Gestational diabetes mellitus (GDM), 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 (NIPT) sequencing data and detailed prenatal records. We identified 13 novel loci for GDM 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 GDM and gestational glycemic levels, distinct from type 2 diabetes and general glycemic levels in East Asians. A machine learning model integrating polygenic risk scores (PRS) and prenatal records predicted GDM before 20 weeks of gestation, achieving an AUC of 0.729 and an accuracy of 0.835. Shapley values highlighted PRS as key contributors. This model offers a cost-effective strategy for early GDM prediction using clinical NIPT.

The authors have declared no competing interest.

The Guangdong Basic and Applied Basic Research Foundation (2022B1515120080 and 2020A1515110859), the Shenzhen Science and Technology Program (20220818100717002), the Shenzhen Health Elite Talent Training Project, the Economic and Technological Development Special Fundation of Shenzhen Longgang District (LGKCYLWS2022008), the National Natural Science Foundation of China (31900487 and 82203291),National Natural Science Foundation of China (82203291),Shenzhen Health Elite Talent Training Project,the Economic and Technological Development Special Fundation of Shenzhen Longgang District （LGKCYLWS2022008）

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Ethics Committee of the School of Public Health (Shenzhen), Sun Yat-Sen University (approval number: 2021. No.8), as well as the Institutional Review Boards of Shenzhen Baoan Women and Children Hospital (approval number: LLSC2021-04-01-10-KS) and Longgang District Maternity and Child Healthcare Hospital (approval number: LGFYYXLLL-2022-024). Data collection was authorized by the Human Genetic Resources Administration of China (HGRAC).

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The complete GWAS summary statistics for GDM, FPG, OGTT0H, OGTT1H, OGTT2H, and HbA1c have been deposited in the GWAS Catalog database (https://www.ebi.ac.uk/gwas/) and the National Genomics Data Center (NGDC) (https://ngdc.cncb.ac.cn/gvm/), with approval from the China National Health Commission (permission number: 2024BAT01079). These data will be publicly available upon publication. Raw sequencing data have been deposited in the Genome Sequence Archive (GSA) for Humans at the National Genomics Data Center under the BioProject accession number GSA-Human: HRA006833, with approval from China National Health Commission (permission number: 2024BAT01079). Access to these data can be obtained through formal applications, following the GSA guidelines (https://ngdc.cncb.ac.cn/gsa-human/document). Data access is restricted to academic research purposes only.

https://www.ebi.ac.uk/gwas/