Journal of Medical Genetics

Background: Many at-risk patients lack access to genetic services due to a genetic counselor (GC) workforce shortage. Little is known about how digital alternatives impact patients with and without cancer who meet criteria for genetic testing. Methods: eREACH2 is a randomized 4-arm non-inferiority trial where pre-test (visit 1) and/or return of results (visit 2) GC counseling was replaced with a patient-centered digital intervention. Arms include: A (GC/GC), B (GC/digital), C (digital/GC) and D (digital/digital). Primary outcomes were non-inferiority in uptake of genetic services and change in genetic knowledge and general anxiety from baseline to post-disclosure of results (T0-T2). Secondary cognitive and affective outcomes were assessed using non-inferiority ANOVAs and equivalency chi-squared tests in intention-to-treat and per-protocol analyses. Findings: 773 participants were recruited nationwide; 46.6% from rural areas. Mean age was 51 years (range 20-87), 13% male, 12% non-white, 29% had less than a college education, and 33% had a personal history of cancer. 584 (76%) patients completed testing (14% had a positive result, 16% had a VUS). In the primary ITT analyses, we met the non-inferiority for uptake of genetic services and anxiety, but results were inconclusive for knowledge. Secondary outcomes were heterogeneous across arms. Arm C demonstrated consistently favorable effects, while Arms B and D showed less favorable outcomes in select domains (e.g. satisfaction and MICRA). Patients who received positive or VUS results via digital disclosure had significantly higher MICRA scores - indicating greater negative response to testing. Interpretation: In this large, randomized trial of patients with and without cancer, the eREACH intervention was effective for pre-test counseling, but inconclusive for digital disclosure of results. Exploratory analyses suggest that digital delivery could be a reasonable alternative for individuals receiving negative results, while those receiving positive or VUS results may derive some short-term psychosocial benefit from GC disclosure.

Background and Objectives: Deep sequencing of brain tissue in the research setting has established that mosaic variants are a major cause of malformations of cortical development (MCDs) and epilepsy. However, genetic testing in the clinical setting primarily detects germline variants using clinically accessible samples. We aimed to determine the diagnostic yield and clinical utility of deep sequencing in the clinical setting to identify pathogenic mosaic variants for this population. Methods: We performed a retrospective cohort analysis of individuals at Boston Children's Hospital with MCDs with or without epilepsy who received clinical deep sequencing between September 2017 and February 2026. Demographic, clinical, and genetic testing data were abstracted from the medical record. For individuals without systemic features, we classified brain tissue as an affected tissue sample. For individuals with systemic features, we classified brain or relevant non-brain tissue as affected. The primary outcome was the diagnostic yield of clinical deep sequencing performed using affected vs unaffected tissue samples. The secondary outcome was the clinical utility of genetic diagnoses. Results: Our cohort included 37 individuals (19/37 (51%) female, 18/37 (49%) male) with MCDs, of whom 35/37 (95%) had epilepsy (25 with brain tissue samples available from epilepsy surgery) and 8/37 (22%) had systemic features. Most (35/37 (95%)) had dysplasia phenotypes on MRI and 12/27 (44%) with pathology available had Focal Cortical Dysplasia Type I or II. The diagnostic yield was 53% (17/32; 16 mosaic and 1 germline variant) when clinical deep sequencing was performed using an affected tissue sample vs 0% (0/6) using an unaffected tissue sample (p=0.016). Of the diagnosed cases, 13/17 (76%) had testing performed on brain tissue (1 with systemic features) and 4/17 (24%) on non-brain tissue (3 buccal and 1 duodenal tissue, all with systemic features). All but one diagnosis involved the mTOR pathway. All diagnoses had clinical utility. Discussion: Clinical deep sequencing, when performed using an affected tissue sample, has high diagnostic yield and clinical utility for individuals with MCDs, especially dysplasia phenotypes, and epilepsy. Our findings support implementation of clinical deep sequencing for this population, especially as the genetic diagnoses have implications for emerging precision therapies.

