Evidence mapPaperPMID 41229170Full record

ArticleJournal of diabetes investigation2026

Dual-center prospective validation of an early second-trimester predictive model integrating BMI trajectories and pathophysiological biomarkers for gestational diabetes risk stratification.

Junxiang Gao, Shuoning Song, Yanbei Duo, Xiaolin Qiao, Yuemei Zhang, Jiyu Xu, Jing Zhang, Xiaorui Nie, Qiujin Sun, Xianchun Yang and 8 more

Abstract readMulticenter StudyValidation Study
In one paragraph

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

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

18 authors.

Junxiang GaoDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Shuoning SongDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yanbei DuoDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Xiaolin QiaoDepartment of Obstetrics, Beijing Chaoyang District Maternal and Child Health Care Hospital, Beijing, China.
Yuemei ZhangDepartment of Obstetrics, Haidian District Maternal and Child Health Care Hospital, Beijing, China.
Jiyu XuCore Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China.
Jing ZhangDepartment of Laboratory, Haidian District Maternal and Child Health Care Hospital, Beijing, China.
Xiaorui NieDepartment of Obstetrics, Beijing Chaoyang District Maternal and Child Health Care Hospital, Beijing, China.
Qiujin SunDepartment of Clinical Laboratory, Beijing Chaoyang District Maternal and Child Health Care Hospital, Beijing, China.
Xianchun YangDepartment of Clinical Laboratory, Beijing Chaoyang District Maternal and Child Health Care Hospital, Beijing, China.
Ailing WangNational Center for Women and Children's Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Wei SunCore Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China.
Yong FuDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Mengmeng ZhangDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yingyue DongDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Zechun LuNational Center for Women and Children's Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Tao YuanDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Weigang ZhaoDepartment of Endocrinology, Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.

Funding

Weigang Zhao 2019ZX09734001
6 · The paper itself

Abstract

objectiveTo develop and validate an early second-trimester predictive model integrating body mass index (BMI) trajectories and pathophysiological biomarkers for gestational diabetes mellitus (GDM) risk stratification, with the aim of reducing weight-monitoring frequency while maintaining predictive accuracy.

methodsIn this prospective dual-center cohort study, 1,450 pregnant women (<12 weeks gestation) without preexisting diabetes were enrolled from two Beijing maternal-child hospitals. Serial anthropometric (BMI at 6-10, 12-14, 15-19, and 24-28 weeks) and metabolic biomarkers (fasting glucose, C-peptide, lipids, uric acid) were analyzed. GDM was diagnosed via 75 g OGTT at 24-28 weeks. Multivariable logistic regression identified predictors using a 7:3 training-test split. Model performance was assessed by area under the ROC curve (AUC), calibration, and decision curve analysis.

resultsGDM incidence was 26.1% (378/1,450). Significant weight/BMI disparities emerged as early as 6-10 weeks (GDM vs NGT: +2.3 kg, P < 0.0001), escalating through gestation. The final model incorporated age (OR = 1.07/year, 95% CI = 1.03-1.11), 12-14-week BMI (OR = 1.06/kg/m

conclusionsThis dual-center model pioneers GDM risk stratification by 12-14 weeks using clinically accessible metrics, reducing weight-monitoring frequency without compromising prognostic value. While AUC limitations (0.68-0.69) suggest unmeasured contributors, its operational simplicity and robust negative predictive capacity support implementation in resource-constrained settings. The findings redefine antenatal care paradigms by shifting focus to early metabolic dysregulation rather than late diagnostic thresholds.

Indexed as

BiomarkersBody Mass IndexDiabetes, GestationalPregnancy Trimester, SecondAdultBlood GlucoseFemaleFollow-Up StudiesHumansPregnancyPrognosisProspective StudiesRisk AssessmentRisk FactorsBiomarkersBlood GlucoseBMI trajectoriesEarly prediction modelGestational diabetes mellitus

Identifiers

PMID41229170
PMCPMC12757667

What Socratic holds

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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.