International Journal of Epidemiology

Background: Multiple observational studies have reported associations between greater parity and increased CVD risk. Whether these associations reflect causal effects or are confounded by socioeconomic factors remains unclear. Methods: We investigated associations between number of children ever born (NEB) and 16 cardiometabolic traits in up to 172,122 females and 138,390 males in the UK Biobank, and an independent sample of 53,237 UK Biobank spousal pairs. We additionally conducted sex-stratified two-sample Mendelian randomization (MR) and applied a novel spousal MR framework, in which an individual's spouse's genotype was used as the instrumental variable to estimate the causal effect of NEB on cardiometabolic health outcomes, as an approach to minimize bias from horizontal pleiotropy. Results: NEB was associated with multiple cardiometabolic traits in the multivariable regression, even after adjustment for socioeconomic status, with differences in the strength of association observed between males and females. Traditional MR provided evidence that higher NEB causally increases type 2 diabetes risk in females, body mass index (BMI) in both sexes, female basal metabolic rate (BMR) and male body fat percentage but decreases female blood pressure. Spousal MR corroborated positive effects on female BMI and BMR and additionally suggested inverse causal effects on female HDL cholesterol and ApoA1 and male blood glucose. Conclusion: These findings indicate a possible causal relationship between NEB and long-term cardiometabolic health, although causal effects are likely to be small.

Background: Reliable estimation of excess mortality is central to population health surveillance. We introduce NeMMo (New Mortality Model), an evolution of the EuroMOMO model for estimating weekly all-cause expected mortality, and assess its behaviour and performance on empirical data. Methods: NeMMo incorporates population offsets, stratifies observed deaths by age group and models seasonality using a periodic B-spline rather than a Serfling-type sinusoidal function. Baseline weeks are selected by a data-driven procedure minimizing the skewness of the residuals before refitting the model, instead of relying solely on fixed calendar windows. NeMMo enables pooling across age groups, direct age standardization and incorporation of external predictors. We applied NeMMo and EuroMOMO to mortality and population data downloaded from Eurostat for 31 countries from 2015 onwards, excluding the COVID-19 pandemic period from baseline estimation. Results: For most countries NeMMo produced a higher expected mortality baseline that better tracked observed deaths, as well as tighter prediction intervals and higher maximum Z-scores, suggesting improved discrimination of mortality excesses. Z-scores and P-scores during non-pandemic weeks were closer to zero with NeMMo than with EuroMOMO but further elevated during pandemic weeks, providing greater separation between pandemic and non-pandemic mortality. Incorporating population offsets resulted in negative linear trends across all countries, consistent with declining mortality after accounting for demographic changes. The periodic B-spline identified substantial heterogeneity in the shape and timing of seasonal mortality that was not captured by a sinusoidal function. Conclusions: NeMMo provides a flexible and parsimonious framework for all-cause mortality surveillance that improves the established EuroMOMO model and offers theoretical, empirical and practical advantages. It is thus suitable both for detecting short-term spikes and for the long-term, age-adjusted quantification and comparison of mortality excesses that has become increasingly important since the COVID-19 pandemic. The accompanying 'nemmo' package for R facilitates its widespread adoption and application.

Background: Vitamin D deficiency has been associated with mortality and chronic disease. In a recent study of Israeli Leumit Health Services (LHS) and US TriNetX populations, severe deficiency was reproducibly associated with mortality and multisystem morbidity, while longitudinal supplementation reconstructed from pharmacy dispensing was associated with lower risk. Methods: We sought to reproduce these associations and address a wide range of potential confounders in the deeply phenotyped UK Biobank. Baseline circulating 25-hydroxyvitamin D [25(OH)D] was standardized to a common late-winter nadir using an eight-harmonic model, and participants were categorized according to nadir-equivalent 25(OH)D as <5, 5-10, 10-15, 15-20, or [&ge;]20 ng/mL. Primary survival analyses used one-year delayed entry and complete-case multivariable adjustment. Results: Analyses included 412,020 participants and 31,197 deaths. Mortality hazard ratios were 2.03 (95% CI 1.90-2.18), 1.34 (1.29-1.39), 1.08 (1.04-1.11), respectively, for <5, 5-10 and 10-15, versus [&ge;]20 ng/mL. Associations previously observed in LHS and TriNetX were reproduced, notably for diabetes, myocardial infarction, cerebrovascular disease, dementia, diabetic retinopathy, and end-stage renal disease, whereas skin malignancies showed the opposite gradient, consistent with greater ultraviolet exposure. DAG-informed causal analyses using generalized overlap weighting achieved acceptable balance across measured covariates; weighted 10-year mortality was 9.7% with very severe deficiency versus 4.8% with [&ge;]20 ng/mL, supporting a causal interpretation. Estimates remained stable with landmark lags extending to 5 years after measurement. Conclusions: Seasonally normalized 25(OH)D identifies a reproducible state of systemic vulnerability while reducing exposure misclassification caused by calendar timing of measurement. An interactive implementation of the normalization model is available: https://vitamind.medil.ai.

Background: Underweight, overweight, and linear growth during and after cancer treatment can impact treatment efficacy and survival in childhood cancer patients, and long-term health in childhood cancer survivors (CCS). However, scientific evidence on the weight status and growth of CCS is limited. Previous studies have been small, focused on specific cancer types, or lacked objectively measured longitudinal data. The availability of standardized, semantically interoperable routine data from oncology clinics offers an unprecedented opportunity to investigate nutritional status across all childhood cancer types, treatment regimens, and critical time windows. Objectives: We will investigate underweight, overweight, and growth trajectories from birth to adulthood in childhood cancer patients and survivors. We will identify predictors associated with distinct growth and weight trajectories and assess the consistency of findings across European countries. Methods: The Growth study will collect routinely recorded sociodemographic, clinical, and anthropometric data from paediatric and adolescent cancer patients (aged <20 years) from Switzerland, Portugal and Slovenia, We will include patients diagnosed between 2004-2026, by linking data from the Swiss Childhood Cancer Registry (ChCR), the Swiss Childhood Cancer Survivor Study (SCCSS), the Swiss Federal Statistical Office (FSO), and oncology clinics in Switzerland. Deterministic linkage of data sources (ChCR, SCCSS, oncology clinics, and FSO) will be performed using social security numbers. We will calculate age- and sex-specific z-scores for weight, height, and BMI based on country-specific and World Health Organization growth references. Latent trajectory modelling will be applied to identify distinct growth and weight trajectories and to detect subgroups at increased risk of growth impairment. We will use logistic regression models to identify predictors of impaired growth. Results: The project was funded in 2025; first study results are expected to be published by autumn 2027. We will obtain routinely collected data from approximately 12,250 childhood cancer patients and survivors - 7,800 from Switzerland, 3,350 from Portugal, and 1,100 from Slovenia. Conclusions: The Growth study will provide insights into the nutritional status of childhood cancer patients and survivors and improve understanding of growth patterns in this population. Identifying high risk groups and tumour or treatment related factors will inform targeted surveillance strategies and early nutritional interventions.

Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) describes health inequalities through three components: (i) specific contextual effects (SCE), (ii) general contextual effects (GCE), and (iii) discriminatory accuracy of the context. We present Simple-Means MAIHDA (S-MAIHDA), which estimates each stratum directly from its observed individuals, with no distributional assumption. The observed proportions are unbiased whatever the stratum size, and their confidence intervals report the uncertainty honestly. S-MAIHDA operationalises the three components on the probability scale. The SCE are the raw and standardised stratum prevalences and the modification of the sociodemographic average differences by the area. The GCE are the variance partition coefficient (VPC) and the contextual structuring of the between-stratum inequality, expressed as the contextual clustering of inequalities, the additive sociodemographic differences, and the contextual modification of inequalities (CMI). The contextual discriminatory accuracy is expressed by the area under the ROC curve (AUC), and the sensitivity and specificity at the population prevalence as the threshold for a possible intervention. Because its estimates are the observed data themselves, S-MAIHDA is the canonical description, and the compare diagnostic quantifies how Random-Effects MAIHDA (RE-MAIHDA), the usual implementation, departs from it: RE shrinkage pulls small strata towards the overall mean and can hide the very inequalities the analysis seeks. The approach is implemented in the smaihda Stata command and reproduced in free Python code. We illustrate S-MAIHDA on register data from Malmo, Sweden (43,291 individuals; 300 area-sociodemographic strata), showing how the three components separate two contrasting outcomes: psychotropic medication use, almost purely sociodemographic, stable across areas, with weak contextual structuring (VPC {approx} 4%, CMI {approx} 0%); and choice of a private general practitioner, strongly geographical (VPC {approx} 11%, CMI {approx} 17%), with the sociodemographic differences reshaped and amplified in wealthy areas. RE-MAIHDA attenuated inequalities. For describing inequalities, S-MAIHDA preserves what the data show.

Longitudinal household studies, combined with mathematical modeling, are widely used to characterize the drivers of respiratory pathogen transmission, including the effects of age and symptoms. In practice, household recruitment protocols vary across studies, potentially introducing biases into observed data. However, these biases are typically overlooked in statistical inference, and their impact on parameter estimates remains unknown. Here, we use synthetic household outbreak data simulated under different recruitment protocols to evaluate how recruiting through infected children affects estimates of age-specific infectiousness and susceptibility. We show that, under child-based recruitment, the standard likelihood, which accounts only for transmission dynamics, leads to underestimating child infectiousness and overestimating child susceptibility by more than 30%. We then propose a novel estimation framework that explicitly incorporates the household recruitment process into the likelihood and show that it substantially reduces these biases. Applying this new approach to a French household study conducted during the COVID-19 pandemic, we estimated that children under 6 had 49% lower infectiousness than teenagers and adults during the Alpha wave, whereas no difference was observed during the Omicron wave. This study demonstrates that ignoring recruitment protocols can bias key epidemiological parameter estimates and highlights the importance of accounting for study design.

BackgroundEarly-life air pollution exposure has been associated with childhood asthma, but its relative contribution is poorly characterised in cohorts with repeated indoor measurements. Short sampling windows and seasonal bias limit how well any single measurement can represent cumulative exposure. ObjectiveTo evaluate the association between early-life indoor and outdoor pollutant exposure from birth to 18 months and asthma development at ages 7, 12, and 18 years, while testing potential exposure-exposure and gene-environment interactions. MethodsIn the COPSAC2000 birth cohort (n = 411), indoor PM2.5, black carbon, NO2, NOx and VOCs, and modelled outdoor PM2.5, NO2 and NOx, were assessed at 6, 12 and 18 months of age. Indoor metrics were corrected for pump-flow drift and seasonal bias via a Gaussian process with a periodic kernel. Hierarchical Bayesian logistic regression(HBLR) and calibrated random forests (CRF) were evaluated using stratified nested cross-validation for discrimination using AUC-ROC, Calibration using integrated calibration index (ICI), and Brier skill scores. Exposure-exposure and gene-environment interactions were quantified via Shapley interaction indices. ResultsDiscrimination was weak at all endpoints with test AUC-ROC: 0.58-0.67, BSS near zero (0.012 to 0.05) and ICI: 0.043-0.061 for random forest and AUC-ROC 0.53-0.61, BSS equal to zero and ICI: 0.054-0.067 for hierarchical Bayesian logistic regression (HBLR). No pollutant reached a highest density interval excluding OR = 1. The polygenic risk score(PRS) was the only term non-null at all three ages in the adjusted models. While gene-environment interactions were detected, effect sizes were negligible and statistically indistinguishable from zero. SignificanceWhere ambient pollution is low, cumulative early-life exposure adds little to long-term asthma prediction beyond genetic and contextual factors, and an improved exposure correction does not uncover an effect. ImpactRepeated bedroom measurements across infancy are rare, and short sampling windows make them hard to read as cumulative exposure. We corrected measured concentrations for pump-flow drift and for seasonal sampling bias on the pollutants and used two complementary machine learning models to predict interaction and risk. In this low-exposure Danish cohort, genetic risk and residential context dominated, and the measured pollutants added almost nothing to the asthma risk at 7,12 and 18 years. These null results indicate that early cumulative exposures of indoor and outdoor air pollution contribute little to nothing to asthma up to 18 years.

Background: In England, a national school-based human papillomavirus (HPV) vaccination programme was introduced in September 2008. Routine vaccination against HPV16/18 is offered to girls aged 12-13 and a 'catch-up' offer ran for girls aged 14-18 during the programme's first two years. Methods: Data from 19,407 women aged 25-29yrs attending cervical screening across England 2016-2025 were collected and residual samples from 4,837 high-risk (HR) HPV-screening-positive women were genotyped to monitor type-specific HR-HPV prevalence and evaluate vaccination impact. Results: Type-specific HR-HPV results were available for 3,683 HR-HPV-screen-positive samples. Estimated age-adjusted prevalence of HPV16 and HPV18 was 91% (95% confidence interval [CI]: 87-94%) and 84% (95%CI: 65-93%) lower respectively among routine-eligible women than vaccination-ineligible women. Large declines were also observed for HPV31 (85% [95%CI: 72-92%]) and HPV45 (84% [95%CI: 73-90%]) but not for other HR-HPV types. Among 618 women with cytological abnormalities, HPV16/18 was present in samples from 53.9% (95%CI: 34.8-73.1%) of vaccination-ineligible, 18.1% (95%CI: 13.5-22.7%) of catch-up-eligible, and 6.5% (95%CI: 3.6-9.3%) of routine-eligible women. Conclusions: The early years of the HPV vaccination programme in England conferred strong protection against HPV16, 18, 31 and 45, and dramatically changed the target remaining for screening strategies to detect and reduce cervical disease.

Researchers must often rely on observational data to draw conclusions about the causal relationship between an exposure and an outcome. This is common in environmental epidemiology research, where the assignment to an exposure can be unethical or impossible. Consider a setting in which we seek to estimate excess mortality during heat waves, but are unable to randomize individuals to moderate or extreme heat. We propose to follow a causal framework for non-randomized time series that uses what we call historical time series matching to construct hypothetical randomized experiments, in which background covariates are balanced. We applied this framework and assessed the following causal question: Are daily mortality counts among elderly Americans higher during hot versus warm summer time periods? We addressed this causal question by drawing frequentist, Fisherian, and Bayesian inferences for five American cities. We estimated the average treatment effect of heat on three-day mortality with 95% Fisherian intervals to be 0.5 [-3.6, 4.4] deaths in Pittsburgh, 3.7 [0.7, 6.6] deaths in Seattle, 12.5 [6.1, 18.9] deaths in Chicago, 8.5 [1.4, 15.6] deaths in Los Angeles, and 20.3 [11.2, 29.5] deaths in New York.

In competing risks settings, covariate effects and group comparisons are usually assessed one event at a time - through log-rank or Cox tests on the cause-specific hazards, or Gray's test or Fine-Gray regression on a cumulative incidence function (CIF). This can obscure a clinically important quantity: the ratio between the event of interest and the competing event, since groups may differ little on the individual events yet differ sharply in their ratio. The generalized competing event (GCE) framework makes this ratio the object of inference; on the cause-specific scale the hazard ratio omega+(t) = lambda_1(t)/lambda_2(t) is estimated efficiently from a single stacked (Lunn-McNeil) model. We extend the framework to two scales that describe realized incidence. The subdistribution hazard ratio omega-tilde+(t) = lambda-tilde_1(t)/lambda-tilde_2(t) is estimated by a stacked, risk-set-weighted extension of the Lunn-McNeil construction; the cumulative-incidence ratio rho(t) = F_1(t)/F_2(t) - the odds that a subject's realized event by time t is the event of interest - by jackknife pseudo-observation regression of the Aalen-Johansen estimator. We relate the three contrasts: rho equals omega+ exactly under proportional cause-specific hazards, and equals omega-tilde+ only in the small-time limit under proportional subdistribution hazards, drifting toward 1 thereafter. The orthogonality that makes omega+ efficient is lost on both cumulative-incidence scales - omega tilde+ through overlapping weighted risk sets and shared censoring weights, rho through the shared all-cause survivor - so each carries a covariance term that must be handled and that bounds efficiency relative to the hazard-scale test. We derive the corresponding variances, study operating characteristics by simulation, illustrate on hypothetical prostate and head-and-neck cohorts, and provide an implementation in the gcemod R package.

Background: Anaemia in women of reproductive age is a persistent contributor to maternal morbidity and adverse birth outcomes in sub-Saharan Africa. Ghanaian surveys have repeatedly placed national anaemia prevalence in the World Health Organization's severe public health problem range, yet the geographical distribution of this burden, and the socioeconomic factors that may account for it after geographical adjustment, remain insufficiently elucidated. Methods: This cross-sectional study analysed the women's and geospatial files of the 2022 Ghana Demographic and Health Survey. Anaemia was defined as altitude- and smoking-adjusted haemoglobin below World Health Organization thresholds among 7,557 women aged 15-49 years with a valid measurement. Survey-weighted multivariable logistic regression, accounting for clustering, stratification, and sampling weights, was used to elucidate socioeconomic and demographic correlates. Cluster-level prevalence was tested for spatial clustering with global Moran's I and local Getis-Ord Gi* statistics (k-nearest neighbour weights, k = 8). Results: Weighted anaemia prevalence was estimated at 41.1% (mild: 22.6%, moderate: 17.3%, and severe: 1.1%). Pregnant women appeared to have higher adjusted odds of anaemia than non-pregnant women (adjusted odds ratio [AOR] 1.71, 95% CI 1.38-2.12), as did women in the Northern (AOR 1.54, 95% CI 1.16-2.06) and Savannah (AOR 1.35, 95% CI 1.04-1.76) regions relative to Greater Accra, while the Ahafo region showed lower odds (AOR 0.64, 95% CI 0.43-0.94). Associations with rural residence, education, and wealth that were significant in crude analysis appeared substantially attenuated after adjustment. Moran's I suggested significant positive spatial autocorrelation (I = 0.156, p = 0.001); Getis-Ord Gi* identified 109 hotspot and 117 cold spot clusters among 618 clusters, with hotspots concentrated in the north and cold spots in the mid-southern forest belt. Conclusion: Two in five women of reproductive age in Ghana were estimated to have been anaemic in 2022. Risk appeared to be shaped more by region and pregnancy status than by household wealth or education once geography was accounted for. Anaemia-control programming may benefit from prioritising the identified northern hotspot areas alongside routine antenatal supplementation.

Health inequalities are often examined through single axes such as class or ethnicity, obscuring how these dimensions interlock. This study applies a longitudinal intersectional approach to examine physical and mental health trajectories across five generations in the UK. We analysed 14 waves of the UK Household Longitudinal Study (2009-2024) using longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). We modelled SF-12 physical and mental health trajectories for 398 intersectional strata defined by gender, ethnicity, occupational class, and generation. Physical health inequalities widened with age, consistent with cumulative disadvantage, while mental health inequalities were more evident among later-born cohorts. The 'Boomer' generation exhibited a relatively flat mental health trajectory across its observed age range. In contrast, 'Generation Z' had lower predicted mental health, particularly among White British women with no work history - a group with poorer predicted trajectories than their South Asian peers. Our study shows how health inequalities are structurally differentiated by cohort and intersectional position. Policy responses should support proportionate universalism, combining broad population-level support with intensified action for groups showing poorer health.

Background: Observational research reports positive associations between type 2 diabetes mellitus (T2DM) and obesity-related cancers (ORCs), but causality remains unclear due to confounding (namely the shared risk factor of obesity, commonly approximated as body mass index, BMI), immortal time bias, and detection-time bias. Here, we aimed to use causal inference methods to minimise the above problems and estimate causal associations between new-onset T2DM and incident cancer. Methods: We performed a cohort study within UK Biobank, comparing new-onset T2DM with unexposed individuals matched 1 to 3 on BMI, age, and sex using a sequential longitudinal approach. The primary outcomes were total incident cancer, divided into ORCs and non-obesity-related cancers (NORCs). The secondary outcomes were site-specific cancers. We developed Cox models to estimate time-split hazard ratios (tsHRs) and 95% confidence intervals (CIs) stratified by sex. Findings: 23,771 participants with new-onset T2DM were matched with 71,170 unexposed participants. During a median follow-up of 5 years, there were 7694 (T2DM: 2432; unexposed: 5262) incident cancers. In men, there was evidence for an effect of T2DM on obesity-related cancer (tsHR 1.39, 95% CI 1.21-1.59), particularly on hepatocellular carcinoma (tsHR 3.97, 95% CI 2.38-6.65), pancreatic (tsHR 1.77, 95% CI 1.15-2.72) and kidney (tsHR 1.62, 95% CI 1.13-2.32) cancers. In women, there was evidence for an effect on obesity-related cancers (tsHR 1.33, 95% CI 1.16-1.52). Importantly, there were no associations with post-menopausal breast and endometrial cancers, two cancer types consistently associated with elevated BMI. There was no effect of new-onset T2DM on incidence of NORCs. There was evidence of detection-time bias, particularly in men. Interpretation: This is the first large-scale study to demonstrate evidence of a BMI-independent associations between new-onset T2DM and incident cancer. In men, this was primarily driven by hepatocellular carcinoma, pancreatic cancer, and kidney cancer. In women, the underlying cancers driving this relationship were less clearly defined. Funding: This study was funded by Cancer Research UK and administered through the Manchester Cancer Research Centre MB-PhD scheme (SEBCATP-2023/100010).

Abstract Background Persistent pain in later life is associated with functional decline. Identifying resources associated with preserved daily function may broaden pain research beyond symptom burden alone. Objective To examine six prespecified resource indicators-physical activity, depressive symptoms, recall performance, current partner status, education and wealth-in relation to subsequent maintenance of independence in activities of daily living (ADL). Methods This longitudinal observational study analysed adults aged 50 years or older with pain at both the initial and baseline assessments in the Health and Retirement Study (HRS), the English Longitudinal Study of Ageing (ELSA) and the Survey of Health, Ageing and Retirement in Europe (SHARE). ADL maintenance was defined as no difficulty in all five prespecified ADL activities at baseline and no difficulty in all five at follow-up. Indicator-specific risk ratios were estimated using modified Poisson regression with robust variance and pooled using random-effects meta-analysis. The six meta-analysis P values were adjusted using the Holm procedure. Each indicator used a separate complete-case sample; no common six- indicators sample was constructed. Results The descriptive samples comprised 8,279 HRS person-windows contributed by 4,688 unique participants, 1,200 ELSA participants and 6,995 SHARE participants; indicator-specific denominators varied. Physical activity was associated with a higher probability of ADL maintenance across all three cohorts (pooled risk ratio, 1.145; 95% CI, 1.097-1.195; P = .005; Holm-adjusted P = .032) and was the only prespecified hypothesis to meet the Holm-adjusted criterion. The other five hypotheses did not meet this criterion; failure to do so should not be interpreted as evidence that the corresponding associations were absent. Several of these pooled estimates showed substantial heterogeneity. Conclusions Among older adults with persistent pain, physical activity was associated with a higher probability of ADL maintenance, and the physical activity hypothesis was the only one of the six prespecified hypotheses to meet the Holm-adjusted criterion in the three-cohort meta-analysis. These observational findings support further study of physical activity and functional maintenance but do not establish causal benefit.

Establishing causality in nutrition research is challenging. While randomised controlled trials (RCTs) provide robust evidence, long-term dietary intervention studies with disease endpoints are often impractical. Short-term RCTs can instead identify intermediate traits that may lie on the causal pathway between diet and disease. Mendelian randomisation (MR) is an epidemiological approach that uses genetic variants as proxies for modifiable exposures to estimate the effects of lifelong differences in exposure on disease risk. However, the utility of MR is limited for complex dietary patterns because genetic variants typically reflect biological mechanisms rather than specific diets. We propose a two-step framework integrating dietary RCTs with MR to infer potential lifetime effects of dietary interventions. First, RCT data identify molecular traits altered by an intervention. Second, MR evaluates whether these traits are associated with long-term disease risk. We demonstrate this framework using the Diabetes Remission Clinical Trial (DiRECT), which measured circulating proteins and diabetes remission. Using protein data alone, 216 of 4,601 proteins changed following the intervention (step 1), and 10 were associated with diabetes risk using MR (step 2). We then compared these MR estimates with observed protein-remission associations from DiRECT. The broad agreement between the two (r{approx}-0.645, R2=0.416) supports this framework as a useful approach for estimating long-term effects of dietary interventions.

Abstract Purpose: Haemochromatosis due to HFE-C282Y homozygosity can lead to excess iron absorption and is typically associated with liver malignancy, plus widespread arthritis. Recent evidence suggests that limb fractures are more common, but little is known about vertebral effects. This study investigated the association of vertebral compression fractures, assessed with intelligent dual-energy X-ray absorptiometry (iDXA), and HFE genotype in a large community cohort. Methods: UK Biobank data from 227 European genetic ancestry C282Y homozygotes (mean 64.6 years) and 234 age, sex, and BMI-matched controls without common HFE haemochromatosis variants were included. Lateral vertebral assessment scans (iDXA, GE-Lunar) were acquired at imaging reassessment (2014-2020) and reviewed, blind to genotype, for radiological evidence of vertebral fracture. Matched logistic regression models assessed associations between C282Y homozygosity and vertebral fractures. Results: 78 vertebral fractures (16.9%) were identified within 461 participants. Male C282Y homozygotes had increased odds of vertebral fracture (n=22/89, 24.7%) compared to participants without HFE alleles (n=9/90, 10.0%); Odds Ratio [OR]: 2.95, 95%CI: 1.28-6.85, p=0.01. The association persisted after excluding individuals with a diagnosis of haemochromatosis (OR: 3.37, 95% CI: 1.41-8.10, p=0.007). No excess fracture risk was observed in female C282Y homozygotes (n=23/138, 16.7%) vs those without HFE alleles (n=24/144, 16.7%); OR: 0.99, 95%CI: 0.53-1.87, p=1.00. Conclusion: In this community-based imaging study, male HFE C282Y homozygotes had a markedly higher likelihood of vertebral fractures than those without HFE variants. These findings support further evaluation of vertebral fracture assessment in C282Y homozygous men to ensure prompt treatment to prevent future fracture if appropriate.

OBJECTIVE To assess associations of ambient UV with cardiovascular and cancer mortality across US Health Service Areas (HSAs), prioritise outcomes by signal and attributable burden, assess racial and ethnic variation, with UK Biobank corroboration. DESIGN Ecological analysis of HSAs using linear regression of crude mortality on ambient UV and social vulnerability, with correction for multiple comparisons. SETTING US HSAs and UK Biobank. DATA SOURCES Publicly available US data and UK Biobank data accessed under Application 82526. PARTICIPANTS 704 808 annual cardiovascular deaths (aged [&ge;]65) and 565 287 annual cancer deaths (aged [&ge;]50) from populations of 52.8 and 115.2 million; UK Biobank analyses included 412 665 individuals. MAIN OUTCOME MEASURES Cardiovascular, all-cancer, and body-site-specific cancer mortality; regression slope per interquartile-range increase in UV ({beta} UV/IQR), modelled attributable burden for US data, and hazard ratios for the UK Biobank data. RESULTS Higher UV was associated with lower cardiovascular ({beta}UV/IQR -68.3 deaths/100 000; P<0.001) and all-cancer mortality (-43.2; P<0.001), corresponding to 36 043 (5.1%) and 49 789 (8.8%) fewer US deaths annually, respectively. Associations varied by race and ethnicity. Cardiovascular findings were directionally consistent between US and UK datasets. All-cancer mortality and 10 of 18 cancers were inversely associated with UV in the US; UK analyses found inverse associations for all-cancer and two individual body-site-specific cancers. Twelve cancers showed directionally concordant associations across the datasets, of which ten were inverse. Lung cancer showed the strongest inverse association in each dataset. CONCLUSIONS Higher UV was associated with lower cardiovascular and cancer mortality across US HSAs, with modelled attributable burdens exceeding 5%; UK Biobank findings provided partial corroboration. Twelve of 18 site-specific cancers were directionally concordant between datasets. US race-stratified heterogeneity did not follow a skin-colour gradient, suggesting residual confounding.

BACKGROUND: Regional stroke-prevention priorities usually rest on what a bloc's members share. Where members span multiple Global Burden of Disease (GBD) super-regions and genetic ancestry groups, population, individual, and genetic evidence face different transportability limits, and earlier combined studies did not prespecify what counts as disagreement. METHODS: We studied the Shanghai Cooperation Organisation, which spans four super-regions and five ancestry groups and is land-contiguous. For five metabolic risk factors we triangulated GBD 2023 attributable fractions of stroke DALYs in ten states, incident stroke among 7896 adults in the China Health and Retirement Longitudinal Study (2011-18; SHRs per 1 SD), and subtype-specific Mendelian randomisation in three ancestries. An explicit rule set, with one pre-submission amendment, classified convergence and divergence by whether a listed mechanism accounted for it. FINDINGS: High systolic blood pressure converged: 53-71% of attributable DALYs, the strongest cohort exposure (SHR 1.37, 95% CI 1.26-1.48), and genetically supported in both ancestries. LDL cholesterol (GBD attribution restricted to ischaemic stroke; European genetic evidence for that subtype; no cohort association) and body-mass index (last in attribution but second in the cohort, with a 1.80-fold [1.48-2.18] gradient inside the 20-25 kg/m2 minimum-risk window) showed structural discordance. The cohort lacked subtype data to establish LDL dilution. Fasting glucose and kidney dysfunction diverged without such a mechanism. INTERPRETATION: Systolic blood pressure had the strongest cross-stream support. LDL cholesterol and body-mass index require qualified interpretation; fasting glucose and kidney dysfunction could not be ranked reliably. The framework identifies where priorities require scrutiny. FUNDING: None.

Doubly robust survival estimators require nuisance models that the available information can support. We examine design and estimator dependence in TCGA breast cancer data. Treatment indicators disagree for 337 of 1076 patients; 67.4% have unobserved five-year binary outcomes; and receptor imputation expands the triple-negative subgroup from 156 directly measured patients to 226. We compare chemotherapy initiation by day 90 with non-initiation, using adjusted restricted mean survival time (RMST) over the next 1095 days. Weighting-only estimates are 55 days (95% bootstrap interval: -24, 133) in triple- negative disease (16 deaths) and 38 days (12, 72) in the whole cohort (72 deaths). A Cox outcome-model benchmark yields 108 and 35 days, respectively, demonstrating material model dependence in the smaller population. A parameter-to-information ratio summarizes the burden on complete-outcome regressions within cross-fitting. Its simulation-calibrated warning level is 0.31 (0.28 - 0.35) for the original regime; additional simulations examine covariate-dependent censoring, delayed benefit and nonlinear misspecification. The ratio does not certify estimator validity or replace weight diagnostics. The contribution is a reproducible workflow that separates design, nuisance-model support and estimator stability before interpreting observational survival contrasts.

Introduction Higher physical activity (PA) is associated with lower risk of several cancers, but the biological mechanisms underlying these associations remain unclear. We integrated observational and Mendelian randomization (MR) approaches to investigate circulating proteins as potential intermediates linking PA to cancer risk. Methods We analysed accelerometer-derived PA measures (~100,000 participants), circulating protein levels (~53,000 participants) derived using the Olink Explore 3072 panel and cancer incidence (breast, colorectal, endometrial, and prostate cancers) via linked registry data among UK Biobank (UKB) participants. Observational associations were assessed using regression models and Cox proportional hazard models adjusting for demographic, lifestyle, and clinical covariates, including body mass index (BMI). Proteins associated with both overall acceleration average and cancer incidence were taken forward in a two-sample and two-step MR analyses using genome-wide association data. Results Higher overall acceleration average was observationally associated with lower risk of breast, colorectal, and endometrial cancer, with attenuation after BMI adjustment for endometrial and colorectal cancer, and a positive association with prostate cancer. MR analyses supported a protective causal effect of higher overall PA on breast, colorectal, and prostate cancer risk, and suggested a much larger effect than observational analyses. Overall, PA was observationally associated with 436 circulating proteins of which 44 were also associated with incident cancer risk. MR analyses provided some support that TNFSRF13B was influenced by physical activity in the same direction as the observational analyses but found conflicting evidence for an effect of TNFSRF13B on breast cancer, although the MR analysis is consistent with a protective effect of physical activity on breast cancer risk. Conclusions While higher overall physical activity appears to causally reduce risk of several cancers, circulating proteins associated with PA largely do not show strong evidence of being intermediates of these effects, suggesting they may act primarily as biomarkers of physical activity rather than causal intermediates.