Gene-environment interplay explaining individual variation in BMI outcomes: a systematic review and meta-analysis of...

Genetic and environmental factors are both associated with weight outcomes, but it remains unclear to what extent environmental exposures are consistently associated beyond genetic predisposition, and to what extent they modify genetic risk.

We meta-analyzed evidence on gene-environment interplay and individual variation in BMI and related outcomes, focusing on peer-reviewed studies examining environmental exposures and using polygenic indices for BMI. Six electronic databases (APA PsycArticles, APA PsycInfo, ERIC, MEDLINE, Psychology and Behavioral Sciences Collection, SocINDEX) were searched through April 2025. Risk of bias was assessed using a modified Newcastle-Ottawa Scale and quality criteria for gene-environment interaction studies.

The meta-analysis included 1161 estimates from 88 studies: 153 estimates of main genetic associations, 283 of associations between environmental factors and BMI outcomes controlling for a polygenic index for BMI, and 725 of gene-environment interactions (G×E). Genetic predisposition for BMI was positively associated with BMI for all ancestry groups. The overall association between environmental exposures and BMI, controlling for genetic risk, was r = 0.12 (p < 0.001; 95% CI: 0.08 to 0.16) for both European and combined ancestry groups. Overall G×E effects ranged from r = 0.06 to 0.12 (all p < 0.001), depending on ancestry group and whether imputed effect sizes were included. Separate meta-analyses by type of environmental exposure yielded overall associations that were largely comparable in magnitude, consistent with moderation analyses indicating that there were no significant differences in effect sizes across different exposure categories.

This meta-analysis highlights the importance of both genetic and environmental factors in explaining variation in BMI. Most studies did not account for gene-environment correlation confounding and focused primarily on European ancestry populations. Future research should prioritize methodologies that address bias and focus on underrepresented ancestry groups to improve inclusivity.