Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome

Abstract

Background and Objectives: Glaucoma is a major cause of irreversible blindness, with primary open-angle glaucoma (POAG) being the most prevalent form. While elevated intraocular pressure (IOP) is a well-known risk factor for POAG, emerging evidence suggests that the human gut microbiome may also play a role in the disease. This review synthesizes current findings on the relationship between gut microbiome and glaucoma, with a focus on mathematical modeling and artificial intelligence (AI) approaches to uncover key insights. Materials and Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, covering studies from its inception to 1 August 2024. Selected studies included basic science, observational research, and those incorporating mathematical-related models. Results: Traditional statistical and machine learning approaches, such as random forest regression and Mendelian randomization, have identified associations between specific microbiota and POAG features. These findings highlight the potential of AI to explore complex, nonlinear interactions in the gut–eye axis. However, limitations include variability in study designs and a lack of integrative, mechanistic models. Conclusions: Preliminary evidence supports the existence of a gut–eye axis influencing POAG disease. Combining data-driven and mechanism-driven models with AI could identify therapeutic targets and novel biomarkers. Future research should prioritize longitudinal studies in diverse populations and integrate physiological data to improve model accuracy and clinical relevance. Furthermore, physics-based models could deepen our mechanistic understanding of the gut–eye axis in glaucoma, advancing beyond associative findings to actionable insights.