Mohammad L Rahman1, Myriam Doyon2, Melina Arguin2, Patrice Perron2,3, Luigi Bouchard2,4,5, Marie-France Hivert6,7,8. 1. Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA. 2. Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC, Canada. 3. Faculty of Medicine and Life Sciences, Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada. 4. Faculty of Medicine and Life Sciences, Department of Biochemistry, Université de Sherbrooke, Sherbrooke, QC, Canada. 5. Department of Medical Biology, CIUSSS du Saguenay-Lac-Saint-Jean, Saguenay, QC, Canada. 6. Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA. mhivert@partners.org. 7. Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC, Canada. mhivert@partners.org. 8. Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. mhivert@partners.org.
Abstract
BACKGROUND: Fetal exposure to maternal excess adiposity and hyperglycemia is risk factors for childhood adverse metabolic outcomes. Using data from a prospective pre-birth cohort, we aimed to further understand the prenatal determinants of fetal metabolic programming based on analyses of maternal adiposity and glycemic traits across pregnancy with childhood metabolomic profiles. METHODS: This study included 330 mother-child pairs from the Gen3G cohort with information on maternal adiposity and glycemic markers at 5-16 (visit 1) and 24-30 (visit 2) weeks of pregnancy. At mid-childhood (4.8-7.2 years old), we collected fasting plasma and measured 1116 metabolites using an untargeted approach. We constructed networks of interconnected metabolites using a weighted-correlation network analysis algorithm. We estimated Spearman's partial correlation coefficients of maternal adiposity and glycemic traits across pregnancy with metabolite networks and individual metabolites, adjusting for maternal age, gravidity, race/ethnicity, history of smoking, and child's sex and age at blood collection for metabolite measurement. RESULTS: We identified a network of 16 metabolites, primarily glycero-3-phosphoethanolamines (GPE) at mid-childhood that showed consistent negative correlations with maternal body mass index, waist circumference, and body-fat percentage at visits 1 and 2 (ρadjusted = -0.14 to -0.21) and post-challenge glucose levels at visit 2 (ρadjusted = -0.10 to -0.13), while positive correlations with Matsuda index (ρadjusted = 0.13). Within this identified network, 1-palmitoyl-2-decosahexaenoyl-GPE and 1-stearoyl-2-decosahexaenoyl-GPE appeared to be driving the associations. In addition, a network of 89 metabolites, primarily phosphatidylcholines, plasmalogens, sphingomyelins, and ceramides showed consistent negative correlations with insulin at visit 1 and post-challenge glucose at visit 2, while positive correlation with adiponectin at visit 2. CONCLUSIONS: Prenatal exposure to maternal higher adiposity and hyperglycemic traits and lower insulin sensitivity markers were associated with a unique metabolomic pattern characterized by low serum phospho- and sphingolipids in mid-childhood.
BACKGROUND: Fetal exposure to maternal excess adiposity and hyperglycemia is risk factors for childhood adverse metabolic outcomes. Using data from a prospective pre-birth cohort, we aimed to further understand the prenatal determinants of fetal metabolic programming based on analyses of maternal adiposity and glycemic traits across pregnancy with childhood metabolomic profiles. METHODS: This study included 330 mother-child pairs from the Gen3G cohort with information on maternal adiposity and glycemic markers at 5-16 (visit 1) and 24-30 (visit 2) weeks of pregnancy. At mid-childhood (4.8-7.2 years old), we collected fasting plasma and measured 1116 metabolites using an untargeted approach. We constructed networks of interconnected metabolites using a weighted-correlation network analysis algorithm. We estimated Spearman's partial correlation coefficients of maternal adiposity and glycemic traits across pregnancy with metabolite networks and individual metabolites, adjusting for maternal age, gravidity, race/ethnicity, history of smoking, and child's sex and age at blood collection for metabolite measurement. RESULTS: We identified a network of 16 metabolites, primarily glycero-3-phosphoethanolamines (GPE) at mid-childhood that showed consistent negative correlations with maternal body mass index, waist circumference, and body-fat percentage at visits 1 and 2 (ρadjusted = -0.14 to -0.21) and post-challenge glucose levels at visit 2 (ρadjusted = -0.10 to -0.13), while positive correlations with Matsuda index (ρadjusted = 0.13). Within this identified network, 1-palmitoyl-2-decosahexaenoyl-GPE and 1-stearoyl-2-decosahexaenoyl-GPE appeared to be driving the associations. In addition, a network of 89 metabolites, primarily phosphatidylcholines, plasmalogens, sphingomyelins, and ceramides showed consistent negative correlations with insulin at visit 1 and post-challenge glucose at visit 2, while positive correlation with adiponectin at visit 2. CONCLUSIONS: Prenatal exposure to maternal higher adiposity and hyperglycemic traits and lower insulin sensitivity markers were associated with a unique metabolomic pattern characterized by low serum phospho- and sphingolipids in mid-childhood.
Authors: M F Hivert; W Perng; S M Watkins; C S Newgard; L C Kenny; B S Kristal; M E Patti; E Isganaitis; D L DeMeo; E Oken; M W Gillman Journal: J Dev Orig Health Dis Date: 2015-01-29 Impact factor: 2.401
Authors: Wei Perng; Matthew W Gillman; Abby F Fleisch; Ryan D Michalek; Steven M Watkins; Elvira Isganaitis; Mary-Elizabeth Patti; Emily Oken Journal: Obesity (Silver Spring) Date: 2014-09-24 Impact factor: 5.002
Authors: J F Gautier; C Wilson; C Weyer; D Mott; W C Knowler; M Cavaghan; K S Polonsky; C Bogardus; R E Pratley Journal: Diabetes Date: 2001-08 Impact factor: 9.461
Authors: Charmaine S Wright; Sheryl L Rifas-Shiman; Janet W Rich-Edwards; Elsie M Taveras; Matthew W Gillman; Emily Oken Journal: Am J Hypertens Date: 2008-11-20 Impact factor: 2.689
Authors: William L Lowe; Denise M Scholtens; Lynn P Lowe; Alan Kuang; Michael Nodzenski; Octavious Talbot; Patrick M Catalano; Barbara Linder; Wendy J Brickman; Peter Clayton; Chaicharn Deerochanawong; Jill Hamilton; Jami L Josefson; Michele Lashley; Jean M Lawrence; Yael Lebenthal; Ronald Ma; Michael Maresh; David McCance; Wing Hung Tam; David A Sacks; Alan R Dyer; Boyd E Metzger Journal: JAMA Date: 2018-09-11 Impact factor: 56.272
Authors: Ghattu V Krishnaveni; Sargoor R Veena; Jacqueline C Hill; Sarah Kehoe; Samuel C Karat; Caroline H D Fall Journal: Diabetes Care Date: 2009-11-16 Impact factor: 19.112