Cynthia Roy1, Pierre-Yves Tremblay2, Elhadji Anassour-Laouan-Sidi3, Michel Lucas4, Jean-Claude Forest5, Yves Giguère5, Pierre Ayotte6. 1. Centre de Toxicologie du Québec, Institut national de santé publique du Québec (INSPQ), 945 Wolfe, Québec, QC G1V 5B3, Canada; Axe santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec, Hôpital du Saint-Sacrement, 1050, chemin Sainte-Foy, Québec, QC G1S 4L8, Canada. 2. Centre de Toxicologie du Québec, Institut national de santé publique du Québec (INSPQ), 945 Wolfe, Québec, QC G1V 5B3, Canada. 3. Axe santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec, Hôpital du Saint-Sacrement, 1050, chemin Sainte-Foy, Québec, QC G1S 4L8, Canada. 4. Axe santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec, Hôpital du Saint-Sacrement, 1050, chemin Sainte-Foy, Québec, QC G1S 4L8, Canada; Département de médecine préventive et sociale, Université Laval, Pavillon Ferdinand-Vandry, Québec, QC G1V 0A6, Canada. 5. Axe de recherche en santé de la mère et de l'enfant, Centre de recherche du CHU de Québec, Hôpital Saint-François d'Assise, 10, rue de l'Espinay, Québec, QC G1S 1L5, Canada; Département de biologie moléculaire, de biochimie médicale et de pathologie, Université Laval, Pavillon Ferdinand-Vandry, Québec, QC G1V 0A6, Canada. 6. Centre de Toxicologie du Québec, Institut national de santé publique du Québec (INSPQ), 945 Wolfe, Québec, QC G1V 5B3, Canada; Axe santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec, Hôpital du Saint-Sacrement, 1050, chemin Sainte-Foy, Québec, QC G1S 4L8, Canada; Département de médecine préventive et sociale, Université Laval, Pavillon Ferdinand-Vandry, Québec, QC G1V 0A6, Canada. Electronic address: pierre.ayotte@inspq.qc.ca.
Abstract
AIMS: Gestational diabetes mellitus (GDM) affects between 5 and 10% of all pregnancies in Canada and can lead to adverse health outcomes in both the mother and fetus. Amino acids (AA) and acylcarnitines (AC) have been identified as early biomarkers of type 2 diabetes but their usefulness in screening for GDM has yet to be demonstrated. METHODS: We conducted a nested case-control study involving 50 controls and 50 GDM cases diagnosed between the 24th and 28th week of gestation. Heparinized plasma samples were obtained during the first and early second trimester of pregnancy. Case and controls were matched according to date of recruitment, maternal age, gestational age at blood sampling as well as pre-pregnancy body mass index. Eight AA and eight AC were quantified using an ultra-high pressure liquid-chromatography quadrupole time-of-flight mass spectrometry platform. Conditional regression analyses adjusted for matching factors and smoking habits during pregnancy were performed to identify plasma metabolites associated with GDM risk. RESULTS: Odds ratio (OR) and 95% confidence interval (CI) for the prediction of GDM per one standard deviation increase of AA or AC in plasma levels were 0.25 (0.08-0.79) for butyrylcarnitine, 0.31 (0.12-0.79) for glutamic acid, 2.5 (1.2-5.3) for acetylcarnitine, 2.9 (1.3-6.8) for isobutyrylcarnitine and 5.3 (1.7-17.0) for leucine. These five metabolites were selected by stepwise conditional logistic regression to create a predictive model with an OR of 2.7 (1.5-4.9). CONCLUSION: Whether the identified metabolites can predict the risk of developing GDM requires additional studies in a larger sample of pregnant women.
AIMS: Gestational diabetes mellitus (GDM) affects between 5 and 10% of all pregnancies in Canada and can lead to adverse health outcomes in both the mother and fetus. Amino acids (AA) and acylcarnitines (AC) have been identified as early biomarkers of type 2 diabetes but their usefulness in screening for GDM has yet to be demonstrated. METHODS: We conducted a nested case-control study involving 50 controls and 50 GDM cases diagnosed between the 24th and 28th week of gestation. Heparinized plasma samples were obtained during the first and early second trimester of pregnancy. Case and controls were matched according to date of recruitment, maternal age, gestational age at blood sampling as well as pre-pregnancy body mass index. Eight AA and eight AC were quantified using an ultra-high pressure liquid-chromatography quadrupole time-of-flight mass spectrometry platform. Conditional regression analyses adjusted for matching factors and smoking habits during pregnancy were performed to identify plasma metabolites associated with GDM risk. RESULTS: Odds ratio (OR) and 95% confidence interval (CI) for the prediction of GDM per one standard deviation increase of AA or AC in plasma levels were 0.25 (0.08-0.79) for butyrylcarnitine, 0.31 (0.12-0.79) for glutamic acid, 2.5 (1.2-5.3) for acetylcarnitine, 2.9 (1.3-6.8) for isobutyrylcarnitine and 5.3 (1.7-17.0) for leucine. These five metabolites were selected by stepwise conditional logistic regression to create a predictive model with an OR of 2.7 (1.5-4.9). CONCLUSION: Whether the identified metabolites can predict the risk of developing GDM requires additional studies in a larger sample of pregnant women.
Authors: Natalia I Krupenko; Jaspreet Sharma; Peter Pediaditakis; Kristi L Helke; Madeline S Hall; Xiuxia Du; Susan Sumner; Sergey A Krupenko Journal: Hum Genomics Date: 2020-11-09 Impact factor: 4.639
Authors: Izabela Burzynska-Pedziwiatr; Adrian Jankowski; Konrad Kowalski; Przemyslaw Sendys; Andrzej Zieleniak; Katarzyna Cypryk; Monika Zurawska-Klis; Lucyna A Wozniak; Malgorzata Bukowiecka-Matusiak Journal: Int J Mol Sci Date: 2020-10-22 Impact factor: 5.923