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Original Investigation |

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

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.

Figures in this Article

Neonates born to overweight or obese women are more likely to be large for gestational age.1 The precise mechanisms underlying this association and the extent to which confounding factors contribute are poorly understood. It is important to understand which maternal traits are causally associated with birth weight because this may facilitate targeted development of interventions to be tested in randomized clinical trials and enable clear, evidence-based recommendations for pregnant women.

Maternal overweight and obesity are key risk factors for gestational diabetes.2 Even in the absence of diabetes and when following the same controlled diet, obese women have higher glucose levels than normal-weight women.3 The association between gestational diabetes and higher birth weight is well documented.4 Maternal glucose levels below those diagnostic of diabetes also show strong associations with birth weight.5

The fetus of an overweight or obese woman may be exposed to the consequences of higher maternal triglyceride levels and blood pressure, lower levels of high-density lipoprotein cholesterol (HDL-C) and adiponectin, and lower vitamin D status (Box 1).1,6,7 However, associations are not always consistently observed and may be confounded by maternal socioeconomic status and associated behaviors such as smoking and diet. Furthermore, the high intercorrelation of obesity-related traits complicates determination of causal relationships in an observational setting.

Box Section Ref ID

Box 1.
Maternal Traits That May Affect Her Fetus
Maternal Traits Hypothesized to Increase Fetal Growth
  • Higher body mass index

  • Higher fasting glucose

  • Gestational or type 2 diabetes

  • Higher triglycerides

  • Lower high-density lipoprotein cholesterol

  • Lower adiponectin

Maternal Traits Hypothesized to Decrease Fetal Growth
  • High blood pressure

  • Lower vitamin D status

The maternal obesity-related traits hypothesized to cause increased or decreased fetal growth, based on observational associations with birth weight: body mass index (BMI)1; fasting glucose5; gestational or type 2 diabetes32; triglycerides9; HDL-cholesterol8; systolic blood pressure10; vitamin D status (as indicated by 25-hydroxyvitamin D, 25[OH]D level)11; adiponectin.12

Maternal genotypes may be used in a mendelian randomization13,14 approach to provide evidence of a potential causal association between maternal traits and birth outcomes (Figure 1). Mendelian randomization is analogous to a randomized clinical trial: genotypes, which are randomly allocated at conception, are largely free from confounding and can be used to estimate the possible causal effects of maternal traits. In this study, genetic variants were selected to calculate genetic scores representing maternal body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and each of 7 obesity-related maternal traits. The potential causal relationship between maternal BMI and each related trait was estimated by testing associations between maternal genetic risk scores and offspring birth weights.

Place holder to copy figure label and caption
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.

Graphic Jump Location
Study Participants

Single-nucleotide polymorphism (SNP) genotype data were used from 30 487 women participating in 18 population- or community-based studies in Europe, North America, or Australia. The birth weight of 1 child per mother was included (see eTable 1 for full details of participant characteristics and eTable 2 for genotyping information, both in the Supplement). Birth weight was measured by trained study personnel (n = 2 studies), from medical records (n = 10 studies), or from maternal report (n = 6 studies). The offspring years of birth were from 1929 to 2013. Multiple births, stillbirths, congenital anomalies, births before 37 weeks’ gestation, and individuals of non-European ancestry were excluded. Informed consent was obtained from all participants, and study protocols were approved by the local, regional, or institutional ethics committees.

Selection of Maternal Obesity-Related Traits and SNPs

In addition to BMI, traits were selected that are associated with maternal obesity and may affect fetal growth through the intrauterine environment. Their effects were modeled in the directions hypothesized by their relationships to maternal BMI (Box 1).

Single-nucleotide polymorphisms known to be robustly associated (P < 5 × 10−8) with BMI and each obesity-related trait were selected. Full details of the selected SNPs are provided in eTable 3 in the Supplement. Single-nucleotide polymorphisms associated with fasting glucose and type 2 diabetes were used to represent maternal glycemia. The type 2 diabetes SNPs were considered to represent exposure to maternal diabetes in pregnancy, including gestational diabetes, given overlap between type 2 and gestational diabetes’ genetic susceptibility variants.15 For blood pressure, SNPs were selected that are primarily associated with systolic blood pressure (SBP), although all also show strong evidence of association with diastolic blood pressure. For vitamin D status, 2 SNPs with hypothesized roles in vitamin D synthesis were used to represent 25(OH)D levels (an indicator of overall vitamin D status), as previously recommended.16,17 Further details of SNP selection are provided in the eMethods in the Supplement.

