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PubMed2026

Premature closure underlies bias in medical diagnosis in students: A randomised controlled experiment.

OBJECTIVE: The purpose of the study reported in this article was to shed light on the cognitive mechanism mediating between biasing information and diagnostic error. The literature suggests at least two different hypotheses: premature closure leading biased participants to spend less time on diagnosis or increased competition between diagnostic hypotheses. The latter hypothesis predicts that biased participants would spend more time reaching a diagnosis. METHOD: Using the salient distracting findings (SDF) experimental paradigm, we biased 58 fourth-year medical students while diagnosing 12 clinical vignettes in a within-group incomplete block design under three conditions: cases presented without SDF, with SDF at the beginning and with SDF at the end. For each of these conditions, diagnostic accuracy, the number of SDF-related mistakes and time per word needed to process the case were recorded. The data were analysed using linear mixed modelling. Estimated marginal mean scores were reported. RESULTS: Participants confronted with salient distracting features (SDFs) at the beginning of a clinical case demonstrated significantly lower diagnostic accuracy (mean 0.11) compared with the No-SDF condition (0.27), representing a 61% reduction (F2,693 = 11.995, p < 0.001), and made more SDF-related mistakes (F2, 693 = 16.395, p < 0.001). When SDFs were presented at the end of the case, diagnostic accuracy was also reduced (mean 0.17; 36% reduction), but processing time did not differ from the No-SDF condition. Only early presentation of SDFs was associated with reduced processing time per word (F2,636 = 4.799, p < 0.01), consistent with premature closure. CONCLUSION: These findings demonstrate that biasing information increases diagnostic error in medical students and that only early bias is associated with reduced information processing. The data do not support the competition hypothesis for early bias, as processing time did not increase under biasing conditions. Premature closure can therefore be directly observed rather than inferred, inviting further research.

Humans↗

PubMed2024

Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data.

BACKGROUND: Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data. METHODS: For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors' evaluations to probability scores using Gaussian mixture based density estimation. We exploited formal concept analysis and decision tree for elicited model validation and explanation. We also proposed an algorithm for combining independent probability estimates from multiple modalities, such as merging the EM probability score from a deep learning image classifier with the elicited score from patient data. RESULTS: We successfully elicited opinions from fifteen expert doctors to create a model for obtaining EM probability scores from patient data. CONCLUSIONS: The elicited probability score and the proposed algorithm can be utilized to make image based deep learning Lyme disease pre-scanners robust. The proposed elicitation and validation process is easy for doctors to follow and can help address related medical diagnosis problems where it is challenging to collect patient data.

Humans↗

PubMed2024

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping.

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.

Tandem Mass Spectrometry↗

PubMed2026

SGLF-Net:Staged Global-to-Local Cross-Scale Fusion Network for Colonoscopic Polyp Segmentation.

Polyp segmentation in colonoscopy images plays a pivotal role in computer-aided medical diagnosis and the early prevention of colorectal cancer. However, existing methods often suffer from performance degradation when confronted with extreme polyp scale variation and polyp boundary ambiguity. To address these challenges, we propose the Staged Global-to-Local Cross-Scale Fusion Network (SGLF-Net), which adopts a novel staged global-to-local learning paradigm to progressively refine segmentation from coarse global semantics to fine-grained local details. Specifically, the Global Semantic Perception Stage integrates a Swin Transformer Encoder and a Dynamic Attentive Decoder (DAD) to construct comprehensive multi-scale contextual representations. The Local Detail Refinement Stage employs an Edge-aware Dynamic Attentive Decoder (E-DAD) to enhance structural fidelity and boundary precision through explicit edge-guided supervision. Furthermore, we introduce the Cross Spatial-Scale Feature Aggregation and Reconstitution (CSSAR) module, equipped with hybrid attention mechanisms, to facilitate efficient semantic structural interaction between the two cascaded stages. Extensive experiments on five public benchmark datasets demonstrate that SGLF-Net consistently outperforms state-of-the-art methods in both segmentation accuracy and boundary preservation.

Journal Article↗

PubMed2026

ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.

Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding-self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10 × Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.

