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Original Investigation |

Genetic Evidence for Causal Relationships Between Maternal Obesity-Related Traits and Birth Weight

Jessica Tyrrell, PhD1,2; Rebecca C. Richmond, PhD3,4,5; Tom M. Palmer, PhD6,7; Bjarke Feenstra, PhD8; Janani Rangarajan, MS9; Sarah Metrustry, MSc10; Alana Cavadino, MSc11,12; Lavinia Paternoster, PhD5; Loren L. Armstrong, PhD13; N. Maneka G. De Silva, PhD5; Andrew R. Wood, PhD1; Momoko Horikoshi, MD, PhD14,15; Frank Geller, MSc8; Ronny Myhre, PhD16; Jonathan P. Bradfield, BS17; Eskil Kreiner-Møller, MD18; Ville Huikari, MSc19; Jodie N. Painter, PhD20; Jouke-Jan Hottenga, PhD21,22; Catherine Allard, BSc23,24; Diane J. Berry, PhD12; Luigi Bouchard, PhD, MBA24,25,26; Shikta Das, PhD27; David M. Evans, PhD3,5,28; Hakon Hakonarson, MD, PhD17,29,30; M. Geoffrey Hayes, PhD13; Jani Heikkinen, MSc31; Albert Hofman, PhD32,54; Bridget Knight, PhD1; Penelope A. Lind, PhD20; Mark I. McCarthy, MD, PhD14,15,33; George McMahon, PhD3; Sarah E. Medland, PhD20; Mads Melbye, MD, DMSc8,34; Andrew P. Morris, PhD15,35; Michael Nodzenski, MS9; Christoph Reichetzeder, MD36,37; Susan M. Ring, PhD3,5; Sylvain Sebert, PhD19,38; Verena Sengpiel, PhD39; Thorkild I. A. Sørensen, MD5,40,41; Gonneke Willemsen, PhD21,22; Eco J. C. de Geus, PhD21,22; Nicholas G. Martin, PhD20; Tim D. Spector, MD10; Christine Power, PhD12; Marjo-Riitta Järvelin, MD, PhD19,38,42,43,44; Hans Bisgaard, MD, DMSci18; Struan F. A. Grant, PhD17,29,30; Ellen A. Nohr, PhD45; Vincent W. Jaddoe, PhD4,32,46; Bo Jacobsson, MD, PhD16,39; Jeffrey C. Murray, MD47; Berthold Hocher, MD, PhD36,48; Andrew T. Hattersley, DM1; Denise M. Scholtens, PhD9; George Davey Smith, DSc3,5; Marie-France Hivert, MD49,50,51; Janine F. Felix, PhD4,32,46; Elina Hyppönen, PhD12,52,53; William L. Lowe Jr, MD13; Timothy M. Frayling, PhD1; Debbie A. Lawlor, PhD3,5; Rachel M. Freathy, PhD1,5 ; for the Early Growth Genetics (EGG) Consortium
[+] Author Affiliations
1Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom
2European Centre for Environment and Human Health, University of Exeter, the Knowledge Spa, Truro, United Kingdom
3School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
4The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
5Medical Research Council Integrative Epidemiology Unit at the University of Bristol, United Kingdom
6Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
7Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
8Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
9Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
10Department of Twin Research, King's College London, St Thomas' Hospital, London, United Kingdom
11Centre for Environmental and Preventive Medicine, Wolfson Institute of Preventive Medicine, Barts, and the London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom
12Population, Policy and Practice, UCL Institute of Child Health, University College London, United Kingdom
13Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
14Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
15Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
16Division of Epidemiology, Department of Genes and Environment, Norwegian Institute of Public Health, Oslo, Norway
17Center for Applied Genomics, the Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
18Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark, and Danish Pediatric Asthma Center, Copenhagen University Hospital, Gentofte, Denmark
19Institute of Health Sciences, University of Oulu, Oulu, Finland
20QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, Australia
21EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
22Department of Biological Psychology, VU University Amsterdam, Amsterdam, the Netherlands
23Department of Mathematics, Universite de Sherbrooke, Quebec City, Quebec, Canada
24Centre de recherché du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec City, Quebec, Canada
25ECOGENE-21 and Lipid Clinic, Chicoutimi Hospital, Saguenay, Quebec City, Quebec, Canada
26Department of Biochemistry, Université de Sherbrooke, Sherbrooke, Quebec City, Quebec, Canada
27Department of Primary Care and Public Health, Imperial College London, United Kingdom
28University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
29Division of Human Genetics, the Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
30Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
31FIMM Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
32Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
33Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
34Department of Medicine, Stanford University School of Medicine, Stanford, California
35Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
36Institute of Nutritional Science, University of Potsdam, Germany
37Center for Cardiovascular Research/Charité, Berlin, Germany
38Department of Epidemiology and Biostatistics, School of Public Health, Medical Research Council-Health Protection Agency Centre for Environment and Health, Faculty of Medicine, Imperial College London, United Kingdom
39Department of Obstetrics and Gynecology, Sahlgrenska Academy, Sahgrenska University Hospital, Gothenburg, Sweden
40Institute of Preventive Medicine, Bispebjerg and Frederiksberg University Hospital, Capital Region, Copenhagen, Denmark
41Novo Nordisk Foundation Center for Basic Metabolic Research and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
42Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland
43Biocenter Oulu, University of Oulu, Oulu, Finland
44Unit of Primary Care, Oulu University Hospital, Oulu, Finland
45Research Unit of Obstetrics & Gynecology, Institute of Clinical Research, University of Southern Denmark, Odense
46Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, the Netherlands
47Department of Pediatrics, University of Iowa, Iowa City
48The First Affiliated Hospital of Jinan University, Guangzhou, China
49Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts
50Diabetes Center, Massachusetts General Hospital, Boston
51Department of Medicine, Universite de Sherbrooke, Quebec City, Quebec, Canada
52Centre for Population Health Research, School of Health Sciences, and Sansom Institute, University of South Australia, Adelaide
53South Australian Health and Medical Research Institute, Adelaide
54Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
JAMA. 2016;315(11):1129-1140. doi:10.1001/jama.2016.1975.
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Importance  Neonates born to overweight or obese women are larger and at higher risk of birth complications. Many maternal obesity-related traits are observationally associated with birth weight, but the causal nature of these associations is uncertain.

