Setu P. Mehta

While diagnostic disparities by race and age of onset are reported in inherited retinal diseases, their impact on Stargardt disease (STGD)-a clinically and genetically heterogeneous macular dystrophy-remains unclear. We analyzed 246 STGD patients at a U.S. referral center (2003-2024) who completed genetic testing, comparing laboratory-reported results (lab-GT) with manual, phenotype-integrated reinterpretation (m-GT) of ABCA4 sequencing incorporating updated variant databases and genotype-phenotype correlation. Diagnostic yield and variant burden were assessed by race and age of onset. Positive/likely positive (P/LP) lab-GT was identified in 79% (195), with 78% (191) attributable to ABCA4 (ABCA4-positive lab-GT: 57% [141]). M-GT increased ABCA4-P/LP yield to 91% (224). Black participants had lower ABCA4-positive lab-GT than White participants (55% vs. 73%) and fewer pathogenic variants; on multivariable analysis, Black race (OR 0.34) and later age of onset (OR 0.95/year) independently predicted reduced molecular diagnosis. The disparity by race resolved with P/LP m-GT (89% vs. 90%); by age of onset, yield remained lower in late-onset cases (ABCA4-P/LP lab-GT: 86% early- [≤10 yrs], 83% intermediate- [11-44 yrs], 54% late-onset [≥45 yrs], improving to 97%, 94%, and 77% after m-GT). Posttest reinterpretation improves diagnostic yield, particularly for Black and late-onset STGD patients, underscoring the value of ancestry-informed interpretation, historical reanalysis, and genotype-phenotype correlation.

: While diagnostic disparities by race and age of onset are reported in inherited retinal diseases, their impact on Stargardt disease (STGD)—a clinically and genetically heterogeneous macular dystrophy—remains unclear. We analyzed 246 STGD patients at a U.S. referral center (2003-2024) who completed genetic testing, comparing laboratory-reported results (lab-GT) with manual, phenotype-integrated reinterpretation (m-GT) of ABCA4 sequencing incorporating updated variant databases and genotype-phenotype correlation. Diagnostic yield and variant burden were assessed by race and age of onset. Positive/likely positive (P/LP) lab-GT was identified in 79% (195), with 78% (191) attributable to ABCA4 ( ABCA4- positive lab-GT: 57% [141]). M-GT increased ABCA4- P/LP yield to 91% (224). Black participants had lower ABCA4- positive lab-GT than Whites (55% vs. 73%) and fewer pathogenic variants; on multivariable analysis, Black race (OR 0.34) and later age of onset (OR 0.95/year) independently predicted reduced molecular diagnosis. The disparity by race resolved with P/LP m-GT (89% vs. 90%); by age of onset, yield remained lower in late-onset cases ( ABCA4- P/LP lab-GT: 86% early-[≤10yrs], 83% intermediate-[11-44yrs], 54% late-onset [≥45yrs], improving to 97%, 94%, and 77% after m-GT). Post-test reinterpretation improves diagnostic yield, particularly for Black and late-onset STGD patients, underscoring the value of ancestry-informed interpretation, historical reanalysis, and genotype-phenotype correlation.

Importance: Multiple clinical trials for Stargardt disease are ongoing, as there is no treatment available for this condition. However, timely recruitment of clinical trial participants remains challenging, risking incomplete study cohorts and prolonged study timelines that together escalate the possibility of trial failure. Factors associated with clinical trial participation in Stargardt are poorly understood. Identifying drivers of clinical trial participation and genetic testing completion may improve trial design and access. Objective: To identify demographic and clinical factors associated with clinical trial participation and genetic testing completion in Stargardt. Design: Retrospective cohort study of patients seen at a genetic eye disease center from January 2003 to December 2024. Associations were evaluated using bivariate analysis and multivariable logistic regression. Setting: Single-center study at a tertiary referral center. Participants: 280 patients clinically diagnosed with Stargardt disease [median (IQR) age at presentation 37 (20-52) years; follow-up 4.5 (1.6-10.1) years]. Exposures: Demographic and clinical factors, including race, insurance, education, employment, parenting status, age of onset, and genetic counseling history. Main Outcomes and Measures: Genetic testing completion (all participants) and clinical trial participation (among ABCA4- positive participants). Results: Among 280 participants, 246 (88%) completed genetic testing. Among 223 ABCA4 -positive participants, 45 (20%) had enrolled in a clinical trial. Clinical trial participation was associated with later onset [≥60 years; OR (95% CI) 36.7 (2.5-537.1)], part-time employment [9.3 (1.01-172.9)], and was less likely among retired individuals [0.014 (0.0005-0.4)] or those without genetic counseling [0.22 (0.07-0.71)]. In multivariable analysis, genetic testing completion was less likely in participants who were Black [0.40 (0.16-0.98)], under/uninsured [0.47 (0.19-0.97)], without genetic counseling [0.09 (0.03-0.22)], or not planning to have children [0.35 (0.14-0.89)]. Common reasons for non-completion included loss to follow-up, refusal, and awaiting genetic counseling. Conclusions and Relevance: In this large single-center cohort, one in five participants with molecularly confirmed Stargardt had enrolled in a clinical trial. Older age of onset, part-time employment, and genetic counseling may promote clinical trial participation among Stargardt patients, while retirement may be negatively associated. These findings may ultimately support the development of rational strategies to accelerate clinical trial enrollment, thereby broadly supporting the development of effective treatments for Stargardt disease.

