European Journal of Epidemiology

Objectives To quantify the frequency of baseline control-group use in published long COVID prevalence studies and assess their key methodological features. Design Cross-sectional meta-epidemiological evaluation of published post-acute COVID-19 prevalence studies, supplemented by a corresponding-author survey. Setting Published studies identified through a systematic review by Hou et al. (2025) and supplementary data obtained through direct email contact with corresponding authors. Participants A total of 440 published long COVID prevalence studies. Main Outcome measures Presence and type of comparator group, reliance on solely self-reported outcomes, acknowledgment of lack of a control group among uncontrolled studies, and availability of additional comparator data through author survey. Results Among 440 studies, 372 (84.5%) reported no control group on their publication. Healthy or uninfected comparators were reported in 55 studies (12.5%) and other comparator types in 14 (3.2%); 1 study included both categories. Solely self-reported outcomes were used in 279 studies (63.4%). Among 372 uncontrolled studies, 244 (65.6%) did not explicitly acknowledge the absence of a baseline comparator as a limitation anywhere in text. Corresponding authors of 140 studies (31.8%) responded to the survey; among them, 126 (90.0%) reported no additional comparative data, while 14 (10.0%) mentioned some available comparative datasets (19 additional datasets). Almost all of that information (10/14, 17/19) had been already published in other articles not captured by the Hou et al. systematic review. Conclusions Most published long COVID prevalence studies lacked comparator groups and relied exclusively on self-reported outcomes without acknowledging this limitation. Direct author contact identified little additional comparator information. Much of the long COVID prevalence literature may therefore be poorly suited to estimating burden attributable specifically to SARS-CoV-2, underscoring the need for appropriately matched comparators and more objective outcome assessment. Registration The protocol was prospectively registered on the Open Science Framework (https://osf.io/f4hra).

The availability of electronic health records has facilitated data-driven approaches to the understanding of multimorbidity, with clustering becoming a common tool for uncovering relevant groups of associated conditions. Previous studies, however, have found challenges in their reproducibility, with wide disparity in the reported clusters. At the core of this issue lays a vagueness of the definition of a cluster, leading to a lack of standards in their methods and evaluation, while implementation details are often not completely reported or explicit in their assumptions. We present a methodological pipeline that can be adapted to different cluster definitions (e.g. multiple cluster membership or clusters where all nodes are mutually associated) and a set of scores that can be composed into an evaluation metric that explicitly incorporates assumptions that align with the research aims. We apply our pipeline to a healthcare dataset of over 7 million patients in England and show how clusters may drastically differ when varying the parameter choices, exposing the risks of reporting a single clustering realisation. Our methodological pipeline, evaluation framework, and tools for analysis and network visualisation serve as a reference to transparently explore and align methodological decisions to the aims of multimorbidity clustering, contributing to overcome the reproducibility challenges of the field.

Background: Generative AI tools can support data-intensive research by writing code, drafting prose, searching analytical possibilities, and stress-testing claims. They can also produce false citations, drift between statistical specifications, and lose continuity across long investigations. This paper describes a practical workflow for using AI systems in empirical research while keeping discovery, verification, and accountability inspectable. Methods: We developed and applied a three-phase human-AI workflow to a case study of 14 elite Ethiopian distance runners. The dataset contained 22,605 GPS-segments collected across 97 consecutive days in late 2025, supplemented by venue and athlete metadata collected in the field. Phase 1 used an autonomous data-exploration tool to pre-filter the hypothesis space across five seeded research questions. Phase 2 used an AI system under direct human guidance to construct candidate findings into numerical claims, verification scripts, and draft text. Phase 3 used an independent AI system in an adversarial role to stress-test methods, statistics, prose, figures, and citations. The workflow was informed by Pearl's distinction between association, intervention, and counterfactual reasoning, with human judgement retained for research direction, interpretation, and final claims. Results: The workflow produced three empirical analyses and a documented correction process. The analyses estimated an altitude-to-sea-level pace correction of +0.10 min/km per 1,000 m at matched heart rate, showed why pooled altitude-surface regression was not identifiable within this venue system, documented method-dependence in heart-rate-based intensity classification, characterised within-venue route variation as a 64/36 path-fixed-to-trail-variable split with the Sululta label resolving into two functionally distinct sub-venues, and reframed the cohort's training through a 3x3x3 prescription lattice grounded in Ethiopian coaching practice. The adversarial phase identified several hallucinated citations, a terminology error between HC1 and cluster-robust standard errors, and several inconsistencies between prose, figures, and computed results. Verification scripts re-derived nearly all numerical claims from the cleaned lap-level data. Conclusions: The case study shows how researchers can organise AI-assisted empirical work so that candidate discovery, claim construction, independent stress-testing, and final accountability remain separated. The workflow did not remove the need for domain expertise or human judgement. Its value was in making the route from candidate finding to manuscript claim explicit, reproducible, and open to challenge. Trial registration: Not applicable.

