Proteomic Landscapes of Aging and Disease: Multi-Omics Integration, Machine Learning, and Translational Opportunities

Aging is accompanied by progressive changes in molecular, cellular, and systemic homeostasis. At the protein level, these changes involve altered proteostasis, post-translational regulation, and protein interaction networks, but their direction and functional significance vary by tissue, age, and disease context. Mass spectrometry, affinity-based assays, spatial proteomics, and emerging single-cell methods now support complementary views of proteins across tissues, biofluids, and cell populations. Integrating proteomics with genomic, transcriptomic, epigenomic, and metabolomic data can identify regulatory relationships that are not resolved by a single molecular layer; such integration prioritizes mechanisms but does not by itself establish causality. Machine-learning methods support spectral prediction, multi-omics integration, and prediction from high-dimensional protein profiles, although their performance depends on study design, calibration, and independent validation. This narrative review synthesizes protein alterations associated with aging and major age-related diseases, describes what multi-omics and computational methods add, compares emerging measurement technologies, and evaluates biomarker and therapeutic opportunities. Chronological-age prediction, estimation of biological aging, and prediction of clinical outcomes are treated as distinct objectives. Current evidence supports discovery and candidate prioritization; prospective clinical utility and causal therapeutic benefit remain to be established.