Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, but existing methods are insufficient to screen early risk factors of diabetic retinopathy (DR). Here, we introduce non-evoked ERG as a simple, fast modality to record spontaneous activity, from which we developed a single random forest-based model that predicts disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome. Classification performance was validated using a dataset from an independent eye center. Our algorithm can be coupled with different ERG sensors, including ones working with portative, non-mydriatic devices. Principal component and discriminant analysis suggest slow spontaneous ERG frequencies as main discriminators for our predictive model. Our study will facilitate the implementation of interventions for the prevention of overweight and obesity by providing a robust, quantitative, and non-invasive identification and follow-up approach, which should ultimately reduce DR incidence.