Novel Systemic Associations of Idiopathic Epiretinal Membrane Identified via Machine Learning

Objective or Purpose: To discover novel systemic associations that may lead to idiopathic epiretinal membrane (iERM) using interpretable machine learning models. Design: Large Data Retrospective Case-Control Study Subjects: All of Us Dataset, including a total of 10,380 patients: 2,015 iERM patients with 2,015 1:1 matched controls, 3,175 secondary epiretinal membrane (sERM) patients with 3,175 1:1 matched controls.Methods: Electronic health records of epiretinal membrane (ERM) patients from the All of Us Research Program, a nationwide longitudinal cohort of U.S. adults (data from 6/2016-2/2025) were collected.Unsupervised clustering using Principal Component Analysis (PCA) was performed on the dataset to identify distinct patient subgroups.Supervised machine learning models, including gradient-boosted decision trees (GBDT) and logistic regression, were trained to predict iERM.Main Outcome Measures: Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while feature importance was assessed using the Gini index for tree-based models and coefficient magnitudes for logistic regression.Additionally, odds ratios for comorbidities associated with both iERM and sERM were estimated using 22 contingency tables.Results: Unsupervised clustering of idiopathic epiretinal membrane patients revealed four distinct subgroups characterized by unique systemic comorbidity profiles, including cardiometabolic, dermatologic, and joint disorder pathways.Clusters demonstrated significant associations with systemic conditions such as hypertension, hyperlipidemia, type 2 diabetes, inflammatory skin conditions, osteoarthritis, and anemia.Supervised models, including logistic regression and gradient-boosted decision trees, achieved AUC values exceeding 0.679 on a testing set.Key predictors of iERM included knee osteoarthritis, hyperlipidemia, essential hypertension, and sensorineural hearing loss, each demonstrating high coefficient magnitudes, Gini importance, and statistically significant odds ratios. Conclusions:This study challenges conventional distinctions between iERM and sERM, proposing systemic comorbidities as associations to ERM development.The observed associations with cardiometabolic dysfunction, chronic inflammation, and joint and dermatologic disorders suggest that systemic mechanisms may significantly influence ERM pathogenesis.Future studies are necessary to establish causality and explore targeted therapeutic approaches, potentially incorporating anti-inflammatory treatments or cardiovascular risk management to prevent ERM formation.These J o u r n a l P r e -p r o o f findings highlight opportunities for personalized risk assessment and preventative 69 interventions based on systemic comorbidity profiles.