Chemoradiotherapy (CRT) is the standard-of-care therapy for many solid malignancies, yet predictive biomarkers of treatment response remain limited. We identified a germline single nucleotide polymorphism (SNP) in an intrinsically disordered region of the lysine demethylase KDM3C/JMJD1C (p.S464T) that is associated with CRT outcomes in locally advanced rectal cancers (LARC) and head and neck squamous cell carcinoma (LA-HNSCC). In silico modeling with AlphaFold predicted S464T substitution influenced interaction between phosphorylated KDM3C and RNF8 FHA domain. In cellular models, conversion of S464 to T464 increased sensitivity to DNA-damaging agents. S464T substitution impaired damage-induced MDC1-RAP80 signaling and downstream RAP80-BRCA1 colocalization. SNP carrying cells impaired DNA repair causing genotoxic stress that is associated with increased cGAS-cGAMP innate immune signaling and increased apoptosis. Population analyses with the SNP highlighted an increase incidence of UV-induced skin and other cancers, linking inherited variation in the chromatin regulatory gene KDM3C to genome instability, cancer risk, and therapeutic vulnerability.

Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.

Parkinsons disease (PD) arises through disruption of multiple interconnected cellular processes, but the genetic contributions to these processes may differ across ancestries. We investigated functional convergence among genes harboring pathogenic or likely pathogenic (P/LP) variants and variants of uncertain significance (VUS) in a multicenter Indian cohort recruited through the Genetics of Parkinsons Disease in India Young Onset Parkinsons Disease project (GOPI YOPD). The cohort included 668 participants (463 males 69.3%) with a mean age at motor onset of 39.4+/-8.8 years. P/LP variants and VUS identified through previously reported whole-exome or whole genome sequencing were retained as separate evidential categories. The P/LP-associated gene set comprised 11 unique genes and the VUS associated set comprised 40 unique genes. Separate STRING functional-enrichment analyses evaluated Gene Ontology Biological Process, Molecular Function and Cellular Component terms, KEGG pathways, WikiPathways and STRING local network clusters. Terms meeting a Benjamini Hochberg false discovery rate threshold of <0.05 were organized into eight non-mutually-exclusive ontology/pathway categories. Gene to pathway mappings were subsequently projected to individual participants to estimate pathway representation and examine clinical associations. At least one reportable P/LP variant or VUS was identified in 336/668 participants (50.3%): 35 had a P/LP variant alone, 282 had VUS alone and 19 had a P/LP variant together with VUS in one or more additional genes. The most frequently represented categories were mitochondrial organization (247/336, 73.5%), autophagy related processes (228/336, 67.9%) and regulation of synaptic vesicle transport (201/336, 59.8%). PRKN was the most frequent P/LP-associated gene, occurring in 29/54 P/LP carriers, followed by PLA2G6 and PINK1. Lysosomal transport was represented exclusively by VUS-associated genes, particularly GBA1, VPS13C and LRRK2. Among P/LP carriers, additional VUS in distinct genes were not associated with age at onset (P = 0.81) or family history (52.6% versus 31.4%; P = 0.15). No pathway phenotype association remained significant after correction for multiple testing. Genetic findings in this Indian cohort converged across an interconnected mitochondrial autophagic lysosomal vesicular network, with different contributions from P/LP-associated and VUS associated gene sets. This study provides the first pathway resolved South Asian genetic profile and a framework for comparative studies across populations.

Obesity is a significant public health concern. Early-onset obesity in the context of rare disease can reflect genetically-mediated pathology or elevated susceptibility through indirect mechanisms. Mapping the diverse characteristics and needs of young people with obesity in the rare disease population is a first step toward mechanistic and translational research. We carried out a retrospective comparative analysis of demographic, genotypic, phenotypic and health service utilisation data for young people with obesity (cases: n=500) and without obesity (controls: n=11,444) from the UK 100,000 Genomes Project rare disease cohort. Cases and controls were recruited prior to genomic diagnosis, across clinical disorder categories. We observed significant association between socioeconomic deprivation and obesity risk. Young people with obesity had significantly higher utilisations of acute care and mental health services, indicating an overall higher health burden. A curated panel of 519 candidate obesity-associated genes demonstrated aggregate association with obesity, although no single gene reached significance. Phenotypic comparison between cases and controls highlighted increased multi-organ and neurological system involvement, highlighting the overlap between neurodevelopmental and obesity risks. Within the case group, we conducted cluster analysis to identify early-onset obesity groups with different phenotypic profiles, potentially arising from different causal pathways - this identified six obesity subgroups of interest, with differing involvement of neurodevelopmental and other systems. Our study confirms that obesity co-occurs with a wide range of factors within the rare disease population, and is associated with significant physical and mental health needs, requiring holistic lifelong care.

