ArticleJournal of diabetes investigation2026
Lipid metabolism profiles as predictors of gestational diabetes mellitus risk: A retrospective study.
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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Abstract
objectiveGestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy and is associated with significant maternal and fetal risks. The study aims to investigate lipid metabolism profiles associated with GDM to identify independent risk factors.
methodsClinical data from 17,452 women with singleton pregnancies who received regular prenatal care and nutritional assessment at Fujian Maternity and Child Health Hospital between December 2011 and December 2019 were analyzed. Participants were divided into GDM and control groups based on diagnosis. Gestational age was confirmed by first-trimester ultrasound, and lipid profiles were measured in early (10-13 weeks) and late pregnancy (28-32 weeks). GDM was diagnosed using either a two-step approach (2011-2014) or the International Association of Diabetes and Pregnancy Study Groups (IADPSG) one-step 75-g oral glucose tolerance test. Maternal characteristics, pregnancy outcomes, and lipid indicators-including total cholesterol (TC), triglycerides (TG), LDL-C, HDL-C, and apolipoproteins (Apo-A1, Apo-B)-were extracted. Feature selection was performed using LASSO regression and the Boruta algorithm.
resultsAge, TG/HDL-C ratio, TG, pre-pregnancy BMI, and HDL-C were identified as independent risk factors for GDM. Levels of ALT, HGB, Apo-B, TC, TG, and TG/HDL-C ratio were significantly higher in the GDM group, while HDL-C was lower (P < 0.05). Threshold analysis indicated that first-trimester HDL-C and TG/HDL-C ratio critical cut-off points were 2.37 and 1.33, respectively.
conclusionAbnormal lipid metabolism is a hallmark of GDM and may serve as a predictive biomarker. Assessment of TG/HDL-C ratio and HDL-C in early pregnancy could improve risk stratification and facilitate early interventions.
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