# Novel Systemic Associations of Idiopathic Epiretinal Membrane Identified via Machine Learning

## TL;DR

This study uses machine learning to find new systemic health conditions linked to a type of eye membrane, suggesting broader health factors may influence its development.

## Contribution

The study identifies novel systemic associations with idiopathic epiretinal membrane using interpretable machine learning models.

## Key findings

- Four distinct iERM subgroups were identified with unique systemic comorbidity profiles, including cardiometabolic and dermatologic conditions.
- Key predictors of iERM included knee osteoarthritis, hyperlipidemia, and hypertension, with high model importance and odds ratios.
- The study suggests systemic mechanisms like chronic inflammation and cardiovascular dysfunction may influence ERM development.

## Abstract

To discover novel systemic associations that may lead to idiopathic epiretinal membrane (iERM) using interpretable machine learning models.

Large data retrospective case-control study.

All of Us Dataset, including a total of 10 380 patients: 2015 iERM patients with 2015 1:1 matched controls, 3175 secondary epiretinal membrane (sERM) patients with 3175 1:1 matched controls.

Electronic health records of epiretinal membrane (ERM) patients from the All of Us Research Program, a nationwide longitudinal cohort of US adults (data from 6/2016 to 2/2025) were collected. Unsupervised clustering using principal component analysis was performed on the data set to identify distinct patient subgroups. Supervised machine learning models, including gradient-boosted decision trees and logistic regression, were trained to predict iERM.

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 2 × 2 contingency tables.

Unsupervised clustering of iERM patients revealed 4 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.

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 findings highlight opportunities for personalized risk assessment and preventative interventions based on systemic comorbidity profiles.

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.