International Journal of Epidemiology

Background: The increasing availability of routinely collected health data offers new opportunities for population-level research, yet access to comprehensive, linked, and standardised datasets remains limited. We describe EST-Health-30, a large-scale, population-representative health data resource from Estonia. Methods: EST-Health-30 comprises a random 30% sample of the Estonian population (~500,000 individuals), with longitudinal data from 2012 to 2024 and annual updates planned through 2026. Individual-level records are linked across five nationwide databases, including electronic health records, health insurance claims, prescription data, cancer registry, and cause of death records. A privacy-preserving hashing approach ensures consistent cohort inclusion over time while maintaining pseudonymisation. All data are harmonised to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (version 5.4) using international standard vocabularies. Data quality was assessed using established OMOP-based validation frameworks. Results: The dataset contains rich multimodal information on diagnoses, procedures, laboratory measurements, prescriptions, free-text clinical notes, healthcare utilisation, and costs, with high population coverage and longitudinal depth. Data quality assessment showed high completeness and consistency, with 99.2% of applicable checks passing. The age-sex distribution closely reflects the national population, supporting representativeness, though coverage is marginally below the target 30% (29.2%), primarily attributable to recent immigrants without health system contact. The dataset enables construction of detailed clinical cohorts, analysis of disease trajectories, and evaluation of healthcare utilisation and outcomes across the life course. Conclusions: EST-Health-30 is a comprehensive, standardised, and population-representative real-world data resource that supports epidemiological, clinical, and methodological research. Its alignment with the OMOP CDM facilitates reproducible analytics and participation in international federated research networks, while secure access infrastructure ensures compliance with data protection regulations.

Objective To examine ethnic differences in the incidence and age-related trajectories of childhood health conditions from birth to adolescence within a UK birth cohort. Design Longitudinal population-based birth cohort with linkage to primary care electronic health records. Setting Born in Bradford (BiB), a multi-ethnic birth cohort in Bradford, UK. Participants 13,282 children (36% White British, 44% Pakistani British, 20% other ethnicity) born 2007 to 2011 with linked primary care records and over 1 year follow-up. Main outcome measures Incident diagnoses of atopic conditions (asthma, eczema, allergic rhinoconjunctivitis), overweight/obesity, common mental health disorders (anxiety, depression), and neurodevelopmental disorders (including ADHD and autism). Incidence rates, Kaplan-Meier cumulative incidence, and Cox regression hazards ratios (HRs) were estimated. Results Atopic conditions emerged early (median onset 5 to 6 years) and were more common among Pakistani British children, with higher hazards of eczema (HR 2.29, 95% CI 2.01 to 2.61), allergic rhinoconjunctivitis (HR 2.27, 2.00 to 2.58), and asthma (HR 1.35, 1.22 to 1.50). Overweight/ obesity developed later (median 9 to 10 years) and were also more frequent in Pakistani British children (HR 1.25, 1.16 to 1.35). In contrast, common mental health disorders emerged predominantly in early adolescence (median around 13 years), and both mental health and neurodevelopmental diagnoses were more frequently recorded among White British children; Pakistani British children had lower hazards of neurodevelopmental diagnoses (HR 0.28, 0.23 to 0.35) and mental health disorders (HR 0.53, 0.41 to 0.70). Conclusions Ethnic differences in childhood health are condition-specific and vary by age of onset, emerging at distinct stages. These findings inform the timing of prevention, service planning, and research into underlying mechanism.

