Citations

Precis: In this retrospective population-based cohort study, patients with open-angle glaucoma were more likely to develop obstructive sleep apnea than matched controls from the general population of Olmsted County, Minnesota. Purpose: To investigate the risk of obstructive sleep apnea (OSA) in patients with open-angle glaucoma (OAG) compared to the risk of OSA in matched controls from a general population. Patients and Methods: Retrospective population-based case-control study comparing the incidence of OSA with a diagnosis of OAG (cases) to the incidence of OSA in matched patients without OAG (controls) from Olmsted County, Minnesota between January 1, 1980 and December 31, 2018. Patients were identified using the Rochester Epidemiology Project. Medical records were reviewed to confirm OAG diagnoses. OSA diagnoses were based on sleep study data and polysomnography parameters. Cumulative probability of OSA was estimated using the Kaplan-Meier method. Results: This cohort had 7,000 patients: 3,500 case patients with OAG diagnosed at any time point, and 3,500 matched control subjects without OAG, with all OAG and OSA diagnoses manually confirmed. There was no statistically significant difference of OSA frequency prior to index date in cases versus controls ( P =0.52). There were 371 (12.5%) cases who developed OSA after glaucoma diagnosis compared to 297 (10.2%) patients who were diagnosed with OSA after the index date ( P =0.0068). The multivariate hazard ratio for diagnosis of OSA after OAG diagnosis was 1.17 ( P =0.047, 95% CI: 1.00, 1.36). The 10-year absolute risk of OSA after OAG diagnosis was 12.5%, and risk of OSA increased with time. Conclusion: In this population-based study, patients with OAG were more likely to be diagnosed with OSA compared to patients without OAG, suggesting a possible association that may justify increased clinical awareness.

Krabbe disease (KD) is a rare, inherited leukodystrophy associated with early mortality. Infantile Krabbe disease (IKD) was added to the Recommended Uniform Screening Panel (RUSP) in July 2024. Since then, U.S. states have continued to evaluate implementing KD newborn screening (NBS), and RUSP inclusion has hastened adoption efforts. This study aimed to characterize NBS program responses to this recommendation and identify factors influencing adoption and implementation. A mixed-methods approach was used, including a case review of Wisconsin’s process to add conditions to its NBS panel and a cross-sectional survey of U.S. NBS programs. Responses were obtained from 50 of 51 NBS programs. Twenty-two programs actively screen for KD, with ten additional programs in the implementation process. Screening approaches varied, particularly in psychosine (PSY) cutoff thresholds and testing algorithms. Programs reported a range of adoption pathways, including legislative mandates, advisory committee review, and state-specific nomination processes. Key factors impacting KD NBS adoption included uncertainty regarding treatment safety, efficacy, and availability, ethical considerations related to identifying later-onset disease, and logistical challenges such as laboratory readiness and clinical follow-up resources. This study provides a contemporary overview of national implementation of KD NBS and offers practical insights for programs considering adoption.

Purpose: This study examined whether phoneme-level posterior probabilities generated by an articulatory-informed deep learning model (Phonet) can serve as interpretable and sensitive markers of articulatory impairment in motor speech disorders (MSDs). The goal was to determine whether these features distinguish individuals with and without MSDs and reflect varying severity levels of articulatory impairment. Method: A total of 676 participants (199 with MSDs, 477 without MSDs) completed a remotely captured word repetition task. Speech recordings were phonetically aligned, and posterior probabilities were extracted using a retrained version of the Phonet model, which estimates frame-level probabilities for 23 phonological classes. Segment-level posterior values were computed and compared across groups and severity levels using linear mixed-effects regression models. Results: Posterior probabilities were significantly reduced in the MSD group across all phoneme types. Within the MSD group, posterior probabilities decreased progressively with greater clinical severity, as measured by both perceptual sound distortion ratings and a cumulative articulation error score. Segment-specific analyses revealed systematic differences across phoneme classes, indicating that posterior probabilities capture distinct patterns of articulatory degradation associated with motor speech impairment. Conclusions: Phoneme-level posterior probabilities serve as interpretable, segment-specific indicators of articulatory impairment in MSDs. These features differentiate clinical status and reflect gradations of severity, making them promising tools for augmenting traditional perceptual speech assessments. Their linguistic transparency and scalability support future clinical applications in diagnosis, monitoring, and targeted intervention.

Purpose This study aimed to develop and validate a tool to predict the primary tumor site in patients with metastatic cancer of unknown primary using demographic and clinical variables, including imaging-derived sites of metastasis, without pathology or laboratory data. Methods In this population-based study, data were extracted from the SEER database. Patients diagnosed between 2010 and 2020 with a known primary cancer site, metastatic staging, and metastasis in the bone, liver, brain, and lungs were included. Patients were randomly split into training (70%) and/or testing (30%) sets. A total of 27 primary tumor sites were included. A gradient boosting model was trained to predict the primary tumor site, with random forest and classification tree models evaluated as comparators. Random guess and frequency-weighted guessing models were used for baseline comparison. Results A total of 689,715 patients were included from 9,208,295 queried. The most common primary cancer sites were non–small cell lung cancer (NSCLC) (n = 211,338), intestines (n = 64,187), and pancreas (n = 60,310). The baseline models achieved Top-5 accuracies of 0.179 for a random guess and 0.476 for a frequency-weighted guess. The gradient boosting model achieved Top-1, Top-3, and Top-5 accuracies of 0.40 ± 0.001, 0.680 ± 0.001, and 0.790 ± 0.001, respectively, across 100 Monte-Carlo replicates. Top-5 accuracy was highest for primary NSCLC (0.990 ± 0.001) and intestinal cancer (0.900 ± 0.003). Our model was most influenced by sex (gain = 0.27) and metastasis to bone (gain = 0.20). A web app was constructed: primarycancerpredictor.netlify.app. Conclusion This study developed and evaluated a gradient boosting machine learning model and a user-friendly web tool to aid in the diagnosis of cancer of unknown primary.

