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

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 () and Siyang Liu ()
Additional contact information
Yuqin Gu: Shenzhen Campus of Sun Yat-sen University
Hao Zheng: Shenzhen Campus of Sun Yat-sen University
Piao Wang: Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)
Yanhong Liu: Shenzhen Campus of Sun Yat-sen University
Xinxin Guo: Shenzhen Campus of Sun Yat-sen University
Yuandan Wei: Shenzhen Campus of Sun Yat-sen University
Zijing Yang: Shenzhen Campus of Sun Yat-sen University
Shiyao Cheng: Shenzhen Campus of Sun Yat-sen University
Yanchao Chen: Shenzhen Campus of Sun Yat-sen University
Liang Hu: Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)
Xiaohang Chen: Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)
Quanfu Zhang: Shenzhen Baoan Women’s and Children’s Hospital
Guobo Chen: People’s Hospital of Hangzhou Medical College
Fengxiang Wei: Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)
Jianxin Zhen: Shenzhen Baoan Women’s and Children’s Hospital
Siyang Liu: Shenzhen Campus of Sun Yat-sen University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract 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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-59442-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59442-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-59442-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().