Proteomic prediction of common and rare diseases

Background For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Whether measuring thousands of proteins offers predictive information across a wide range of diseases is unknown. Methods In 41,931 individuals from the UK Biobank Pharma Proteomics Project (UKB-PPP), we integrated ∼3000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81 – 6038 cases). We compared prediction models based on proteins with a) basic clinical information alone, b) basic clinical information + 37 clinical biomarkers, and c) genome-wide polygenic risk scores. Results For 67 pathologically diverse diseases, a model including as few as 5 to 20 proteins was superior to clinical models (median delta C-index = 0.07; range = 0.02 – 0.31) and to clinical models with biomarkers for 52 diseases. In multiple myeloma, for example, a set of 5 proteins significantly improved prediction over basic clinical information (delta C-index = 0.25 (95% confidence interval 0.20 – 0.29)). At a 5% false positive rate (FPR), proteomic prediction (5 proteins) identified individuals at high risk of multiple myeloma (detection rate (DR) = 50%), non-Hodgkin lymphoma (DR = 55%) and motor neuron disease (DR = 29%). At a 20% FPR, proteomic prediction identified individuals at high-risk for pulmonary fibrosis (DR= 80%) and dilated cardiomyopathy (DR = 75%). Conclusions Sparse plasma protein signatures offer novel, clinically useful prediction of common and rare diseases, through disease-specific proteins and protein predictors shared across multiple diseases. (Funded by Medical Research Council, NIHR, Wellcome Trust.) ### Competing Interest Statement J. Davitte, P. Surendran, D. Croteau-Chonka, C. Robins, T. Kanno, S. Gade, D. Freitag, F. Ziebell, J. Betts, and R. Scott are all employees of and/or shareholders for GlaxoSmithKline. None of the other authors has a competing interest. ### Funding Statement This study has been funded by the Medical Research Council, the NIHR, and the Wellcome Trust. ### 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: All UK Biobank data was accessed in accordance with GlaxoSmithKline's UK Biobank Application 20361 and the UKB-PPP Consortium Application 65851. 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 All individual level data is publicly available to bona fide researchers from the UK Biobank ().