Background: Sibling-matched designs control for shared familial confounding but remain vulnerable to non-shared confounders. Bi-directional sensitivity analyses, which stratify families by whether the older or younger sibling was exposed, are commonly used to assess carryover effects. We aimed to demonstrate how this methodological approach can introduce severe confounding by parity. Methods: We conducted simulations motivated by a recent epidemiological study. The true causal effect of a hypothetical exposure (prenatal acetaminophen) on neurodevelopmental outcomes was set to strictly null. To introduce parity-related confounding, baseline exposure and outcome probabilities were varied slightly by birth order. We compared conditional logistic regression effect estimates from total sibling models against bi-directional stratified models. Results: In the total simulated sibling cohort, models yielded the true null effect (odds ratio = 1.00) when adjusting for parity. However, the bi-directional analyses exhibited divergent artifactual signals. Because parity is perfectly collinear with exposure in these stratified subsets, it cannot be adjusted for. For example, when the older sibling was exposed, the odds ratio for autism spectrum disorder was 1.68; when the younger was exposed, the odds ratio was 0.60. Conclusions: Divergent estimates in bi-directional sibling analyses can be a predictable artifact of parity confounding rather than evidence of carryover effects or invalidating unmeasured bias. Overall sibling models adjusting for parity may remain robust despite divergent stratified sensitivity results.

Childhood weight development, especially overweight and obesity, has been associated with mental health, but their dynamic, causal relationships, and whether these differ by sex, remain unclear. We applied causal discovery to data from the Danish National Birth Cohort (n=67,593) spanning six periods from pregnancy to late adolescence and considering 67 variables related to child and parental weight, mental health, lifestyle, and socio-economic factors. We found no statistically significant difference between the causal graphs for boys and girls (P=0.079). The data-driven models found causal influence of childhood weight on subsequent weight status. Mental health pathways were exclusively within or across adjacent periods and centered on early adolescent stress. We examined the interplay between a subset of mental health variables, containing information on externalizing and internalizing problems, and weight, and found no direct causal pathway between the two processes. These findings suggest that observed links between weight and these mental health measures may be attributable to confounding. Our findings demonstrate the value of data-driven causal discovery in large cohort studies and how to test for differences in causal mechanisms across subgroups. Results are available in an interactive application, enabling future research to further explore the interplay between weight and mental health.

Objective. Menopause is a significant physiological transition with implications for health outcomes (e.g., cardiometabolic), yet gaps remain in understanding the menopause transition, including how menopause timing and type influence health outcomes. Large-scale cohort studies in midlife (age~40-60) females, including the All of Us Research Program (AoURP), provide opportunities to study menopause across diverse populations and data modalities. We characterized menopause-related data in AoURP, focusing on age distributions and concordance between EHR diagnosis codes and self-reported survey responses. Methods. We analyzed menopause-related survey, EHR diagnostic code, and genomic data among ~396,000 participants in AoURP with female sex. We summarized menopause data across modalities, overlap between survey, EHR, and genomic data, and age distributions overall and across sociodemographic characteristics. Results. Among ~396,000 females, surveys captured ~193,000 menopause observations, nearly seven times more than structured EHR diagnoses (~28,000), suggesting under- ascertainement in EHR data. Nearly all females (~99%) with an EHR menopause diagnosis also reported menopause in the survey. Approximately 22,000 participants had intersected EHR, survey, and genomic menopause-related data. Survey-based age patterns matched expectations, with participants <40 years predominantly reporting pre-menopausal status and those >60 years predominantly reporting post-menopausal status. A small subset (N{approx}1,700; 4%) (age>70 years) reported no menopause, suggesting response or recall bias. EHR menopause codes were concentrated after age>45 years, with a notable spike at age 65. Modest differences in survey-based menopause age distributions were observed by sociodemographic characteristics (e.g., race, ancestry). Conclusions. These findings inform sampling strategies, power calculations, phenotype definition, and study design for menopause research using AoURP.

