Systemic Comorbidities Common in Both Idiopathic and Secondary ERM

Pre-existing factors like systemic vascular dysfunction, chronic inflammation and oxidative stress may lower the threshold for ERM formation by compromising barrier integrity and promoting fibrosis, the researchers proposed in their paper. Photo: Diana Shechtman, OD.  Click image to enlarge. While often asymptomatic, epiretinal membranes (ERMs) can cause retinal distortion and traction, resulting in varying presentations of visual impairment. Ocular conditions such as diabetic retinopathy, uveitis, retinal detachment and ocular trauma can lead to secondary ERM. The condition can also develop idiopathically. Though its exact pathogenesis remains unclear, ERM is strongly associated with posterior vitreous detachment (PVD). Given its increasing prevalence with age and its strong association with this condition, understanding the risk factors and systemic associations of idiopathic ERM is critical for early detection, patient management and potential therapeutic strategies.Researchers based in Pittsburgh, PA, recently used machine learning models on the large nationwide All of Us dataset to reveal previously unrecognized associations and risk factors for idiopathic ERM. They found that broader systemic disease mechanisms may be linked to ERM risk. Identifying distinct patient clusters within idiopathic ERM highlighted multiple potential pathways associated with idiopathic ERM development, including cardiometabolic, inflammatory and dermatologic processes. At the same time, the strong overlap in comorbidities between idiopathic and secondary ERM suggested that systemic comorbidities are common in both and may influence susceptibility or fibrotic remodeling even when a local ocular trigger is present.“While the traditional classification of secondary and idiopathic remains clinically useful for identifying established local ocular risk factors, these results suggest that the presence or absence of a documented local precipitant may not fully capture systemic susceptibility that could influence ERM development across both categories,” the study authors wrote in their paper, which was published in Ophthalmology Science.Electronic health records of patients from the All of Us database were collected, including a total of 10,380 patients: 2,015 with idiopathic ERM, 3,175 with secondary ERM and then matched controls for each group. The majority of patients in both ERM subgroups were white (86% in the idiopathic group and 80% in the secondary group). The gender distribution was relatively balanced, with men comprising 52.8% of the idiopathic group and 50.8% of the secondary group.The research team also identified four distinct subgroups of idiopathic epiretinal membrane patients characterized by unique systemic comorbidity profiles, including cardiometabolic, dermatologic and joint disorder pathways. Their analysis also demonstrated significant associations with systemic conditions such as hypertension, hyperlipidemia, type 2 diabetes, inflammatory skin conditions, osteoarthritis and anemia.“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,” the study concluded. “These findings highlight opportunities for personalized risk assessment and preventative interventions based on systemic comorbidity profiles.”The researchers suggested that, moving forward, longitudinal studies are needed to assess the causal relationships between these systemic conditions and ERM development, while incorporating minimum observation windows and comorbidity burden indices to better distinguish biological associations from documented patterns. Click here for the journal source. Wu E, Jiang J, Hasan N, et al. Novel systemic associations of idiopathic epiretinal membrane identified via machine learning. Ophthalmol Sci. February 18, 2026. [Epub ahead of print]. This article was developed by the editorial staff in conjunction with experts in the field. In the process, AI may have been among the editorial tools used to meet the goals of human editors, who approved all content.