Background: In prospective cohort studies, where an exposure is collected repeatedly, interest often lies in determining whether the timing of that exposure has a differential effect on a later outcome. The Structured Life Course Modeling Approach (SLCMA), where users select between temporal hypotheses of exposure specified a priori, provides one way to analyse such longitudinal data. However, few studies using SLCMA consider the effect of time-varying covariates (TVC) which may impact associations. Methods: We present a modified version of the SLCMA - called direct and mediated effects (DME)-SLCMA - which corrects for TVC. We first develop the DME-SLCMA method, test it through simulation, and apply it to psychosocial data from the Drakenstein Child Health Study (DCHS, n=336) to investigate relationships between maternal psychopathology, TVC of socioeconomic status, and offspring depressive symptoms. Results: We found that, on average, offspring depressive symptoms score increased by 3.9% (95% CI: 1.0%-6.9%, p = 0.039) for each unit of maternal psychopathology (SRQ) at 48 months whilst adjusting for time-varying socioeconomic status (at 18, 30, 42 and 54 months). Our simulations identified several realistic scenarios where selections ignoring TVC - with TVC mediated exposure effects present - were prone to be incorrect, including our DCHS example. Conclusion: DME-SLCMA is a robust new approach for life course modelling in the presence of time-varying covariates. We recommend adjusting for TVC whenever possible, and, when not possible, our simulation study identified that scenarios where mediated effects are comparable, or greater, in magnitude to direct effects are most prone to confounding.

Background Differential contributions to transmission across age groups have been reported for many respiratory infections, including SARS-CoV-2. They are crucial for estimating the impact of age-specific interventions. Disentangling these age-dependent contributions remains challenging, as they may reflect differences in contact rates, biological susceptibility, or infectiousness. Aim We aim to jointly estimate age-specific per-contact infectiousness and susceptibility and their effect on the impact of age-specific interventions. Methods The age-specific infectiousness and susceptibility were jointly estimated in a Bayesian framework by combining contact data with transmission pair data (who-infected-whom). We applied this approach to 197,840 self-reported household transmission pairs collected in the Netherlands during the COVID-19 pandemic. Using these estimates, we projected the expected impact of school closure and work-from-home measures during the early stages of an epidemic in the absence of other interventions. Results Both infectiousness and susceptibility to SARS-CoV-2 infection were lowest in children aged 0-9 years and highest in adults over 30 years old, with 2- to 4.5-fold differences between these groups. Projected impacts of age-specific interventions indicated that school closures would reduce the reproduction number by 8% or 29% when age-specific susceptibility and infectiousness were or were not considered, respectively. Conversely, working-from-home policies would lead to reductions of 41% with and 20% without age-specific infectiousness and susceptibility. Conclusion Our method enables robust estimation of age-specific infectiousness and susceptibility. Accounting for these age heterogeneities is essential for projecting the impact of age-targeted interventions. Our approach is adaptable to other respiratory infections and can guide more tailored public health responses.

Wearable devices offer the ability to objectively characterize free-living physical activity; however, raw step-count data generated by commercial devices require systematic processing before they can support rigorous inference. We describe a transparent, reproducible standard operating procedure (SOP) for transforming epoch-level step-count data from commercial Garmin devices into participant-level analytic variables and demonstrate its application in the STRRIDE-PD Reunion study: a long-term follow-up of older adults originally enrolled in a supervised exercise intervention trial. This data pipeline standardizes timestamps, reconstructs daily epoch grids, infers wear time from observed step patterns, and applies a prespecified valid-day threshold ([≥]10 hours inferred wear time) to generate participant-level summaries. Among 67 participants (mean age 71.4 years, 65.7% women), the median valid-day count was 10 days, median average daily steps were 5,794, and participant-level estimates were identical across [≥]10-hour and [≥]6-hour valid-day thresholds. Wearable-derived step counts were significantly associated with 9 of 16 cardiometabolic and fitness outcomes, including cardiorespiratory fitness, body composition, and lipid profiles. By contrast, self-reported exercise - assessed via a frequency-by-duration composite ranked into deciles - was not significantly associated with any outcome. A regression calibration framework applied to the full sample quantified the attenuation underlying this discrepancy: the naive self-report model systematically underestimated associations relative to both the observed Garmin model and calibration-corrected estimates. These findings demonstrate that measurement approach is a determinant of scientific conclusions in physical activity research, and that reproducible wearable data pipelines are essential infrastructure for aging epidemiology.