Background: Inequalities exist for many health outcomes, but there is limited evidence regarding menstrual symptoms despite their importance for health and wellbeing. We aimed to investigate inequalities in menstrual symptoms according to socioeconomic position and childhood adversity. Methods: In two generations (G0 mothers and G1 offspring) from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK prospective cohort study, we examined associations of multiple indicators of socioeconomic position (SEP) and adverse childhood experiences (ACEs) with menstrual symptoms (pain, abnormal uterine bleeding, and premenstrual syndrome (PMS) measured 3-8-years post-birth in G0 and 17-21-years-old in G1), using multivariable logistic regression. Samples ranged from 4,828 to 9,335 G0 participants and 1,288 to 2,757 G1 participants depending on the exposure-outcome association. Missing data were addressed using multiple imputation and inverse probability weighting. Results: Financial difficulties were associated with greater odds of menstrual pain (G1 OR 1.41; 95% CI 1.07, 1.86: G0 OR 1.55; 95% CI 1.36, 1.76) and irregular cycles (G1 OR 1.60; 95% CI 1.12, 2.29: G0 OR 1.48; 95% CI 1.27, 1.72) in both generations, as well as with short/long cycle lengths in G0 only. Lower education and manual social class were also associated with these three menstrual symptoms in at least one generation. Conversely, higher SEP was associated with PMS in both generations. Higher cumulative ACEs were consistently associated with menstrual pain (4+ compared to none: G1 OR 2.15; 95% CI 1.48, 3.11: G0 OR 1.52; 95% CI 1.29, 1.80) and irregular cycles (G1 OR 1.92; 95% CI 1.20, 3.09: G0 OR 1.54; 95% CI 1.26, 1.87) but not cycle length. Lower parental education, financial difficulties, and cumulative ACEs were associated with heavy bleeding in G1 offspring only, whereas financial difficulties, own manual social class, and cumulative ACEs were associated with prolonged bleeding in G0 mothers only. Higher cumulative ACEs were also associated with PMS in G1 offspring only. Conclusions: We found evidence of inequalities according to socioeconomic disadvantage and childhood adversity for multiple menstrual symptoms, although some associations were only observed in one generation. Findings suggest that menstrual symptoms are disproportionately experienced by socially and socioeconomically disadvantaged women.

Background The time trend in long-term survival after a stroke is to some extent unknow due to (relatively) short follow up periods in available data. The objective of this study is to identify and quantify differences in long-term stroke survival in Sweden between men and women and patients with different attained educational levels, comparing two time-periods, 2000-2009 and 2010-2022. Methods This study employs total population Swedish register data pertaining to hospital-based care and mortality due to stroke for the period 2000-2022 in order to estimate survival (all-cause mortality) after ischaemic and haemorrhagic stroke, respectively, and pertaining to attained educational level. Kaplan-Meier survival functions are estimated stratifying for time-period, sex and educational level. Cox regressions are employed to quantify mortality hazard ratios between the strata. Age is taken into account in complementary analyses (supplement). Results Taking only time-period (2000-2009 vs 2010-2022) into account resulted in significantly higher survival in the second period for ischaemic stroke patients (HR: 0.84; 95% CI: 0.83-0.84), while no significant difference could be detected for haemorrhagic stroke. Stratifying for sex showed that men gained more than women in terms of reduced mortality hazard rate between the periods. Further stratifying by educational level and estimating survival separately for men and women showed that, for both men and women, patients with the lowest education were relatively worse off (compared to patients with higher education) in the second period. Further analyses, taking age into account, reversed the relative hazard ratio between men and women, but corroborated the result that low education is associated with poorer outcome than high education. Conclusions The results suggest that there are considerable differences in expected long-term survival after stroke between the sexes, but that this may be due to differences in age between the sexes at the time of stroke. Moreover, lower educational level is significantly associated with lower long-time survival.

