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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement R.N.I. is a Doctoral student from the Programa de Posgrado en Ciencias, Universidad Nacional Autonoma de Mexico (UNAM) and received fellowships from the National Council of Science and Technology of Mexico (CONACYT; #620199). This study was supported by the UNAM grant IN209317 (ST), IN205420 (ST), CONACYT 299625 (ST), CONACYT CF-2019-1759 (ST), and Shedid grant (R. Miledi and A. Martinez Torres). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval was obtained from the Instituto de Oftalmologia Human Participants Ethics committee (reference: CEI/029-1/2015), the National Ethics Committee (reference: CONBIOETICA-09-CEI-006-20170306), the Research Committee at Asociacion Para Evitar la Ceguera (17 CI 09 003 142), and the Research Ethics Committee at Escuela Nacional de Educacion Superior Leon (reference: CEI\_22\_06_S21). Written informed consent was provided by all subjects. All procedures were conducted in accordance with the tenets of the Declaration of Helsinki. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The clinical data used for the training and test sets were collected at the IMO, APEC, INDEREB, and ENES Leon and transferred to a secure data center with strict access controls in de-identified format. Clinical data were used with individual permissions. They are not publicly available, and restrictions apply for their use. The de-identified dataset (or a test subset) can be provided by Stephanie C. Thebault pending scientific review and a completed material transfer agreement. Requests for the clinical data should be submitted to: sthebault{at}comunidad.unam.mx. The R code and documentation for the analysis are available online at https://github.com/airetinopathydx/AIRetinopathyDx_.