Spatial Transcriptomics↗

PubMed2024

End of Life Events and Causes of Death in Danish Long-Lived Siblings: Reduced Dementia Risk Compared to Sporadic Long-Livers.

BACKGROUND: Better physical robustness and resilience of long-lived siblings compared to sporadic long-livers has been demonstrated in several studies. However, it is unknown whether long-lived siblings also end their lives better. OBJECTIVE: To investigate end-of-life (EoL) events (dementia diagnosis, medication, hospitalizations in the last 5 years of life), causes of death, and location of death in long-lived siblings compared to matched sporadic long-livers from the Danish population. METHODS: Long-lived siblings were identified through three nationwide Danish studies in which the inclusion criteria varied, but 99.5% of the families had at least two siblings surviving to age 90 + . Those who died between 2006 and 2018 were included, and randomly matched with sex, year-of-birth and age-at-death controls (i.e., sporadic long-lived controls) from the Danish population. RESULTS: A total of 5,262 long-lived individuals were included (1,754 long-lived siblings, 3,508 controls; 63% women; median age at death 96.1). Long-lived siblings had a significantly lower risk of being diagnosed with dementia in the last years of life (p = 0.027). There was no significant difference regarding the number of prescribed drugs, hospital stays, days in hospital, and location of death. Compared to controls, long-lived siblings presented a lower risk of dying from dementia (p = 0.020) and ill-defined conditions (p = 0.030). CONCLUSIONS: In many aspects long-lived siblings end their lives similar to sporadic long-livers, with the important exception of lower dementia risk during the last 5 years of life. These results suggest that long-lived siblings are excellent candidates for identifying environmental and genetic protective factors of dementia.

Humans↗

PubMed2026

[Tics and Tourette Syndrome].

Tics disorders and Tourette syndrome (TS) are neurodevelopmental conditions characterized by motor and/or vocal tics with onset in childhood. Their clinical presentation is heterogeneous and fluctuating over time, with exacerbations related to emotional, environmental, and medical factors. Diagnosis is clinical and based on medical history and neurological examination, following DSM-5-TR criteria, with ancillary testing rarely required. The prevalence of TS is estimated at 0.7%, while transient tic disorders affect up to 10% of children. The natural history is generally favorable, with symptom improvement during adolescence, although a subset of patients continues to experience tics into adulthood. Most individuals with TS present psychiatric comorbidities, particularly attention-deficit/hyperactivity disorder and obsessive-compulsive disorder, which significantly impact quality of life and should be prioritized in management decisions. Treatment is recommended only when tics cause functional impairment and follows a stepwise approach including psychoeducation, behavioral interventions, and individualized pharmacological therapy. Comprehensive behavioral intervention for tics is considered first-line treatment when available. Alpha-2 adrenergic agonists and dopamine antagonists are the most commonly used pharmacological options. Neuromodulation therapies are reserved for severe, refractory cases. These recommendations from the Ibero-American Academy of Pediatric Neurology summarize current evidence and provide a practical, updated framework for the diagnosis and management of tic disorders and Tourette syndrome in pediatric patients.

Humans↗

PubMed2026

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

Humans↗

PubMed2026

Congenital Laryngomalacia: Pathophysiology, Clinical Spectrum, and Holistic Management.

Congenital laryngomalacia is the most common cause of infant stridor and arises from interacting structural, neuromuscular, and inflammatory mechanisms that produce dynamic supraglottic collapse. Disease severity spans mild stridor to significant obstruction, aspiration, and failure to thrive, often influenced by comorbid medical conditions. Diagnosis relies on flexible laryngoscopy supported by instrumental swallowing studies and microdirect laryngoscopy and bronchoscopy when indicated. Most infants respond to conservative management, particularly targeted feeding modifications, while acid suppression offers benefit in selective cases. Supraglottoplasty provides effective, durable improvement for severe disease and significantly enhances infant outcomes and family quality of life.

Humans↗

PubMed2024

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Humans↗

PubMed2026

Polygenic risk scores and lifestyle factors predicting new onset of type 2 diabetes in the Japanese general population.