A body of research suggests that autistic individuals are less likely to drink alcohol than neurotypicals. However, emerging studies support a link between autism and alcohol use. This complex relationship is also reflected in studies that have examined the genetic overlap between the two traits. However, it is unclear whether there is a direct causal relationship between them. To explore this, we applied a combination of polygenic score and Mendelian randomisation analyses using publicly available genome-wide summary statistics and phenotypic measures of autism and alcohol consumption from UK Biobank. LD score regression analyses did not provide evidence of a genetic correlation between genetic liability for autism and drinks consumed per week (rg=-0.08; CI95%=-0.19, 0.03). Further, findings from polygenic score analyses did not support an association between genetic liability for autism and overall monthly alcohol intake. Univariable Mendelian randomisation analyses showed little evidence for a total effect of autism, attention deficit hyperactivity disorder (ADHD) or depression on overall monthly alcohol consumption. Multivariable Mendelian randomisation analyses also showed little evidence of a direct effect of autism on drinks per week when controlling for ADHD and depression. It is plausible that genetic liability for autism does not directly increase the amount of alcohol consumed but instead operates via commonly co-occurring difficulties in the autistic community. However, our findings may be due to methodological shortcomings, including weak instruments biasing effects towards to the null. Consequently, results should be interpreted with caution and further research conducted to address these issues.

Autosomal dominant gain-of-function mutations in platelet-derived growth factor receptor beta (PDGFRb) cause overgrowth of the skeleton and other connective tissue in Kosaki overgrowth syndrome. However, the target cell type and signaling pathways underlying PDGFRb-driven overgrowth are unknown. Normal postnatal growth is controlled by pituitary-secreted growth hormone (GH), which activates the STAT5 transcriptional factor to upregulate insulin-like growth factor 1 (IGF1). To investigate the role of the GH-STAT5-IGF1 pathway in PDGFRb-related overgrowth, we generated mice with a PDGFRb gain-of-function mutation in skeletal and fibroblast lineages, which resulted in STAT5 activation and gigantism. Conditional deletion of Stat5ab in connective tissue lineages rescued skeletal overgrowth and keloid-like fibrosis in the skin. Conditional deletion of GH receptor (Ghr) did not rescue overgrowth, indicating the physiological activator of STAT5 is not required for overgrowth. However, deletion of Igf1, the STAT5 target gene, and its receptor, Igf1r, in connective tissue, rescued the overgrowth phenotype. These findings demonstrate a GHR-independent STAT5-IGF1 signaling pathway in mutant connective tissue cells, which mediates PDGFRb-driven overgrowth in mice and potentially in humans with similar PDGFRB mutations.

