B Gelaye1, C B Clish2, M Denis3, G Larrabure4, M G Tadesse5, A Deik2, K Pierce2, K Bullock2, C Dennis2, D A Enquobahrie6, M A Williams7. 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677, Huntington Avenue, K505F, 02115 Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. Electronic address: bgelaye@hsph.harvard.edu. 2. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 3. UMR amélioration génétique et adaptation des plantes méditerranéennes et tropicales, CIRAD, Montpellier, France. 4. Instituto Nacional Materno Perinatal de Lima, Lima, Peru; Departamentos de Medicina y Ginecología y Obstetricia Universidad Nacional Mayor de San Marcos, Lima, Peru. 5. Department of Mathematics and Statistics, Georgetown University, Washington DC, USA. 6. Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA; Center for Perinatal Studies, Swedish Medical Center, Seattle, WA, USA. 7. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677, Huntington Avenue, K505F, 02115 Boston, MA, USA.
Abstract
AIM: The oral glucose tolerance test (OGTT), widely used as a gold standard for gestational diabetes mellitus (GDM) diagnosis, provides a broad view of glucose pathophysiology in response to a glucose challenge. We conducted the present study to evaluate metabolite changes before and after an oral glucose challenge in pregnancy; and to examine the extent to which metabolites may serve to predict GDM diagnosis in pregnant women. METHODS: Peruvian pregnant women (n=100) attending prenatal clinics (mean gestation 25 weeks) participated in the study with 23% of them having GDM diagnosis. Serum samples were collected immediately prior to and 2-hours after administration of a 75-g OGTT. Targeted metabolic profiling was performed using a LC-MS based metabolomics platform. Changes in metabolite levels were evaluated using paired Student's t-tests and the change patterns were examined at the level of pathways. Multivariate regression procedures were used to examine metabolite pairwise differences associated with subsequent GDM diagnosis. RESULTS: Of the 306 metabolites detected, the relative concentration of 127 metabolites were statistically significantly increased or decreased 2-hours after the oral glucose load (false discovery rate [FDR] corrected P-value<0.001). We identified relative decreases in metabolites in acylcarnitines, fatty acids, and diacylglycerols while relative increases were noted among bile acids. In addition, we found that C58:10 triacylglycerol (β=-0.08, SE=0.04), C58:9 triacylglycerol (β=-0.07, SE=0.03), adenosine (β=0.70, SE=0.32), methionine sulfoxide (β=0.36, SE=0.13) were significantly associated with GDM diagnosis even after adjusting for age and body mass index. CONCLUSIONS: We identified alterations in maternal serum metabolites, representing distinct cellular and metabolic pathways including fatty acid metabolism, in response to an oral glucose challenge. These findings offer novel perspectives on the pathophysiological mechanisms underlying GDM.
AIM: The oral glucose tolerance test (OGTT), widely used as a gold standard for gestational diabetes mellitus (GDM) diagnosis, provides a broad view of glucose pathophysiology in response to a glucose challenge. We conducted the present study to evaluate metabolite changes before and after an oral glucose challenge in pregnancy; and to examine the extent to which metabolites may serve to predict GDM diagnosis in pregnant women. METHODS: Peruvian pregnant women (n=100) attending prenatal clinics (mean gestation 25 weeks) participated in the study with 23% of them having GDM diagnosis. Serum samples were collected immediately prior to and 2-hours after administration of a 75-g OGTT. Targeted metabolic profiling was performed using a LC-MS based metabolomics platform. Changes in metabolite levels were evaluated using paired Student's t-tests and the change patterns were examined at the level of pathways. Multivariate regression procedures were used to examine metabolite pairwise differences associated with subsequent GDM diagnosis. RESULTS: Of the 306 metabolites detected, the relative concentration of 127 metabolites were statistically significantly increased or decreased 2-hours after the oral glucose load (false discovery rate [FDR] corrected P-value<0.001). We identified relative decreases in metabolites in acylcarnitines, fatty acids, and diacylglycerols while relative increases were noted among bile acids. In addition, we found that C58:10 triacylglycerol (β=-0.08, SE=0.04), C58:9 triacylglycerol (β=-0.07, SE=0.03), adenosine (β=0.70, SE=0.32), methionine sulfoxide (β=0.36, SE=0.13) were significantly associated with GDM diagnosis even after adjusting for age and body mass index. CONCLUSIONS: We identified alterations in maternal serum metabolites, representing distinct cellular and metabolic pathways including fatty acid metabolism, in response to an oral glucose challenge. These findings offer novel perspectives on the pathophysiological mechanisms underlying GDM.
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