ArticleCommunications medicine2024
Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Genetic basis of Asian diabetes.Journal of diabetes investigation · 2026Review
- Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.Scientific reports · 2026Article
- Plasma Multiomics Links Early-Life Adversity to Disease and Cardiometabolic Health: A Population-Based Cohort Study.Journal of the American Heart Association · 2026Article
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Sociodemographic Determinants of Hospitalized Exacerbations in Exposure-Defined COPD Phenotypes: A Nationwide Prospective Cohort Study in China.International journal of chronic obstructive pulmonary disease · 2026Article
- Review
- An overview of modern machine learning methods for effect measure modification analyses in high-dimensional settings.SSM - population health · 2025Article
- Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores.Communications medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundAlthough polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level.
methodsWe employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369,942) and BioBank Japan (BBJ; n = 149,421).
resultsHere we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman's ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention.
conclusionsOur study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.
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Registered trials
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 OpenQuestion graph.