A weighted genetic score was calculated for each maternal trait (see eMethods in the Supplement for full details). Very few of the selected SNPs have been tested in pregnancy. Genetic scores were validated by confirming that each was associated with its respective maternal trait, measured during pregnancy (with the exception of BMI, for which the prepregnancy value was used). Maternal prepregnancy BMI was available from registry data (n = 2 studies) or calculated from self-reported weight and height (n = 3 studies). In the Avon Longitudinal Study of Parents and Children (ALSPAC) study, the self-report was validated with a clinic measure.18 Details of traits measured in pregnancy and their sources are given in eTable 4 in the Supplement. In each available study, linear regression of the maternal trait (eg, BMI) against the genetic score was performed, adjusting for maternal age. To confirm that associations between each genetic score and its respective maternal trait were similar in the same individuals during and after pregnancy, available data were used from 2 longitudinal studies (ALSPAC and the Exeter Family Study of Childhood Health [EFSOCH]). To check that the strategy for SNP selection had resulted in genetic scores that were specific to each maternal trait, the association was tested between each of the 8 genetic scores and each maternal trait in addition to indicators of maternal socioeconomic status and smoking.

Analyses of Maternal Obesity-Related Traits and Birth Weight

For BMI and each related maternal trait, 2 mendelian randomization approaches were used to test the hypothesis that the trait was causally related to birth weight. First, associations were tested between genetic scores representing maternal traits and offspring birth weight using the maximum number of participants (ie, for each trait, those with genetic score and offspring birth weight data available, irrespective of whether they had the maternal trait measured). An association of the genetic score with birth weight would support a possible causal relationship between the trait (eg, prepregnancy BMI) and birth weight but would not provide information on the size of that association. Second, we performed analyses in those with the measured trait that enabled an estimate of the size of a possible causal relationship. The analyses took into account the association between each genetic score and the maternal trait it represented (eg, BMI), in addition to the association between the same genetic score and birth weight. These 2 results were used to calculate an association between the maternal trait (eg, BMI) and birth weight that was free from confounding. This second approach measures the relationship between variation in maternal BMI (or BMI-related trait) and birth weight that is attributable only to genetic factors (see Figure 1 for an explanation of the method). For each approach, meta-analysis was used to combine data from individual studies (see eMethods in the Supplement).

Using the first approach, we investigated the association between each genetic score and (1) birth weight and (2) ponderal index (an index of neonatal leanness, calculated as birth weight in kilograms divided by birth length in meters cubed). Within each study, birth weight or ponderal index Z scores were regressed against each maternal genetic score, adjusted for offspring sex and gestational age. Analyses using the type 2 diabetes genetic score were repeated after excluding participants with preexisting and gestational diabetes. Analyses using the SBP genetic score were repeated after excluding participants with preeclampsia and existing or gestational hypertension.

The genetic estimate of the association between each maternal trait and birth weight or ponderal index from the second approach was compared with the corresponding observational association. To obtain the observational estimates, linear regression was performed using birth weight or ponderal index as the dependent variable, and each of 7 maternal traits as independent variables, adjusting for sex and gestational age. There was insufficient information on maternal type 2 diabetes prevalence, so it was not possible to estimate the causal relationship for that trait. Full details of the analysis are provided in the eMethods (in the Supplement).

Maternal BMI, Birth Weight, and Fasting Glucose

To estimate how much of the association between maternal BMI and birth weight might be mediated by fasting glucose, available data were used first to estimate the approximate causal relationship between a 1-SD higher maternal BMI (≈4 points) and (1) fasting glucose and (2) SBP. Then, using each of those estimates, the results of the mendelian randomization analyses were rescaled to represent the effects of fasting glucose and SBP that could be directly compared with the causal relationship between a 1-SD higher maternal BMI and birth weight (see eMethods in the Supplement for a detailed description of the method).