PURPOSE: This study investigated the association of polygenic risk scores (PRS) and lifestyle factors with type 2 diabetes mellitus development in Japanese populations and evaluated whether PRS can improve diabetes risk prediction beyond traditional risk factors. METHODS: We conducted a cross-sectional and a longitudinal study using the Shika resident cohort (n = 895) and the Toshiba worker cohort (n = 7019), respectively. Participants were categorized into low, intermediate, and high genetic risk groups using PRS constructed with genome-wide association study data from East Asian populations. We defined diabetes based on hemoglobin A1c, fasting blood glucose, self-reported diagnosis, or medication use. The associations of PRS and lifestyle factors with diabetes development were analyzed using multivariate logistic regression and Cox proportional hazards models. RESULTS: Higher PRS were associated with increased diabetes risk in both cohorts (resident cohort: odds ratio 4.51, 95% CI 2.53-8.04; worker cohort: hazard ratio 1.50, 95% CI 1.23-1.83 for high vs low PRS), which remained consistent across age, body mass index, and comorbidities. Regular exercise, absence of hypertension, and absence of dyslipidemia were associated with lower diabetes risk, particularly in the high PRS group. The addition of PRS to conventional prediction models improved the discrimination of diabetes risk. MAIN CONCLUSION: PRS are associated with diabetes risk in Japanese general populations, independent of traditional risk factors. Nonetheless, healthy lifestyle habits may reduce diabetes risk even among genetically susceptible individuals, which support the utility of PRS for personalized diabetes risk assessment and prevention strategies.

Adult↗

PubMed2026

Association of Genetic Liability to Psychiatric Disorders with Peripheral Metabolic Dysregulation.

IMPORTANCE: Individuals with psychiatric disorders face elevated cardiometabolic risk which is linked to increased mortality. The extent to which this reflects shared pathogenesis or the downstream effects of illness and treatment remains poorly understood. OBJECTIVE: To characterize the direct pleiotropic effects of psychiatric genetic liability on circulating metabolites and aggregate cardiometabolic risk, independent of psychiatric diagnosis and psychotropic medication use. DESIGN SETTING AND PARTICIPANTS: Cross-sectional analysis of Mass General Brigham Biobank participants with metabolomic profiling, genomic data, and linked electronic health records. EXPOSURES: Genetic liability to nine psychiatric disorders quantified using polygenic risk scores (PRS): attention deficit/hyperactivity disorder (ADHD), anorexia nervosa (ANO), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), PTSD, schizophrenia (SCZ), and substance use disorder (SUD). MAIN OUTCOMES AND MEASURES: 249 circulating metabolites and four metabolomic risk scores (MRS) for type 2 diabetes, myocardial infarction, ischemic stroke, and vascular dementia. PRS-metabolite associations were estimated using nested models adjusting for lifetime psychiatric diagnosis and psychotropic medication use. RESULTS: Across 25,290 participants, we identified 604 significant PRS-metabolite associations (Bonferroni p< 1.36 x 10-4), of which 89% persisted after adjustment for lifetime diagnosis and medication use, suggesting that the direct genetic effects on metabolism are largely independent of illness or treatment. PRS for MDD, PTSD, and ADHD showed the most extensive dysregulation, with a transdiagnostic pattern of elevated lipids and systemic inflammation, specifically triglycerides (β = 0.04 to 0.05, all p< 4.4 x10-13) and glycoprotein acetyls (β = 0.05, all p< 2.2 x10-16). Notably, PRS for SCZ and BD showed minimal metabolite dysregulation despite having the strongest association with their target diagnoses. PRS for MDD, PTSD, ADHD, and SUD were associated with increased MRS across cardiometabolic conditions (β = 0.03 to 0.08, all p< 2.1 x10-4). Sensitivity analyses controlling for BMI or excluding participants without any psychiatric history (N: 21,305 and 11,150, respectively) showed a similar pattern. CONCLUSIONS AND RELEVANCE: Psychiatric genetic liability is associated with systemic metabolic dysregulation independent of illness onset or treatment, supporting a partially pleiotropic basis for psychiatric-cardiometabolic comorbidity.

Journal Article↗

PubMed2025

Modeling Early-Onset Cancer Kinetics Reveals Changes in Underlying Risk and the Impact of Population Screening.

