Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population.

PURPOSE: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs. METHODS: The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010. An Autoencoder was employed to categorize RFs as primary or secondary. This study evaluated six primary RFs coupled independently with each secondary RF on five machine-learning models. RESULTS: The secondary RF 'gender' gave a maximum increase in Area under the curve (AUC) score to predict DR when combined separately with 'insulin treatment,' 'fasting plasma glucose,' 'hypertension history,' and 'glycosylated hemoglobin' with a maximum increase in AUC for the Naive Bayes model from 0.573 to 0.646, for the Support Vector Machines (SVM) model from 0.644 to 0.691, for the SVM model from 0.487 to 0.607, and for the Decision Tree model from 0.8 to 0.848, respectively. The secondary RFs 'age' and 'BMI' gave a maximum increase in AUC score to predict DR when combined separately with 'diabetes mellitus duration' and 'systolic blood pressure,' with a maximum increase in AUC for the SVM model from 0.389 to 0.621, and for the Decision Tree model from 0.617 to 0.713, respectively. CONCLUSION: The risk factor 'gender' was the best secondary RF in predicting DR compared to 'age' and 'BMI,' increasing the predictive power of four primary RFs.

Bridging the Camera Domain Gap With Image-to-Image Translation Improves Glaucoma Diagnosis.

Purpose: The purpose of this study was to improve the automated diagnosis of glaucomatous optic neuropathy (GON), we propose a generative adversarial network (GAN) model that translates Optain images to Topcon images. Methods: We trained the GAN model on 725 paired images from Topcon and Optain cameras and externally validated it using an additional 843 paired images collected from the Aravind Eye Hospital in India. An optic disc segmentation model was used to assess the disparities in disc parameters across cameras. The performance of the translated images was evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), 95% limits of agreement (LOA), Pearson's correlations, and Cohen's Kappa coefficient. The evaluation compared the performance of the GON model on Topcon photographs as a reference to that of Optain photographs and GAN-translated photographs. Results: The GAN model significantly reduced Optain false positive results for GON diagnosis, with RMSE, PSNR, and SSIM of GAN images being 0.067, 14.31, and 0.64, respectively, the mean difference of VCDR and cup-to-disc area ratio between Topcon and GAN images being 0.03, 95% LOA ranging from -0.09 to 0.15 and -0.05 to 0.10. Pearson correlation coefficients increased from 0.61 to 0.85 in VCDR and 0.70 to 0.89 in cup-to-disc area ratio, whereas Cohen's Kappa improved from 0.32 to 0.60 after GAN translation. Conclusions: Image-to-image translation across cameras can be achieved by using GAN to solve the problem of disc overexposure in Optain cameras. Translational Relevance: Our approach enhances the generalizability of deep learning diagnostic models, ensuring their performance on cameras that are outside of the original training data set.

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.

BACKGROUND: Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions. METHODS: We developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes). FINDINGS: Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36·0 U/L, calcium <8·6 mg/dL, eGFR <60·0 mL/min/1·73 m2, haemoglobin <11·0 g/dL, platelets <150·0 × 103/µL, ACR ≥300 mg/g, and WBC <4·0 × 103/µL on validation set A (a population resembling the development datasets), with the area under the receiver operating characteristic curve (AUC) of the DLS exceeding that of the baseline by 5·3-19·9% (absolute differences in AUC). On validation sets B and C, with substantial patient population differences compared with the development datasets, the DLS outperformed the baseline for ACR ≥300·0 mg/g and haemoglobin <11·0 g/dL by 7·3-13·2%. INTERPRETATION: We found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications. FUNDING: Google.

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.

Validation of diagnostic accuracy of retinal image grading by trained non-ophthalmologist grader for detecting diabetic retinopathy and diabetic macular edema.

