A multimodal ocular aging index reveals proteomic pathways and predicts incident age-related eye diseases.
Abstract
Chronological age incompletely captures heterogeneity in biological aging. In this prospective study of 45,819 UK Biobank participants, we developed a multimodal ocular aging index (MOAI) by integrating ophthalmic phenotypes with plasma proteomic and metabolomic profiles using machine learning. The MOAI quantifies divergence between ocular biological and chronological age. Over 13.80 years of follow-up, accelerated ocular aging was significantly associated with higher risks of incident age-related macular degeneration and cataract, even after adjustment for chronological age and established risk factors. Incorporation of the MOAI significantly improved risk reclassification beyond traditional predictors. Explainable modeling and pathway enrichment analyses identified inflammation-related proteins and pathways, including cytokine-cytokine receptor interactions and PI3K-Akt signaling, as key drivers of accelerated ocular aging. These findings establish a multimodal framework for quantifying organ-specific biological aging, link ocular aging to systemic inflammatory processes, and highlight the eye as a sensitive readout of aging biology with implications for healthspan.
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Research summary
The study claims that the multimodal ocular aging index (MOAI) can predict incident age-related eye diseases by quantifying biological aging in the eye. This paper is relevant as it addresses biological aging mechanisms and their implications for healthspan, linking ocular aging to systemic inflammatory processes, which are central to understanding and potentially mitigating aging.
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