Abstract Importance: Irregular sleep-wake patterns have been associated with poor health and cognitive outcomes, yet evidence linking 24-hour sleep-wake regularity to cognitive decline or dementia remains inconsistent. Particularly, regularity can be measured as regularity of rest-wake, sleep-wake or overall 24-hour activity, but it is unclear which aspects are most relevant for cognitive aging. Objective: To assess associations of rest-wake, sleep-wake, and 24-hour activity regularity with cognitive decline and dementia risk. Design: Observational prospective study comprised of six US and European cohorts: MrOS (sleep study between 2003-2005, mean follow-up: 7.1 years), Rotterdam Study (2004-2007, 11.6 years), MESA (2010-2013, 8.2 years), MAP (2005-2018, 7.2 years), Whitehall II (2012-2013, 6.9 years), and UKB (2013-2015, 7.9 years). Setting: Cohort-specific estimates were pooled using random-effects meta-analysis. Analyses were done between June 2025 and March 2026. Participants 74,733 dementia-free adults with multi-day actigraphy were included across cohorts: MrOS (age: 67-96 years, female:0%), MESA (54-95y, female:54.6%), Rotterdam Study (46-98y, female:55.0%), MAP (56-100y, female:77.1%), Whitehall II (59-83y, female:25.9%), and UKB (55-78y, female:55.5%). Exposure: Day-to-day rest-wake regularity (Rest Regularity Index, RRI), day-to-day sleep-wake regularity (Sleep Regularity Index, SRI), and 24-hour activity regularity (Interdaily Stability, IS) were derived from multi-day actigraphy. Main Outcome: Outcomes were risk of dementia and changes in global cognition. Results: Across six cohorts, 1,906 dementia cases occurred among 74,733 participants. After adjusting for demographics, health behaviors, depressive symptoms and cardiovascular comorbidities, each 1-SD higher regularity score was associated with an 9-14% lower dementia risk (pooled hazard ratios: RRI 0.86 95%CI: [0.79-0.95]; SRI 0.87[0.79-0.97]; IS: 0.91[0.88-0.95]). Associations were approximately linear. Age-stratified analyses showed directionally stronger associations among adults aged < 65, although meta-regression did not support an interaction(p > 0.55). Greater regularity was associated with modestly slower decline in global cognition (pooled {beta} per 1-SD higher score of RRI per year: 0.003, 95%CI [0.001-0.006]). Conclusions & Relevance: Greater regularity of rest-wake, sleep-wake, and 24-hour activity rhythms was associated with lower dementia risk and modestly slower global cognitive decline. These findings suggest that 24-hour sleep-wake regularity is a relevant behavioral marker of cognitive aging and may inform future efforts to identify or intervene on early risk.

Background During infectious disease outbreaks, characteristics of reported cases are routinely collected. These give information on becoming infected but not on infecting others. We assess whether linking infectees to infectors, together with their characteristics, can help understand transmission. Methods From the start of the COVID-19 pandemic in the Netherlands, reported cases were asked to identify their most probable infector in routine surveillance, enabling the linking of cases. We assess for the period 27 February 2020 - 11 April 2022 whether the infectees of these transmission pairs are representative of all reported cases, whether the transmission pairs yield verifiable estimates of epidemiological characteristics (here the serial interval), and whether they provide information on transmission that cannot be obtained otherwise. Results Of 8,003,008 reported cases, 678,482 (8.5%) could be linked to their most probable infector. These infectees were largely representative of the reported cases regarding age group, sex, and geographical location. The mean serial interval of 3.6 days (sd 3.4 days) from transmission pairs aligns with literature. Transmissions between age groups largely follow known contact patterns. Most transmissions in September 2021 occurred between persons who were not (fully) vaccinated, indicating the effectiveness of the vaccine, and relatively few between persons with different vaccination status, indicating assortative mixing in vaccination status. Conclusion Transmission pairs can be efficiently collected in routine surveillance, providing insight into disease transmission. The current post-pandemic period provides an excellent opportunity to adjust reporting systems for linking infectees to their most probable infector as preparation for future outbreaks.

Atherosclerosis is a systemic vascular process linked to cardiovascular, cognitive and renal outcomes. DNA methylation (DNAm)-based scores of atherosclerosis may capture cumulative biological processes underlying vascular aging. Here, we examined associations of DNAm scores for coronary artery calcification (DNAm-CAC) and carotid plaque (DNAm-cPlaque), derived from a large study of imaging-based subclinical atherosclerosis, with prevalent and incident outcomes in two population-based cohorts of older adults: the Health and Retirement Study (HRS; n = 3,875) and The Irish Longitudinal Study on Ageing (TILDA; n = 487). Higher DNAm scores were associated with adverse cardiometabolic profiles and socioeconomic indicators. In HRS, higher DNAm-CAC was associated with prevalent cardiovascular disease (odds ratio per SD, 1.16; 95% confidence interval (CI), 1.07-1.26), lower cognitive function ({beta} = -0.50, 95% CI -0.68 to -0.32) and lower estimated glomerular filtration rate (eGFR; -1.7 ml min-1 1.73 m-2, 95% CI -2.6 to -0.8) in unadjusted models. After adjustment for demographic and clinical risk factors, DNAm-CAC ({beta} = -0.29, 95% CI -0.46 to -0.13) and DNAm-cPlaque ({beta} = -0.24, 95% CI -0.42 to -0.06) remained associated with lower cognitive function, and DNAm-cPlaque was associated with incident cognitive impairment or dementia (hazard ratio per SD, 1.16; 95% CI, 1.01-1.32). Associations were attenuated after further adjustment for race/ethnicity and socioeconomic indicators. In TILDA, higher DNAm-cPlaque was associated with worse cognitive performance (incidence rate ratio, 1.11; 95% CI, 1.01-1.21), increased risk of incident cardiovascular disease (hazard ratio, 1.18; 95% CI, 1.00-1.42) and lower eGFR, with consistent associations observed for DNAm-CAC. These findings suggest that DNAm-based scores of atherosclerosis capture systemic vascular processes linked to multiple age-related outcomes across populations. Further work is needed to clarify the biological pathways reflected by these scores and their relation to cumulative and socially patterned vascular risk.

Background: It is well-established that males have a higher mortality risk than females. Immune cells and their function are known to undergo characteristic changes during aging, and immune cells are known to have sex differences. Immune cells and their function have been linked to mortality risk, but no studies have investigated to what degree, if at all, Immune Cell Biomarkers (ICBs) contribute to the known differences in mortality risk by sex. Methods: Using participant data from the Health and Retirement Study (n = 8,822), we applied multivariable linear regressions adjusting for age, cytomegalovirus (CMV) serostatus, sex, and race/ethnicity to identify differences by sex in 48 immune cell biomarker (ICB, e.g. T cells, B cells, Monocytes, etc.) percentages and counts (measured in 2016). We studied how the associations between ICBs and mortality risk differ by sex using stratified Cox Proportional Hazard (CPH) models. We estimated how inclusion of sex explained the relationship between ICBs and all-cause mortality, and conversely, how inclusion of individual and all ICBs combined explain the relationship between sex and all-cause mortality using multivariable modeling approaches. Results: Differences in ICBs by sex range between 2-38% (39/48 statistically significant). 9 ICBs were significantly associated with mortality risk in the entire sample. While different ICBs were significantly associated with mortality risk in the stratified analyses, particularly with respect to monocyte, B cell, and NK cell populations, adjusting for sex modestly influenced the hazard ratios of the ICBs (sex: 8 ICBs, percent change <5.4%). Furthermore, individual and cumulative contributions of ICBs in explaining the differences in mortality risk by sex were not significant.

Background. Blood-based biomarkers are increasingly proposed for identifying high-risk individuals before clinical disease and for making prevention-oriented trials more efficient. Prognostic enrichment can increase event rates, but trial efficiency also depends on whether the intervention effect is preserved in the enriched population. Methods. Using the UK Biobank Pharma Proteomics Project, we trained disease-specific proteomic risk scores (ProRS) from 2,916 plasma proteins with elastic-net Cox models. We compared ProRS, polygenic risk scores (PRS), and combined PRS--ProRS scores across ten incident diseases. We estimated cumulative incidence and theoretical two-arm time-to-event trial sample sizes across risk strata. To evaluate effect preservation, we examined six intervention-analogue exposure--outcome pairs spanning genetic (PCSK9/coronary artery disease, APOE/Alzheimer's disease, PPARG/type 2 diabetes, IL23R/Crohn's disease), behavioural (physical activity/all-cause mortality), and pharmacological (RAAS inhibitors versus calcium channel blockers/coronary artery disease) examples. Results. ProRS outperformed PRS for 9 of 10 diseases (median C-index 0.75 versus 0.61). ProRS and PRS were weakly correlated (median Pearson |r| = 0.04), and joint PRS--ProRS stratification identified groups with higher observed incidence than either score alone for several endpoints. In the top risk quartile, combined-score enrichment reduced theoretical required sample sizes by 32--74\% under a fixed 20\% relative hazard reduction. These gains were not always preserved when stratum-specific intervention-analogue effects were used. Effects were broadly preserved for APOE/Alzheimer's disease and physical activity/mortality. The PPARG/type 2 diabetes effect attenuated toward the null under all three score types, showing that event-rate enrichment does not guarantee effect preservation. For IL23R/Crohn's disease and the antihypertensive comparison, point estimates differed across score types -- preserved under polygenic but attenuated under proteomic enrichment -- but confidence intervals were wide and overlapping. Conclusions. Proteomic risk scores can identify high-event-rate populations for prevention-oriented trials, but event-rate enrichment alone is insufficient for trial design. Biomarker-guided enrichment should evaluate mechanism-specific effect preservation and may be preferable as a stratification or adaptive-design variable rather than as a restrictive eligibility criterion.

Objective: To estimate the adjusted relative risk (RR) of administrative grant disruption faced by first-time and mechanism-first principal investigators (PIs) during the 2025 National Institutes of Health (NIH) grant disruptions. Design: Retrospective cohort study linking NIH RePORTER data to a publicly curated registry of grants disrupted in 2025. Setting: All NIH active research grants in fiscal years 2024 to 2025. Participants: 80,976 active projects: 4,961 disrupted during the wave that peaked in May 2025, 76,015 non-disrupted controls. Main outcome measures: Adjusted RR of disruption by two pre-specified first-time PI constructs: absolute first-time PI (no prior NIH grant) and mechanism-first PI (no prior NIH grant with the same activity code). Modified Poisson regression with institution-clustered standard errors adjusted for project, institutional, and geographic covariates. A pre-specified fiscal year 2024 common-anchor analysis addressed year-of-disruption confounding. Results: Of 4,961 disrupted grants, 237 (4.8%) had an absolute first-time PI and 396 (8.0%) had a mechanism-first PI. After adjustment, absolute first-time PIs faced 77% elevated risk of disruption (RR 1.77, 95% CI 1.34 to 2.32) and mechanism-first PIs faced 57% elevated risk (RR 1.57, 1.16 to 2.11). Under the common-anchor analysis, the absolute first-time effect attenuated to RR 1.22 (0.95 to 1.58); the mechanism-first effect persisted (RR 1.48, 1.07 to 2.06). The elevated risk was concentrated in research-mechanism grants (RR 1.78, 1.26 to 2.52) and was robust across 8 of 9 pre-specified sensitivity analyses. The Track A start-time construct, which asks whether the disrupted project was the PI's debut grant, yielded null estimates (RR 0.98, 0.93 to 1.04), with any effect concentrated entirely in newly started projects. Conclusions: First-time and mechanism-first PIs faced disproportionately elevated risk of disruption during the 2025 NIH wave, concentrated in research-mechanism grants and robust to year-confounding-free identification. The relevant exposure was being early-career at the moment of administrative action, not at project initiation. The findings have direct implications for workforce equity in US biomedical research.

