Purpose:A growing body of evidence points to complement dysregulation in diabetes. Early studies have indicated the presence of complement components inside the eye in patients with diabetic retinopathy, but these data have been confounded by leakage of proteins from the systemic circulation into the vitreous cavity. Methods:We took samples of plasma and vitreous from patients with and without proliferative diabetic retinopathy (PDR) and measured levels of 16 complement components as well as albumin. We employed a normalized ratio using local and systemic complement and albumin levels to control for vascular leakage into the vitreous cavity. Results:Before normalizing, we found significantly higher levels of 16 complement components we measured in PDR eyes compared to controls. After normalizing, levels of C4, factor B, and C5 were decreased compared to controls, while C3a and Ba levels were elevated compared to controls. We also found higher ratios of C3a/C3, C5a/C5, and Ba/factor B in PDR eyes compared to controls. Conclusions:We found evidence of local, intraocular activation of C3, C5, and factor B. The normalized data suggest involvement of the alternative complement pathway. By showing activation of specific complement components in PDR, this study identifies targets for diagnostic and therapeutic potential.

BACKGROUND Diabetes and hypertension are among top public health priorities, particularly in low and middle-income countries where their health and socioeconomic impact is exacerbated by the quality and accessibility of health care. Moreover, their connection with severe or deadly COVID-19 illness has further increased their societal relevance. Tools for early detection of these chronic diseases enable interventions to prevent high-impact complications, such as loss of sight and kidney failure. Similarly, prognostic tools for COVID-19 help stratify the population to prioritize protection and vaccination of high-risk groups, optimize medical resources and tests, and raise public awareness. METHODS We developed and validated state-of-the-art risk models for the presence of undiagnosed diabetes, hypertension, visual complications associated with diabetes and hypertension, and the risk of severe COVID-19 illness (if infected). The models were estimated using modern methods from the field of statistical learning (e.g., gradient boosting trees), and were trained on publicly available data containing health and socioeconomic information representative of the Mexican population. Lastly, we assembled a short integrated questionnaire and deployed a free online tool for massifying access to risk assessment. RESULTS Our results show substantial improvements in accuracy and algorithmic equity (balance of accuracy across population subgroups), compared to established benchmarks. In particular, the models: i) reached state-of-the-art sensitivity and specificity rates of 90% and 56% (0.83 AUC) for diabetes, 80% and 64% (0.79 AUC) for hypertension, 90% and 56% (0.84 AUC) for visual diminution as a complication, and 90% and 60% (0.84 AUC) for development of severe COVID disease; and ii) achieved substantially higher equity in sensitivity across gender, indigenous/non-indigenous, and regional populations. In addition, the most relevant features used by the models were in line with risk factors commonly identified by previous studies. Finally, the online platform was deployed and made accessible to the public on a massive scale. CONCLUSIONS The use of large databases representative of the Mexican population, coupled with modern statistical learning methods, allowed the development of risk models with state-of-the-art accuracy and equity for two of the most relevant chronic diseases, their eye complications, and COVID-19 severity. These tools can have a meaningful impact on democratizing early detection, enabling large-scale preventive strategies in low-resource health systems, increasing public awareness, and ultimately raising social well-being.

Background The automated screening of patients at risk of developing diabetic retinopathy represents an opportunity to improve their midterm outcome and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. Objective This study aimed to develop and evaluate the performance of an automated deep learning–based system to classify retinal fundus images as referable and nonreferable diabetic retinopathy cases, from international and Mexican patients. In particular, we aimed to evaluate the performance of the automated retina image analysis (ARIA) system under an independent scheme (ie, only ARIA screening) and 2 assistive schemes (ie, hybrid ARIA plus ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the 3 schemes. Methods A randomized controlled experiment was performed where 17 ophthalmologists were asked to classify a series of retinal fundus images under 3 different conditions. The conditions were to (1) screen the fundus image by themselves (solo); (2) screen the fundus image after exposure to the retina image classification of the ARIA system (ARIA answer); and (3) screen the fundus image after exposure to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of 3 retina specialists for each fundus image. Results The ARIA system was able to classify referable vs nonreferable cases with an area under the receiver operating characteristic curve of 98%, a sensitivity of 95.1%, and a specificity of 91.5% for international patient cases. There was an area under the receiver operating characteristic curve of 98.3%, a sensitivity of 95.2%, and a specificity of 90% for Mexican patient cases. The ARIA system performance was more successful than the average performance of the 17 ophthalmologists enrolled in the study. Additionally, the results suggest that the ARIA system can be useful as an assistive tool, as sensitivity was significantly higher in the experimental condition where ophthalmologists were exposed to the ARIA system’s answer prior to their own classification (93.3%), compared with the sensitivity of the condition where participants assessed the images independently (87.3%; P=.05). Conclusions These results demonstrate that both independent and assistive use cases of the ARIA system present, for Latin American countries such as Mexico, a substantial opportunity toward expanding the monitoring capacity for the early detection of diabetes-related blindness.

Background: The automated screening of patients at risk of developing diabetic retinopathy (DR), represents an opportunity to improve their mid-term outcome and lower the public expenditure associated with direct and indirect costs of a common sight-threatening complication of diabetes. Objective: In the present study, we aim at developing and evaluating the performance of an automated deep learning-based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, we study the performance of the automated retina image analysis (ARIA) system under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis. Methods: We ran a randomized controlled experiment where 17 ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the opinion of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the opinion of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists9 opinion in each condition and the opinion of the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three retina specialists for each fundus image. Results: The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC), sensitivity, and specificity of 98%, 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, 90% respectively for Mexican patient-cases. The results achieved on Mexican patient-cases outperformed the average performance of the 17 ophthalmologist participants of the study. We also find that the ARIA system can be useful as an assistive tool, as significant specificity improvements were observed in the experimental condition where participants were exposed to the answer of the ARIA system as a second opinion (93.3%), compared to the specificity of the condition where participants assessed the images independently (87.3%). Conclusions: These results demonstrate that both use cases of ARIA systems, independent and assistive, present a substantial opportunity for Latin American countries like Mexico towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.