Correcting for Direct Fetal Genotype Effects

Genotypes of maternal-fetal pairs were available in up to 8 studies (total for analysis, 11 493). Analyses were repeated including the fetal genotype at each SNP in the model to correct for potential confounding caused by direct effects of the fetal genotype. A 2-sided P value <.05 was considered to provide evidence against the null hypothesis. Statistical software used for data analysis within each individual study is detailed in eTable 2 in the Supplement. All meta-analyses were performed using Stata v.13 (StataCorp).

The characteristics of included participants from the 18 contributing studies are shown in Table 1. Among the 30 487 newborns the mean birth weight ranged from 3325 g to 3679 g. The mean prepregnancy BMI was available in 11 studies and ranged from 22.78 to 24.83. The mean maternal age at delivery, available in 16 studies, ranged from 24.5 years to 31.5 years.

Table Graphic Jump LocationTable 1.  Key Characteristics of Participants by Study

There was evidence of an association between each genetic score and its corresponding maternal trait measured in pregnancy (P ≤ .003; Table 2). For BMI, fasting glucose, and SBP, data from multiple studies were meta-analyzed, with similar effect estimates among studies for BMI and fasting glucose (P for heterogeneity>.05) and evidence of heterogeneity for SBP (P for heterogeneity = .04). The effect sizes of associations between maternal traits and their respective genetic scores were very similar when compared in the same individuals during and outside pregnancy, with the exception of the SBP genetic score, which had a weaker effect during pregnancy (eTable 5 in the Supplement). There was no evidence of association between any genetic score and potentially confounding variables. No individual genetic score was associated with any of the other maternal traits, except for the genetic score for BMI, which was positively associated with SBP (P < .003 Bonferroni-corrected for 15 tests; eTable 6 in the Supplement).

Table Graphic Jump LocationTable 2.  Associations Between Maternal Genetic Scores and Maternal Obesity-Related Traits
Higher Maternal BMI and Higher Birth Weight

The maternal BMI genetic score was associated with higher birth weight (Table 3) and ponderal index (eTable 7 in the Supplement) with similar effect sizes before and after adjusting for possible effects of fetal genotype. Using the genetic score to quantify the possible causal association, a 1-SD genetically higher maternal BMI was associated with a 55-g higher offspring birth weight (95% CI, 17-93 g). After adjusting for fetal genotype, the estimated effect was 104-g increase (95% CI, 32-176 g) (Table 4). These mendelian randomization causal estimates were similar to the observational association of 62 g per SD of higher maternal BMI (95% CI, 56-70 g) (Figure 2). Similar results were obtained for ponderal index (eTable 8 and eFigure 1 in the Supplement).

Table Graphic Jump LocationTable 3.  Associations Between Maternal Genetic Scores and Birth Weight of Offspring
Table Graphic Jump LocationTable 4.  Observational and Genetic Associations Between Each Maternal Trait and Offspring Birth Weight
Place holder to copy figure label and caption
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.

Graphic Jump Location
Higher Maternal Fasting Glucose, Higher Birth Weight

The maternal fasting glucose and type 2 diabetes genetic scores were associated with higher birth weight (Table 3) and ponderal index (eTable 7 in the Supplement) with similar effect size estimates before and after adjusting for fetal genotype and before and after excluding preexisting and gestational diabetes. Using the genetic score to estimate the possible causal relationship, a 1-SD (7.2 mg/dL) of genetically higher maternal glucose was associated with a 114-g higher birth weight (95% CI, 80-147 g). After adjusting for fetal genotype, the association was 145 g (95% CI, 91-199 g) (Table 4). These genetic estimates were similar to the observational association of 92 g (95% CI, 80-104) per each SD higher maternal glucose (7.2 mg/dL) (Figure 2). Similar results were obtained for ponderal index (eTable 8 and eFigure 1 in the Supplement).(To convert glucose from mg/dL to mmol/L, multiply by 0.0555.)

Maternal Lipids, Adiponectin, and Birth Weight

The maternal triglyceride genetic score was not associated with offspring birth weight (Table 3) or ponderal index (eTable 7 in the Supplement). Using the genetic score to estimate the possible causal relationship, a genetically higher maternal triglyceride level was not associated with offspring birth weight and the 95% CIs around the genetic estimate excluded the observational association between maternal triglycerides and birth weight (P = .007 testing difference between genetic and observational association; Table 4; Figure 2). Likewise, the genetic estimate of the possible effect of maternal adiponectin levels on offspring birth weight was different from the observational association (P = .002). The genetic score for HDL-C was not associated with birth weight or ponderal index. The analysis was consistent with no causal relationship; however, this could not be distinguished from the negative observational association between maternal HDL-C and birth weight.