Background: It is an everyday observation that people of the same chronological age differ with respect to their physical and mental capacity. However, assessing these differences in biological age remains challenging. Methods: Here, we aggregate 89 age-associated variables from the Berlin Aging Study II (BASE-II, n=1,631) to generate MultiAge, a new marker of biological age that summarizes information from ten domains reflecting organ health and global biological age. We then used methylation data obtained from an Illumina MethylationEPIC array and supervised machine learning to translate MultiAge into a DNA methylation signature, MultiAgeEpi (309 CpGs), which was subsequently validated in four independent external validation cohorts (KORA FF4, KORA Age, SHIP-TREND, BiDirect, total n=4,339). MultiAgeEpi results were compared with previously published epigenetic clocks (GrimAge, DunedinPACE, SystemsAge). Results: We report that MultiAgeEpi showed similar, and in several cases, stronger associations with age-associated outcomes such as diabetes, metabolic syndrome, multimorbidity, frailty and mortality (q < 0.05) compared to the other clocks. Conclusions: MultiAge and MultiAgeEpi thus provide a comprehensive assessment of biological age through aggregation of numerous age-associated variables and the use of the high-resolution methylomics data makes transfer of this marker to other cohorts possible.

Background: Mortality rates have declined across most high-income countries for decades, but recent evidence suggests a slowdown in improvements or a shift to increasing mortality, particularly among working-age populations. The international distribution and drivers of these trends remain incompletely understood. Methods: Mortality trends during 2012-2019 were analysed using all-cause and cause-specific data from 30 countries. Trends were estimated via linear regression. K-means clustering with Dynamic Time Warping identified countries and ICD-10 chapters with similar temporal trajectories. Results: Trends varied substantially by nation. While Japan, Switzerland, and the Republic of Korea maintained consistent declines in all-cause mortality rates, increases were concentrated in the United States, Canada, and the United Kingdom, most prominently in persons aged 30-59 years. However, cause-specific analysis showed that rising mortality was not confined to these countries: most countries exhibited increases in at least one ICD-10 chapter, with several European countries showing increases across multiple chapters. Across countries, a small set of causes recurred among increasing trends, including external causes (self-harm, drug poisoning) at younger ages and chronic conditions (cardiovascular and liver diseases, specific cancers) in mid-life. Notably, ill-defined causes of death consistently appeared among the increasing causes across countries and age groups. Conclusions: Mortality increases in the 2010s were geographically more widespread than previously recognized. The recurrent rise in mortality from ill-defined causes suggests that an important component of mortality change remains poorly characterized. These findings indicate that stalled health progress is a systemic challenge across many high-income societies.

Machine learning models for causal inference aim to adjust for confounding factors that are associated with both an exposure and an outcome, creating a spurious biased association. But, these methods are rarely empirically evaluated to assess their success in mitigating such bias. Recent advances in knowledge representation, including both foundation models and knowledge graphs, could enrich these models, but rigorous evaluations are needed in order to assess their potential. Here, we ask whether enriching existing causal inference models with knowledge representations from foundation models can improve confounding control. Rather than using semi-simulated data to address this question, we focus on examples of real confounding: we emulate target randomized active comparator trials that are subject to confounding by indication. Our results can guide researchers aiming to develop or apply methods for discovering causal effects from observational data.

ObjectivesPhenotype-driven workflows in clinical and translational research require standardized ontology-based representation, ontology-aware cohort discovery, and provenance inspection for each assertion. Existing approaches optimize either for semantic traversal or scalable batch analytics, but not both. We describe PheBee, a hybrid system that links semantic assertions to scalable evidence storage via a deterministic identifier, preserving provenance while supporting ontology-aware discovery at cohort scale. Materials and MethodsPheBee represents phenotype assertions in a knowledge graph as ontology-linked nodes with clinical modifier context (e.g., negated, family history), and stores supporting evidence records in a scalable row-oriented evidence table for cohort-scale access. The two layers are connected by a deterministic identifier enabling stable joins across repeated ingestions without duplicating high-volume evidence in the graph. We evaluated PheBee using synthetic datasets designed to exercise end-to-end ingestion and query workflows. ResultsFunctional evaluation validated hierarchical term expansion, qualifier-aware retrieval, duplicate-free assertion handling under re-ingestion, and privacy-conscious management of subjects shared across multiple research projects. At scale (10,000 subjects producing 12M evidence records) PheBee completed ingestion in [~]30 minutes and responded to interactive queries within 6 seconds under concurrent load. DiscussionPheBee exposes a unified API for ontology-aware cohort discovery with hierarchical term expansion, subject-centric retrieval of phenotypes and clinical modifiers, and evidence and provenance queries. Its data model aligns with GA4GH Phenopackets, facilitating interoperability with phenotype exchange standards. ConclusionBy combining ontology-aware semantics with scalable, provenance-bearing evidence storage, PheBee provides a practical open-source foundation for phenotype-driven research workflows that demand both semantic precision and cohort-scale traceability. LAY SUMMARYResearchers often use "phenotypes" (observable clinical features) to describe individual subjects and find groups of similar subjects. Those phenotypes come from many sources and need both standard terminology and clear evidence for why a phenotype has been associated with a subject. PheBee is a software system that stores phenotype assertions in a way that supports both "ontology-aware" searching (for example, finding patients with any subtype of a condition) and scalable storage of supporting evidence across large research cohorts. PheBee uses multiple types of data storage so researchers can perform interactive phenotype searches and also store millions of pieces of supporting evidence. A shared identifier connects the two storage layers, so subjects phenotypes and their supporting evidence remain linked even as new data is added over time. We evaluated PheBee using fully synthetic (non-patient) data to confirm correct query behavior, evidence traceability, and system performance at large scale.

Importance: Problematic use of the internet (PUI) behaviors, including problematic gaming, social media use, smartphone use, and general internet use, have been increasingly studied worldwide. So far, it is unclear what the global prevalence of PUI is. Objective: To critically appraise existing systematic reviews and meta-analyses on the prevalence of PUI behaviors and generate aggregated global prevalence estimates across different manifestations and definitions. Data Sources: MEDLINE (Ovid), Embase (Ovid), Scopus, Web of Science, CINAHL, and the Cochrane Review Library were searched for relevant articles from database inception to the most recent available search prior to manuscript preparation. Searches targeted systematic reviews and meta-analyses reporting prevalence for PUI-related behaviors. Study Selection: Systematic reviews and meta-analyses of observational studies reporting prevalence estimates for problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, or sexting were included. Scoping reviews were retained for descriptive synthesis only. Data Extraction and Synthesis: An umbrella review methodology was used. Data extraction and methodological appraisal were conducted using AMSTAR-2 to assess the quality of included systematic reviews up to February 2026. Primary studies included in each review were extracted and pooled using random-effects meta-analysis. Analyses were conducted to estimate pooled prevalence with 95% confidence intervals (CIs) and heterogeneity across non-overlapping primary studies. Small-study effects were examined. Main Outcomes and Measures: Global pooled prevalence estimates for PUI behaviors, including problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, and sexting. Results: Eleven reviews, including 10 systematic reviews and 1 scoping review, met inclusion criteria, representing data from 3,145,428 individuals, of whom 3,030,023 were included in pooled prevalence analyses. Across regions, pooled prevalence estimates were 6% (95% CI, 5%-7%) for problematic gaming, 16% (95% CI, 15%-17%) for problematic internet use, 32% (95% CI, 28%-35%) for problematic smartphone use, and 23% (95% CI, 19%-28%) for problematic social media use. Substantial heterogeneity (I2 > 99%) was observed across primary studies, reflecting variation in study methodologies, sampled populations, and definitions of PUI behaviors. Conclusions and Relevance: PUI behaviors appear to affect a substantial proportion of the global population. However, methodological concerns were common, with 9 of 10 systematic reviews rated as having low or critically low confidence according to AMSTAR-2. Evidence remains concentrated in East Asia and Europe, and many reviews combine heterogeneous populations and sampling strategies. Additional high-quality epidemiological research, including studies in underrepresented regions, is needed to refine prevalence estimates, clarify risk factors, and support the development of standardized criteria for PUI behaviors.

