Daniel A Enquobahrie1, Marie Denis1, Mahlet G Tadesse1, Bizu Gelaye1, Habtom W Ressom1, Michelle A Williams1. 1. Center for Perinatal Studies (D.A.E.), Swedish Medical Center, Seattle, Washington 98121; Department of Epidemiology (D.A.E.), School of Public Health, University of Washington, Seattle, Washington 98195; Department of Epidemiology (M.D., B.G., M.A.W.), Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115; UMR Amélioration Génétique et Adaptation des Plantes Méditerranéennes et Tropicales (M.D.), CIRAD, International Campus of Baillarguet TA A-108/C, 34398 Montpellier, France; Department of Mathematics and Statistics (M.G.T.), Georgetown University, Washington, District of Columbia 20057; and Department of Oncology (H.W.R.), Georgetown University Medical Center, Washington, District of Columbia 20057.
Abstract
CONTEXT: Significant gaps remain in the understanding of genetic and environmental risk factors, as well as related mechanisms that contribute to gestational diabetes mellitus (GDM). OBJECTIVES: This study aimed to investigate early pregnancy maternal serum metabolites and subsequent risk of GDM. DESIGN: Information on participant characteristics and GDM diagnosis was collected using in-person interviews and medical record abstraction, respectively. Early pregnancy serum samples were used for nontargeted metabolite profiling using a gas chromatography-mass spectrometry platform. Lasso regression was used to select a set of metabolites that are jointly associated with GDM case-control status. We evaluated the predictive performance of the set of selected metabolites using a receiver operating characteristics curve and area under the curve. PARTICIPANTS: A total of 178 GDM cases and 180 controls participated in a pregnancy cohort study. RESULTS: A set of 17 metabolites (linoleic acid, oleic acid, myristic acid, d-galactose, d-sorbitol, o-phosphocolamine, l-alanine, l-valine, 5-hydroxy-l-tryptophan, l-serine, sarcosine, l-pyroglutamic acid, l-mimosine, l-lactic acid, glycolic acid, fumaric acid, and urea) differentiated GDM cases from controls. Fold changes of relative abundance of these metabolites among GDM cases compared with controls ranged from 1.47 (linoleic acid) to 0.78 (5-hydroxy-l-tryptophan). Addition of these selected metabolites to a set of well-known GDM risk factors improved the area under the curve significantly from 0.71 to 0.87 (P = 3.97E-07). CONCLUSIONS: We identified combinations of metabolites in early pregnancy that are associated with subsequent risk of GDM. Replication of findings may improve understanding of GDM pathogenesis and may have implications for the design of GDM prevention and early diagnosis protocols.
CONTEXT: Significant gaps remain in the understanding of genetic and environmental risk factors, as well as related mechanisms that contribute to gestational diabetes mellitus (GDM). OBJECTIVES: This study aimed to investigate early pregnancy maternal serum metabolites and subsequent risk of GDM. DESIGN: Information on participant characteristics and GDM diagnosis was collected using in-person interviews and medical record abstraction, respectively. Early pregnancy serum samples were used for nontargeted metabolite profiling using a gas chromatography-mass spectrometry platform. Lasso regression was used to select a set of metabolites that are jointly associated with GDM case-control status. We evaluated the predictive performance of the set of selected metabolites using a receiver operating characteristics curve and area under the curve. PARTICIPANTS: A total of 178 GDM cases and 180 controls participated in a pregnancy cohort study. RESULTS: A set of 17 metabolites (linoleic acid, oleic acid, myristic acid, d-galactose, d-sorbitol, o-phosphocolamine, l-alanine, l-valine, 5-hydroxy-l-tryptophan, l-serine, sarcosine, l-pyroglutamic acid, l-mimosine, l-lactic acid, glycolic acid, fumaric acid, and urea) differentiated GDM cases from controls. Fold changes of relative abundance of these metabolites among GDM cases compared with controls ranged from 1.47 (linoleic acid) to 0.78 (5-hydroxy-l-tryptophan). Addition of these selected metabolites to a set of well-known GDM risk factors improved the area under the curve significantly from 0.71 to 0.87 (P = 3.97E-07). CONCLUSIONS: We identified combinations of metabolites in early pregnancy that are associated with subsequent risk of GDM. Replication of findings may improve understanding of GDM pathogenesis and may have implications for the design of GDM prevention and early diagnosis protocols.
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