Background: Higher Body Mass Index (BMI) is an established risk factor of sleep disturbance. It is not known if the effect is homogeneous across the lifecourse or if there is a particular time point in life that might be best to target. Methods: Two-sample Mendelian randomization (MR) was used to investigated the effect of childhood adiposity (adjusting on adulthood adiposity and obstructive sleep apnea (OSA)) on insomnia, morning chronotype, sleep duration, daytime sleepiness and daytime napping. Similarly, total, and direct effect of adulthood adiposity on these outcomes was explored. We used summary statistics from a genome-wide association study (GWAS) of UK Biobank for childhood and adulthood adiposity (n=453,169) and large-scale consortia of OSA (Million Veteran Program) (n=410,268), insomnia, and chronotype (23andMe) (n=1,978,022 and n=248,1000, respectively). Results: Two-sample univariable MR analysis provided no evidence of an effect of genetically predicted childhood adiposity on later life insomnia (Odds ratio (OR)= 0.94, 95% Confidence interval (CI)= 0.87, 1.03). Whereas, multivariable MR (adjusted for adulthood adiposity) analysis provide strong evidence of direct protective effect of genetically predicted childhood adiposity on later life insomnia (OR= 0.70, CI= 0.64, 0.77). Further, both in univariable and multivariable MR, a strong positive effect of increased childhood body size on morning chronotype was observed (OR= 1.16, CI= 1.01, 1.33 and OR= 1.36, CI= 1.15, 1.62, respectively) after accounting for adulthood body size. In both analysis the estimate did not change considerably after aditionally adjusting for OSA. However, childhood and adulthood adiposity found to be associated with OSA and OSA with insomnia. In both univariable and multivariable analysis, increased body size in adulthood increased the risk of having insomnia and a morning chronotype. Conclusions: The findings suggest that higher body size in childhood is not a risk factor for later life insomnia, whereas higher body size in adulthood was. Further, if healthy body size is maintained in adulthood, high childhood adiposity may decrease the risk of insomnia and increase the risk of being a morning person in later life. Keywords: childhood, adulthood, obesity, insomnia, morning chronotype, medelian randomization

Sub-Saharan African populations carry pharmacogenomic alleles poorly represented in the European-derived reference panels underlying most clinical genotyping tools. We present a curated, machine-readable catalog of nine actionable alleles across six pharmacogenes (CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP3A5, NAT2) with African-specific frequency ranges, functional annotations, and evidence levels derived from reanalysis of 661 high-coverage whole-genome sequences across seven 1000 Genomes Project African populations. Direct comparison against PharmCAT v3.4.0 shows that CYP2D6 produces zero diplotype calls (0/661 samples callable) due to monomorphic reference positions absent from standard variant-only VCF output, a known limitation whose consequences for African allele carriers had not been reported. afripharmagen's reduced-position strategy identifies 243 CYP2D617 and 134 CYP2D629 carriers from the same input. For CYP2B6, CYP2C9, CYP2C19, and NAT2, both tools show concordance of 95-100%. Frequency gradients (CYP2B66: 30-50%; CYP2D617: 15-35% in West Africa; CYP3A5*1: 60-95%) translate directly into prescribing risk for efavirenz, tramadol, tacrolimus, and isoniazid. Pharmacogenomic decision support in African settings must incorporate population-specific allele definitions and input-format-aware strategies.

Background: Atrial fibrillation detected after stroke (AFDAS) is frequently diagnosed after embolic stroke of undetermined source (ESUS) and has important implications for secondary stroke prevention. Although prediction scores have been proposed to identify patients at increased risk of AFDAS, prospective evidence supporting their implementation to guide rhythm monitoring in routine clinical practice is limited. Methods: In this prospective, population-based implementation cohort study, adults with ESUS were enrolled between January 2022 and December 2024 across all stroke centers in Styria, Austria. The Graz AF Risk Score was prospectively implemented as part of a risk-adapted diagnostic pathway for cardiac rhythm monitoring. Patients with a score [&ge;]4 were recommended for implantable loop recorder monitoring, whereas monitoring in those with scores <4 remained at the treating physician's discretion. The primary outcome was AFDAS detection; recurrent ischemic stroke and recurrent stroke etiology were secondary outcomes. Results: Among 784 patients (median age 73 years [IQR 64-80], 45.7% women), AFDAS was detected in 166 patients (21.2%) during a median follow-up of 26.3 months (IQR 20-34). AFDAS detection was substantially higher in patients with a Graz AF Risk Score [&ge;]4 than <4 (38.1% vs. 3.9%; p<0.001). After adjustment for age, sex and ILR monitoring, a score [&ge;]4 independently predicted AFDAS (HR 6.3, 95% CI 3.5-11.2; p<0.001) and recurrent ischemic stroke (HR 2.2, 95% CI 1.1-4.1; p=0.023). Only one recurrent stroke in patients with a score <4 was attributable to atrial fibrillation (AF) (1/18, 5.6%). Conclusions: Prospective implementation of the Graz AF Risk Score identified patients with ESUS at markedly different risks of AFDAS. A Graz AF Risk Score [&ge;]4 was also independently associated with recurrent ischemic stroke. These findings support a risk-adapted approach to cardiac rhythm monitoring after ESUS.

Purpose: To evaluate whether initiation of GLP-1 receptor agonists (GLP-1RAs) is associated with anti-VEGF treatment burden in type 2 diabetes patients with diabetic macular edema (DME) in the IRIS(R) Registry (Intelligent Research in Sight). Methods: Incident GLP-1RA initiators were matched 1:1 with controls via Mahalanobis distance matching (9,896 pairs; N=19,792) on sociodemographics, DME risk factors, and factors influencing GLP-1RA prescription including hypertension, obesity, chronic kidney disease. A longitudinal mixed-effects event-study model evaluated monthly anti-VEGF injection frequency over a 36-month window (12 months before through 24 months after initiation), adjusting for DME duration. Visual acuity (VA) and central subfield thickness (CST) were secondary outcomes. Results: Following GLP-1RA initiation, anti-VEGF injection trajectories did not significantly differ between the matched GLP-1RA and control cohorts (interaction coefficients -0.18 to 1.59, P>0.05). Likewise, no differences in VA were observed between cohorts (-0.05 to 0.04 logMAR, P>0.05) or CST (-14.12 to 33.58 {micro}m, P>0.05). Conclusion: In these matched cohorts, GLP-1RA initiation was not associated with the trajectory of anti-VEGF use or changes in VA or CST. Precis We used the American Academy of Ophthalmology IRIS(R) Registry (Intelligent Research in Sight) to identify patients with DME. In 19,792 matched patients, there was no significant reduction in injection frequency post GLP1-RA initiation and no significant change in VA or CST.

Objectives To analyse the long-term effects of a cruciate ligament (CL) injury on health and socioeconomic outcomes. Methods We used a comprehensive national injury insurance database to identify CL injuries occurring in New Zealand between 2009 and 2022, and employed a doubly robust staggered difference-in-differences research design to identify the effects of these injuries on outcomes up to 10 years after injury. The outcomes of interest were healthcare use (hospitalisations, emergency department visits, medications, knee replacement surgery for osteoarthritis), associated healthcare costs, and labour market outcomes (employment rates, income, and government benefit payments). Results We identified 61 344 CL injuries for inclusion in the analysis. Over 10-year follow-up, a CL injury resulted in increased healthcare use (0.6 more hospitalizations [95%CI 0.4 to 0.7], 1.7 more days spent in hospital [95%CI 1.3 to 2.1], 0.4 more emergency department visits [95%CI 0.3 to 0.6], 2.5 more outpatient visits [95%CI 1.8 to 3.2], and 4.7 more medications dispensed [95%CI -1.8 to 11.2]) and public healthcare costs ($7 537; 95%CI 5 888 to 9 186), reduced income (-$6 060; 95%CI -11 644 to -475), and increased benefit payments ($1 152; 95%CI 542 to 1 761). Conclusion CL injuries have long-term impacts on healthcare use and socioeconomic outcomes. Strategies to reduce the incidence of CL injuries have the potential to realise large health and economic benefits.

Background: Evidence suggests chronic distress influences ovarian cancer (OC) etiology and metabolomic profiles. Here, we evaluated the association of a metabolite-based distress score (MDS) and OC risk. Methods: We included two matched case-control studies nested within the Nurses' Health Studies (N=584) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N=348). Metabolites were measured 3-27 years before diagnosis using liquid-chromatography tandem mass spectrometry. We examined the association of quintiles of MDS and 19 constituent metabolites with OC risk using unconditional logistic regression and stratified by tumor histotype, menopausal status, and age at diagnosis. Results: We observed women in the highest versus lowest quintile of MDS had an increased OC risk (OR=1.62,95%CI=1.03-2.54,ptrend=0.07), and type 2 tumors (OR=1.71,95%CI=1.03-2.83,ptrend=0.11). Associations were suggestively stronger for premenopausal and <69-year-old women, and driven by pseudouridine, and N2,N2-dimethylguanosine. Conclusion: Our findings suggest chronic distress-associated metabolic dysregulation may represent a novel OC risk factor, especially among younger women.

