Evidence map›Paper›PMID 42238241›Full record

ArticleFrontiers in endocrinology2026

Glycometabolic profiles and pregnancy outcomes across pathophysiological subtypes of gestational diabetes mellitus.

Junyou Su, Jing Luo, Xiaoting Huang, Yan Huang, Sumei Wang

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Article in Frontiers in endocrinology, 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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5 · Who and what money

Authors and funding

5 authors.

Junyou SuDepartment of Obstetrics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Jing LuoDepartment of Obstetrics, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xiaoting HuangDepartment of Obstetrics, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yan HuangDepartment of Obstetrics, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Sumei WangDepartment of Obstetrics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gestational diabetes mellitus (GDM) exhibits profound pathophysiological heterogeneity, but current management fails to account for subtype-specific differences in clinical features and pregnancy prognoses. This study aimed to characterize the glycometabolic profiles and pregnancy outcomes of GDM subtypes defined by insulin resistance and β-cell function, and provide a foundation for precision medicine in GDM. Methods: In this 1:1 case-control study, 330 singleton women with GDM and 330 healthy singleton controls were enrolled. Using control-derived quartiles of HOMA-IR and HOMA-β, GDM patients were classified into four subtypes: insulin resistance (GDM-IR), insulin secretory defect (GDM-IS), combined defect (GDM-M), and unclassified (GDM-N). Clinical characteristics, glycometabolic parameters, and pregnancy outcomes were compared across groups, and multivariate logistic regression identified independent risk factors for adverse pregnancy outcomes. Results: Subtype distribution was: GDM-IR 36.4% (120/330), GDM-IS 22.7% (75/330), GDM-M 17.9% (59/330), GDM-N 23.0% (76/330). GDM-M showed the most severe glycometabolic derangements: highest first-trimester fasting glucose, all OGTT glucose values, HbA1c, insulin use rate (18.6%, 11/59), and poor glycemic control rate (22.0%, 13/59). It also had the highest rates of cesarean delivery (72.9%, 43/59), postpartum hemorrhage (8.5%, 5/59), preterm birth (18.6%, 11/59), macrosomia (17.0%, 10/59), and neonatal hypoglycemia (11.9%, 7/59) (all P < 0.05). Multivariate regression identified age (OR = 1.067, 95% CI: 1.028-1.108), pre-pregnancy BMI (OR = 1.073, 95% CI: 1.014-1.136), GDM-IS (OR = 2.013, 95% CI: 1.134-3.574), GDM-M (OR = 8.417, 95% CI: 2.661-26.627), and GDM-N (OR = 1.831, 95% CI: 1.076-3.115) as independent risk factors for adverse pregnancy outcomes, with GDM-M conferring the highest risk. Conclusions: GDM subtypes defined by insulin resistance and β-cell function exhibit marked heterogeneity in glycometabolic profiles and pregnancy outcomes. The combined defect subtype (GDM-M) carries the highest risk of adverse maternal-neonatal events and may warrant the most intensive monitoring. These findings support pathophysiological subtyping for risk stratification, but causal inference is limited by the observational, single-center design; validation in multicenter, multi-ethnic prospective cohorts is required.

Indexed as

Blood GlucoseDiabetes, GestationalInsulin ResistancePregnancy OutcomeAdultCase-Control StudiesFemaleHumansInfant, NewbornInsulinPregnancyPrognosisRisk FactorsBlood GlucoseInsulingestational diabetes mellitusglycometabolisminsulin resistancepregnancy outcomessubtypes

Identifiers

PMID42238241
PMCPMC13225975

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.