Modeling Retinal Ganglion Cell Degeneration from Longitudinal Intraocular Pressure Trajectories

Abstract

Many experimental studies collect longitudinal physiological measurements while assessing irreversible biological outcomes only at a terminal endpoint, leaving the timing of disease progression unobserved. This disconnect between continuously measured covariates and latent biological events limits quantitative analysis of how physiological stress drives tissue degeneration. We address this problem by formulating retinal ganglion cell (RGC) degeneration in experimental glaucoma as a latent time-to-event process driven by longitudinal intraocular pressure (IOP) exposure. Using monthly IOP measurements and terminal RGC counts from the DBA/2J mouse model of glaucoma, we develop both Cox proportional hazards models and a time-dependent extension based on the Andersen-Gill counting-process formulation, allowing progression risk to depend on both contemporaneous IOP and cumulative pressure burden. We further reconstruct model-implied survival curves from the fitted hazard functions, providing a continuous-time representation of latent disease progression under observed and hypothetical IOP trajectories. Across all disease thresholds and both modeling approaches, cumulative IOP burden above 19 mmHg emerged as the dominant predictor of RGC degeneration, whereas peak and contemporaneous IOP contributed little additional predictive information once sustained exposure was taken into account. HDAP2, a mitochondria-targeted neuroprotective peptide, significantly reduced progression hazard after adjustment for longitudinal IOP exposure, supporting a pressure-independent neuroprotective mechanism. Beyond identifying cumulative pressure exposure as the dominant predictor of neurodegeneration in this experimental model, the proposed framework provides a general strategy for relating longitudinal physiological measurements to latent biological progression. By linking exposure histories to model-implied survival trajectories, it enables trajectory-based risk assessment, prediction under hypothetical IOP trajectories, and quantitative evaluation of therapeutic interventions in experimental systems where biological outcomes are observed only at terminal endpoints.