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Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome
Madeline C Rocks1, Priyanka Bhatnagar1, Alice Verticchio Vercellin2
1George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA.
Medicina (Kaunas, Lithuania)
|February 26, 2025
Summary
Emerging research suggests a gut-eye axis influences primary open-angle glaucoma (POAG). Mathematical modeling and artificial intelligence (AI) can uncover complex interactions between the gut microbiome and POAG, identifying potential therapeutic targets.
Area of Science:
- Ophthalmology
- Microbiome Research
- Computational Biology
Background:
- Glaucoma, a leading cause of irreversible blindness, is often associated with elevated intraocular pressure (IOP).
- Growing evidence implicates the human gut microbiome in the pathogenesis of primary open-angle glaucoma (POAG).
Purpose of the Study:
- To review current research on the gut microbiome's role in glaucoma.
- To explore the application of mathematical modeling and artificial intelligence (AI) in understanding the gut-eye axis in POAG.
Main Methods:
- Comprehensive literature search of PubMed and Google Scholar up to August 1, 2024.
- Inclusion of basic science, observational studies, and mathematical modeling research.
- Analysis of traditional statistical and machine learning approaches.
Main Results:
- Identified associations between specific gut microbiota and POAG features using methods like random forest regression and Mendelian randomization.
- Highlighted AI's potential for analyzing complex, nonlinear interactions within the gut-eye axis.
- Noted limitations including study design variability and lack of integrated mechanistic models.
Conclusions:
- Preliminary evidence supports a gut-eye axis influencing POAG.
- AI and integrated models can identify novel biomarkers and therapeutic targets for POAG.
- Future research should focus on longitudinal studies, diverse populations, and integrating physiological data for improved model accuracy and clinical relevance.
