Lauren E McMichael1, Hannah Heath1, Catherine M Johnson1, Rob Fanter2,3, Noemi Alarcon4,5, Adilene Quintana-Diaz4,5, Kari Pilolla1,5, Andrew Schaffner5,6, Elissa Jelalian7, Rena R Wing7, Alex Brito8,9, Suzanne Phelan4,5, Michael R La Frano10,11,12. 1. Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA, USA. 2. College of Agriculture, Food and Environmental Sciences, California Polytechnic State University, San Luis Obispo, CA, USA. 3. Cal Poly Metabolomics Service Center, California Polytechnic State University, San Luis Obispo, CA, USA. 4. Department of Kinesiology and Public Health, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA, 93407, USA. 5. Center for Health Research, California Polytechnic State University, San Luis Obispo, CA, USA. 6. Department of Statistics, California Polytechnic State University, San Luis Obispo, CA, USA. 7. Department of Psychiatry and Human Behavior, Warren Alpert Medical School at Brown University, Providence, RI, USA. 8. Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology. I.M. Sechenov First, Moscow Medical University, Moscow, Russia. 9. World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia. 10. Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA, USA. mlafrano@calpoly.edu. 11. Cal Poly Metabolomics Service Center, California Polytechnic State University, San Luis Obispo, CA, USA. mlafrano@calpoly.edu. 12. Center for Health Research, California Polytechnic State University, San Luis Obispo, CA, USA. mlafrano@calpoly.edu.
Abstract
INTRODUCTION: Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood. OBJECTIVES: Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM. METHODS: Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays. RESULTS: One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel". CONCLUSION: Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.
INTRODUCTION: Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood. OBJECTIVES: Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM. METHODS: Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays. RESULTS: One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel". CONCLUSION: Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.
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