ObjectiveThere is little longitudinal research investigating links between violence exposure and mental disorders among children in low- and middle-income countries (LMICs), despite high rates of violence. We examined cross-sectional and longitudinal violence-mental health associations among children in a large South African birth cohort, the Drakenstein Child Health Study, including direct clinical interviews capturing childrens mental disorders. MethodIn this birth cohort (N=974), we assessed lifetime violence exposure and four subtypes (witnessed community, community victimization, witnessed domestic, domestic victimization) at ages 4.5 and 8-years via caregiver reports. At 8-years, caregivers completed the Child Behaviour Checklist; and psychiatric disorders were assessed using the Mini-International Neuropsychiatric Interview for Children and Adolescents, a self-report measure. We tested for associations using linear/logistic regressions, adjusted for confounders. ResultsMost children (91%) had experienced violence by 8-years. Cross-sectionally, total violence exposure was associated with total (B =0.49 [95% CI 0.32, 0.66]), internalizing (0.32 [0.17, 0.47]), and externalizing problems (0.46 [0.31, 0.61]), and with increased odds of disorder at 8 years (aOR=1.09 [1.05, 1.13]). Longitudinally, total violence exposure up to 4.5-years was associated with total (B=0.27 [0.03, 0.52]), internalizing (0.24 [0.04. 0.44]), and externalizing scores (0.23 [0.008, 0.45]) at 8-years, but not with increased risk of psychiatric disorders. The strongest and most consistent associations were observed for domestic versus community violence subtypes. ConclusionOur strong cross-sectional but weaker longitudinal findings suggest that recent violence exposures may be more critical than early exposures for childrens mental health. Longitudinal exploration of other violence-affected LMIC populations is urgently needed.

Background India represents 18% of the global population yet remains underrepresented in health research. Moreover, existing national surveys miss critical variation across its 4,600 ethnolinguistic groups. We present a comprehensive phenotypic characterisation of 81 populations from the GenomeIndia project. Methods We analysed 67 sociodemographic, anthropometric, and blood biochemistry variables from 17,777 individuals sampled across 81 ethnolinguistic populations from India, examining population-level variation, disease reporting fractions, and age- and sex-specific life-course trends. Findings Ethnolinguistic identity predicted health outcomes independently of administrative state, improving phenotypic variance explained by an average of 7.4%. 95% of participants had at least one abnormal biochemical or anthropometric marker, driven by low HDL (52.2%) and elevated triglycerides (43.6%). Metabolic risk, however, was highly stratified: adjusted prevalence for low HDL ranged four-fold across ancestry groups from 17.2% to 67.7%. We also identified an "awareness gap"; only 17.6% of people with hypertension and 2.2% of people with dyslipidemia were aware of their condition. This awareness gap was higher in tribal populations, in which women did not show the higher HDL levels typically seen compared to men, pointing to distinct metabolic profiles and healthcare access barriers across India. Interpretation The Indian phenotypic landscape is highly structured along ethnolinguistic lines, where ancestry and environment both influence risk. The high systemic burden of abnormalities necessitates population-specific reference intervals. GenomeIndia provides a foundational map for precision public health, shifting the focus from state-level averages to population-specific risk profiles. Funding This work was funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India.

Background Prenatal exposure to pesticides and psychosocial factors often co-occurs, particularly in low- and middle-income settings, yet their joint effects on epigenetic age acceleration (EAA) in early life remain unknown. We investigated the joint associations of prenatal pesticides metabolites and psychosocial factors on EAA in the first five years of life in the South African Drakenstein Child Health Study. Methods In 643 mothers, we measured 11 urinary pesticide metabolites and seven psychosocial factors during the second trimester of pregnancy. Child DNA methylation was measured in whole blood at ages 1, 3, and 5 years. EAA was estimated using the Horvath, Skin & Blood Horvath (skinHorvath), and Wu epigenetic clocks. Longitudinal associations were estimated using generalized estimating equations, adjusted for confounders. Joint mixture associations were evaluated using weighted quantile sum regression (WQS) and quantile g-computation (QGCOMP). Results The joint prenatal exposure mixture was positively associated with Wu ({beta} per one quintile increase in the mixture [95% CI]: 0.41 years [0.15, 0.80]), skinHorvath (0.11 years [0.06, 0.16]), and Horvath EAA (0.31 years [0.20, 0.46]) over time using WQS. Psychosocial factors, particularly food insecurity, physical interpersonal violence, and stress biomarkers, contributed most to the total mixture effect for all clocks. Pyrethroid metabolites PBA and TDCCA were top pesticide contributors to Wu EAA. Pathway enrichment analyses of clock-specific CpGs revealed distinct biological architectures, with the Wu clock enriched for neurodevelopmental and immune pathways, and metabolic pathways for the Horvath clock. Discussion Joint prenatal exposure to pesticides and psychosocial factors was associated with increased EAA across early childhood, with psychosocial factors contributing the most to the total effect. These findings highlight the importance of assessing chemical and non-chemical stressors jointly and clock-specific biological interpretation in epigenetic aging research.

