Machine Learning Flags Key Risks for Early Macular Degeneration: Hypertension, Joint Disorders, and Dyslipidemia: Study

To assess predictive potential, the researchers applied machine learning models—including gradient-boosted decision trees and random forests—to comorbidity data. The gradient-boosted decision tree model achieved a prediction accuracy of 75.84%, relying solely on comorbidities to determine the likelihood of early AMD onset.

In analyzing which comorbidities were most predictive, hypertension emerged as the top-ranked factor, followed by dyslipidemia and inflammatory joint disorders. These findings highlight the potential of systemic health factors in influencing the earlier manifestation of AMD.

The study also found that patients with early-onset AMD had a significantly higher average number of comorbid conditions compared to those with late-onset AMD (3.32 ± 0.24 versus 0.16 ± 0.02), indicating a potential role of overall disease burden in accelerating retinal degeneration.

The authors concluded that hypertension, dyslipidemia, and inflammatory joint disorders are strongly associated with early AMD onset. They emphasized the need for ophthalmologists to consider patients’ systemic health profiles during routine evaluations. Integrating comorbidity screening and proactive management may enable earlier diagnosis and personalized interventions to slow disease progression.

This innovative study emphasizes the promise of machine learning in ophthalmology and highlights the importance of interdisciplinary approaches in tackling complex, age-related diseases like AMD.

Reference:

Ethan Wu, Nasiq Hasan, Sharat Chandra Vupparaboina, Jessica Ye Jiang, Joseph DeCicco, Kiran Vupparaboina, Sandeep Bollepalli, José-Alain Sahel, Jay Chhablani; Identifying Key Co-Morbidities for Predicting Early-Onset Age-Related Macular Degeneration Using Machine Learning. Invest. Ophthalmol. Vis. Sci. 2025;66(8):293.