Objective  To test for genetic evidence of causal associations of maternal body mass index (BMI) and related traits with birth weight.

Design, Setting, and Participants  Mendelian randomization to test whether maternal BMI and obesity-related traits are potentially causally related to offspring birth weight. Data from 30 487 women in 18 studies were analyzed. Participants were of European ancestry from population- or community-based studies in Europe, North America, or Australia and were part of the Early Growth Genetics Consortium. Live, term, singleton offspring born between 1929 and 2013 were included.

Exposures  Genetic scores for BMI, fasting glucose level, type 2 diabetes, systolic blood pressure (SBP), triglyceride level, high-density lipoprotein cholesterol (HDL-C) level, vitamin D status, and adiponectin level.

Main Outcome and Measure  Offspring birth weight from 18 studies.

Results  Among the 30 487 newborns the mean birth weight in the various cohorts ranged from 3325 g to 3679 g. The maternal genetic score for BMI was associated with a 2-g (95% CI, 0 to 3 g) higher offspring birth weight per maternal BMI-raising allele (P = .008). The maternal genetic scores for fasting glucose and SBP were also associated with birth weight with effect sizes of 8 g (95% CI, 6 to 10 g) per glucose-raising allele (P = 7 × 10−14) and −4 g (95% CI, −6 to −2g) per SBP-raising allele (P = 1×10−5), respectively. A 1-SD ( ≈ 4 points) genetically higher maternal BMI was associated with a 55-g higher offspring birth weight (95% CI, 17 to 93 g). A 1-SD ( ≈ 7.2 mg/dL) genetically higher maternal fasting glucose concentration was associated with 114-g higher offspring birth weight (95% CI, 80 to 147 g). However, a 1-SD ( ≈ 10 mm Hg) genetically higher maternal SBP was associated with a 208-g lower offspring birth weight (95% CI, −394 to −21 g). For BMI and fasting glucose, genetic associations were consistent with the observational associations, but for systolic blood pressure, the genetic and observational associations were in opposite directions.

Conclusions and Relevance  In this mendelian randomization study, genetically elevated maternal BMI and blood glucose levels were potentially causally associated with higher offspring birth weight, whereas genetically elevated maternal SBP was potentially causally related to lower birth weight. If replicated, these findings may have implications for counseling and managing pregnancies to avoid adverse weight-related birth outcomes.

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Figure 1.
Principle of Mendelian Randomization

If a maternal trait causally influences offspring birth weight, then a risk score of genetic variants associated with that trait will also be associated with birth weight. Because genotype is determined at conception, it should not be associated with factors that normally confound the association between maternal traits and birth weight (eg, socioeconomic status). Estimates of the genetic score–maternal phenotype association (w) and the genetic score-birth weight association (x) may be used to estimate the association between the maternal trait variation that is due to genetic score and birth weight (y = x/w), which is expected to be free from confounding. If the estimated causal relationship, y, is different from the observational association between the measured maternal phenotype and birth weight, this would suggest that the observational association is confounded (assuming that the assumptions of the mendelian randomization analyses are valid).14 The dashed line connecting maternal trait with fetal growth indicates that the causal nature of the association is uncertain. It is important to adjust for possible direct effects of fetal genotype (z). Body mass index is calculated as weight in kilograms divided by height in meters squared; ponderal index of neonatal leanness, calculated as birth weight in kilograms divided by birth length in meters cubed.

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Figure 2.
Comparison of the Observational With the Genetic Change in Birth Weight (in grams) for an Standard Deviation Change in Each Maternal Obesity-Related Trait

aFor 25[OH]D and adiponectin, we present the change in birth weight for a 10% change in maternal trait level because these variables were logged for analysis. The genetic change was estimated from mendelian randomization analysis, in which a genetic score was used to estimate the possible causal relationship between the maternal trait and birth weight. The genetic estimate is presented twice: in the second case it was adjusted for fetal genotype using a subset of available studies. The error bars represent the 95% CIs around the effect size estimates. For maternal prepregnancy body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) and fasting glucose, the 95% CIs for both the observational and genetic approaches exclude the null, suggesting positive possible causal relationships between maternal BMI and fasting glucose and birth weight. For maternal systolic blood pressure, the observational analysis suggested a weak positive association with birth weight, whereas the genetic analysis showed evidence of a negative possible causal relationship. Observational analyses suggested that higher maternal triglyceride levels, lower maternal adiponectin and lower maternal high-density lipoprotein (HDL) cholesterol levels were associated with higher birth weight, whereas lower maternal vitamin D status was associated with lower birth weight, but none of these was supported by the genetic analyses. To convert glucose from mg/dL to mmol/L, multiply by 0.0555; HDL-C from mg/dL to mmol/L, 0.0259; triglycerides from mg/dL to mmol/L, 0.0113.

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