Background: On-call clinicians frequently report the anecdotal impression of 'theme shifts' during which specific acute neurological diagnoses appear to cluster. Whether such clustering reflects a statistically true and reproducible phenomenon has not been systematically investigated; the present paper examines seasonality and temporal clustering within six different acute neurological conditions. Methods: In this retrospective, single-center cohort study, we identified all patients admitted to a tertiary neurological department between July 2016 and June 2026 with acute unilateral vestibulopathy, cerebral artery dissection, generalized epileptic seizures, primary intracerebral hemorrhage, peripheral facial nerve palsy, or transient global amnesia (TGA) (n = 2,140). Monthly and seasonal distributions were assessed using chi-squared goodness-of-fit and cosinor analysis. Short-term temporal clustering was tested by Monte Carlo permutation across time windows from 24 hours to 90 days, and endogenous cluster dynamics were characterized using Hawkes self-exciting point process modeling. Results: Admissions for generalized epileptic seizures showed a statistically significant deviation from a uniform monthly distribution with a winter distribution (p<0.001 and q = 0.002), and a significant temporal clustering across time windows from 72 hours to 90 days (all q < 0.05). Peripheral facial nerve palsy presented significant clustering at the 90-day window (q = 0.029) and TGA at 60-day time window (q = 0.041) without seasonality; the diagnostic groups of acute unilateral vestibulopathy, cerebral artery dissection and primary intracerebral hemorrhage showed neither seasonality nor clustering after correction for multiple comparison. No diagnostic group showed clustering within a 24-hour window, statistically significant self-excitation in Hawkes process modelling, or a significant linear trend in monthly case counts over the study period. Conclusion: The anecdotal impression of diagnostic 'theme shifts' among on-call neurologists appears to have a measurable basis, although clustering is confined to specific conditions and rather on a time scale of weeks to months. Generalized epileptic seizures were the only diagnostic group that uniquely combined seasonality with temporal clustering, suggesting a shared trigger, while facial palsy and TGA showed episodic, yet non-seasonal clustering.

Disseminating study results to participants is an ethical responsibility for researchers but remains uncommon in low- and middle-income countries, and participants preferences for receiving study results are poorly understood. This study examined study result dissemination preferences among pregnant women in a phase III malaria prevention trial in Papua New Guinea (PNG). Participants completed an interviewer-administered questionnaire (survey) assessing their interest in and motivation for receiving trial results and preferences for dissemination methods and content. Associations between participants characteristics and dissemination preferences were explored using multivariable logistic regression analysis. Of 1172 trial participants, 96.0% (1125/1172) completed the survey, and of these 99.6% (1121/1125) wanted to learn about the trial results. The main motivation factors driving participants interest were an acknowledgment of their contribution to research (51.7%; n=579) and a better understanding of the study (45.0%; n=505). Most participants (78.9%; n=884) wanted to learn about the trial findings through written summary and a group meeting with other participants at the nearest clinic (31.1%, n=349). Multivariable regression analysis indicated that participants from rural/peri-urban clinics were more likely to choose non-electronic media dissemination approaches such as a group meeting as compared to urban-dwelling participants. Frequently selected items (>50% of participants) for content included information regarding good results of the study, purpose of the study, medical treatment advances, results specific to me, and how study was conducted. There was heterogenicity in the preference for dissemination content: compared to urban clinics rural clinics are less likely to want to learn about how and why study was conducted and medical and scientific advances. Overall, the majority wanted to learn about trial results, highlighting the importance of integrating dissemination into research activities in PNG. Variation in preferences for mode and content of dissemination between study clinics suggests that dissemination activities could be tailored to local context and preferences.

Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk.

Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System IISDS, an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [&ge;]0.011 Dice score, reducing lesion volume estimation error by [&ge;]0.3 average symmetric surface distance (ASSD), and increasing classification performance by [&ge;]0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation.