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Objectives: The All of Us Research Program harnesses advances in technology, science, and engagement for precision medicine research. We describe informatics innovations which support that goal and return value to the participant cohort and community., Materials and Methods: Research data from the All of Us Research Program are available to authorized users on the All of Us Researcher Workbench. We describe the technical infrastructure that enables data access and usage for researchers. Participants are considered partners. To ensure return of value, we outline participant access to information., Results: The All of Us Research Hub allows broad access to data, regardless of background. The innovations described are rooted in the program's core values: participation is open and reflects the diversity of the United States; participants are partners and have access to their information; transparency, security, and privacy are of the highest importance; data are broadly accessible; and the program promotes positive change. We assess research impact and reflect on how All of Us can increase existing return of value to participant communities through future informatics advancements., Discussion: The program will continue to support efforts to ensure equitable access to data and return of value to participants. Looking ahead, we invite the community to join us., Conclusion: All of Us research findings can change clinical care, inform guidelines, and set a new bar for data sharing. The ultimate return of value is better care for all., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Aims: Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs)., Methods and Results: Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACEs: fatal and non-fatal myocardial infarction, stroke, and coronary artery revascularization) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. Data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC = 0.771, P < 001, NRI = 0.140, and AUC = 0.767, P = 0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not., Conclusion: Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank., Methods and Results: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate, and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P-values), particularly for sex, age, and body mass index. AUCs for all logistic regressions were higher for deep learning volumes than standard volumes (P < 0.001 for all four chambers at ED and ES)., Conclusion: Neural network reconstructions of whole heart volumes had significantly stronger associations with CVD and risk factors than standard volume estimation methods in an automatic processing pipeline., Competing Interests: Conflict of interest: K.P.K. is an employee of Siemens Healthcare Ltd. S.E.P. provides consultancy to Circle Cardiovascular Imaging, Inc., Calgary, Alberta, Canada. All others have no conflicts of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Shift work has become popular along with adverse effects such as disrupted biological rhythms, metabolic changes, sleep disorders and myocardial infarction. Studies have shown a link between myocardial infarction and shift work, but evidence is still lacking., Aims: We aim to explore the association between present and past shift work and risk of myocardial infarction in a large population of European workers., Methods: We analysed data from the UK Biobank with >500 000 participants and an average 12-year follow-up duration. Cox proportional hazard models were employed to analyse the relationship between present shift work (n = 265 064), lifetime duration or frequency of shift work (n = 71 428) and the risk of myocardial infarction, as well as the association between rest day during shift work and myocardial infarction incidents in night shift workers (n = 14 588)., Results: Night shift workers had a higher risk of myocardial infarction compared to day workers, including 'shift but never/rarely night shifts' (hazard ratio [HR] = 1.09, 95% confidence interval [CI] 1.00-1.20), 'some night shifts' (HR = 1.13, 95% CI 1.01-1.27) and 'usual/permanent night shifts' (HR = 1.21, 95% CI 1.07-1.37), respectively. Similarly, higher frequency and longer duration of night shift work were associated with the increased risk of myocardial infarction (<10 years: HR = 1.20, 95% CI 1.01-1.42; ≥10 years: HR = 1.51, 95% CI 1.28-1.77; or an average of more than eight nights per month: HR = 1.45, 95% CI 1.23-1.71). However, longer rest days couldn't decrease myocardial infarction risk compared to those who rest 1 day., Conclusions: Present and lifetime exposure to night shifts were associated with a risk of myocardial infarction and did not benefit from longer rest days., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Late-onset seborrhoeic dermatitis seriously affects patients' quality of life. Studies have shown an association between air pollution and other inflammatory skin diseases. However, associations between air pollution exposures and the incidence of late-onset seborrhoeic dermatitis have not been elucidated., Objectives: To investigate air pollution's role in the incidence of late-onset seborrhoeic