Proteomic prediction of disease largely reflects environmental risk exposure

Abstract 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. Extending this approach to alcohol intake phenotypes, our results generally suggest that many plasma proteins identified in observational associations are more likely to be readouts of environmental risk factors than disease- specific signals. We conclude that the plasma proteome may provide critical objective biomarkers for quantifying the impacts of environmental risk factors on human health and disease. Our results have significant implications for implementing predictive plasma protein biomarkers in disease prevention, and can help guide interpretation of putative protein-disease associations as actionable therapeutic targets or quantitative indications of upstream exposures that represent potential intervention points. Competing Interest Statement DB, BS, and C-YC were employees of Biogen during the period of the study. DG was an employee of Optima Partners during the period of the study. CF is an employee of Optima Partners. Funding Statement A.R.M is funded by NIH U01HG011719 as well as Broad Institute Next Gen and Merkin awards. K.T. is funded by F31HL167378 and supported by the ECOR Claflin Award to A.R.M. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UK Biobank has Research Tissue Bank (RTB) approval from the North West Multi-centre Research Ethics Committee (MREC), and researchers with approved applications operate under the RTB approval. This study used data from UK Biobank via the application 31063. The FinnGen study is approved by the Finnish Institute for Health and Welfare (THL) (approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019 and THL/1721/5.05.00/2019), the Digital and Population Data Service Agency (VRK43431/2017-3, VRK/6909/2018-3 and VRK/4415/2019-3), the Social Insurance Institution (KELA) (KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019 and KELA 98/522/2019) and Statistics Finland (TK-53-1041-17). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Individual-level data from the UK Biobank can be accessed via application at https://www.ukbiobank.ac.uk/. We accessed UK Biobank data under application 31063. GWAS summary statistics can be accessed as described in their respective papers in Supplementary Table 2 and in the FinnGen study. Weights for the proteomic scores will be made available upon publication. Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Kristin Tsuo (ktsuo{at}broadinstitute.org), upon reasonable request.