Changing genetic architecture of body mass index from infancy to early adulthood: an individual based pooled analysis of 25 twin cohorts

Karri Silventoinen, Weilong Li, Aline Jelenkovic, Reijo Sund, Yoshie Yokoyama, Sari Aaltonen, Maarit Piirtola, Masumi Sugawara, Mami Tanaka, Satoko Matsumoto, Laura A. Baker, Catherine Tuvblad, Per Tynelius, Finn Rasmussen, Jeffrey M. Craig, Richard Saffery, Gonneke Willemsen, Meike Bartels, Catharina E.M. van Beijsterveldt, Nicholas G. MartinSarah E. Medland, Grant W. Montgomery, Paul Lichtenstein, Robert F. Krueger, Matt McGue, Shandell Pahlen, Kaare Christensen, Axel Skytthe, Kirsten O. Kyvik, Kimberly J. Saudino, Lise Dubois, Michel Boivin, Mara Brendgen, Ginette Dionne, Frank Vitaro, Vilhelmina Ullemar, Catarina Almqvist, Patrik K.E. Magnusson, Robin P. Corley, Brooke M. Huibregtse, Ariel Knafo-Noam, David Mankuta, Lior Abramson, Claire M.A. Haworth, Robert Plomin, Morten Bjerregaard-Andersen, Henning Beck-Nielsen, Morten Sodemann, Glen E. Duncan, Dedra Buchwald, S. Alexandra Burt, Kelly L. Klump, Clare H. Llewellyn, Abigail Fisher, Dorret I. Boomsma, Thorkild I.A. Sørensen, Jaakko Kaprio

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Body mass index (BMI) shows strong continuity over childhood and adolescence and high childhood BMI is the strongest predictor of adult obesity. Genetic factors strongly contribute to this continuity, but it is still poorly known how their contribution changes over childhood and adolescence. Thus, we used the genetic twin design to estimate the genetic correlations of BMI from infancy to adulthood and compared them to the genetic correlations of height. Methods: We pooled individual level data from 25 longitudinal twin cohorts including 38,530 complete twin pairs and having 283,766 longitudinal height and weight measures. The data were analyzed using Cholesky decomposition offering genetic and environmental correlations of BMI and height between all age combinations from 1 to 19 years of age. Results: The genetic correlations of BMI and height were stronger than the trait correlations. For BMI, we found that genetic correlations decreased as the age between the assessments increased, a trend that was especially visible from early to middle childhood. In contrast, for height, the genetic correlations were strong between all ages. Age-to-age correlations between environmental factors shared by co-twins were found for BMI in early childhood but disappeared altogether by middle childhood. For height, shared environmental correlations persisted from infancy to adulthood. Conclusions: Our results suggest that the genes affecting BMI change over childhood and adolescence leading to decreasing age-to-age genetic correlations. This change is especially visible from early to middle childhood indicating that new genetic factors start to affect BMI in middle childhood. Identifying mediating pathways of these genetic factors can open possibilities for interventions, especially for those children with high genetic predisposition to adult obesity.

Original languageEnglish (US)
Pages (from-to)1901-1909
Number of pages9
JournalInternational Journal of Obesity
Volume46
Issue number10
DOIs
StatePublished - Oct 2022

Bibliographical note

Funding Information:
This study was conducted within the CODATwins project. Support for collaborators: Colorado Twin Registry is funded by NIDA funded center grant DA011015, & Longititudinal Twin Study HD10333; Author Huibregtse is supported by National Institute on Drug Abuse (5T32DA017637) and National Institute on Aging (5T32AG052371). Finnish Twin Cohort is supported by the Academy of Finland (grants 312073 and 336823) and the Sigrid Juselius Foundation. Michigan State University Twin Registry was supported by National Institute of Mental Health (NIMH) (R01-MH081813, R01-MH0820–54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01, 1R01-MH118848-01), Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) (R01-HD066040) and MSU Foundation (11-SPG-2518). PETS was funded by the Australian National Health and Medical Research Council (grant numbers 437015 and 607358); the Bonnie Babes Foundation (grant number BBF20704); the Financial Markets Foundation for Children (grant number 032-2007); and the Victorian Government’s Operational Infrastructure Support Program. We acknowledge The Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no 2017-00641. TEDS was supported by a program grant to RP from the UK Medical Research Council (MR/M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (AG046938). The West Japan Twins and Higher Order Multiple Births Registry was supported by Grant-in-Aid for Scientific Research (B) (grant number 20H04019) from the Japan Society for the Promotion of Science. Open Access funding provided by University of Helsinki including Helsinki University Central Hospital.

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Twin Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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