PURPOSE: To validate the fundus image grading results by a trained grader (Non-ophthalmologist) and an ophthalmologist grader for detecting diabetic retinopathy (DR) and diabetic macular oedema (DMO) against fundus examination by a retina specialist (gold standard). METHODS: A prospective diagnostic accuracy study was conducted using 2002 non-mydriatic colour fundus images from 1001 patients aged ≥40 years. Using the Aravind Diabetic Retinopathy Evaluation Software (ADRES) images were graded by both a trained non-ophthalmologist grader (grader-1) and an ophthalmologist (grader-2). Sensitivity, specificity, positive predictive value and negative predictive value were calculated for grader-1 and grader-2 against the grading results by an independent retina specialist who performed dilated fundus examination for every study participant. RESULTS: Out of 1001 patients included, 42% were women and the mean ± (SD) age was 55.8 (8.39) years. For moderate or worse DR, the sensitivity and specificity for grading by grader-1 with respect to the gold standard was 66.9% and 91.0% respectively and the same for the ophthalmologist was 83.6% and 80.3% respectively. For referable DMO, grader-1 and grader-2 had a sensitivity of 74.6% and 85.6% respectively and a specificity of 83.7% and 79.8% respectively. CONCLUSIONS: Our results demonstrate good level of accuracy for the fundus image grading performed by a trained non-ophthalmologist which was comparable with the grading by an ophthalmologist. Engaging trained non-ophthalmologists potentially can enhance the efficiency of DR diagnosis using fundus images. Further study with multiple non-ophthalmologist graders is needed to verify the results and strategies to improve agreement for DMO diagnosis are needed.

Prevalence and Pattern of Geographic Atrophy in Asia: The Asian Eye Epidemiology Consortium.

PURPOSE: Although there have been many population-based studies of age-related macular degeneration (AMD), only limited information is available in Asia on the epidemiology of geographic atrophy (GA). We aimed to determine the prevalence and patterns of GA through an analysis of multiple studies conducted within the Asian Eye Epidemiology Consortium (AEEC). DESIGN: Cross-sectional meta-analyses. PARTICIPANTS: A total of 97 213 individuals aged 40 years and older. METHODS: Data from 22 population-based studies from countries belonging to the AEEC were included. In all studies, AMD was defined on the basis of standardized grading systems. Geographic atrophy was defined as an area of pallor in the fundus with visibility of the underlying choroidal blood vessels and sharply defined borders. Random-effects meta-analysis was performed to estimate overall and age-, gender-, and region-specific pooled prevalence of GA. MAIN OUTCOME MEASURES: Prevalence of GA per 1000 persons. RESULTS: The mean age was 60.8 ± 10.0 years, and 42 673 (43.9%) were male. Overall, a total of 223 individuals (0.2%) had GA. The pooled overall prevalence of GA was 1.57 per 1000 persons (95% confidence interval [CI], 1.04-2.10), which was 3 times less than that of neovascular AMD of 5.20 per 1000 persons (95% CI, 3.97-6.43). Compared with those aged 50 to 59 years, the prevalence of GA increased from 0.34 per 1000 persons (95% CI, 0.07-0.62) to 2.90 per 1000 persons (95% CI, 1.55-4.25) in those aged ≥70 years. The GA prevalence per 1000 persons was similar between urban (2.22; 95% CI, 1.22-3.23) and rural residents (1.33; 95% CI, 0.70-1.96). Geographic atrophy was more prevalent in South Asia (based on studies from India and Nepal, 3.82 per 1000 persons; 95% CI, 1.72-5.93) compared with East Asia (based on studies from China, Korea, Hong Kong, Taiwan, and Japan, and the Singapore Chinese Eye Study, 0.76 per 1000 persons; 95% CI, 0.31-1.22, P = 0.005). CONCLUSIONS: Geographic atrophy is uncommon in Asian populations compared with those of European ancestry. Even within Asia, geographic differences in GA prevalence were seen. The findings of this meta-analysis suggest that better dissection of risk factors in the Asian population for GA may provide insights into the biological pathways that drive these late-stage manifestations, thus suggesting better targets for prevention.

A partnership model for capacity-building of primary care physicians in evidence-based management of diabetic retinopathy in India.

In India, more than 72 million people have diabetes. Diabetic retinopathy (DR), a vision-threatening complication of people with diabetes, is an important cause of avoidable blindness. The delay in the detection of DR is due to lack of awareness and shortage of ophthalmologists trained in the management of DR. With this background, in 2015, we initiated a capacity-building program "Certificate Course in Evidence Based Management of Diabetic Retinopathy (CCDR)" with an objective to build the skills and core competencies of the physicians across India in the management of diabetes and DR. The program has completed four cycles and 578 physicians have been trained. The course elicited an excellent response, which reflects the much-felt need for skill improvement in DR diagnosis and management for physicians in India. This model demonstrates an innovative modality to address DR-related avoidable blindness in a resource-restraint country like India.

Exploring choroidal angioarchitecture in health and disease using choroidal vascularity index.