Background Obstructive sleep apnea (OSA)–chronic obstructive pulmonary disease overlap syndrome has been linked to worse cardiovascular outcomes. Preserved ratio impaired spirometry (PRISm) is recognized as an early stage of chronic obstructive pulmonary diseas. However, whether the coexistence of OSA and PRISm confers increased cardiovascular risk remains unknown. Methods Using data from the Sleep Heart Health Study, participants were categorized into 4 groups according to OSA and PRISm status. The primary outcome was major adverse cardiovascular and cerebrovascular events. The secondary outcome was cardiovascular disease mortality. Multivariable Cox regression models were used to evaluate the association of these groups with the risk of cardiovascular outcomes. Results During a median follow‐up of 11.45 years, the incidence of major adverse cardiovascular and cerebrovascular events was 17.00% in non‐OSA with normal lung function, 27.52% in non‐OSA with PRISm, 25.28% in OSA with normal lung function, and 49.40% in OSA with PRISm. After multivariable adjustment, compared with non‐OSA with normal lung function, the adjusted hazard ratios (HRs) for major adverse cardiovascular and cerebrovascular events were 1.20 (95% CI, 0.82–1.75) in non‐OSA with PRISm, 1.04 (95% CI, 0.89–1.21) in OSA with normal lung function, and 1.52 (95% CI, 1.08–2.12) in OSA with PRISm. Corresponding HRs for CVD mortality were 2.02 (95% CI, 1.26–3.24), 1.05 (95% CI, 0.81–1.35), and 2.17 (95% CI, 1.41–3.34), respectively. A significant sex interaction was observed ( P for interaction=0.009), with the association present only in women. Conclusions The coexistence of OSA and PRISm is independently associated with increased risk of adverse cardiovascular outcomes. This association is observed predominantly in women.

Obstructive sleep apnea (OSA) is a common, heterogeneous disorder managed through a multidisciplinary pathway that connects recognition, physiologic testing, treatment selection, specialty referral, insurance coverage, and longitudinal reassessment. This narrative review provides a clinically oriented, system-level overview of the adult OSA care ecosystem in the United States for an international clinical audience, with particular emphasis on the evolving role of otolaryngology. We describe the organization and complementary functions of sleep physicians, otolaryngologists, dentists, and allied health professionals. We also outline the roles of polysomnography and home sleep apnea testing, along with major therapeutic pathways including positive airway pressure, oral appliance therapy, weight management, upper-airway surgery, and neurostimulation. Otolaryngology provides an important interface between physiologic diagnosis and airway-specific treatment by integrating anatomic evaluation, sleep-state airway phenotyping, intervention selection, and post-treatment reassessment. Recent advances in neurostimulation, pharmacologic weight management, wearable and home-based monitoring, airflow analysis, and artificial intelligence are expanding opportunities for accessible and phenotype-informed care. Important challenges include underdiagnosis, treatment attrition, fragmented transitions, variable coverage, and reliance on the apnea–hypopnea index. Although health-care structures differ internationally, multidisciplinary coordination, individualized treatment selection, and longitudinal reassessment provide broadly applicable principles for developing patient-centered OSA programs.

Cardiovascular responses exhibited both sex-dependent and sex-independent alterations in acute pancreatitis models of differing severity. In cerulein-induced mild acute pancreatitis, both sexes showed enhanced cardiac systolic function with only modest differences in vascular permeability. In severe acute pancreatitis, vascular permeability was similar between sexes, but compensatory cardiac systolic augmentation was preserved only in females. Angiopoietin-2 was identified as a compensatory cardiac mediator in severe disease.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the destruction of insulin-producing pancreatic β cells. Both genetic susceptibility and epigenetic dysregulation contribute to T1D risk. Recent studies have reported β-cell dysfunction before disease onset, suggesting that β-cell-intrinsic mechanisms contribute to pathogenesis. However, the mechanistic link between disease-associated variants and β-cell dysfunction remains poorly understood. We hypothesized that a subset of T1D-associated variants directly influences cell type-specific candidate cis-regulatory elements (cCREs) by disrupting transcription factor (TF) binding and altering gene expression. To define cell type-specific cCREs in human islets, we integrated publicly available epigenomic datasets, including bulk histone modification profiles and single-cell multiomic data from human pancreatic islets. We combined these data with T1D genome-wide association study (GWAS) variants to identify disease-associated SNPs located within enhancer regions. Using multiomic datasets from PANC-DB, we characterized cell type-specific chromatin accessibility in nondiabetic and T1D islets and identified regulatory regions that may control gene expression in β cells and other islet cell types. We then applied ChromBPNet, a deep learning model that predicts base-pair-resolution chromatin accessibility from scATAC-seq data, to evaluate how specific variants may alter local regulatory activity. In parallel, we used TF footprinting to nominate TFs likely to bind these variant-containing regions. These analyses identified several T1D-associated SNPs predicted to alter chromatin accessibility at candidate TF binding sites, suggesting mechanisms by which noncoding variants may contribute to β-cell dysfunction and T1D susceptibility. Our findings link T1D-associated variants to cell type-specific enhancer activity and regulatory pathways and provide candidates for future mechanistic studies.