Triangulating evidence from the UK Biobank and China shows the health and behavioral impact of vegetarianism
Zhu, C.; Yang, X.; Song, Y.; Xu, W.; Gong, J.; Wang, X.; Si, W.; Fan, S.
Show abstract
BackgroundVegetarianism is widely recognized for its health and environmental benefits. However, its broader impacts on physical, mental, and social well-being remain underexplored. This study investigates the health and behavioral outcomes associated with vegetarianism across diverse populations. MethodsWe analysed polygenic scores for vegetarianism (VegPGS) in 495,971 UK Biobank (UKB) participants and performed phenome-wide association studies (PheWAS) on 443 health and behavioral traits. Cross-validation analyses were conducted using data from 9,009 vegetarians and 486,962 non-vegetarians. One- and two-sample Mendelian randomization (MR) analyses explored causal relationships. Findings were further validated in 11,642 participants from the China Health and Nutrition Survey (CHNS). Additionally, machine-learning classification models were developed to predict vegetarian status using behavioral, physiological, and genetic factors. FindingsPheWAS identified 57 health-related and 1 behavior-related factor significantly associated with VegPGS, with cross-validation confirming these links. MR analyses supported causal effects of vegetarianism on lower basal metabolic rate, reduced body mass index (BMI), decreased fat mass, and lower risk of type 2 diabetes. CHNS data confirmed associations with lower BMI and diabetes risk in East Asian populations. Machine-learning models achieved high accuracy in predicting vegetarian status (AUC 0.913C{+/-}C0.018). InterpretationThis study provides robust evidence supporting the metabolic health benefits of vegetarianism. The integration of multimodal genetic, behavioral, and physiological data enhances understanding and prediction of dietary choices, offering valuable insights for policymakers and individuals considering a transition to plant-based diets to achieve sustainability. FundingNational Natural Science Foundation of China (Nos. 72103187 and 72061147002) and the 2115 Talent Development Program at China Agricultural University.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.