The choroid is one of the most vascularized structures of the human body and plays an irreplaceable role in nourishing photoreceptors. As such, choroidal dysfunction is implicated in a multitude of ocular diseases. Studying the choroid can lead to a better understanding of disease pathogenesis, progression and discovery of novel management strategies. However, current research has produced inconsistent findings, partly due to the physical inaccessibility of the choroid and the lack of reliable biomarkers. With the advancements in optical coherence tomography technology, our group has developed a novel quantitative imaging biomarker known as the choroidal vascularity index (CVI), defined as the ratio of vascular area to the total choroidal area. CVI is a potential tool in establishing early diagnoses, monitoring disease progression and prognosticating patients. CVI has been reported in existing literature as a robust marker in numerous retinal and choroidal diseases. In this review, we will discuss the current role of CVI with reference to existing literature, and make postulations about its potential and future applications.

Effectiveness of Teleretinal Imaging-Based Hospital Referral Compared With Universal Referral in Identifying Diabetic Retinopathy: A Cluster Randomized Clinical Trial.

Importance: Studies in high-income countries provide limited evidence from randomized clinical trials on the benefits of teleretinal screening to identify diabetic retinopathy (DR). Objective: To evaluate the effectiveness of teleretinal-screening hospital referral (TR) compared with universal hospital referral (UR) in people with diabetes. Design, Setting, and Participants: A cluster randomized clinical trial of 8 diabetes clinics within 10 km from Aravind Eye Hospital (AEH), Madurai, India, was conducted. Participants included 801 patients older than 50 years. The study was conducted from May 21, 2014, to February 7, 2015; data analysis was performed from March 12 to June 16, 2015. Interventions: In the TR cohort, nonmydriatic, 3-field, 45° retinal images were remotely graded by a retinal specialist and patients with DR, probable DR, or ungradable images were referred to AEH for a retinal examination. In the UR cohort, all patients were referred for a retinal examination at AEH. Main Outcomes and Measures: Hospital-diagnosed DR. Results: Of the 801 participants, 401 were women (50.1%) (mean [SD] age, 60.0 [7.3] years); mean diabetes duration was 8.6 (6.6) years. In the TR cohort, 96 of 398 patients (24.1%) who underwent teleretinal imaging were referred with probable DR (53 [13.3%]) or nongradable images (43 [10.8%]). Hospital attendance at AEH was proportionately higher with TR (54 of 96 referred [56.3%]) compared with UR (150 of 400 referred [37.5%]). The intention-to-treat analysis based on all patients eligible for referral in each arm showed that proportionately more patients with TR (36 of 96 [37.5]%) were diagnosed with DR compared with UR (50 of 400 [12.5%]) (unadjusted risk ratio [RR], 3.00; 95% CI, 2.01-4.48). These results were little changed by inclusion of covariates (RR, 2.72; 95% CI, 1.90-3.91). The RR was lower in the per-protocol analysis based on all patients who adhered to referral (covariate-adjusted RR, 1.75; 95% CI, 1.12-2.74). Diagnoses of DR were predominantly mild or moderate nonproliferative DR (36 in TR and 43 in UR). In the UR arm, there were 4 cases of severe nonproliferative DR and 2 cases of proliferative DR. Age (RR, 0.98; 95% CI, 0.95-0.99), female sex (RR, 0.79; 95% CI, 0.64-0.98), and hypertension diagnosis (RR, 0.81; 95% CI, 0.68-0.95) were factors associated with lower attendance. Those with higher secondary educational level or more were twice as likely to attend (RR, 2.00; 95% CI, 1.32-3.03). Conclusions and Relevance: The proportionate yield of DR cases was higher in the TR arm, confirming the potential benefit, at least in the setting of eye hospitals in India, of a targeted referral approach using teleretinal screening to identify patients with DR. Trial Registration: ClinicalTrials.gov identifier: NCT02085681.

Choroidal structural analysis and vascularity index in retinal dystrophies.

