Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

TOPIC: To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.CLINICAL RELEVANCE: Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.METHODS: The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).RESULTS: There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.CONCLUSION: With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p

Identification of biomarkers for diabetic retinopathy

In this thesis Ward Fickweiler identified certain protective factors that help prevent diabetic eye disease from developing in some individuals with type 1 diabetes. One protein, known as retinol binding protein 3, was shown to protect both the neuroretina and vascular retina from diabetes-induced disease. Also we found that common protective factors between diabetic eye disease and cardiovascular disease may exist. We also examined a potential new pathway in the development of diabetic eye disease, the kallikrein kinin system. Lastly, we evaluated the usefulness of non-invasive imaging techniques and peripheral blood compounds to predict treatment response in diabetic eye disease. We found several characteristics on retinal imaging and peripheral blood compounds that may be helpful in predicting treatment response in diabetic eye disease

PREDICTIVE VALUE OF OPTICAL COHERENCE TOMOGRAPHIC FEATURES IN THE BEVACIZUMAB AND RANIBIZUMAB IN PATIENTS WITH DIABETIC MACULAR EDEMA (BRDME) STUDY: 812–819

To establish the predictive value of specific optical coherence tomography retinal features on visual outcomes and retinal thickness during anti-vascular endothelial growth factor treatment in patients with diabetic macular edema. Post hoc analysis of compound data of a prospective, 6-month, multicenter, randomized controlled trial of 119 patients with diabetic macular edema receiving either intravitreal bevacizumab or ranibizumab were analyzed to assess the associations between baseline retinal morphologic parameters and change in best-corrected visual acuity and central subfield thickness. Based on the study protocol of the core study, best-corrected visual acuity and central subfield thickness were obtained before each mandatory monthly injection during 6 months. The presence of serous retinal detachment at baseline was associated with significant improvement in best-corrected visual acuity letter score at Month 3 and Month 6 (P < 0.001 and P = 0.01, respectively). In addition, the presence of disorganization of retinal inner layers was associated with lower best-corrected visual acuity letter score at Month 3 and Month 6 (P < 0.05 and P = 0.01, respectively). This study found that serous retinal detachment and disorganization of retinal inner layers were associated with different treatment responses to anti-vascular endothelial growth factor therapy in patients with diabetic macular edem

Association of Circulating Markers With Outcome Parameters in the Bevacizumab and Ranibizumab in Diabetic Macular Edema Trial

PURPOSE. The purpose of this study was to evaluate selected candidate biomarkers as potential markers for patients with diabetic macular edema (DME) who receive antivascular endothelial growth factor (VEGF) therapy METHODS. Selected biomarkers included blood levels of messenger RNA (mRNA) of retinoschisin, RPE65, rhodopsin, and endothelial progenitor cell markers CD34 and CD133. Blood samples were obtained from 89 patients with DME according to the study protocol of the Bevacizumab and Ranibizumab in Diabetic Macular Edema (BRDME) study. During each monthly visit, patients underwent optical coherence tomography scanning and visual acuity was measured. Anti-VEGF injections were administered at fixed monthly intervals over 6 months. Analyses of covariance using simplified and linear mixed models were used to examine the correlations between candidate markers and changes in visual acuity and central subfield thickness. RESULTS. Plasma mRNA levels of retinoschisin were negatively associated with visual acuity, and plasma mRNA levels of rhodopsin were positively associated with visual acuity in patients with DME (P <0.01 and P <0.05, respectively). In addition, changes in central subfield thickness between baseline and months 1, 2, and 3 during anti-VEGF treatment were associated with mRNA levels of retinoschisin, rhodopsin, and the ratio of retinoschisin-torhodopsin (P <0.01, all). CONCLUSIONS. This prospective, multicenter study found that circulating mRNA levels of retinoschisin and rhodopsin are associated with visual acuity and changes in central subfield thickness during anti-VEGF therapy in patients with DME

Retinal artery to vein ratio is associated with cerebral microbleeds in individuals with type 1 diabetes

Peer reviewe

Automated machine learning for predicting diabetic retinopathy progression from ultra-widefield retinal images

Importance Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. Objective To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images.Design, Setting and Participants Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022.Exposure Automated ML models were generated from baseline on-axis 200° UWF retinal images. Baseline retinal images were labeled for progression based on centralized reading center evaluation of baseline and follow-up images according to the clinical Early Treatment Diabetic Retinopathy Study severity scale. Images for model development were split 8-1-1 for training, optimization, and testing to detect 1 or more steps of DR progression. Validation was performed using a 328-image set from the same patient population not used in model development.Main Outcomes and Measures Area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy.Results A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moderate (799 [67.8%]) NPDR were included. DR progression was present in half of the training set (590 of 1179 [50.0%]). The model’s AUPRC was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. On the validation set for eyes with mild NPDR, sensitivity was 0.72 (95% CI, 0.57-0.83), specificity was 0.63 (95% CI, 0.57-0.69), prevalence was 0.15 (95% CI, 0.12-0.20), and accuracy was 64.3%; for eyes with moderate NPDR, sensitivity was 0.80 (95% CI, 0.70-0.87), specificity was 0.72 (95% CI, 0.66-0.76), prevalence was 0.22 (95% CI, 0.19-0.27), and accuracy was 73.8%. In the validation set, 6 of 8 eyes (75%) with mild NPDR and 35 of 41 eyes (85%) with moderate NPDR progressed 2 steps or more were identified. All 4 eyes with mild NPDR that progressed within 6 months and 1 year were identified, and 8 of 9 (89%) and 17 of 20 (85%) with moderate NPDR that progressed within 6 months and 1 year, respectively, were identified.Conclusions and Relevance This study demonstrates the accuracy and feasibility of automated ML models for identifying DR progression developed using UWF images, especially for prediction of 2-step or greater DR progression within 1 year. Potentially, the use of ML algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes.<br/