Proteomic signatures improve risk prediction for common and rare diseases

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed…

• G
  GlaxoSmithKline
• W
  Wellcome

  Award: 220044/Z/19/Z
• WT
  Wellcome Trust
• CR
  Cancer Research UK

  Award: C864/A14136
• NI
  National Institute for Health and Care Research
• UC
  University College London
• CT
  Cambridge Trust
• MR
  Medical Research Council

  Awards: MC_PC_21036, MC_PC_21036, MC_UU_00006/1, C864/A14136, MR/N003284/1, HDR-23004, MR/N003284/1 and MC_UU_00006/1 and MC_PC_21036, MR/N003284/1