Background: Population-based cohort studies are increasingly expected to demonstrate benefits for public health and wider society. However, there is limited systematic evidence on what such impact entails or how it is generated and sustained. To address this gap, we examined researcher perspectives on the impact of cohort studies. Methods: We conducted, to our knowledge, the first quantitative study of researcher views on cohort impact, recruiting active cohort researchers through national and international networks between August and December 2025. The anonymous cross-sectional survey captured researcher characteristics, perceived contributions, impact processes, challenges, and open-ended reflections. Results: A total of 163 cohort researchers participated, primarily from Australia (42%) and the UK (23%). Participants perceived their work as informing a wide range of societal issues and reported investing an average of 24% of their work time in impact-related activities. While most respondents (73%) believed their research leads to tangible policy or practice change, two thirds indicated that impact is rarely or never demonstrable shortly after study completion (67%) and seldom attributable to a single study (67%). Key concerns included pressure to overstate contributions (80%), perceived disadvantages for cohort studies in impact assessments (78%), and inadequate skills or resources to achieve impact (65%). Conclusions: Cohort researchers perceive their work as generating broad societal contributions and invest substantial effort in supporting impact. However, they face systemic challenges in both achieving and demonstrating impact. These findings highlight the need for impact frameworks that better capture complexity, long-term influence, and cumulative contributions, while mitigating unintended consequences.

The etiology of autism is influenced by genetic and non-genetic factors, with observational studies suggesting associations between early maternal health diagnoses and offspring autism. However, these associations may partly reflect shared familial genetic liability rather than direct causal effects. Using comprehensive national health registers and individual-level genetic data from the iPSYCH cohort (N=117,542), we examined whether maternal health diagnoses are associated with offspring polygenic scores (PGS) for autism. Such associations between maternal health and offspring autism would indicate shared genetic factors and the possibility of genetic confounding in the observational associations. We also tested such associations with PGSs for other neuropsychiatric and neurodevelopmental conditions that are genetically correlated with autism, but with better-powered PGS (due to larger GWAS sample sizes and likely more polygenic genetic architecture), as well as height, a negative control. Several maternal diagnoses were nominally associated with autism PGS in the child, including, e.g., certain obstetric complications, asthma, and obesity. After adjustment for multiple testing, the only statistically significant results included those between maternal diagnoses, predominantly psychiatric, and other neuropsychiatric and neurodevelopmental PGSs in the child. Sensitivity analyses confirmed the robustness of our results across exposure windows, diagnostic settings, and socioeconomic adjustments. These findings indicate that maternal diagnoses associated with autism partially reflect shared genetic liabilities between mothers and their children. However, such genetic effects, as captured by child PGS do not fully explain the observed associations, suggesting additional factors, including e.g., non-genetic familial factors, rare variants, and indirect effects.

1Mendelian randomization (MR) uses genetic variants as instrumental variables to infer causal relationships between complex traits. Standard MR can be used to estimate an average causal effect at the population level, and typically assumes a linear exposure-outcome relationship. Recently, several methods for estimating nonlinear effects have been developed. However, many have been found to produce spurious empirical findings when subjected to negative control analyses. We propose that this poor performance may be attributable to heterogeneity in variant-exposure associations. We demonstrate that heterogeneous genetic effects on exposure lead to biased estimates, poor coverage, and inflated type I error in control function and stratification-based methods. In contrast, two-stage least squares (TSLS) methods are robust to such heterogeneity, but suffer from low precision and low power in some circumstances. We show that a statistical test for heterogeneity can be used to guide the choice of nonlinear MR methods. Using UK Biobank data, we reassess the causal effects of BMI, vitamin D, and alcohol consumption on blood pressure, lipid, C-reactive protein, and age (negative control). We find strong evidence of heterogeneity for all three exposures, and also recapitulate previous results that control function and stratification-based methods are prone to false positives. Finally, using nonparametric TSLS, we identify evidence of nonlinear causal effects of BMI on HDL cholesterol, triglycerides, and C-reactive protein; however, specific estimates of the shape of these relationships are imprecise. Altogether, our results suggest that common nonlinear MR methods are unreliable in the presence of realistic levels of heterogeneity, and that more methodological development is required before practically useful nonlinear MR is feasible.

Background: Parental diabetes and obesity influence offspring phenotype, but their relative contributions remain unclear. Aim: To examine the relative contributions of parental diabetes and obesity to offspring overweight-obesity and glucose intolerance. Methods: We studied 200 offspring of mothers with diabetes in pregnancy (ODM; 176 indexes, 24 siblings), 176 mothers (133 gestational diabetes (GDM), 22 type 1, 21 type 2 diabetes), and 150 fathers. Controls included 177 offspring of non-diabetic mothers (ONDM), 177 mothers without diabetes in pregnancy, and 163 fathers. Overweight-obesity was defined by WHO criteria, central obesity as waist-to-height ratio >0.5, and glucose intolerance by ADA criteria (fasting glucose for <10 years; oral glucose tolerance test (OGTT) for >=10 years). Generalized linear mixed-effects models assessed parental determinants of offspring outcomes. Results: ODM were more overweight-obese, centrally obese, and glucose intolerant than ONDM. Younger ODM had higher capillary glucose (5.6 vs 5.1 mmol/L, p<0.001). Among ODM >=10 years, 37% had prediabetes and 5% diabetes versus 20% and 0% in ONDM. Overweight-obesity was associated with maternal (OR 7.81; 95% CI 2.19-27.85), paternal (OR 6.21; 95% CI 1.57-24.53), and biparental obesity (OR 9.59; 95% CI 2.73-33.69), but not parental diabetes. Glucose intolerance was associated only with maternal diabetes in pregnancy (OR 3.90; 95% CI 2.05-7.41). Conclusions: Preventing offspring obesity will require addressing parental obesity, whereas preventing glucose intolerance will require optimal glycemic control in the mothers before and during pregnancy.

Background. Vietnam's disease burden has shifted from communicable, maternal, neonatal, and nutritional (CMNN) causes to non-communicable diseases (NCDs), but the tempo, drivers, and regional positioning of this transition have not been jointly quantified. We characterised Vietnam's epidemiological transition 1990-2023 against ten Southeast-Asian (SEA) peers. Methods. Using Global Burden of Disease 2023 data, we computed joinpoint-regression AAPC with 95% CI (BIC-penalised, up to three break-points) for age-standardised DALY rates and cause-composition shares. We applied Das Gupta three-factor decomposition to 1990-2023 absolute DALY change (population-size, age-structure, age-specific-rate effects) and benchmarked Vietnam's NCD share against an SDI-conditional peer trajectory via leave-one-out quadratic regression. Premature mortality was quantified as WHO 30q70 under both broad NCD and strict SDG 3.4.1 definitions, using Chiang II life-table adjustment identically across all eleven countries. Findings. The CMNN age-standardised DALY rate fell from 13,295.9 to 4,022.1 per 100,000 (AAPC -4.63%/year; 95% CI -4.80 to -4.46); the NCD rate fell only from 21,688.2 to 19,282.8 (AAPC -0.37; -0.45 to -0.30). NCD share of total DALYs rose from 52.99% to 70.67% (+17.67 pp; AAPC +1.09). Vietnam ranked fourth of eleven SEA countries in 2023 (up from sixth in 1990) and sat 5.3% above the SDI-expected trajectory. Das Gupta decomposition attributed the +10.63 million NCD DALY increase to population growth (+6.26 M) and ageing (+6.08 M); rate change removed only 1.71 M. Premature NCD mortality fell from 25.02% to 21.80% (broad, 12.9% reduction) and from 22.17% to 19.50% (SDG 3.4.1, 12.0%; Vietnam sixth of eleven) - far short of the SDG 3.4 one-third-reduction target. Interpretation. Vietnam has entered a disability- and ageing-dominated NCD phase. Meeting SDG 3.4 by 2030 requires population-scale primary prevention sized to demographic momentum.

Aims Type 2 diabetes mellitus (T2DM) increases risks of stroke and dementia, yet these risks vary across individuals. We hypothesized that clinically derived diabetes subtypes contribute to this heterogeneity. We aimed to identify data-driven subtypes using routine clinical features and examine their associations with dementia, stroke, mortality, and brain structure. Methods K-means clustering was applied to 14,353 UK Biobank participants with prevalent T2DM using age at diagnosis, body mass index, glycated hemoglobin, insulin resistance (triglyceride/HDL ratio), systolic blood pressure, and C-reactive protein. Cox models assessed associations with incident dementia (all-cause, Alzheimers disease [AD], vascular dementia [VaD]), stroke (all-cause, ischemic [IS], intracerebral hemorrhage [ICH]), and mortality. Brain MRI outcomes were analyzed in 779 participants using inverse probability-weighted linear regression. Results Three subtypes were identified: severe obesity-related inflammatory diabetes (SOID), mild metabolic diabetes (MMD, reference), and mild age-related hypertension-predominant diabetes (MARD-H). Compared with MMD, SOID showed higher risks of dementia (HR 1.24), VaD (HR 1.42), stroke (HR 1.38), IS (HR 1.48), all-cause mortality (HR 1.59), and cardiovascular death (HR 1.88). MRI showed lower gray matter volume and greater white matter hyperintensity burden in SOID. Conclusions Data-driven subtyping revealed heterogeneity in neurological risk in T2DM, with the obesity-inflammation subtype showing elevated vascular and neuroimaging risk.

Background: The two-child benefit cap, implemented in April 2017, restricted Universal Credit and Child Tax Credit to the first two children in households with three or more children. We evaluate the impact of the two-child benefit cap on parental mental, general, and financial health, as well as investigate how this may differ in particular sociodemographic and economic subgroups based on sex, ethnicity and income. Methods: Data was obtained from parents (youngest child aged 5 or under) in the UK Household Longitudinal Survey from 2009 to 2023. Outcomes included parental mental health (psychological distress and life satisfaction), general health (health-related quality of life (HRQoL), self-rated health and health satisfaction), and financial health (current financial situation and financial outlook). We used complementary policy evaluation methods with different strengths and assumptions to triangulate evidence and strengthen inference: interrupted time series (ITS), difference-in-differences (DiD) and controlled time series analysis (CITS). Subgroup analyses were stratified by sex, ethnicity, and income. Findings: Across methods, findings consistently indicate that the policy worsened life satisfaction, self-rated health, health satisfaction, and financial health for parents of 3+ children. Findings were less consistent across methods for psychological distress and HRQoL. For instance, for psychological distress ITS and CITS indicate adverse impacts of the policy; however, one DiD model did not support this conclusion due to greater average worsening in the control group between the pre- and post-periods. Subgroup analyses indicate greater mental health and general health impacts in lower income, male and ethnic minority parents; while financial health was negatively impacted in all subgroups examined. Conclusions: Using repeated cross-sectional panel data and triangulating across causal inference methods, we conclude that the two-child benefit cap in the UK had a measurable adverse impact on most health outcomes examined, with worse outcomes for male, lower income and ethnic minority parents.

Methods: Using Global Burden of Disease (GBD) data, we analyzed prevalence, incidence, mortality, and disability-adjusted life years (DALYs) rates across global and 21 GBD regions from 1990-2023. Joinpoint regression identified temporal trends, age-period-cohort models analyzed effect contributions, Das Gupta decomposition quantified demographic and epidemiological impacts, inequality indices assessed health equity, and Bayesian models projected 2024-2038 trends. Results: In 2023, the global number of children and adolescents with asthma reached 131 million, with an age-standardized prevalence rate (ASPR) of 1,789.9 per 100,000. From 1990 to 2023, the global ASPR and age-standardized incidence rate (ASIR) of asthma in children and adolescents showed an upward trend, while the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate (ASDR) exhibited a downward trend. Among the 0-14 age group, the disease burden was greater in males than in females, whereas in the 15-19 age group, males had a lower disease burden than females. Projections indicate that over the next 15 years, the overall disease burden will continue to decline; however, female mortality rates and DALYs rates are projected to show an upward trend. Conclusions: The increasing prevalence and incidence rates, coupled with declining mortality and DALYs rates of asthma among children and adolescents globally, underscore the necessity for targeted public health interventions. These findings provide crucial insights for early diagnosis, treatment optimization, and global health policy formulation.

Summary statistics fine-mapping methods offer advantages over classical methods, including avoiding data-sharing constraints and improved modelling of correlated variables and sparse effects. However, its performance has not been comprehensively evaluated in breast cancer using real-world data. Previous multinomial stepwise regression (MNR) fine-mapping analyses for breast cancer identified 196 credible sets. Here, we apply summary statistics fine-mapping, compare methods, and assess parameters influencing performance. Using summary statistics from the Breast Cancer Association Consortium, we compared finiMOM, SuSiE, and FINEMAP to published MNR results across 129 regions. Performance was assessed by recall using in-sample and out-of-sample LD. Discordant credible sets were examined for technical factors, and target genes were defined using the INQUISIT pipeline. SuSiE showed the closest agreement with MNR. Results varied across regions depending on the assumed number of causal variants (L), with higher values reducing recall and no single L maximising performance. At optimal L per region, SuSiE identified 8,192 CCVs in 244 credible sets, with recall of 88%, 86%, and 72% for overall, ER-positive, and ER-negative breast cancer. Thirty MNR sets were missed. Discordance was partially explained by allele flips, imputation quality, and array heterogeneity. Fifty-two MNR-identified genes, including BRCA2, WNT7B and CREBBP were not recovered, while additional candidate genes were identified. Using out-of-sample LD reduced recall by 3% but identified novel variants. Fine-mapping results vary across methods, and no single approach is sufficient. The choice of L strongly influences results, and combining analytical approaches with functional validation can improve causal variant identification.

Background and aims: Direct evidence to connect early life metabolism with cardiometabolic diseases in old age is limited due to the rarity of multi-decadal biochemical follow-up studies. To gain deeper insight into metabolic ageing, we conducted a longitudinal study that integrates serial data on clinical biomarkers, metabolomics and clinical events across the human life course. Methods: Children born in 1962-1992 were included from four European cohorts. Time-series of clinical biomarkers and metabolomics data were available for 8,653 participants (ages 0-49 years, 142 molecular and four physiological variables). Comparable data for 13,795 UK Biobank participants at two visits (ages 40-79 years) were linked with retrospective and prospective records of diabetes and cardiovascular disease. Lifetime metabolic trajectories were reconstructed by unsupervised machine learning and local polynomial regression. Results: A stable stratification in metabolic health emerged in children between ages 3 and 12 years and persisted to old age. We summarized this population pattern by assigning each participant into one of seven metabolic subgroups with characteristic biomarker trajectories. Two subgroups (MetDys TG+ and MetDys TG-) featured increased waist-height ratio from childhood, persistently higher C-reactive protein throughout life and rapidly increasing fasting insulin between 30 and 49 years of age. Both subgroups exhibited high risk for diabetes (HR > 13) and ischemic heart disease (HR > 2.5) when compared against the lowest risk subgroup (High HDL ApoB-). Conclusions: This life-course analysis shows that metabolic dysfunction associated with excess weight gain begins in early childhood and is associated with cardiometabolic morbidity in later life.

Introduction: Although temporal variation is the hallmark of asthma, recommended diagnostic approaches largely rely on single clinic-based measurements. Ambulatory monitoring captures diurnal and day-to-day variability and may therefore enhance diagnostic accuracy. We evaluated the clinical feasibility and potential utility of home spirometry and fractional exhaled nitric oxide (FeNO) monitoring in asthma diagnosis. Methods: Symptomatic, untreated adults with GP-suspected asthma underwent diagnostic tests including bronchodilator reversibility, in-clinic FeNO, blood eosinophil counts and bronchial challenge. Participants measured spirometry and FeNO four times daily over one week; during the second week spirometry were measured twice daily. The reference standard was provided (asthma/not-asthma) by an expert panel of at least two asthma specialists based on clinical history and the results of all in-clinic testing; home spirometry (except for peak expiratory flow) and FeNO measurements were blinded to the panel. Results: Of 67 eligible participants, 51(76%) were recruited, and 38 had asthma confirmed or excluded by the panel. 1058 home spirometry measurements were obtained from 37(73%) participants; 848 home FeNO readings were obtained from 39(76%) participants. Among those completing at least one home measurement, median (IQR) adherence was 66.7(58.6-97.6)% for spirometry and 78.5(51.8-103.6)% for FeNO. Collection of health impact data for economic evaluation was feasible. In participants with a confirmed diagnostic outcome who completed home measurements (FeNO: n=32; spirometry: n=28), the putative home-testing metrics demonstrated high sensitivities at [&ge;]90% specificity, and outperformed peak expiratory flow diurnal variability. Incorporating home testing into the BTS/NICE/SIGN 2024 diagnostic pathway had the potential to reduce reliance on bronchial challenge testing by 57%. Conclusions: Home spirometry and FeNO testing and the prospective collection of health-economic data in the diagnostic setting were feasible. Home-based testing strategy showed early potential to improve asthma diagnosis and pathway efficiency. These findings support further evaluation through an adequately powered diagnostic accuracy study and health-economic assessment.

Aims: Although metabolic dysregulation is implicated in DCM, the involvement of metabolic syndrome (MetS) remains unclear. This study aims to systematically examine MetS in DCM pathogenesis. Materials and methods: By leveraging 378,837 UK Biobank participants, instead of the conventional binary MetS, we calculated a continuous metabolic risk score (MRS) and evaluated its influence on DCM risk within a multi-model evidence framework. Bidirectional weighted quantile sum regression identified key MRS components, a nested case-control study assessed 14-year pre-diagnosis MRS trajectories, mediation analyses evaluated MRS mediating lifestyle-DCM links and inflammation mediating MRS-DCM relationships, and Mendelian randomization (MR) evaluated causality for genetically predicted MetS and components on DCM. Results: During a median follow-up period of 13.4 years (interquartile range 12.7~14.1 years), 820 (0.2%) participants developed DCM. Higher MRS (HR=1.26 [1.18~1.34]) was associated with increased DCM risk, and such an association persisted across all robustness assessments even among non-MetS individuals. Waist circumference (WC, HR=1.36 [1.28~1.45], weight=0.58) and glycated hemoglobin (HR=1.23 [1.16~1.30], weight=0.22) dominated MRS' risk contribution. The trajectories of MRS diverged in cases approximately 10 years pre-diagnosis. MRS mediated 5.1~26.2% of lifestyle-related DCM risk, while inflammation mediated 16.4% of the MRS-DCM association. MR analysis further confirmed causal effects of MetS (OR=1.65 [1.45~1.88]), WC (OR=1.79 [1.58~2.03]) on DCM risk. Conclusions: Metabolic dysfunction, which was dominated by central adiposity and hyperglycemia, plays a key role in the occurrence of DCM. Early intervention targeting metabolic factors may prevent DCM onset.