Elmar W. Tobi, Diana L. Juvinao-Quintero, Justiina Ronkainen, Raffael Ott, Rossella Alfano, Mickaël Canouil, Madelon L. Geurtsen, Amna Khamis, Leanne K. Küpers, Ives Y. Lim, Patrice Perron, Giancarlo Pesce, Johanna Tuhkanen, Anne P. Starling, Toby Andrew, Elisabeth Binder, Robert Caiazzo, Jerry K.Y. Chan, Romy Gaillard, Peter D. Gluckman, Elina Keikkala, Neerja Karnani, Sanna Mustaniemi, Tim S. Nawrot, François Pattou, Michelle Plusquin, Violeta Raverdy, Kok Hian Tan, Evangelia Tzala, Katri Raikkonen, Christiane Winkler, Anette-G. Ziegler, Isabella Annesi-Maesano, Luigi Bouchard, Yap Seng Chong, Dana Dabelea, Janine F. Felix, Barbara Heude, Vincent W.V. Jaddoe, Jari Lahti, Brigitte Reimann, Marja Vääräsmäki, Amélie Bonnefond, Philippe Froguel, Sandra Hummel, Eero Kajantie, Marjo-Riita Jarvelin, Regine P.M. Steegers-Theunissen, Caitlin G. Howe, Marie-France Hivert, Sylvain Sebert; Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies. Diabetes Care 1 March 2022; 45 (3): 614–623. https://doi.org/10.2337/dc21-1701
Maternal glycemic dysregulation during pregnancy and neonatal blood DNA methylation : meta-analyses of epigenome-wide association studies
|Author:||Tobi, Elmar W.1; Juvinao-Quintero, Diana L.2; Ronkainen, Justiina3;|
1Division of Obstetrics and Prenatal Medicine, Department of Obstetrics and Gynaecology, Erasmus MC, Rotterdam, the Netherlands
2Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA
3Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
4Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany
5Forschergruppe Diabetes, Technical University Munich, Klinikum rechts der Isar, Munich, Germany
6Forschergruppe Diabetes e.V., Helmholtz Zentrum München, Munich-Neuherberg, Germany
7Center for Environmental Sciences, University of Hasselt, Hasselt, Belgium
8INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
9University of Lille, Lille University Hospital, Lille, France
10The Generation R Study Group, Erasmus MC, Rotterdam, the Netherlands
11Department of Pediatrics, Erasmus MC, Rotterdam, the Netherlands
12Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K.
13Bioinformatics Institute, A*STAR, Singapore
14Singapore Institute for Clinical Sciences, A*STAR, Singapore
15Department of Medicine, Universite de Sherbrooke, Sherbrooke, Canada
16Research Center, Centre hospitalier Universitaire de Sherbrooke, Sherbrooke, Canada
17Paris-Saclay University, Paris-South University, UVSQ, Center for Research in Epidemiology and Population Health (CESP), INSERM, Villejuif, France
18Sorbonne Université and INSERM, Team EPAR, Institut Pierre Louis D’Épidémiologie et de Santé Publique, Paris, France
19Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
20Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
21Lifecourse Epidemiology of Adiposity and Diabetes Center, University of Colorado Anschutz Medical Campus, Aurora, CO
22Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
23Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA
24University of Lille, CHU Lille, Inserm, Institut Pasteur Lille, U1190 Translational Research for Diabetes, Lille, France
25Department of Reproductive Medicine, KK Women’s and Children’s Hospital, Singapore
26Academic Clinical Program in Obstetrics and Gynaecology, Duke-NUS Medical School, Singapore
27Liggins Institute, University of Auckland, Aukland, New Zealand
28Population Health Unit, Finnish Institute for Health and Welfare, Oulu, Finland
29PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
30Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
31Department of Maternal Fetal Medicine, KK Women’s and Children’s Hospital, Singapore
32MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K.
33Montpellier University, INSERM, Institut Desbrest d’Épidémiologie et de Santé Publique (IDESP), Montpellier, France
34Department of Biochemistry and Functional Genomics, Universite de Sherbrooke, Sherbrooke, Canada
35Department of Laboratory Medicine, CIUSSS du Saguenay–Lac-St-Jean, Hôpital Universitaire de Chicoutimi, Canada
36Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore
37Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
38Université de Paris, Inserm, INRAE, Centre for Research in Epidemiology and Statistics (CRESS), Paris, France
39Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
40Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
41Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland
42Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, U.K.
43Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH
44Diabetes Unit, Massachusetts General Hospital, Boston, MA
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022042831107
American Diabetes Association,
|Publish Date:|| 2022-04-28
Objective: Maternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring, a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations.
Research design and methods: To address this hypothesis, we conducted fixed-effects meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmaximum = 3,503), insulin (Nmaximum = 2,062), and area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (Nmaximum = 1,505). We performed lookup analyses for identified cytosine-guanine dinucleotides (CpGs) in independent observational cohorts to examine associations between DNAm and cardiometabolic traits as well as tissue-specific gene expression.
Results: Greater maternal AUCgluc was associated with lower cord blood DNAm at neighboring CpGs cg26974062 (β [SE] −0.013 [2.1 × 10⁻³], P value corrected for false discovery rate [PFDR] = 5.1 × 10⁻³) and cg02988288 (β [SE]−0.013 [2.3 × 10⁻³], PFDR = 0.031) in TXNIP. These associations were attenuated in women with GDM. Lower blood DNAm at these two CpGs near TXNIP was associated with multiple metabolic traits later in life, including type 2 diabetes. TXNIP DNAm in liver biopsies was associated with hepatic expression of TXNIP. We observed little evidence of associations between either maternal glucose or insulin and cord blood DNAm.
Conclusions: Maternal hyperglycemia, as reflected by AUCgluc, was associated with lower cord blood DNAm at TXNIP. Associations between DNAm at these CpGs and metabolic traits in subsequent lookup analyses suggest that these may be candidate loci to investigate in future causal and mediation analyses.
|Pages:||614 - 623|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3123 Gynaecology and paediatrics
E.W.T. was supported by a VENI grant from the Netherlands Organization for Scientific Research (91617128). This work was funded by the Joint Programming Initiative – A Healthy Diet for a Healthy Life (JPI HDHL) (proposal number 655). In the U.K. it is jointly funded by the Medical Research Council and the Biotechnology and Biological Sciences Research Council (grant MR/S03658X/1), in Spain by Instituto de Salud Carlos III (PCI2018-093147), in Germany by the German Federal Ministry of Education and Research (FKZ 01EA1905), in the Netherlands by ZonMw (529051023), and in France by French National Research Agency (ANR18-HDHL-0003-05). J.R. and S.S. received funding from the Healthy Diet for a Healthy Life (JPI HDHL) (PREcisE proposal no. 655) and the European Union's Horizon 2020 research and innovation program under grant agreement nos. 733206 (LifeCycle), 824989 (EUCAN-Connect), 874739 (Longitools), and 848158 (EarlyCause). Information regarding funding for the contributing cohorts can be found in Supplementary Material.
|EU Grant Number:||
(733206) LIFECYCLE - Early-life stressors and LifeCycle health
(824989) EUCAN-Connect - A federated FAIR platform enabling large-scale analysis of high-value cohort data connecting Europe and Canada in personalized health
(874739) LONGITOOLS - Dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases
© 2022 by the American Diabetes Association. This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes Care. The American Diabetes Association (ADA), publisher of Diabetes Care, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes Care in print and online at http://care.diabetesjournals.org.