The retinal age gap-particulate hypothesis: environmental exposure as a latent contributor to deep learning-based retinal age prediction

Deep learning models applied to retinal fundus photographs and optical coherence tomography (OCT) can predict chronological age with remarkable accuracy. The discrepancy between predicted and chronological age—the "retinal age gap”—is associated with mortality and multiple systemic diseases. However, the biological and physical correlates underlying these predictions remain incompletely understood. We propose that cumulative environmental particulate exposure—including microplastics, nanoplastics, and other airborne or ingested particulates—may contribute as a previously unrecognized component of the retinal age signal captured by deep learning models. Specifically, we hypothesize that particulate-associated retinal features, potentially manifesting as hyperreflective foci on OCT or tiny discrete foci on fundus imaging, may constitute one of several feature classes utilized by these models. We outline seven falsifiable predictions spanning epidemiology, imaging analysis, experimental manipulation, and post-mortem validation. Importantly, this framework does not posit that particulate burden is the primary determinant of retinal age, but rather that it may act as a latent, cumulative exposure signal embedded within a multifactorial aging phenotype. If supported, this hypothesis would expand the interpretability of retinal age models and position retinal imaging as a potential tool for studying long-term environmental exposure. This work is intended as a testable framework to guide empirical investigation rather than a definitive explanatory model.