dermatitis., Methods: We engaged a prospective cohort analysis utilizing the UK Biobank database. Exposure data spanning various years for specific air pollutants, namely particulate matter [PM; with an aerodynamic diameter of ≤ 2.5 µm (PM2.5), between 2.5 and 10 μm (PM2.5-10), ≤ 10 μm (PM10)] along with nitrogen oxides (NO plus NO2, denoted NOx) and NO2, were incorporated. Through a composite air pollution score constructed from five pollutants and employing Cox proportional hazards models, the relationship between air pollution and seborrhoeic dermatitis was delineated., Results: Our examination of 193 995 participants identified 3363 cases of seborrhoeic dermatitis. Higher concentrations of specific pollutants, particularly in the upper quartile (Q4), were significantly linked to an elevated risk of seborrhoeic dermatitis. Notably, PM2.5, PM10, NO2 and NOx exhibited hazard ratios of 1.11, 1.15, 1.22 and 1.15, respectively. The correlation was further solidified with a positive association between air pollution score increments and onset of seborrhoeic dermatitis. Intriguingly, this association was accentuated in certain demographics, including younger men, socioeconomically deprived people, smokers, daily alcohol consumers, and those engaging in regular physical activity., Conclusions: Our findings revealed that air pollution exposures were associated with incidence of late-onset seborrhoeic dermatitis. These results emphasize the importance of preventing environmental air pollution exposures to mitigate the risk of developing the condition., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aims: Advanced age is associated with an increased risk of adverse cardiovascular events. The relationship between biological age acceleration (BAA), cardiac size, cardiac function, and heart failure (HF) is not well-defined., Methods and Results: Utilizing the UK Biobank cohort, we assessed biological age using the Klemera-Doubal and PhenoAge methods. BAA was quantified by residual analysis compared with chronological age. Cardiovascular magnetic resonance (CMR) imaging provided detailed insights into cardiac structure and function. We employed multivariate regression to examine links between BAA and CMR-derived cardiac phenotypes. Cox proportional hazard regression models analysis was applied to explore the causative relationship between BAA and HF. Additionally, Mendelian randomization was used to investigate the genetic underpinnings of these associations. A significant correlation was found between increased BAA and deleterious changes in cardiac structure, such as diminished left ventricular mass, lower overall ventricular volume, and reduced stroke volumes across ventricles and atria. Throughout a median follow-up of 13.8 years, participants with greater biological aging showed a heightened risk of HF [26% per standard deviation (SD) increase in KDM-BA acceleration, 95% confidence intervals (CI): 23-28%; 33% per SD increase in PhenoAge acceleration, 95% CI: 32-35%]. Mendelian randomization analysis suggests a likely causal link between BAA, vital cardiac metrics, and HF risk., Conclusion: In this cohort, accelerated biological aging may serve as a risk indicator for altered cardiac dimensions, functionality, and the onset of heart failure among middle-aged and elderly adults. It holds promise as a focal point for evaluating risk and developing targeted interventions., Competing Interests: Conflict of interest: none declared, (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Context: The interplay between cardiovascular health metrics (CVHMs) and microvascular disease (MVD) in relation to the risk of incident coronary heart disease (CHD) among individuals with type 2 diabetes mellitus (T2DM) remains to be evaluated., Objective: To investigate the role of MVD and CVHMs in the development of CHD among T2DM., Design: We included 19 664 participants with T2DM from the UK Biobank who had CVHM data and were free of CHD during recruitment. CVHMs were defined based on 5 behavioral (body mass index, diet, sleep duration, smoking, and regular exercise) and 3 biological (glycemic control, hyperlipidemia, and hypertension) factors. MVD was defined as the presence of retinopathy, peripheral neuropathy, or chronic kidney disease. Hazard ratio (HR) and 95% CI of CHD were estimated by multivariable Cox regression models., Results: There were 3252 incident cases of CHD recorded after a median follow-up of 12.3 years. After multivariable adjustment, each MVD was separately associated with risk of CHD, and those who had 1 or ≥ 2 MVD had a 27% and an 87% increased risk of developing CHD, respectively. Each unfavorable CVHM was associated with a higher risk of CHD. As compared with MVD-free participants who had ideal CVHMs, those who had ≥ 2 MVD and had poor CVHMs were at particularly high risk of incident CHD (HR = 4.58; 95% CI: 3.58, 5.86), similarly when considering behavioral CVH or biological CVH separately. On an additive scale, there was a positive statistically significant interaction between number of MVD and CVHMs., Conclusion: Coexistence of multiple MVDs was associated with a substantially higher risk of CHD among individuals with T2DM. Such association may be amplified by unfavorable CVHMs., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The UK Biobank (UKB) is a large cohort study that recruited over 500,000 British participants aged 40-69 in 2006-2010 at 22 assessment centers from across the United Kingdom. Self-reported health outcomes and hospital admission data are 2 types of records that include participants' disease status. Coronary artery disease (CAD) is the most common cause of death in the UKB cohort. After distinguishing between prevalence and incidence CAD events for all UKB participants, we identified geographical variations in age-standardized rates of CAD between assessment centers. Significant distributional differences were found between the pooled cohort equation scores of UKB participants from England and Scotland using the Mann-Whitney test. Polygenic risk scores of UKB participants from England and Scotland and from different assessment centers differed significantly using permutation tests. Our aim was to discriminate between assessment centers with different disease rates by collecting data on disease-related risk factors. However, relying solely on individual-level predictions and averaging them to obtain group-level predictions proved ineffective, particularly due to the presence of correlated covariates resulting from participation bias. By using the Mundlak model, which estimates a random effects regression by including the group means of the independent variables in the model, we effectively addressed these issues. In addition, we designed a simulation experiment to demonstrate the functionality of the Mundlak model. Our findings have applications in public health funding and strategy, as our approach can be used to predict case rates in the future, as both population structure and lifestyle changes are uncertain., Competing Interests: Conflicts of interest The author(s) declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Objectives: To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data., Materials and Methods: We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results., Results: For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates., Discussion: Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis., Conclusion: EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Whether metformin and its adenosine 5'monophosphate-activated protein kinase (AMPK) activation protect from psoriasis risk is unconcluded. We investigated the effect of AMPK, a pharmacological target of metformin, on the risk of psoriasis and its comorbidities and mortality among participants in the UK Biobank (UKB)., Methods: To avoid immortal time biases in pharmacoepidemiologic studies, Mendelian randomization was used to infer the AMPK pathway-dependent effects. The cut-off age for distinguishing early-onset/late-onset psoriasis (EOP/LOP) was set at 60 years, based on the incident psoriasis peak in UKB. A genetic instrument comprising 44 single-nucleotide polymorphisms associated with glycated haemoglobin (HbA1c), serving as a proxy for AMPK genetic risk score (negatively associated with AMPK activation), was employed as previously reported in the literature. Log-binomial models were used to estimate the effect size of AMPK regarding relative risk (RR) and 95% CI., Results: A total of 407 159 participants were analysed, including 9126 EOP and 3324 LOP. The AMPK genetic risk score was associated with a 12.4% increase in the risk of LOP in men (RR = 1.124, 95% CI: 1.022-1.236). This association was not significant for EOP or women. AMPK genetic risk score exhibited an elevated risk of ischemic heart disease (RR = 1.217, 95% CI 1.062-1.395) in male psoriasis patients., Conclusions: AMPK activation may protect against LOPs and associated ischemic heart disease in men. A sex-specific, comorbidity-targeted intervention for psoriasis is needed., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aims: The relationship between the long-term joint exposure to ambient air pollution and incidence of myocardial infarction (MI) and modification by genetic susceptibility remain inconclusive., Methods and Results: We analysed 329 189 UK Biobank participants without MI at baseline. Exposure concentrations to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were obtained. Air pollution score assessing the joint exposure was calculated, and its association with MI was evaluated via Cox model under the P value aggregation framework. Genetic susceptibility to MI was evaluated by incorporating polygenic risk score (PRS) into models. Risk prediction models were also established. During a median follow-up of 13.4 years, 9993 participants developed MI. Per interquartile range increase of PM2.5, PM10, NO2, and NOx resulted in 74% [95% confidence intervals (CIs) 69%-79%], 67% (63%-72%), 46% (42%-49%), and 38% (35%-41%) higher risk of MI. Compared with the lowest quartile (Q1) of air pollution score, the multivariable adjusted hazard ratio (HR) (95%CIs) of Q4 (the highest cumulative air pollution) was 3.50 (3.29-3.72) for MI. Participants with the highest PRS and air pollution score possessed the highest risk of incident MI (HR = 4.88, 95%CIs 4.35-5.47). Integrating