Related Experiment Video
Updated: May 3, 2026
Biobanking of Human Aqueous and Vitreous Liquid Biopsies for Molecular Analyses
Published on: September 11, 2023
Integrated plasma proteomics and metabolomics reveal immunometabolic pathways and predictive signatures for
Xuehao Cui1, Qiuchen Zhao2, Jiajia Yuan3
1Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0XY, UK; John van Geest Centre for Brain Repair, University of Cambridge, Cambridge, CB2 0PY, UK; Cambridge Eye Unit, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK.
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.