UNLABELLED: Recent studies have reported increases in early-onset cancer cases (diagnosed less than 50 years of age) and raised questions about whether the increase is related to earlier diagnosis from nonspecific medical tests as reflected by decreasing tumor-size-at-diagnosis (apparent effects) or actual increases in underlying cancer risk (true effects), or both. The classic Multistage Clonal Expansion (MSCE) model assumes cancer detection at the first malignant cell's emergence, although later modifications have included lag-times or stochasticity in detection to represent the delay in tumor detection. In this study, we introduced an approach to explicitly incorporate tumor-size-at-diagnosis in the MSCE framework accounting for improvements in cancer detection over time to distinguish between apparent and true increases in early-onset cancer incidence. The model was structurally identifiable and provided better parameter estimation than the classic model. The model was applied to colorectal, breast, and thyroid cancers to examine changes in cancer risk while accounting for detection improvements over time in three representative birth cohorts (1950-1954, 1965-1969, and 1980-1984). The analyses suggested accelerated carcinogenic events and shorter mean sojourn times (the average time from the first malignant cell emergence to cancer detection) in more recent cohorts. Furthermore, using this model to examine the screening impact on the incidence of breast and colorectal cancers, for which both have established screening protocols, provided results that align with well-documented differences in screening effects between these cancers. These findings underscore the importance of incorporating tumor-size-at-diagnosis in cancer modeling and support true increases in early-onset cancer risk in recent years for breast, colorectal, and thyroid cancers. SIGNIFICANCE: A model of early-onset cancer trends that distinguishes true risk from detection effects accurately captures cancer kinetics, trends in cancer progression, and the impact of screening, which could inform cancer prevention strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗

PubMed2026

Global inequalities in cardiometabolic care and achievable cardiovascular risk reduction by wealth, region, and sex: a pooled analysis of individual participant data from 76 countries.

BACKGROUND: Wealth-related inequalities affect cardiometabolic health worldwide, but their implications for cardiometabolic care and potentially preventable cardiovascular disease remain poorly understood. We aimed to quantify wealth-related inequalities in the care cascade for hypertension, diabetes, and hypercholesterolaemia by wealth quintile, region, and sex. METHODS: In this cross-sectional, individual-level analysis, we analysed harmonised, nationally representative health examination surveys conducted in five WHO regions. Adults aged 18 years or older with data on age, sex, wealth, and at least one cardiometabolic outcome were eligible. All variables in the surveys were obtained from standardised in-person examinations. We evaluated hypertension, diabetes, and hypercholesterolaemia and applied a care cascade of disease awareness, treatment, and control for each condition uniformly across all surveys. Disease status was defined from measured biomarkers, self-reported diagnosis, or current medication; awareness and treatment were based on self-reported information, and control on measured biomarkers. Each indicator was expressed as the proportion of all individuals with the corresponding condition. Socioeconomic position was assessed using household wealth indices derived within each survey, and participants were ranked within each country and categorised into country-specific quintiles (quintile 1 to quintile 5), with quintile 1 including those with the least household wealth. Inequality was quantified by the quintile 5 minus quintile 1 difference, the slope index of inequality (SII), and relative index of inequality (RII). Predicted 10-year cardiovascular risk was estimated with the Globorisk equations, and trial-derived relative risk reductions were applied to estimate achievable absolute risk reduction. The ASANDE consortium is registered with ClinicalTrials.gov (NCT07427355). FINDINGS: We analysed data from 109 surveys conducted in 76 countries between 2002 and 2024. 315 403 (65·9%) of 478 947 survey participants with available data were included in this analysis (median age 40 years [IQR 30-52], 185 209 [58·7%] women, and 130 194 [41·3%] men). Inequalities widened progressively across the care cascade in all regions and were most pronounced for disease control. Pooled across regions, the SII for control was 4·4% (95% CI 2·4-6·4) for hypertension (RII 1·1, 1·1-1·2), 4·8% (0·6-9·0) for diabetes (RII 1·1, 1·0-1·2), and 6·5% (3·8-9·2) for hypercholesterolaemia (RII 1·1, 1·0-1·1). However, regional patterns varied substantially. In the region of the Americas, disease control consistently favoured wealthier individuals (SII 9·2% for hypertension, 4·8-13·5; RII 1·2, 1·1-1·3). In the African region, coverage was uniformly low, and the largest absolute inequality favoured individuals with the least wealth, particularly for hypercholesterolaemia treatment (SII -37·6%, -49·7 to -25·5; RII 0·6, 0·5 to 0·7). Baseline cardiovascular risk was higher in individuals with the least wealth than among the wealthiest (13·6% vs 12·2%), but achievable absolute risk reduction was correlated with baseline risk rather than with treatment coverage: achievable reduction was greatest in the European Region (3·9%) and lowest in the Africa region (2·5%). Across all regions, achievable absolute risk reduction was greater in men than in women (4·6% vs 3·5% in the European region). INTERPRETATION: The populations with the largest treatment gaps are not necessarily those that could achieve the greatest absolute reduction in cardiovascular risk through treating individuals who are currently untreated. In settings where coverage is uniformly low, expanding the supply of care matters more than redistributing access to it. Moreover, because socioeconomic inequalities widen after diagnosis, screening alone is unlikely to reduce disparities unless accompanied by sustained access to treatment. Policy should prioritise overall population health over maximise equity within the population. FUNDING: None.

