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Autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV) is an autoimmune condition of the eye that sequentially mimics uveitis, retinitis pigmentosa, and proliferative diabetic retinopathy as it progresses to complete blindness. We identified two different missense mutations in the CAPN5 gene in three ADNIV kindreds. CAPN5 encodes calpain-5, a calcium-activated cysteine protease that is expressed in retinal photoreceptor cells. Both mutations cause mislocalization from the cell membrane to the cytosol, and structural modeling reveals that both mutations lie within a calcium-sensitive domain near the active site. CAPN5 is only the second member of the large calpain gene family to cause a human Mendelian disorder, and this is the first report of a specific molecular cause for autoimmune eye disease. Further investigation of these mutations is likely to provide insight into the pathophysiologic mechanisms of common diseases ranging from autoimmune disorders to diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Purpose: We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows.Patients and Methods: A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow ("Human Workflow"), an AI-based workflow involving IDx-DR ("AI Workflow"), and a two-step hybrid workflow ("AI–Human Hybrid Workflow"). The AI Workflow provided results within 48 hours, whereas the other workflows took up to 7 days. Patients were surveyed by phone about follow-up decisions.Results: Under the AI Workflow, 279 patients screened positive for MTMDR. Of these, 69.2% followed up with an ophthalmologist within 90 days. Altogether 70.5% (N=48) of patients who followed up chose their location based on primary care referral. Among the subset of patients that were seen in person at the university eye institute under the Human Workflow and AI–Human Hybrid Workflow, 12.0% (N=14/117) and 11.7% (N=12/103) of patients with a referrable screening result followed up compared to 35.5% of patients under the AI Workflow (N=99/279; χ2df=2 = 36.70, p < 0.00000001).Conclusion: Ophthalmology follow-up after a positive DR screening result is approximately three-fold higher under the AI Workflow than either the Human Workflow or AI–Human Hybrid Workflow. Improved follow-up behavior may be due to the decreased time to screening result. [ABSTRACT FROM AUTHOR]

While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy. [ABSTRACT FROM AUTHOR]

Differences in regional protein expression within the human retina may explain molecular predisposition of specific regions to ophthalmic diseases like age-related macular degeneration, cystoid macular edema, retinitis pigmentosa, and diabetic retinopathy. To quantify protein levels in the human retina and identify patterns of differentially-expressed proteins, we collected foveomacular, juxta-macular, and peripheral retina punch biopsies from healthy donor eyes and analyzed protein content by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein expression was analyzed with 1-way ANOVA, gene ontology, pathway representation, and network analysis. We identified a mean of 1,974 proteins in the foveomacular retina, 1,999 in the juxta-macular retina, and 1,779 in the peripheral retina. Six hundred ninety-seven differentially-expressed proteins included those unique to and abundant in each anatomic region. Proteins with higher expression in each region include: heat-shock protein 90-alpha (HSP90AA1), and pyruvate kinase (PKM) in the foveomacular retina; vimentin (VIM) and fructose-bisphosphate aldolase C (ALDOC); and guanine nucleotide-binding protein subunit beta-1 (GNB1) and guanine nucleotide-binding protein subunit alpha-1 (GNAT1) in the peripheral retina. Pathway analysis identified downstream mediators of the integrin signaling pathway to be highly represented in the foveomacular region (P = 6.48 e–06). Metabolic pathways were differentially expressed among all retinal regions. Gene ontology analysis showed that proteins related to antioxidant activity were higher in the juxta-macular and the peripheral retina, but present in lower amounts in the foveomacular retina. Our proteomic analysis suggests that certain retinal regions are susceptible to different forms of metabolic and oxidative stress. The findings give mechanistic insight into retina function, reveal important molecular processes, and prioritize new pathways for therapeutic targeting. [ABSTRACT FROM AUTHOR]

Background: While metabolic syndrome has been strongly implicated as a risk factor for macrovascular diseases, such as stroke and cardiovascular disease, its relationship with microvascular diseases, including diabetic retinopathy, has been less defined. The purpose of this pilot study was to investigate the association between metabolic syndrome and the presence and severity of diabetic retinopathy. Methods: A retrospective case-control chart review at the University of Iowa ophthalmology and primary care clinics included 100 patients with proliferative diabetic retinopathy (PDR), 100 patients with nonproliferative diabetic retinopathy (NPDR), 100 diabetic patients without diabetic retinopathy, and 100 nondiabetic patients who were randomly selected. Using the International Diabetes Foundation definition, the prevalence of metabolic syndrome and the number of components of metabolic syndrome were compared among these groups. Results: The prevalence of metabolic syndrome in patients with diabetes was 69.3%, which was significantly higher than that in patients without diabetes (27%; P<0.0001) (odds ratio [OR] =6.28; 95% confidence interval [CI]: 3.76-10.49; P=0.0004). However, there was no significant difference in the prevalence of metabolic syndrome between diabetics with and without diabetic retinopathy, with rates of 67.5% and 73%, respectively (P=0.36) (OR =0.77; 95% CI: 0.45-1.32; P=0.34). In addition, there was no significant difference between the PDR and NPDR groups, with rates of 63% and 72%, respectively (P=0.23) (OR =0.70; 95% CI: 0.38-1.30; P=0.26). Conclusion: The metabolic syndrome was highly prevalent in patients with diabetes, but it was not associated with the presence or severity of retinopathy. [ABSTRACT FROM AUTHOR]