Evidence mapPaperPMID 42568080Full record

ArticleCardiovascular diabetology2026

A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.

Shuo Ma, Chen Zhang, Yuming Yao, Meiling Zhou, Ai Chen, Yaya Chen, Yinhao Chen, Jiwei Wang, Gulinaizhaer Abudushalamu, Shijie Cai and 13 more

Abstract readMulticenter Study
In one paragraph

Article in Cardiovascular diabetology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

23 authors.

Shuo Ma *Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Chen Zhang *Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Yuming YaoCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Meiling ZhouCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Ai ChenCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Yaya ChenCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Yinhao ChenDepartment of Integrated Oncology, Center for Integrated Oncology (CIO), University Hospital Bonn, 53127, Bonn, Germany.
Jiwei WangCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Gulinaizhaer AbudushalamuCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Shijie CaiCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Fengfeng ZhaoCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Dawen ChenCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Xiao LiCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Yue ZhengCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Juxiang FanChangzhou Maternity and Child Health Care Hospital, Changzhou, 213000, Jiangsu, China.
Xun GaoCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Yonghui LiuCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Weimin FanWomen's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, 210004, Jiangsu, China.
Feng ZhuJiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, Jiangsu, China.
Jin YangDepartment of Clinical Laboratory, The Affiliated Lianyungang Hospital of Xuzhou Medical University, The First People's Hospital of Lianyungang), Lianyungang, 222002, Jiangsu, China.
Miao MiaoWomen's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, 210004, Jiangsu, China.
Xiaobo FanDepartment of Laboratory Medicine, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China.
Guoqiu WuCenter of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, 210009, Jiangsu, China. 101008404@seu.edu.cn.ORCID https://orcid.org/0000-0002-0403-7607

Funding

Jiangsu Provincial Key Laboratory of Critical Care Medicine JSKLCCM202202015National Natural Science Foundation of China 82302609National Natural Science Foundation of China 82373781Natural Science Foundation of Jiangsu Province BK20230840
6 · The paper itself

Abstract

backgroundGestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes and long-term metabolic and cardiovascular risk. However, oral glucose tolerance testing at 24-28 gestational weeks limits early risk stratification. Gut microbiota-associated metabolites may reflect early metabolic abnormalities, including those relevant to cardiometabolic health, but robust early-pregnancy biomarkers remain limited.

methodsWe conducted a multicenter nested case-control and prospective study involving 2,693 pregnant women. Untargeted metabolomics and metagenomics were integrated to identify GDM-associated metabolites and gut microbial alterations. Three consistently dysregulated metabolites, 3-hydroxydecanoic acid, γ-Glu-Leu, and propionic acid, were quantified by targeted LC-MS/MS. Candidate algorithms were compared using repeated 10-fold cross-validation, and a final generalized linear model was externally and prospectively validated.

resultsWomen who later developed GDM showed an adverse early-pregnancy metabolic profile, including higher BMI, triglycerides, and platelet count. Untargeted metabolomics identified 14 persistently altered metabolites enriched in energy, oxidative stress, and amino acid metabolism pathways. Metagenomics revealed taxonomic restructuring and coordinated microbiota-metabolite associations. The three-metabolite model achieved AUCs of 0.838 (95% CI, 0.791-0.885) in training, 0.840 (95% CI, 0.769-0.911) in internal validation, 0.955 (95% CI, 0.925-0.985) and 0.917 (95% CI, 0.875-0.958) in two external cohorts, and 0.969 (95% CI, 0.937-1.000) in the prospective cohort.

conclusionEarly microbiota-associated metabolic dysregulation is detectable before routine GDM diagnosis. This compact three-metabolite panel may support early GDM risk stratification and provides metabolic evidence relevant to broader cardiometabolic risk assessment in pregnancy.

Indexed as

BacteriaDiabetes, GestationalGastrointestinal MicrobiomeMetabolomicsPropionatesAdultBiomarkersCase-Control StudiesFemaleGestational AgeHumansMetagenomicsMultiomicsPredictive Value of TestsPregnancyPrognosisBiomarkersPropionatesEarly predictionGestational diabetes mellitusGut microbiotaMachine learningMetabolomicsMulti-omics integration

Identifiers

PMID42568080
PMCPMC13450031

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.