| Title: | Triangulated evidence from the UK and China reveals the health impact of vegetarianism |
| Journal: | BMJ Nutrition Prevention & Health |
| Published: | 9 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41768504/ |
| DOI: | https://doi.org/10.1136/bmjnph-2025-001245 |
| URL: | https://nutrition.bmj.com/content/early/2025/09/09/bmjnph-2025-001245.full.pdf |
Publication 19531
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Abstract
Background: Vegetarianism is widely recognised 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 behavioural outcomes associated with vegetarianism across diverse populations.</p>
Methods: We analysed polygenic scores for vegetarianism (VegPGS) in 495 971 UK Biobank participants and performed phenome-wide association studies (PheWAS) on 443 health and behavioural traits. Cross-validation analyses were conducted using data from 9009 vegetarians to 486 962 non-vegetarians. One-sample and two-sample Mendelian randomisation (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 behavioural, physiological and genetic factors.</p>
Findings: PheWAS identified 65 health-related and 2 behaviour-related factors 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 (area under the receiver operating characteristic curve=0.913±0.018, area under the precision-recall curve (PR-AUC)=0.40).</p>
Interpretation: This study provides robust evidence supporting the metabolic health benefits of vegetarianism. The integration of multimodal genetic, behavioural 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.</p>
8 Authors
- Chen Zhu
- Xiaosong Yang
- Yanjun Song
- Wenyan Xu
- Jiakai Gong
- Xiaolu Wang
- Wei Si
- Shenggen Fan
1 Application
| Application ID | Title |
|---|---|
| 89068 | Using genetic information to improve the prediction of food choices: A machine learning-based classification approach |
Enabling scientific discoveries that improve human health