Objective: To identify clinical and demographic predictors of genetic testing (GT) completion and diagnostic yield among patients with genetic eye disorders (GED) at a large U.S. tertiary academic center. Design: Retrospective cohort study. Participants: Patients with clinically diagnosed GEDs evaluated at the Wilmer Eye Institute’s Genetic Eye Disease (GEDi) Center between 2002 and 2025. Methods: Demographic, clinical, and GT data were extracted. Bivariate analyses and multivariable logistic regression identified factors associated with GT completion and molecular diagnosis. Subgroup analyses examined racial disparities between Black, non-Hispanic White, and Other race participants. Main Outcome Measures: Proportion of patients completing GT, molecular diagnostic yield, and clinical/demographic predictors of each. Results: Of 1466 participants (median age at presentation 44 years, symptom onset 28 years; median follow-up 6 years), 74% (1088) completed genetic testing, with a likely molecular diagnosis achieved in 62%, inconclusive results in 22%, and no diagnosis in 16%. GT completion was more likely among younger participants with earlier symptom onset, and longer follow-up. Likely molecular diagnosis was more likely in participants with earlier symptom onset, worse visual acuity (VA), male sex, and syndromic or X-linked phenotypes. Black and Other race participants had significantly lower odds of completing GT (Black: OR [95% CI] 0.40 [0.30-0.53]; Other: 0.57 [0.36-0.92]) and receiving a likely molecular diagnosis (Black: 0.37 [0.26-0.51]; Other: 0.58 [0.35-0.98]), and consistently exhibited worse VA at both baseline and follow-up. Notably, among Black and Other race participants, disparities in GT completion and visual outcomes persisted despite equivalent or shorter time from presentation to GT completion, suggesting barriers arise independently of delays in care engagement. We identified 118 causative genes among participants with likely molecular diagnoses, with ABCA4 , USH2A , PRPH2 , RHO , and BEST1 accounting for 47% of cases. Conclusions: This is the largest single-center GED genetic testing cohort reported in the U.S. and reveals significant disparities in GT completion and yield by race, age, sex, and disease-level factors. Our findings underscore the need to expand early access to GT, diversify genomic databases, and address systemic barriers to ensure equity in GED diagnosis, clinical trial access, and delivery of emerging therapies.

Introduction: Prediabetes is a prevalent condition in which early detection and lifestyle interventions can prevent or delay progression to diabetes. Artificial intelligence (AI) and machine learning (ML) offer enhanced tools for diagnosis, risk stratification, and scalable delivery of lifestyle interventions. This review synthesizes current applications of AI/ML in patients with prediabetes. Methods: We conducted a scoping review using PubMed, EMBASE, and Web of Science (through May 2025) to identify original studies applying AI/ML to prediabetes prediction or management. Population-level forecasting and models combining prediabetes with other conditions were excluded. Data were extracted via structured REDCap instruments and validated through secondary review. Descriptive statistics summarized findings. Results: Of 2072 records screened, 149 studies met criteria: 118 prediction model studies, 20 intervention studies, and 11 miscellaneous. Machine learning models primarily targeted prediction of prediabetes, progression to diabetes, diabetic complications, and glucose metrics. Overall model performance was favorable (mean C-statistic 0.81), with random forests, neural networks, and support vector machines showing better performance. Only 20 studies reported external validation, few compared ML to standard risk tools, and data/code availability was limited. Six AI-based diabetes prevention programs showed positive clinical outcomes, though randomized controlled trial (RCT) evidence was limited. Three personalized nutrition interventions showed mixed efficacy. Conclusion: Most AI/ML research in prediabetes focused on predictive modeling, which shows promise but limited translation to real-world settings. Artificial intelligence-based interventions may scale behavioral change support but need further evaluation versus standard care. Future efforts should prioritize external validation, assess added value over standard tools, and address barriers to integration into care.