Higher SBP and Lower Birth Weight

The maternal SBP genetic score was associated with lower birth weight (Table 3) and ponderal index (eTable 7 in the Supplement) with similar effect-size estimates before and after adjusting for fetal genotype and before and after excluding maternal preeclampsia and hypertension. Using the genetic score to estimate the possible causal relationship, 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). After adjusting for fetal genotype, the estimated effect was −151 g (95% CI, −390 to 89 g) (Table 4). The genetic estimate of the association between maternal SBP and birth weight in the full sample of women was in the opposite direction to the observational association (P = .01 for difference between genetic and observational associations; Table 4; Figure 2). Similar results were obtained for ponderal index (eTable 8 and eFigure 1 in the Supplement).

The maternal genetic score for lower vitamin D status was associated with lower birth weight (P = .03; Table 3). However, the estimated causal relationship was not significantly different from 0 (the estimated change in birth weight for a 10% genetically lower maternal 25[OH]D level was −26 g (95% CI, −54 to 2 g); Table 4, Figure 2).

Consistency Among Studies in the Meta-analysis

Associations between maternal genetic scores and offspring birth weight were similar between studies in the meta-analysis (Table 3; P for heterogeneity>.05). When data were combined from observational analyses, the associations between maternal fasting glucose or SBP and birth weight were similar (P for heterogeneity>.05), and there was evidence of heterogeneity for the BMI-birth weight observational association (Table 4; P for heterogeneity = .03).

Maternal BMI, Maternal Fasting Glucose, and Offspring Birth Weight

To estimate how much of the association between maternal BMI and birth weight might be mediated by fasting glucose, the BMI and fasting glucose genetic scores were used. A 1-SD genetically higher maternal BMI was associated with a 0.34 SD ( ≈ 2.5 mg/dL) higher maternal fasting glucose. From the mendelian randomization analyses, a 1-SD genetically higher maternal fasting glucose was associated with a 114-g higher birth weight (95% CI, 80-147 g). Consequently, it was predicted that a 0.34-SD higher fasting glucose would be associated with a 114 g × 0.34 = 39 g; (95% CI, 27-50 g) higher birth weight. This approximation is broadly similar to the total estimated effect of an SD higher BMI on birth weight (55 g; 95% CI, 17-93 g). However, using the same method with the BMI and SBP genetic scores, we estimated that a an SD higher maternal BMI would be associated with a −40 g (95% CI, −75 to −4) lower birth weight via its association with maternal SBP (eFigure 2 in the Supplement), which would oppose the positive association with maternal fasting glucose.

This study provides evidence for a possible causal association between maternal BMI and offspring birth weight. A genetically higher maternal BMI of 4 points was associated with a 55 g (95% CI, 17-93 g) higher offspring birth weight. In addition, a genetically higher circulating maternal fasting glucose of 7.2 mg/dL was associated with a 114 g (95% CI, 80-147 g) higher birth weight, whereas genetically higher maternal SBP of 10 mm Hg was associated with a −208 g (95% CI, −394 to −21 g) lower birth weight. These results provide evidence that genetically elevated maternal glucose and SBP may have directionally opposite causal associations with birth weight. The estimated associations between these maternal traits and birth weight (either increased or reduced) are substantial and of clinical importance. They support efforts to maintain healthy gestational glucose and blood pressure levels to ensure healthy fetal growth. The positive association between maternal BMI and birth weight may be partially mediated by the effect of higher BMI on circulating maternal fasting glucose. There was no evidence of association of offspring brith weight with a genetic score for maternal triglycerides, which have also been hypothesized to be important contributors to higher birth weight in overweight or obese women. Other lipids, or specific subclasses of triglycerides, might be important but require further study.

These results provide genetic evidence of a potentially causal association between maternal glycemia and birth weight and ponderal index, even in women with no preexisting or gestational diabetes, which is consistent with published observational data.5 A possible explanation for this finding is that women with a higher genetic score for type 2 diabetes have relatively higher glucose levels in pregnancy, as a result of inadequate beta-cell compensation in response to gestational insulin resistance,19,20 leading to increased placental glucose transfer and fetal insulin secretion,21 and consequently higher birth weight.

These data did not support a causal association between maternal triglyceride, HDL-C or adiponectin levels and birth weight or ponderal index. The genetic associations between maternal triglycerides and adiponectin and birth weight were null, in contrast to the observational associations, suggesting that the observational associations seen herein, and in other published studies,8,9,12 are confounded.

The mendelian randomization analysis showed that the positive observational association between SBP and birth weight is confounded, most likely by BMI, which is both an important risk factor for higher SBP in pregnancy and positively associated with birth weight.1 Using genetic variants that are independent of confounding by BMI, genetically higher maternal SBP was associated with lower birth weight, even after excluding preeclampsia and hypertension. The precision of our estimate of the change in birth weight per 1 SD in maternal SBP could be affected by the heterogeneity between studies in the genetic score-SBP association (P = .04, I2 = 76.0%; Table 2). However, associations between the SBP genetic score and birth weight were consistent across all 13 meta-analyzed studies (P = .14; I2 = 30.4%; Table 3) and supportive of a causal association between higher maternal SBP and lower birth weight. These findings support observational associations between maternal SBP and birth weight that were adjusted for a wide range of confounders22 and are consistent with laboratory and population studies suggesting a link between hypertensive disorders of pregnancy and impaired fetal growth due to placental pathology.23 There are increasing concerns about the effect the obesity epidemic might have on birth size, via greater maternal BMI. However, the focus of that concern has been largely on increased birth size as a result of greater maternal glucose and other fetal nutrients. Our findings suggest that there may be opposing effects of maternal blood pressure and glucose.

Published mendelian randomization analyses provide evidence that higher BMI is causally associated with lower vitamin D status,6 and evidence from multiple observational studies suggests that lower maternal vitamin D is associated with lower birth weight.11,24 Our analysis of the vitamin D genetic score provided some evidence to support a possible causal association with birth weight, but this requires further exploration in larger numbers of pregnancies.

Socioeconomic factors and related behaviors such as smoking are key confounders of observational associations between maternal BMI (or BMI-related traits) and offspring birth weight, since they are associated with both variables (see eTable 9 in Supplement for a demonstration of these associations in the ALSPAC study). The genetic scores used in our analyses were not associated with socio-economic factors or smoking, and this illustrates a key strength of the mendelian randomization approach: since genotypes are determined at conception, such confounding is avoided.

There are some limitations to this study. Despite attempts to maximize specificity of the genetic scores, we cannot fully exclude the possibility that the selected genetic variants act on more than one maternal trait. Although all available information was used, there was limited power to detect associations between the genetic scores and other traits. For example, the known association between BMI-associated variants and triglyceride levels was not detected.25 With the potential for high-throughput metabolomic studies and a growing public database of genetic associations,2628 future studies will likely improve the specificity (for different lipid sub-fractions) of selected genetic variants.

Despite the large sample in this study, statistical power to detect potentially causal relationships was limited for some maternal traits (see eMethods and eTable 10 in Supplement for power calculations). The total sample provided more than 99% power to detect associations at P < .05 between birth weight and genetic scores such as fasting glucose and systolic blood pressure that explain at least 0.1% variance in birth weight. However, larger samples (>80 000) will be needed to confidently detect or rule out the association with vitamin D status suggested by our data, or smaller positive or negative causal associations between maternal triglycerides, HDL-C or adiponectin and birth weight.

Although adjusting for the fetal genetic scores was necessary to separate maternal effects from the direct effects of genetic variants in the fetus, this could introduce bias via association with paternal genotypes. Assortative mating for BMI could additionally result in a correlation between maternal and paternal genotypes, leading to similar bias. However, a father’s genetic score would only confound the mendelian randomization estimates if the father’s phenotype were related to birth weight, and we found only very weak associations of fathers BMI and related traits with offspring birth weight (eTable 11 in Supplement). Another potential bias could be induced by the use of the genetic score for SBP, which was derived from a genome-wide association study of blood pressure conditional on BMI. Because BMI is also associated with birth weight, this could bias the results. However, similar results were obtained using an alternative genetic score that was unadjusted for BMI (eMethods).

In mendelian randomization analysis, a weak statistical association between a genetic score and a maternal trait (due to low variance explained or small sample size) has the potential to cause weak instrument bias toward the observational results.29 The proportions of maternal trait variance explained by the genetic scores are modest in our study (Table 2). However, the large overall sample size ensured that the possible causal associations identified are unlikely to be due to weak instrument bias (see eMethods).

Our analyses assume that maternal BMI and related traits are linearly associated with offspring birth weight. We have not tested for nonlinear associations which, in a mendelian randomization design, would require very large numbers.30 However, for maternal BMI, fasting glucose and SBP, there is observational evidence of such linear associations across the distribution, with no evidence of threshold or curvilinear associations.5,10,31

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.

Corresponding Authors: Rachel M. Freathy, PhD, University of Exeter, Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK (r.freathy@ex.ac.uk) and Debbie A. Lawlor, PhD, University of Bristol, MRC Integrative Epidemiology Unit, Oakfield House, Oakfield Road, Bristol, BS8 2BN, UK(d.a.lawlor@bristol.ac.uk)

Correction: This article was corrected online April 19, 2016, to include additional grant support.

Author Contributions: Drs Freathy and Lawlor had full access to all of the data in the ALSPAC, EFSOCH, and HAPO (non-GWAS) studies and access to summary data from all contributing studies and take responsibility for the integrity of the data and accuracy of the data analysis. Drs Palmer, Frayling, Lawlor, and Freathy jointly directed this work.

Study concept and design: Tyrrell, Frayling, Lawlor, Freathy.

Acquisition, analysis, or interpretation of data: Tyrrell, Richmond, Metrustry, Feenstra, Geller, Cavadino, Bradfield, de Geus, Evans, Ring, Murray, Bouchard, Hottenga, Willemsen, Hakonarson, Sørensen, Martin, Nohr, Knight, Spector, Grant, Jacobsson, Power, Sebert, Järvelin, Reichetzeder, Hocher, Hayes, Jaddoe, Hofman, Melbye, Kreiner-Møller, Bisgaard, Davey Smith, Hattersley, Hyppönen, Hivert, Felix, Lowe, Frayling, Lawlor, Freathy.

Drafting of the manuscript: Tyrrell, Richmond, Metrustry, Feenstra, Cavadino, Hyppönen, Hivert, Felix, Lowe, Frayling, Lawlor, Freathy.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: De Silva, Wood, McMahon, Evans, Kreiner-Møller, Geller, Allard, Hottenga, Bradfield, Hayes, Scholtens, Armstrong, Rangarajan, Horikoshi, McCarthy, Nodzenski, Paternoster, Das, Huikari, Painter, Morris, Medland, Lind, Berry, Myhre, Sengpiel, Tyrrell, Richmond, Metrustry, Feenstra, Cavadino, Felix, Lawlor, Freathy.

Obtained funding: Tyrrell, Feenstra, Evans, Paternoster, Spector, Jacobsson, Power, Martin, Serbert, Sørensen, Järvelin, Hocher, Hayes, Lowe, Grant, Hakonarson, Medland, Bouchard, Bradfield, de Geus, Jaddoe, Hofman, McCarthy, Melbye, Morris, Murray, Bisgaard, Davey Smith, Hattersley, Hyppönen, Hivert, Felix, Lowe, Frayling, Lawlor, Freathy.

Administrative, technical, or material support: Heikkinen.

Study supervision: Spector, Jacobsson, Power, Martin, Serbert, Sørensen, Järvelin, Hocher, Hayes, Lowe, Grant, Hakonarson, Bradfield, de Geus, Jaddoe, Hofman, McCarthy, Melbye, Murray, Bisgaard, Davey Smith, Hattersley, Hyppönen, Hivert, Felix, Lowe, Frayling, Lawlor, Freathy.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Feenstra reported that he is supported by an Oak Foundation fellowship. Dr Paternoster reported that she receives grant support from the UK Medical Research Council. Dr Hocher reports receiving grant support from Deutsche Forschungsgemeinschaft. Dr Frayling reports that he has received personal fees from Boeringer Ingelheim. No other financial disclosures were reported. Dr Lawlor reported that she has received grant support from Wellcome Trust, UK Medical Research Council, UK National Institute for Health Research, and the US National Institutes of Health. Dr Freathy reports that she receives grant support from Wellcome Trust and the Royal Society. No other disclosures were reported.

Funding/Support: Funding/support of authors is as follows (funding details for individual studies are reported in the Supplement). Drs Frayling and Wood are supported by grant 323195 SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC from the European Research Council; Drs Hattersley and McCarthy are Wellcome Trust senior investigators; Dr McCarthy is a National Institutes of Health Research senior investigator. Dr Freathy is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant 104150/Z/14/Z); Dr Tyrrell is funded by a Diabetes Research and Wellness Foundation Fellowship; Dr Richmond is funded by the Wellcome Trust 4-year studentship (grant code, WT083431MF). Drs Lawlor, Davey Smith, Evans, and Ring all work in a unit that receives funding from the University of Bristol and the UK Medical Research Council (grants MC_UU_1201/1/5, MC_UU_1201/1 and MC_UU_1201/4). Dr Lawlor is supported by awards from the Wellcome Trust (WT094529MA and WT088806), US National Institutes of Health (R01 DK10324), and European Research Council (669545), and is a National Institutes of Health Research Senior Investigator (NF-SI-0611-10196). Drs Evans and Medland were supported by an Australian Research Council Future Fellowship (FT130101709 and FT110100548). Dr Järvelin is supported by a DynaHEALTH grant (European Union H2020-PHC-2014; 633595). Dr Feenstra is supported by an Oak Foundation Scholarship. Dr Bouchard is a junior research scholar from the Fonds de la recherché en santé du Québec (FRQS) and a member of the FRQS-funded Centre de recherché du CHUS. Dr M-F. Hivert is a Fonds de la recherché en santé du Québec research scholars and was awarded a Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics–Canadian Institute of Health Research. Dr Allard was awarded the Canadian Institute of Health Research–Frederick Banting and Charles Best Canada Graduate Scholarships. Dr Jaddoe is supported by the Netherlands Organization for Health Research and Development (ZonMw –VIDI 016.136.361). Dr Morris is a Wellcome Trust Senior Research Fellow (grant number WT098017). Dr Sørensen is holder of a European Research Council Advanced Principal Investigator award.

Role of the Sponsors: The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, approval of manuscript; or decision to submit manuscript for publication.

Previous Presentations: This work was presented at the Diabetes UK Annual Professional Conference 2014, 5-7 March, Liverpool, United Kingdom.

Additional Contributions: We thank the participants and families who contributed to all of the studies and the teams of investigators involved in each one. These include interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. For additional study-specific acknowledgments, please see Supplementary Material.

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Figures

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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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Tables

Table Graphic Jump LocationTable 1.  Key Characteristics of Participants by Study
Table Graphic Jump LocationTable 2.  Associations Between Maternal Genetic Scores and Maternal Obesity-Related Traits
Table Graphic Jump LocationTable 3.  Associations Between Maternal Genetic Scores and Birth Weight of Offspring
Table Graphic Jump LocationTable 4.  Observational and Genetic Associations Between Each Maternal Trait and Offspring Birth Weight

References

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Multimedia

Supplement.

eMethods. Technical descriptions of the methods, including how we selected the genetic variants and calculated the genetic scores, in addition to details of statistical analyses

eFigure 1. Comparison of the observational with the genetic change in ponderal index (in kg/m3) for a 1 standard deviation (SD) change in each maternal trait

eFigure 2. Estimating how much of the possible causal effect of maternal BMI on birth weight is mediated by maternal fasting glucose

eTable 1. Basic characteristics of study participants and their offspring

eTable 2. Genotyping information Genotyping information (studies 15-18)

eTable 3. Details of single nucleotide polymorphisms (SNPs) used to construct the genetic scores

eTable 4. Studies with maternal traits ascertained during pregnancy (or for BMI, pre-pregnancy) and available for association analysis with genetic scores

eTable 5. Associations between maternal genetic scores and maternal traits during and post-pregnancy in the same individuals

eTable 6. Associations between each maternal genetic score and potentially confounding or mediating variables

eTable 7. Associations between maternal genetic scores and ponderal index of offspring at birth

eTable 8. A comparison of the observational with the genetic association between each maternal trait and offspring ponderal index at birth

eTable 9. Observational associations between offspring birth weight and maternal socio-economic status or maternal smoking in the ALSPAC study

eTable 10. Power calculations

eTable 11. Association between father’s phenotypes and offspring birth weight using data from the ALSPAC study

eReferences

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