Santos Ferreira DL, Williams DM, Kangas AJ, Soininen P, Ala-Korpela M, Smith GD, et al. (2017) Association of pre-pregnancy body mass index with offspring metabolic profile: Analyses of 3 European prospective birth cohorts. PLoS Med 14(8): e1002376. https://doi.org/10.1371/journal.pmed.1002376
Association of pre-pregnancy body mass index with offspring metabolic profile : analyses of 3 European prospective birth cohorts
|Author:||Santos Ferreira, Diana L.1,2; Williams, Dylan M.3,4; Kangas, Antti J.5,6;|
1MRC Integrative Epidemiology Unit at the University of Bristol
2School of Social and Community Medicine, University of Bristol
3Department of Epidemiology and Biostatistics, MRC–PHE Centre for Environment and Health, School of Public Health, Imperial College London
4Department of Medical Epidemiology & Biostatistics, Karolinska Institutet
5Computational Medicine, Faculty of Medicine, University of Oulu
6Biocenter Oulu, University of Oulu
7NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland
8Center for Life-Course Health Research and Northern Finland Cohort Center, Faculty of Medicine, University of Oulu
9Unit of Primary Care, Oulu University Hospital
|Online Access:||PDF Full Text (PDF, 3.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201709228700
Public Library of Science,
|Publish Date:|| 2017-09-22
Background: A high proportion of women start pregnancy overweight or obese. According to the developmental overnutrition hypothesis, this could lead offspring to have metabolic disruption throughout their lives and thus perpetuate the obesity epidemic across generations. Concerns about this hypothesis are influencing antenatal care. However, it is unknown whether maternal pregnancy adiposity is associated with long-term risk of adverse metabolic profiles in offspring, and if so, whether this association is causal, via intrauterine mechanisms, or explained by shared familial (genetic, lifestyle, socioeconomic) characteristics. We aimed to determine if associations between maternal body mass index (BMI) and offspring systemic cardio-metabolic profile are causal, via intrauterine mechanisms, or due to shared familial factors.
Methods and findings: We used 1- and 2-stage individual participant data (IPD) meta-analysis, and a negative-control (paternal BMI) to examine the association between maternal pre-pregnancy BMI and offspring serum metabolome from 3 European birth cohorts (offspring age at blood collection: 16, 17, and 31 years). Circulating metabolic traits were quantified by high-throughput nuclear magnetic resonance metabolomics. Results from 1-stage IPD meta-analysis (N = 5327 to 5377 mother-father-offspring trios) showed that increasing maternal and paternal BMI was associated with an adverse cardio-metabolic profile in offspring. We observed strong positive associations with very-low-density lipoprotein (VLDL)-lipoproteins, VLDL-cholesterol (C), VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and strong negative associations with high-density lipoprotein (HDL), HDL-diameter, HDL-C, HDL₂-C, and HDL₃-C (all P < 0.003). Slightly stronger magnitudes of associations were present for maternal compared with paternal BMI across these associations; however, there was no strong statistical evidence for heterogeneity between them (all bootstrap P > 0.003, equivalent to P > 0.05 after accounting for multiple testing). Results were similar in each individual cohort, and in the 2-stage analysis. Offspring BMI showed similar patterns of cross-sectional association with metabolic profile as for parental pre-pregnancy BMI associations but with greater magnitudes. Adjustment of parental BMI–offspring metabolic traits associations for offspring BMI suggested the parental associations were largely due to the association of parental BMI with offspring BMI. Limitations of this study are that inferences cannot be drawn about the role of circulating maternal fetal fuels (i.e., glucose, lipids, fatty acids, and amino acids) on later offspring metabolic profile. In addition, BMI may not reflect potential effects of maternal pregnancy fat distribution.
Conclusion: Our findings suggest that maternal BMI–offspring metabolome associations are likely to be largely due to shared genetic or familial lifestyle confounding rather than to intrauterine mechanisms.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3142 Public health care science, environmental and occupational health
This study was funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement (Grant number 669545; DevelopObese) and the US National Institutes of Health (R01 DK10324). The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. DLSF, DAL, GDS, and MA-K, work in a Unit that receives funds from the University of Bristol and the UK Medical Research Council (MC_UU_12013/1, MC_UU_12013/ 5), and DAL is a UK National Institute of Health Research Senior Investigator (NF-SI-0166-10196). MA-K was supported by the Sigrid Juselius Foundation and the Strategic Research Funding from the University of Oulu. DMW is funded by a European Union Horizon 2020 research and innovation program grant (agreement 634821). No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All ALSPAC data are available to scientists on request to the ALSPAC Executive via this website, which also provides full details and distributions of the ALSPAC study variables: http://www.bristol.ac.uk/alspac/researchers/access/. Consistent with other studies funded by UK funders, ALSPAC uses a business model to offset the expense of preparing and supporting data access. The ALSPAC data management plan (available here: http://www.bristol.ac.uk/alspac/researchers/data-access/documents/alspac-data-management-plan.pdf) describes in detail the policy regarding data sharing, including the real costs for providing data. The system for accessing data via the executive applies to all researchers including ALSPAC investigators and scientists at the University of Bristol. The Executive do not scrutinise the proposed science by those wishing to access data, nor do they check for scientific overlap with other data requests (all data requests are published online—https://proposals.epi.bristol.ac.uk/); requests are only refused if the requested data are not available in ALSPAC or there are concerns that the proposed research might bring the study into disrepute. Restrictions (including collapsing categories) might be applied to some variables if cell numbers are small, to ensure participant anonymity. DAL was a member of the ALSPAC Executive from April 2007 to June 2017. An independent scientific advisory board reviews the very small number of data access requests that are declined. The study website contains details of all the data that are available through a fully searchable data dictionary (http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). The Northern Finland Cohorts has an open policy in regards to collaboration with other research groups (http://www.oulu.fi/nfbc). Requests for data access and collaboration are discussed with the Northern Finland Cohorts Management Team.
© 2017 Santos Ferreira et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.