BackgroundThe increase in human lifespan without a proportional increase in healthspan imposes a substantial burden on individuals and society. Exceptionally long-lived individuals and members of long-lived families exhibit compression of multi-morbidity. Genetics, and in particular rare protein-altering variants, appear to play an important role in their longevity. MethodsIn this study, we employed a targeted pathway approach to provide functional evidence of the significance of rare variants in the insulin/insulin-like growth factor 1 signalling - mechanistic target of rapamycin (IIS/mTOR) signalling pathway identified in long-lived individuals. To this end, we used CRISPR/Cas9 to introduce these rare genetic variants into mouse embryonic stem cells (mESCs). We subsequently assessed several functional readouts that have previously been associated with lifespan regulation in model organisms and/or IIS/mTOR and mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) signalling pathway activity. ResultsFunctional characterisation revealed that the variants exhibit both shared and distinct effects on the signalling pathways. Principal component analysis of omics-based datasets showed that the variants clustered into two groups, a distribution that corresponds with the grouping observed for a subset of functional readouts. All variant mESC lines exhibited a downregulation in IIS/mTOR and MAPK/ERK signalling pathway activity as well as an increase in Foxo3 expression and FOXO3 binding activity. We identified alterations in lipid and mitochondrial metabolism, including a reduction in mitochondrial DNA levels, which were mostly shared among all variants. All variant mESC lines exhibited a signature implying increased pluripotency. The effects on stress resistance and growth rate diverged between the two variant groups, with partially opposing effects. Group 1 demonstrated a reduced growth rate and increased resistance to a subset of stressors, while Group 2 demonstrated an increased growth rate and reduced resistance to a subset of stressors. ConclusionsHere, we provide evidence that rare genetic variants in the IIS/mTOR and MAPK/ERK signalling pathways identified in long-lived human individuals result in shared functional effects associated with longevity in model organisms. These insights can serve as a foundation to better understand the role of rare variants in the insulin signalling network in the regulation of human longevity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/728260v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1bf5ebdorg.highwire.dtl.DTLVardef@e4e5dcorg.highwire.dtl.DTLVardef@1aee276org.highwire.dtl.DTLVardef@95f170_HPS_FORMAT_FIGEXP M_FIG C_FIG

Resuscitation in the oldest old at the end of life is associated with potential harm, raising concerns about misalignment with patients goals of care. This study aimed to elucidate changes in the use of resuscitation among the oldest old in Japan following the revision of the national guideline on end-of-life care which explicitly incorporates the concept of advance care planning. We conducted a repeated cross-sectional study using the National Database of Health Insurance Claims Open Data, including adults aged [&ge;]85 years, from April 2014 to March 2024. The annual number of resuscitation procedures per 100,000 individuals aged [&ge;]85 years was used as the measure of frequency. Resuscitation included closed-chest cardiopulmonary resuscitation (CPR) and endotracheal intubation. Interrupted time series analysis was used to examine changes following the 2018 revision of the national end-of-life care guideline. The frequencies of CPR and endotracheal intubation declined before 2018 (CPR: age 85-89, -68.4 [-87.9 to -48.8]; age [&ge;]90, -106.7 [-131.5 to -82.0]; intubation: age 85-89, -57.5 [-71.8 to -43.2]; age [&ge;]90, -69.5 [-80.7 to -58.3]), but the decline attenuated thereafter (CPR: age 85-89, +56.2 [28.0 to 84.5]; age [&ge;]90, +84.1 [50.7 to 117.6]; intubation: age 85-89, +36.6 [8.5 to 64.7]; age [&ge;]90, +38.3 [23.8 to 52.8]). These findings provide insight into the changes in resuscitation trends following policy interventions supporting end-of-life decision-making. Further studies are needed to better understand the mechanisms underlying this change.

Cultural engagement is associated longitudinally with better mental health and reduced depression incidence, but evidence has largely relied on self-reported symptoms and diagnoses, leaving uncertainty about clinically recorded disorders, and residual confounding remains a concern. Here, we examined whether cultural engagement (including going to cinemas, museums, galleries, exhibitions, theatre, concerts, or opera) predicts hospital-treated mental disorders in 8,274 adults aged 50 years or older from the English Longitudinal Study of Ageing. Participant records were linked to ICD-10 diagnoses in Hospital Episode Statistics and mortality records with follow-up of up to 20 years. In fully adjusted Cox models accounting for sociodemographic, lifestyle, and social factors and multiple testing, frequent cultural engagement was associated with lower risk of any mental disorders (HR 0.71, 95% CI 0.62-0.82, FDR adjusted P value<0.001), dementia (0.71, 0.56-0.89, FDR adjusted P value=0.010), substance misuse (0.75, 0.59-0.95,FDR adjusted P value=0.040), and mood disorders (0.73, 0.56-0.95, FDR adjusted P value=0.044), but not neurotic disorders. Associations persisted after excluding early incident cases and adjusting for baseline depressive symptoms and cognition, and showed robustness to unmeasured confounders. To further probe causality, eye disease, ear disease, and traumatic brain injury, which share similar socio-demographic profiles to mental disorders, were prespecified as negative control outcomes. Cultural engagement was not associated with any negative control outcomes. These findings provide triangulated statistical data to suggest that cultural engagement is associated with reduced risk of several clinically recorded mental disorders and support further testing of cultural engagement as a population mental health strategy.