PURPOSE: To assess choroidal structural changes in patients with retinal dystrophies using choroidal vascularity index (CVI), a novel optical coherence tomography (OCT) based tool. METHODS: This retrospective study included 26 patients with retinal dystrophies (17 with retinitis pigmentosa, four with Stargardt disease, three with cone-rod dystrophy, one each with Best disease and Bietti crystalline dystrophy) and 32 healthy controls. Subfoveal OCT images were used for analysis. Mean CVI was compared between retinal dystrophy and control group, as well as among the retinal dystrophy subgroups. RESULTS: Mean CVI in eyes with retinal dystrophies was 52 ± 9% and it was significantly lower compared to that in normal eyes (70 ± 3%, p < 0.001). The differences among subgroups of retinal dystrophy were not statistically significant (p = 0.084). All types of retinal dystrophy were associated with lower CVI (all p < 0.001), after adjusting for age, gender, visual acuity and duration of symptoms. Older age was also shown to be independently associated with lower CVI (p = 0.012). Gender, visual acuity (VA) and duration of symptoms did not significantly affect CVI. CONCLUSION: Decreased choroidal vascularity was seen in eyes with retinal dystrophies. (CVI) may be a helpful tool in monitoring choroidal involvement in retinal dystrophies.

Choroidal structural analysis in eyes with diabetic retinopathy and diabetic macular edema-A novel OCT based imaging biomarker.

PURPOSE: To evaluate structural changes in the choroid among patients with diabetic macular edema (DME), with varying grades of diabetic retinopathy (DR), using enhance depth imaging spectral domain optical coherence tomography (EDI SD-OCT) scans. METHODS: A cross-sectional study was conducted on 82 eyes with DR and DME and 86 healthy control eyes. Eyes with DME were classified according to the severity of DR as per the international DR severity scale. Sub foveal choroidal thickness (SFCT)was obtained using EDI SD-OCT scans. These scans were binarized into luminal and stromal areas, to derive the choroidal vascularity index (CVI). CVI and SFCT were analyzed between the study and control group using paired-T test. Tukey's test was used to correlate the differences in CVI and SFCT between different grades of DR. Further analysis was done to look for the effect of DR severity and type of DME on CVI as well as SFCT using correlation coefficient and linear regression analysis. RESULTS: SFCT was significantly increased in eyes with DME as compared to the controls (334.47±51.81µm vs 284.53±56.45µm, p<0.001), and showed an ascending trend with worsening of DR, though this difference was not statistically significant [mild non-proliferative diabetic retinopathy (NPDR) = 304.33±40.39µm, moderate NPDR = 327.81±47.39µm, severe NPDR = 357.72±62.65µm, proliferative DR (PDR) = 334.59±47.4µm, p-0.09]. CVI was significantly decreased in DME with DR eyes as compared to controls (63.89±1.89 vs 67.51±2.86, p<0.001). CVI was also significantly decreased with worsening DR (mild NPDR = 66.38±0.3, moderate NPDR = 65.28±0.37, severe NPDR = 63.50±0.47, PDR = 61.27±0.9, p<0.001). CONCLUSION: SFCT and CVI are dynamic parameters that are affected by DME. Unlike CVI, SFCT is also affected by ocular and systemic factors like edema and hypertension. CVI may be a more accurate surrogate marker for DME and DR and can potentially be used to monitor the progression of DR.

Comparison of Choroidal Vascularity Markers on Optical Coherence Tomography Using Two-Image Binarization Techniques.

Purpose: To compare the agreement between optical coherence tomography (OCT)-based choroidal vascularity markers measured by two previously reported image binarization techniques. Methods: Spectral-domain OCT using enhanced-depth imaging was performed in 100 eyes from 52 normal subjects. Choroidal images were binarized to luminal area and stromal area using two different algorithms. Choroidal vascularity marker was defined as the ratio of luminal area to total choroidal area and they were termed "luminal/choroidal area ratio (L/C ratio)" and "choroidal vascularity index (CVI)" per the algorithm. The agreement between choroidal vascularity markers measured by the two techniques was compared using intraclass correlation coefficient (ICC) and Bland-Altman analysis. Results: The mean values of choroidal vascularity markers were 70.12% (range, 56.76%-78.55%) for CVI and 67.44% (range, 51.09%-81.31%) for L/C ratio. Low level of absolute agreement between the two binarization techniques was reflected by an adjusted ICC of 0.353 using linear mixed model with age, sex, and spherical equivalent as covariates. Conclusions: There was discrepancy between measurements of choroidal vascularity using two commonly adopted image binarization techniques. It remained unclear what was the true choroidal vascularity and which binarization algorithm was more accurate. Future studies with enhanced image quality and improved image analysis algorithm are required to decipher the ground truth for choroidal vascularity.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting: A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. Exposure: Deep learning-trained algorithm. Main Outcomes and Measures: The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. Results: The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%. Conclusions and Relevance: In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.

Whole exome sequencing identified novel CRB1 mutations in Chinese and Indian populations with autosomal recessive retinitis pigmentosa.

