AIMS/HYPOTHESIS: Metabolomic profiling in populations with impaired glucose tolerance has revealed that branched chain and aromatic amino acids (BCAAs) are predictive of type 2 diabetes. Because gestational diabetes mellitus (GDM) shares pathophysiological similarities with type 2 diabetes, the metabolite profile predictive of type 2 diabetes could potentially identify women who will develop GDM. METHODS: We conducted a nested case-control study of 18- to 40-year-old women who participated in the Massachusetts General Hospital Obstetrical Maternal Study between 1998 and 2007. Participants were enrolled during their first trimester of a singleton pregnancy and fasting serum samples were collected. The women were followed throughout pregnancy and identified as having GDM or normal glucose tolerance (NGT) in the third trimester. Women with GDM (n = 96) were matched to women with NGT (n = 96) by age, BMI, gravidity and parity. Liquid chromatography-mass spectrometry was used to measure the levels of 91 metabolites. RESULTS: Data analyses revealed the following characteristics (mean ± SD): age 32.8 ± 4.4 years, BMI 28.3 ± 5.6 kg/m(2), gravidity 2 ± 1 and parity 1 ± 1. Six metabolites (anthranilic acid, alanine, glutamate, creatinine, allantoin and serine) were identified as having significantly different levels between the two groups in conditional logistic regression analyses (p < 0.05). The levels of the BCAAs did not differ significantly between GDM and NGT. CONCLUSIONS/ INTERPRETATION: Metabolic markers identified as being predictive of type 2 diabetes may not have the same predictive power for GDM. However, further study in a racially/ethnically diverse population-based cohort is necessary.
AIMS/HYPOTHESIS: Metabolomic profiling in populations with impaired glucose tolerance has revealed that branched chain and aromatic amino acids (BCAAs) are predictive of type 2 diabetes. Because gestational diabetes mellitus (GDM) shares pathophysiological similarities with type 2 diabetes, the metabolite profile predictive of type 2 diabetes could potentially identify women who will develop GDM. METHODS: We conducted a nested case-control study of 18- to 40-year-old women who participated in the Massachusetts General Hospital Obstetrical Maternal Study between 1998 and 2007. Participants were enrolled during their first trimester of a singleton pregnancy and fasting serum samples were collected. The women were followed throughout pregnancy and identified as having GDM or normal glucose tolerance (NGT) in the third trimester. Women with GDM (n = 96) were matched to women with NGT (n = 96) by age, BMI, gravidity and parity. Liquid chromatography-mass spectrometry was used to measure the levels of 91 metabolites. RESULTS: Data analyses revealed the following characteristics (mean ± SD): age 32.8 ± 4.4 years, BMI 28.3 ± 5.6 kg/m(2), gravidity 2 ± 1 and parity 1 ± 1. Six metabolites (anthranilic acid, alanine, glutamate, creatinine, allantoin and serine) were identified as having significantly different levels between the two groups in conditional logistic regression analyses (p < 0.05). The levels of the BCAAs did not differ significantly between GDM and NGT. CONCLUSIONS/ INTERPRETATION: Metabolic markers identified as being predictive of type 2 diabetes may not have the same predictive power for GDM. However, further study in a racially/ethnically diverse population-based cohort is necessary.
Authors: Thomas J Wang; Martin G Larson; Ramachandran S Vasan; Susan Cheng; Eugene P Rhee; Elizabeth McCabe; Gregory D Lewis; Caroline S Fox; Paul F Jacques; Céline Fernandez; Christopher J O'Donnell; Stephen A Carr; Vamsi K Mootha; Jose C Florez; Amanda Souza; Olle Melander; Clary B Clish; Robert E Gerszten Journal: Nat Med Date: 2011-03-20 Impact factor: 53.440
Authors: Eliana S Di Cairano; Alberto M Davalli; Lucia Perego; Silvia Sala; V Franca Sacchi; Stefano La Rosa; Giovanna Finzi; Claudia Placidi; Carlo Capella; Paola Conti; Victoria E Centonze; Francesca Casiraghi; Federico Bertuzzi; Franco Folli; Carla Perego Journal: J Biol Chem Date: 2011-02-18 Impact factor: 5.157
Authors: Wei Perng; Brandy M Ringham; Harry A Smith; Gregory Michelotti; Katerina M Kechris; Dana Dabelea Journal: Diabetologia Date: 2019-11-13 Impact factor: 10.122