Integrated plasma proteomics and metabolomics reveal immunometabolic pathways and predictive signatures for age-related eye diseases.

BACKGROUND AND AIMS: Age-related eye diseases (AREDs) share aging as a major risk factor, but the systemic molecular changes preceding disease onset remain incompletely understood. We aimed to define the shared and disease-specific immunometabolic architecture of major AREDs and to examine how circulating molecular features relate to retinal phenotypes, pre-diagnostic patterns, and disease risk.

METHODS

We performed a large-scale prospective multi-omics study in the UK Biobank integrating baseline plasma proteomics, metabolomics, retinal imaging-derived phenotypes, and longitudinal follow-up across five major AREDs: age-related macular degeneration, cataract, diabetic retinopathy, glaucoma, and retinal vascular occlusion. Cox regression, functional enrichment, protein-metabolite correlation, mediation analysis, trajectory analysis, and machine-learning models were applied.

RESULTS

Proteome-wide analyses identified both shared and disease-specific circulating signatures, mainly involving immune, extracellular matrix, vascular, and stress-response pathways. Reconstructed population-level molecular patterns diverged from controls up to 15 years before diagnosis, with marked heterogeneity across diseases. Integration with retinal imaging linked immune- and matrix-related proteins to retinal neurodegenerative and microvascular phenotypes. Metabolite clustering and mediation analyses highlighted recurrent lipoprotein-related pathways, particularly HDL-related structure and composition, as cross-layer features associated with systemic protein signals, metabolic states, and disease risk. Combined proteomic-metabolomic models improved prediction of incident disease compared with protein-only models.

CONCLUSIONS

Major AREDs share a systemic immunometabolic aging architecture while retaining substantial disease-specific molecular features. Circulating molecular alterations are detectable years before clinical onset and may support future biological stratification and risk prediction.