What question did this study set out to answer?

This analysis investigates whether early pregnancy metabolic or lipid measures enhance the prediction of gestational diabetes (GDM) and large-for-gestational age (LGA) outcomes beyond standard clinical factors.

June 9, 2026

1234-OR: Do First-Trimester Metabolic or Lipid Measures Improve Prediction of GDM and LGA?

Key Points

This analysis investigates whether early pregnancy metabolic or lipid measures enhance the prediction of gestational diabetes (GDM) and large-for-gestational age (LGA) outcomes beyond standard clinical factors.
Evaluated 2178 singleton gravidas without preexisting diabetes from 9 US centers.
Measured insulin sensitivity (IS) and β-cell function (βCF) along with fasting lipids at 10-14 weeks using a 75g OGTT.
Logistic regression models assessed the predictive accuracy of IS, βCF, and lipids against established clinical factors for GDM and LGA.
91% of participants completed OGTTs with 15% diagnosed with GDM.
For GDM, neither IS nor βCF added predictive accuracy over clinical models including OGTT.
For LGA, the addition of metabolic measures and lipids also did not enhance prediction accuracy.

Abstract

Introduction and Objective: In GO MOMs, early pregnancy OGTT did not sufficiently improve prediction of gestational diabetes (GDM) or large-for-gestational age birthweight (LGA) beyond clinical factors to warrant universal first-trimester screening. This secondary analysis evaluated if early metabolic or lipid measures improved predictions of these outcomes. Methods: Singleton gravidas without preexisting diabetes from 9 US centers had 75g OGTT, metabolic measures Matsuda insulin sensitivity (IS), β-cell function (βCF) and fasting lipids free fatty acids (FFA), triglycerides (TG) at 10-14 weeks gestation. Logistic regression models evaluated addition of IS and βCF to clinical factors (age, BMI, history of GDM or macrosomia) and OGTT values for prediction of GDM at 24-28 weeks using Carpenter-Coustan criteria. For prediction of LGA, FFA and TG were added to the models. Binary variable cutoffs maximized area under the receiver operating characteristic curve (AUC) in a training data set (4 centers), with results validated in remaining centers. Results: Of 2178 GO MOMs participants, 91% completed OGTTs (15% GDM) and 96% had live births (11% LGA). Neither IS nor βCF improved predictive accuracy compared to models with clinical factors or clinical factors + OGTT for GDM. For LGA, neither metabolic measures nor lipids improved prediction (Figures). Conclusion: Metabolic or lipid measures in early pregnancy do not improve prediction of GDM or LGA over clinical factors + OGTT. Disclosure E. LeBlanc: None. A. Kuang: Stock/Shareholder; Current; Dexcom, Inc. F.L. Facco: None. M. Feghali: None. W. Grobman: None. A.A. Killeen: None. W. Lowe: None. A. Merriam: None. M. Mourad: None. C. Oshiro: None. C. Powe: Research Support; Current; Dexcom, Inc. Other - Associate Editor of Diabetes Care, Honoraria for Educational Materials; Current; American Diabetes Association. Other - Royalties for Up To Date chapters; Current; Wolters Kluwer (Up To Date). Other - Speaker; Ended; Medscape. U. Reddy: None. D. Rouse: None. J. Sherr: Other - research support, consultant, advisory board member; Current; Abbott Diabetes. Other - advisory board member, consultant; Current; Vertex Pharmaceuticals Incorporated. Consultant; Current; Ypsomed AG. Research Support; Current; Dexcom, Inc., JDRF, Provention Bio, Inc., National Institutes of Health. Other - research support, consultant, advisory board member; Current; Insulet Corporation, Medtronic. Research Support; Current; Sanofi. Advisory Panel; Current; sequel med tech. A.C. Spadola: None. K. Vesco: None. E. Werner: None. L.M. Yee: None. N. Zork: None. D. Scholtens: None. P. Catalano: None. Funding The GO MOMs study is supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases. U01DK123795 to Massachusetts General Hospital; U01DK123791 to Kaiser Permanente; U01DK123759 and U01DK123745 to Northwestern University; U01DK123799 to Yale University; U01DK123783 to Women & Infants Hospital of Rhode Island. Dexcom provided the CGM systems used in the study free of charge.

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