Evidence map›Paper›PMID 40325049›Full record

ArticleNature communications2025

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 and 6 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Yuqin Gu *School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0005-9817-466X
Hao Zheng *School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0005-1766-8255
Piao Wang *The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong, 518172, China.
Yanhong LiuSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0006-3319-3429
Xinxin GuoSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0006-1037-2694
Yuandan WeiSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0009-6433-3940
Zijing YangSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0004-7522-5754
Shiyao ChengSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.ORCID http://orcid.org/0009-0006-4387-4121
Yanchao ChenSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.
Liang HuThe Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong, 518172, China.ORCID http://orcid.org/0000-0002-4416-0546
Xiaohang ChenThe Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong, 518172, China.
Quanfu ZhangCentral Laboratory, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, Guangdong, 518102, China.
Guobo ChenDepartment of Genetic and Genomic Medicine, Center for Productive Medicine, Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0001-5475-8237
Fengxiang WeiThe Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong, 518172, China. haowei727499@163.com.
Jianxin ZhenCentral Laboratory, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, Guangdong, 518102, China. jxzhen@qq.com.ORCID http://orcid.org/0000-0003-4899-4422
Siyang LiuSchool of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, 518107, China. liusy99@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0001-6780-9419

Funding

National Natural Science Foundation of China (National Science Foundation of China) 31900487National Natural Science Foundation of China (National Science Foundation of China) 32470642National Natural Science Foundation of China (National Science Foundation of China) 82203291
6 · The paper itself

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.

Indexed as

Diabetes, GestationalGenetic Predisposition to DiseaseAdultBlood GlucoseChinaDiabetes Mellitus, Type 2East Asian PeopleFemaleGene FrequencyGenome-Wide Association StudyHumansMachine LearningMultifactorial InheritancePolymorphism, Single NucleotidePregnancyRisk FactorsBlood Glucose

Identifiers

PMID40325049
PMCPMC12053562

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.