Journal Article↗

PubMed2026

Psychiatric Diagnoses and Psychotropic Medications Among Military-Affiliated Adolescents and Young Adults With Polycystic Ovary Syndrome.

PURPOSE: To study rates of psychiatric diagnoses and psychotropic medication prescription among U.S. military-affiliated adolescents and young adults (AYA) with polycystic ovary syndrome (PCOS). METHODS: This retrospective matched cohort study included U.S. military-affiliated AYA (aged 15-21 years) enrolled in TRICARE Prime for at least 6 months during the surveillance period (January 2016 to October 2023). Military-affiliated AYA were grouped into three categories: individuals diagnosed with PCOS (N = 6,911), age-matched individuals with no diagnosed PCOS symptoms (N = 35,814), and individuals with diagnosed symptoms suggestive of PCOS (N = 2,136). The presence of a psychiatric diagnoses and prescriptions for psychotropic medications were obtained via the International Classification of Diseases, 10th Revision, Clinical Modification codes and National Drug codes, respectively. RESULTS: AYA with diagnosed PCOS had higher odds of having a psychiatric diagnosis and being prescribed a psychotropic medication compared to an age-matched comparison group (psychiatric diagnosis odds ratio [OR] = 2.48 [2.35-2.62], medication OR = 2.14 [2.03-2.25]) and individuals with symptoms suggestive of PCOS (psychiatric diagnosis OR = 1.11 [1.003-1.23], medication OR = 1.16 [1.05-1.28]). DISCUSSION: The odds of psychiatric comorbidities and psychotropic medication prescription were more than twice as high as among U.S. military-affiliated AYA with PCOS. More research is needed to determine whether health-care utilization and military-related factors impact mental health outcomes among AYA with PCOS. Additionally, tailored, multidisciplinary mental health services for AYA with PCOS are needed.

Humans↗

PubMed2025

A prediction model for metachronous colorectal cancer: development and validation.

BACKGROUND: Being able to estimate the risk of metachronous disease in a patient with colorectal cancer (CRC) could enable risk-appropriate surveillance. The aim of this study was to develop a risk-prediction model to estimate individual 10-year risk of metachronous disease following a CRC diagnosis. METHODS: A population-based cohort of patients with CRC was recruited soon after diagnosis between 1997 and 2012 from the United States, Canada, and Australia. Cox regression with the least absolute shrinkage and selection operator penalization was used to identify factors that predicted the risk of a new primary CRC diagnosed at least 1 year after the initial CRC diagnosis. Potential predictors included demography, anthropometry, lifestyle factors, comorbidities, personal and family cancer history, medication use, age at diagnosis, and pathological features of the first CRC. Internal validation through bootstrapping was used to evaluate the discrimination and calibration. RESULTS: We included 6085 CRC cases; 138 (2.3%) of these cases were diagnosed with metachronous disease over a median of 12 years (IQR = 5-17 years). Metachronous CRC risk was predicted by body mass index; smoking status; level of physical activity; family history of cancer and synchronous CRC; stage, grade, histological type, and DNA mismatch repair status; and age at diagnosis of the first CRC. The model was valid with a C statistic of 0.65 (95% CI = 0.63 to 0.68) and a calibration slope of 0.873 (SD = 0.087). CONCLUSIONS: Metachronous CRC can be predicted with reasonable accuracy using a prediction model that consists of clinical variables collected as part of routine practice.

Humans↗

PubMed2026

Acute COVID-19 severity and impaired cognitive function up to 32 months after diagnosis: an observational study.

BACKGROUND: Cognitive dysfunction ("brain fog") is a commonly reported post-COVID-19 symptom. Leveraging data from five general population cohorts across four European countries (Estonia, Iceland, Norway, and Sweden), we assessed long-term prevalence of impaired subjective cognitive function among individuals diagnosed with COVID-19 by acute illness severity. METHODS: The included cohorts consisted of adult participants recruited from March 2020 and followed with self-report measures of cognitive function and past COVID-19 infection (except one cohort consisting of clinically confirmed COVID-19 cases) through February 2023. In a cross-sectional analysis we contrasted the prevalence of impaired cognitive function among individuals with and without a COVID-19 diagnosis, overall and by illness severity up to 32 months post-diagnosis. We adjusted for age, gender, education, relationship status, binge drinking, body mass index, previous psychiatric diagnosis, number of chronic medical conditions, and response period. In a longitudinal analysis, we assessed potential changes in cognitive function scores before and after COVID-19 diagnosis. RESULTS: The study population consisted of 153,841 participants (71% women), with 31,359 (20.4%) reporting a positive COVID-19 test. Overall, a COVID-19 diagnosis was not statistically significantly associated with increased prevalence ratio (PR) of impaired cognitive function (PR 1.30 [95% CI: 0.98-1.71]). Individuals bedridden due to COVID-19 for 1-6 days (PR 1.38 [95% CI 0.96-1.99]) or ≥ 7 days (2.59 [1.55-4.33]) had higher prevalence of impaired cognitive function compared to those never diagnosed, while individuals never bedridden had a lower prevalence to those never diagnosed with COVID-19 (0.89 [0.80-1.00]). These findings were corroborated in the longitudinal analysis where a pre- to post diagnosis decline in cognitive function was observed among individuals bedridden due to COVID-19 (p < 0.0001). CONCLUSIONS: The data indicates that a severe COVID-19 acute illness course is associated with impaired cognitive function up to 18-32 months after COVID-19 diagnosis.

Adult↗

PubMed2026

Exome sequencing and large-scale analysis of electronic medical record-linked biobank data identify candidate deafness genes.

INTRODUCTION: Rapid advances in whole-exome sequencing (WES) have enabled large-scale detection of pathogenic variants. Although hundreds of genes are implicated in hearing loss, up to half of inherited cases remain unsolved, limiting eligibility for gene therapy trials that require genetic diagnosis. Biobanks and electronic medical records (EMRs) offer opportunities to integrate genomic and clinical data at scale and expand the spectrum of hearing loss genes. Despite clinical value, EMRs often lack key information such as inheritance patterns, posing challenges for accurate interpretation. METHODS: WES was performed on DNA samples from 1038 hearing-impaired patients enrolled in the Maccabi Research and Innovation Center Tipa Biobank. Clinical data were extracted from EMRs. Audiograms were available for all cases, although data on age of onset, family history and mode of inheritance were mostly unavailable. We applied a scalable bioinformatics analysis strategy for high-throughput annotation, filtering and prioritisation of WES variants across more than 1000 patients, designed to accommodate incomplete and heterogeneous clinical records. RESULTS: Using this approach, 15% of cases were solved or potentially solved through known or novel variants in established deafness genes. Homozygous variants in novel candidate genes were identified in 3% of cases. Functional characterisation was performed for promising candidate genes to validate their role in the ear. CONCLUSION: These findings demonstrate that WES can determine disease aetiology in large, genetically heterogeneous populations, even in the context of incomplete clinical data. This approach supports large-scale genetic screening and provides a framework for identifying patients who may benefit from emerging gene-based therapies.

Genetic Testing↗