Purpose: Whole exome or genome sequencing (WES and WGS, respectively) can be valuable in identifying the molecular basis of inherited retinal disorders (IRD), particularly when panel-based testing yields negative or inconclusive results. However, WES or WGS may reveal secondary findings (SF) associated with severe diseases such as cancer, neurodegenerative and cardiovascular conditions. The American Society of Medical Genetics and Genomics has defined a list of reportable SFs that entail significant health implications. Acknowledging the limited awareness of SFs among ophthalmologists, we sought to understand the frequency, characteristics, and impact of SF following WES/WGS. Design: Retrospective cross-sectional study Subjects, Participants and/or Controls: We included patients with suspected IRD who underwent WES or WGS at Johns Hopkins Hospital from 2019-2024. WES/WGS was ordered following genetic counseling and consenting by certified genetic counselors (CGCs). Methods/ Main Outcome Measures: We conducted a qualitative analysis of WES/WGS indications and SF. We also analyzed SF consent status and recommended clinical actions. Results: The study included 36 patients, 50% of whom were females. 34 patients received WES, and two received WGS. The mean age was 45 ± 24 years. 89% (32/36) patients consented to examine SF, while 11% declined analysis. Of the 32 patients who consented to SF analysis, 5 had SFs (16%). Detected SF included pathogenic or likely pathogenic variants in the APC, RET, TP53, MUTYH, and BRCA2 genes associated with familial adenomatous polyposis, multiple endocrine neoplasia, Li-Fraumeni syndrome, MYH -associated polyposis, and hereditary breast and ovarian cancer, respectively. After WES/WGS, patients were advised to continue managing their symptoms with their healthcare team, take further medical actions, and/or encourage family members to pursue SF genetic testing. Of the 33 individuals who received post-test genetic counseling, 79% (26/33) were counseled by a CGC, 18% (6/33) by an ophthalmologist, and 3% (1/33) by a primary care provider. IRD gene results were positive in six (16%) patients, none of whom had SF. Conclusions: SF are not infrequent when pursuing WES/WGS for diagnostic testing in IRDs. This study highlights the need for ophthalmologists to be prepared to discuss and manage SF. Multidisciplinary care, including a CGC, enables successful clinical management in this context.

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The growth of administrative data made available publicly, often in near-real time, offers new opportunities for monitoring conditions that impact community health. Urban blight—manifestations of adverse social processes in the urban environment, including physical disorder, decay, and loss of anchor institutions—comprises many conditions considered to negatively affect the health of communities. However, measurement strategies for urban blight have been complicated by lack of uniform data, often requiring expensive street audits or the use of proxy measures that cannot represent the multifaceted nature of blight. This paper evaluates how publicly available data from New York City’s 311-call system can be used in a natural language processing approach to represent urban blight across the city with greater geographic and temporal precision. We found that our urban blight algorithm, which includes counts of keywords (‘tokens’), resulted in sensitivity ~90% and specificity between 55% and 76%, depending on other covariates in the model. The percent of 311 calls that were ‘blight related’ at the census tract level were correlated with the most common proxy measure for blight: short, medium, and long-term vacancy rates for commercial and residential buildings. We found the strongest association with long-term (>1 year) commercial vacancies (Pearson’s correlation coefficient = 0.16, p < 0.001). Our findings indicate the need of further validation, as well as testing algorithms that disambiguate the different facets of urban blight. These facets include physical disorder (e.g., litter, overgrown lawns, or graffiti) and decay (e.g., vacant or abandoned lots or sidewalks in disrepair) that are manifestations of social processes such as (loss of) neighborhood cohesion, social control, collective efficacy, and anchor institutions. More refined measures of urban blight would allow for better targeted remediation efforts and improved community health.