xinxin Guo,
  • Yuandan Wei,
  • Zijing Yang,
  • Shiyao Cheng,
  • Yanchao Chen,
  • Liang Hu,
  • Xiaohang Chen,
  • Quanfu Zhang,
  • Guobo Chen,
  • Fengxiang Wei,
  • Jianxin Zhen,
  • Siyang Liu

摘要

Gestational diabetes mellitus, a heritable metabolic disorder and the most common pregnancy-related condition, remains understudied regarding its genetic architecture and its potential for early prediction using genetic data. Here we conducted genome-wide association studies on 116,144 Chinese pregnancies, leveraging their non-invasive prenatal test sequencing data and detailed prenatal records. We identified 13 novel loci for gestational diabetes mellitus and 111 for five glycemic traits, with minor allele frequencies of 0.01-0.5 and absolute effect sizes of 0.03-0.62. Approximately 50% of these loci were specific to gestational diabetes mellitus and gestational glycemic levels, distinct from type 2 diabetes and general glycemic levels in East Asians. A machine learning model integrating polygenic risk scores and prenatal records predicted gestational diabetes mellitus before 20 weeks of gestation, achieving an area under the receiver operating characteristic curve of 0.729 and an accuracy of 0.835. Shapley values highlighted polygenic risk scores as key contributors. This model offers a cost-effective strategy for early gestational diabetes mellitus prediction using clinical non-invasive prenatal test.