Retinitis pigmentosa (RP) is a leading cause of inherited blindness characterized by progressive degeneration of the retinal photoreceptor cells. This study aims to identify genetic mutations in a Chinese family RP-2236, an Indian family RP-IC-90 and 100 sporadic Indian individuals with autosomal recessive RP (arRP). Whole exome sequencing was performed on the index patients of RP-2236, RP-IC-90 and all of the 100 sporadic Indian patients. Direct Sanger sequencing was used to validate the mutations identified. Four novel mutations and one reported mutation in the crumbs homolog 1 (CRB1) gene, which has been known to cause severe retinal dystrophies, were identified. A novel homozygous splicing mutation c.2129-1G>C was found in the three patients In family RP-2236. A homozygous point mutation p.R664C was found in RP-IC-90. A novel homozygous mutation p.G1310C was identified in patient I-44, while novel compound heterozygous mutations p.N629D and p.A593T were found in patient I-7. All mutations described above were not present in the 1000 normal controls. In conclusion, we identified four novel mutations in CRB1 in a cohort of RP patients from the Chinese and Indian populations. Our data enlarges the CRB1 mutation spectrums and may provide new target loci for RP diagnose and treatment.

Whole Exome Sequencing Analysis Identifies Mutations in LRP5 in Indian Families with Familial Exudative Vitreoretinopathy.

BACKGROUND: Familial exudative vitreoretinopathy (FEVR, OMIM 133780) is a severe inherited retinal disorder characterized by incomplete retinal vascular development and neovascularization. At least five genes have been reported to be associated with FEVR, including NDP, LRP5, FZD4, TSPAN12, and ZNF408. Recently reported data showed that mutations in the KIF11 gene can also lead to FEVR conditions. Previous studies suggested that known mutations only explain approximately 40-60% of FEVR cases in different populations. PURPOSE: To investigate the causative genetic mutations in four Indian families with FEVR. METHODS: Whole exome sequencing was carried out to analyze the genomic DNA samples from the four FEVR proband patients and Sanger sequencing was utilized to verify all identified polymorphisms. A luciferase assay was used to test the mutant protein activity. RESULTS: We identified four novel LRP5 missense mutations in these FEVR families: c.C1042T (p.R348W), c.G1141A (p.D381N), c.C1870T (p.R624W), and c.A4550G (p.Y1517C). The luciferase assay demonstrated that all four of these LRP5 mutations led to significant reduction of enzymatic activity with response to NORRIN, suggesting that they are pathogenic. CONCLUSION: Our findings expand the mutational spectrum of FEVR in the Indian population and provide some guidelines in clinical diagnosis.

Defining a Minimum Set of Standardized Patient-centered Outcome Measures for Macular Degeneration.

PURPOSE: To define a minimum set of outcome measures for tracking, comparing, and improving macular degeneration care. DESIGN: Recommendations from a working group of international experts in macular degeneration outcomes registry development and patient advocates, facilitated by the International Consortium for Health Outcomes Measurement (ICHOM). METHODS: Modified Delphi technique, supported by structured teleconferences, followed by online surveys to drive consensus decisions. Potential outcomes were identified through literature review of outcomes collected in existing registries and reported in major clinical trials. Outcomes were refined by the working group and selected based on impact on patients, relationship to good clinical care, and feasibility of measurement in routine clinical practice. RESULTS: Standardized measurement of the following outcomes is recommended: visual functioning and quality of life (distance visual acuity, mobility and independence, emotional well-being, reading and accessing information); number of treatments; complications of treatment; and disease control. Proposed data collection sources include administrative data, clinical data during routine clinical visits, and patient-reported sources annually. Recording the following clinical characteristics is recommended to enable risk adjustment: age; sex; ethnicity; smoking status; baseline visual acuity in both eyes; type of macular degeneration; presence of geographic atrophy, subretinal fibrosis, or pigment epithelial detachment; previous macular degeneration treatment; ocular comorbidities. CONCLUSIONS: The recommended minimum outcomes and pragmatic reporting standards should enable standardized, meaningful assessments and comparisons of macular degeneration treatment outcomes. Adoption could accelerate global improvements in standardized data gathering and reporting of patient-centered outcomes. This can facilitate informed decisions by patients and health care providers, plus allow long-term monitoring of aggregate data, ultimately improving understanding of disease progression and treatment responses.

