Large-scale multi-omics enhance risk prediction for type 2 diabetes
Authors' conclusion
Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.
Study context
- Population
- UK Biobank participants without diagnosed diabetes at baseline; derivation set (Phase 1 metabolomics release, N = 23,108), validation set (Phase 2 release, N = 19,732)1,090 incident T2D
- Participants
- 42,840 participants
- Comparison
- Clinical Cambridge Diabetes Risk Score (CDRS) extended with polygenic risk scores (PRS), metabolomics and proteomics vs clinical CDRS alone
- Outcome
- Incident T2D risk prediction (Harrell's C-index, net reclassification index (NRI))
- Follow-up
- 10 years
Methodological detailsderivation and independent validation sets, UK Biobank
- Derivation and independent validation sets
- UK Biobank
Performance
As reported in the abstract
ΔC-index
+ 0.022
P < 0.001
CDRS + proteomics vs CDRS
ΔC-index
+ 0.007
P < 0.001
Full multi-omics vs CDRS + proteomics
CDRS
0.862
CDRS + proteomics
0.884
Full multi-omics
0.891
Original publication
Composed by AI from the published abstract and checked against it. How Briefs are made →