Evidence mapPaperPMID 40763113Full record

ArticlePLoS genetics2025

The association between parental BMI and offspring adiposity: A genetically informed analysis of trios.

Liam Wright, Gemma Shireby, Tim T Morris, Neil M Davies, David Bann

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Article in PLoS genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Liam WrightCentre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-6347-5121
Gemma ShirebyCentre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom.
Tim T MorrisCentre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-8178-6815
Neil M DaviesDivision of Psychiatry, University College London, London, United Kingdom.
David BannCentre for Longitudinal Studies, Social Research Institute, University College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChildren with obesity are more likely to have parents with obesity than those without. Several environmental explanations have been proposed for this correlation, including foetal programming and parenting practices. However, body mass index (BMI) is a heritable trait; child-parent correlations may reflect direct inheritance of adiposity-related genes. There is some evidence that mothers' BMI associates with offspring BMI net of direct genetic inheritance, consistent with both intrauterine and parenting effects, but this requires replication. Here, we also investigate the role of fathers' BMI as well as offsprings' diet as a mediating factor.

methodsWe used Mendelian Randomization (MR) with genetic trio (mother-father-offspring) data from 2,630 families in the Millennium Cohort Study, a UK birth cohort study of individuals born in 2000/02, to examine the association between parental BMI (kg/m2) and offspring birthweight and BMI and diet measured at six-time points between ages 3y and 17y. Paternal and maternal BMI were instrumented with polygenic indices (PGI) for BMI conditioning upon offspring PGI. This allowed us to separate direct and indirect ("genetic nurture") genetic effects. We compared these results with associations obtained using standard multivariable regression techniques using phenotypic BMI data only.

resultsMothers' and fathers' BMI were positively associated with offspring BMI to similar degrees. However, in MR analysis, associations between father's BMI and offspring BMI were close to the null. In contrast, mother's BMI was consistent in MR analysis with phenotypic associations. Maternal indirect genetic effects were between 25-50% the size of direct genetic effects. There was limited and inconsistent evidence of associations with offspring diet and some evidence that mothers', but not fathers', BMI was related to birthweight in both MR and multivariable regression models.

conclusionsResults suggest maternal BMI may be particularly important for offspring BMI: associations may arise due to both direct transmission of genetic effects and indirect (genetic nurture) effects. Associations of father's and offspring adiposity that do not account for direct genetic inheritance may yield biased estimates of paternal influence. Larger studies are required to confirm these findings.

Indexed as

AdiposityBody Mass IndexObesityPediatric ObesityAdolescentAdultBirth WeightChildChild, PreschoolCohort StudiesFathersFemaleHumansMaleMendelian Randomization AnalysisMothers

Identifiers

PMID40763113
PMCPMC12324121

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.