Whole-exome Sequencing Analysis Identifies Mutations in the EYS Gene in Retinitis Pigmentosa in the Indian Population.

Retinitis pigmentosa (RP) is a rare heterogeneous genetic retinal dystrophy disease, and despite years of research, known genetic mutations can explain only approximately 60% of RP cases. We sought to identify the underlying genetic mutations in a cohort of fourteen Indian autosomal recessive retinitis pigmentosa (arRP) families and 100 Indian sporadic RP cases. Whole-exome sequencing (WES) was performed on the probands of the arRP families and sporadic RP patients, and direct Sanger sequencing was used to confirm the causal mutations identified by WES. We found that the mutations of EYS are likely pathogenic mutations in two arRP families and eight sporadic patients. Specifically, we found a novel pair of compound heterozygous mutations and a novel homozygous mutation in two separate arRP families, and found two novel heterozygous mutations in two sporadic RP patients, whereas we found six novel homozygous mutations in six sporadic RP patients. Of these, one was a frameshift mutation, two were stop-gain mutations, one was a splicing mutation, and the others were missense mutations. In conclusion, our findings expand the spectrum of EYS mutations in RP in the Indian population and provide further support for the role of EYS in the pathogenesis and clinical diagnosis of RP.

Whole-exome sequencing reveals a novel frameshift mutation in the FAM161A gene causing autosomal recessive retinitis pigmentosa in the Indian population.

Retinitis pigmentosa (RP) is a heterogenous group of inherited retinal degenerations caused by mutations in at least 50 genes. To identify genetic mutations underlying autosomal recessive RP (arRP), we performed whole-exome sequencing study on two consanguineous marriage Indian families (RP-252 and RP-182) and 100 sporadic RP patients. Here we reported novel mutation in FAM161A in RP-252 and RP-182 with two patients affected with RP in each family. The FAM161A gene was identified as the causative gene for RP28, an autosomal recessive form of RP. By whole-exome sequencing we identified several homozygous genomic regions, one of which included the recently identified FAM161A gene mutated in RP28-linked arRP. Sequencing analysis revealed the presence of a novel homozygous frameshift mutation p.R592FsX2 in both patients of family RP-252 and family RP-182. In 100 sporadic Indian RP patients, this novel homozygous frameshift mutation p.R592FsX2 was identified in one sporadic patient ARRP-S-I-46 by whole-exome sequencing and validated by Sanger sequencing. Meanwhile, this homozygous frameshift mutation was absent in 1000 ethnicity-matched control samples screened by direct Sanger sequencing. In conclusion, we identified a novel homozygous frameshift mutations of RP28-linked RP gene FAM161A in Indian population.

Angiogenic potential of vitreous from Proliferative Diabetic Retinopathy and Eales' Disease patients.

PURPOSE: Proliferative Diabetic Retinopathy (PDR) and Eales' Disease (ED) have different aetiologies although they share certain common clinical symptoms including pre-retinal neovascularization. Since there is a need to understand if the shared end-stage angiogenic pathology of PDR and ED is driven by common stimulating factors, we have studied the cytokines contained in vitreous from both patient groups and analyzed the angiogenic potential of these samples in vitro. MATERIAL AND METHODS: Vitreous samples from patients with PDR (n = 13) and ED (n = 5) were quantified for various cytokines using a cytokine biochip array and sandwich ELISA. An additional group of patients (n = 5) with macular hole (MH) was also studied for comparison. To determine the angiogenic potential of these vitreous samples, they were analyzed for their ability to induce tubulogenesis in human microvascular endothelial cells. Further, the effect of anti-VEGF (Ranibizumab) and anti-IL-6 antibodies were studied on vitreous-mediated vascular tube formation. RESULTS: Elevated levels of IL-6, IL-8, MCP-1 and VEGF were observed in vitreous of both PDR and ED when compared to MH. PDR and ED vitreous induced greater levels of endothelial cell tube formation compared to controls without vitreous (P<0.05). When VEGF in vitreous was neutralized by clinically-relevant concentrations of Ranibizumab, tube length was reduced significantly in 5 of 6 PDR and 3 of 5 ED samples. Moreover, when treated with IL-6 neutralizing antibody, apparent reduction (71.4%) was observed in PDR vitreous samples. CONCLUSIONS: We have demonstrated that vitreous specimens from PDR and ED patients share common elevations of pro-inflammatory and pro-angiogenic cytokines. This suggests that common cytokine profiles link these two conditions.