Proteomic prediction of disease largely reflects environmental risk exposure

作者：Kristin Tsuo, M. Austin Argentieri, Danni A. Gadd, Mitja Kurki, Zhili Zheng, Denis Baird, Riccardo E. Marioni, Christopher N. Foley, Hailiang Huang, Benjamin B. Sun, Chia‐Yen Chen, Mark J. Daly, Alicia R. Martin · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.08.27.25334571 · 被引用次数：3 · 研究领域：Alcohol Consumption and Health Effects、Metabolomics and Mass Spectrometry Studies、Genetic Associations and Epidemiology

Plasma proteomic signatures accurately predict disease risk, but our understanding of the mechanisms contributing to the predictive value of the proteome remains limited. Here, we characterized proteomic biomarkers of 19 age-related diseases, based on observational associations between 2,923 protein levels and incidence of these outcomes in the UK Biobank (N = 45,438). To identify the subset of these biomarkers that may represent causal drivers of disease, we first employed Mendelian Randomization (MR) and found that only 8% of the protein-disease associations with genetic instruments showed suggestive evidence of causal relationships, and were more likely to pertain to only a single disease. We then tested the hypothesis that many proteomic biomarkers, particularly the non-causal proteins, are impacted by environmental factors that might independently affect disease risk and protein levels. We discovered that the vast majority (>90%) of proteins associated with diseases like lung cancer and COPD are also associated with smoking, and more than half of all disease-associated proteins tested in MR were associated with smoking. These proteins showed no evidence of causal effects on disease, suggesting their predictive value is as an environmental sensor. Given the sensitivity of the plasma proteome to smoking, we developed a proteomic score for smoking (SmokingPS) and demonstrated that the plasma proteome can serve as a quantitative index of smoking behavior and history. Extendi...