Multi-omics integration predicts 17 disease incidences in the UK Biobank.

ABSTRACT:

Importance

Traditional clinical predictors for disease risks have limitations in capturing underlying disease complexity. Multi-omics technologies, such as metabolomics and proteomics, offer deeper molecular perspectives that could enhance risk prediction, but large-scale studies integrating the two omics are scarce.

Objectives

The primary objective is to systematically evaluate whether adding metabolomics and/or proteomics data to traditional clinical predictors improves risk prediction for 17 common incident diseases. A secondary objective is to identify key disease-related omics features.

Data sources and participants

Our study incorporated 23,776 UK Biobank participants who had complete baseline omics data for 159 NMR-based metabolites and 2,923 Olink affinity-bas

SUBMITTER: Du J 

PROVIDER: S-EPMC12340880 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature