Citations

ABSTRACT Background Sunlight has profound impacts on physical and mental health, beyond vision, including effects on circadian rhythms, alertness, mood, and sleep. A family history of any mood disorders is strongly associated with psychiatric disorders including bipolar disorder. The purpose of this study was to evaluate the association between a family history of any mood disorder in patients with bipolar I disorder and solar insolation at varied international onset locations. Methods Data for this analysis were available from 5842 patients with a diagnosis of bipolar I disorder obtained at 83 collection sites in both hemispheres. This included 4752 patients from 71 collection sites in the northern hemisphere and 1090 patients from 12 collection sites in the southern hemisphere. Patient data variables were obtained from records or interviews. Solar insolation data were obtained from The National Aeronautics and Space Administration (NASA) Power database for each onset location, and the ratio of the mean monthly minimum/mean monthly maximum solar insolation was calculated. Typically, the ratio is largest near the equator (little yearly change in solar insolation) and smallest near the poles (large yearly change in solar insolation). Results A significant relationship was found between a family history of any mood disorder, the ratio of the mean monthly minimum/mean monthly maximum solar insolation, and gender. The odds of a family history of mood disorder increased as patient location nears the poles and decreased near the equator. Female gender also increased the odds of having a family history of a mood disorder. Conclusions This study highlighted the association between family history, solar insolation, and gender in international patients with bipolar I disorder. Given the profound effects of sunlight on human health, the family of patients with bipolar disorder who live in the same location with the same solar insolation, and especially females, may be at increased risk for a mood disorder.

Abstract INTRODUCTION Determining apolipoprotein E ( APOE ) ε4 allele status, a key genetic risk factor for Alzheimer's disease (AD), requires molecular genotyping infrastructure not widely accessible beyond specialized centers. METHODS A fully automated high‐throughput apoE E4 proteotyping immunoassay was evaluated for clinical performance (460 participants across three cohorts) and analytical validity. Concordance with polymerase chain reaction (PCR)‐based genotyping and measures of analytical validity were reported. RESULTS The apoE E4 immunoassay demonstrated 99.6% (95% confidence interval [CI]: 98.4% to 99.9%) concordance with PCR‐based APOE ε4 genotype results across the pooled clinical cohort; 100.0% (95% CI: 97.1% to 100.0%) in those with AD ( N = 127) and 99.4% (95% CI: 97.8% to 99.8%) in those without AD (333). The assay met analytical validity criteria for E4 isoform specificity, interference, precision, and stability. DISCUSSION The apoE E4 immunoassay demonstrated high concordance with PCR‐based genotyping and robust analytical validity, offering an accessible alternative for APOE ε4 zygosity assessment. Highlights A novel high‐throughput plasma‐based proteotyping immunoassay for APOE ε4 zygosity classification was developed and evaluated for clinical performance and analytical validity. The apoE E4 immunoassay demonstrated high concordance (99.6%) with PCR‐based APOE ε4 genotyping across a diverse international cohort, and a robust analytical profile. An apoE E4 immunoassay may offer a more cost‐effective and accessible alternative to DNA genotyping approaches currently used for AD risk evaluation and anti‐amyloid treatment decisions.

Abstract Objectives Traditional laboratory regulatory frameworks provide robust guidance for conventional clinical testing but lack requirements for data documentation for artificial intelligence and machine learning (AI/ML) solutions. This gap creates significant risks since AI solutions directly inherit patterns, biases, and limitations from their training data. Here, we describe the development and implementation of a comprehensive data documentation checklist with accompanying data lineage for use within an AI lifecycle framework for clinical laboratories. Methods Building on the previously established Clinical AI Readiness Evaluator (CARE) framework, we developed a comprehensive data checklist and data lineage methodology through a multiphase process, including (1) comprehensive review of existing AI/ML data documentation frameworks, (2) focused meetings with 3 institutional AI operations teams, and (3) 3 rounds of iterative refinement by our multidisciplinary team. The checklist’s effectiveness was then assessed using 3 diverse AI/ML projects moving through the CARE framework. Results The CARE Data Checklist and Data Lineage provide a structured approach to documenting critical aspects of datasets used in AI/ML projects, including data composition, demographics, collection methods, labeling processes, usage constraints, maintenance requirements, and a data readiness assessment. The checklist addresses unique data-centric challenges of AI/ML applications, facilitating transparency, reproducibility, and regulatory compliance. Conclusions The CARE Data Checklist and Data Lineage serve as both a technical guide and a communication tool bridging gaps between technical and clinical stakeholders. By working on these documents early in the AI lifecycle, laboratories can anticipate and address data-related challenges, ultimately saving time, optimizing resources, and improving the reliability of AI-augmented laboratory solutions.

Abstract Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework’s utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.

Abstract Introduction Left atrial-to-coronary sinus (LA-to-CS) shunting through the APTURE transcatheter shunt system aims to unload the LA while preserving the interatrial septum. This article aims to examine the 3-year health status, functional capacity, and clinical outcomes of the ALT-FLOW Early Feasibility Study (EFS). Methods Device safety, patency, and echocardiographic parameters, but also symptoms, health status, and exercise capacity were assessed in patients with left ventricular ejection fraction (LVEF) > 40% (n & 95). Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score predicted 3-year all-cause mortality was compared with observed mortality assessed by Kaplan–Meier analysis using adjudicated events. Predicted and observed HF hospitalization (HFH)-free survival rates were also compared. Results At 3 years, the safety profile remained stable with no new safety signals, and all implanted shunts remained patent. 76.3% of patients improved by at least one NYHA class, and mean KCCQ Overall Summary Scores increased by 25 points from baseline (P < .0001). The 6-min walk distance improved by an average of 39 m (P & .002). These benefits were consistent regardless of baseline pulmonary vascular resistance (PVR). Right heart echocardiographic parameters after 3 years were stable, though a reduction in TAPSE was observed, primarily in the high PVR subgroup. The observed 3-year survival was significantly higher than the MAGGIC-predicted survival (84.4% vs 70.2, respectively; P & .009), as well as the HFH-free survival (68.0% vs 24.0%, respectively; P < 0001). Conclusion In patients with HF and LVEF >40%, the APTURE LA-to-CS shunting confirmed an acceptable safety profile with stable right heart parameters and sustained improvements in symptoms, quality of life, and functional capacity at 3 years. Observed survival and HFH were superior to MAGGIC predictions.

Oversight of Artificial Intelligence in Human Subjects Research (AI HSR) presents unique challenges. These challenges arise from both the non-linear and iterative nature of AI development, as well as from the way AI shifts risk from individual research subjects to larger populations affected by AI-driven decisions and data handling. Traditional Institutional Review Boards (IRBs) often struggle to keep pace with these changes, which can lead to gaps in risk assessment and delays in the review process. There is a growing need for transparent, repeatable methods to manage AI risk in healthcare. This paper introduces the Three-Stage Framework, a risk-based oversight model designed to align ethical and regulatory review with an AI project’s stage of maturity and potential human impact. By aligning the level and timing of IRB review with the types of risks present at each stage of AI system development, the framework supports appropriate regulatory pathways and documents expectations while maintaining effective protection of human subjects. Through gradual, stage-appropriate documentation, the approach supports responsible and adaptive innovation while preparing AI systems for safe and ethical use across biomedical, social, behavioral, and educational domains. This approach prepares AI systems for safe and ethical use, accelerates compliant research, and helps IRBs and institutions maintain trustworthiness while protecting human subjects.

Abstract Chromosome 5p15.33 harbors several independent association signals which demonstrate antagonistic pleiotropy across cancer types, with causal mechanisms largely unresolved. To identify functional variants and enhancer elements at this locus, we performed statistical fine-mapping followed by massively parallel reporter assays (MPRA) and proliferation based CRISPRi screens. This approach identified eight multi-cancer functional variants (MCFVs) across three GWAS signals. Targeting rs421629 (part of the CLPTM1L signal marked by rs465498) with CRISPRi revealed opposing effects on TERT expression in pancreatic versus lung cancer cells, consistent with the antagonistic pleiotropy observed for this signal. Furthermore, CRISPRi nominated an intronic CLPTM1L variable number tandem repeat (VNTR) as a potent enhancer. Long-read sequencing established VNTR polymorphisms as potential causal variants for the rs465498 signal. We showed that Hippo-pathway transcription factors mediate VNTR enhancer activity in lung and pancreatic cancer cells. Together, these findings indicate that cancer susceptibility at 5p15.33 may be mediated by both SNPs and VNTRs and provide an integrated framework for resolving complex pleiotropic loci.

Robotic-assisted reverse shoulder arthroplasty has recently entered early limited clinical use, with the goal of improving the execution of preoperative plans and reducing malposition outliers that increase complication rates and health-care costs. This Innovation article reviews the rationale for this technology, explores its potential impact, examines key implementation challenges, and highlights the future directions needed to determine its ultimate value.

Abstract Context Osteoanabolic therapy for osteoporosis should be followed by antiresorptive treatment (“consolidation”) to preserve bone mass. Although osteoanabolic therapy is increasingly endorsed for primary fracture prevention, real-world implementation of consolidation remains unknown. Objective To assess patterns and predictors of timely consolidation after osteoanabolic therapy for primary fracture prevention. Methods We conducted a retrospective cohort study using US commercial, Medicare Advantage, and traditional Medicare claims data. Adults aged ≥50 years who initiated romosozumab, teriparatide, or abaloparatide between 2011 and 2022 were included. The primary outcome was timely consolidation, defined as initiation of an antiresorptive medication within 3 months after osteoanabolic therapy. Secondary outcomes included delayed consolidation (initiation >3 months after completion), osteoanabolic restart following a treatment gap, and no consolidation therapy. We further distinguished participants who completed the full osteoanabolic course, defined as ≥12 months for romosozumab and ≥18 months for teriparatide or abaloparatide. Multivariable logistic regression models were used to identify predictors of consolidation patterns. Results Among 15 389 patients (mean age 70.6 years; 89% women; 85% White), 30% completed osteoanabolic therapy (median, 12.9 months for romosozumab; 23.4 months for teriparatide/abaloparatide). Overall, 25% had timely consolidation, 24% delayed consolidation, 12% restarted osteoanabolics, and 40% had no consolidation; only 12% completed therapy with timely consolidation. Timely consolidation was more likely with endocrinology (OR 1.31; 95% CI, 1.17-1.48) or rheumatology (OR 1.45; 1.30-1.62) vs primary care, and less likely among patients aged 50-64 years (OR 0.67; 0.58-0.77) vs 65-74, those enrolled in Medicare Advantage (OR 0.85; 0.74-0.98) vs traditional Medicare, or those recently hospitalized (OR 0.88; 0.80-0.97). Odds increased over time (OR 4.27; 3.63-5.05 for 2021 vs 2011 starts). Conclusion Only 1 in 4 patients received timely consolidation therapy after osteoanabolic treatment, and 1 in 8 completed the full sequence. Strategies to improve consolidation in primary fracture prevention are needed.