PRS, air pollution exposure, and traditional factors substantially improved risk prediction of MI., Conclusion: Long-term joint exposure to air pollutants including PM2.5, PM10, NO2, and NOx is substantially associated with increased risk of MI. Genetic susceptibility to MI strengthens such adverse joint association. Air pollutions together with genetic and traditional factors enhance the accuracy of MI risk prediction., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs., Methods: Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex., Results: Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men., Conclusions: We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Inverse associations between dietary fiber (DF) and colorectal cancer risk are well-established. However, evidence is limited in relation to other cancer sites. This study, of 364,856 participants from the UK Biobank, aimed to evaluate the associations between total and source-specific partial DF and risk of 17 specific cancers and all cancers combined. Partial DF was derived from baseline touchscreen questionnaire data on cereal, bread, fruit, and vegetable intake. The outcomes were incident cancer at 17 sites and all cancers combined. Cox proportional hazards models were applied. Over a median 8.8-year follow-up period, 30,725 people were diagnosed with cancer. After adjusting for sociodemographic and lifestyle factors, those in the highest quintile of partial DF compared with the lowest quintile (<9.6 vs ≥19.1 g/day) had 10% lower risk of cancer overall, with the greatest risk reductions observed for cervical (hazard ratio (HR) = 0.33, 95% confidence interval (CI): 0.14; 0.82), esophageal (HR = 0.66, 95% CI: 0.52; 0.84), lung (HR = 0.67, 95% CI: 0.59; 0.76), bladder (HR = 0.72, 95% CI: 0.56; 0.91), and kidney (HR = 0.75, 95% CI: 0.61; 0.92) cancers. Associations between DF and lung cancer were observed only in current and former smokers. Higher DF intake, in particular cereal fiber and fruit and vegetable fiber, was associated with a lower risk of overall and multiple site-specific cancers., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Among people living with frailty, adherence to a healthy lifestyle may be a low-cost and effective strategy to decrease frailty-induced health risks across different social environments., Methods: We included 15 594 frail participants at baseline from the UK Biobank study. We used four lifestyle factors to create a composite healthy lifestyle score and 17 social factors to construct a polysocial score. We classified the lifestyle score into two levels (unhealthy and healthy) and the polysocial score into three levels (low, intermediate and high). We used Cox regression to determine the association of each lifestyle factor and lifestyle score with all-cause mortality, respectively. We also examined the associations across polysocial score categories. We evaluated the joint association of the lifestyle score and the categorical polysocial score with all-cause mortality., Results: During up to 14.41 follow-up years, we documented 3098 all-cause deaths. After multivariable adjustment, we found a significant association between not smoking and adequate physical activity with all-cause mortality across polysocial score categories, respectively. We also found a significant association between a healthy diet and all-cause mortality among frail participants living in an intermediate social environment. A healthy lifestyle was associated with a lower all-cause mortality risk across polysocial score categories, especially among those with a low polysocial score., Conclusions: Adherence to a healthy lifestyle, particularly not smoking, adequate physical activity and a healthy diet, may provide a feasible solution to decreasing mortality risk among frail adults across different social environments, especially for those in the socially disadvantaged group., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Public Health Association.)

Importance: Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort., Objectives: More than 280 000 participants have shared their electronic health records (EHRs) in the All of Us Research Program. We aim to understand the phenomic profiles of this cohort through comparisons with those in the US general population and a well-established nation-wide cohort, UK Biobank, and to test whether association results of selected commonly studied diseases in the All of Us cohort were comparable to those in UK Biobank., Materials and Methods: We included participants with EHRs in All of Us and participants with health records from UK Biobank. The estimates of prevalence of diseases in the US general population were obtained from the Global Burden of Diseases (GBD) study. We conducted phenome-wide association studies (PheWAS) of 9 commonly studied diseases in both cohorts., Results: This study included 287 012 participants from the All of Us EHR cohort and 502 477 participants from the UK Biobank. A total of 314 diseases curated by the GBD were evaluated in All of Us, 80.9% (N = 254) of which were more common in All of Us than in the US general population [prevalence ratio (PR) >1.1, P < 2 × 10-5]. Among 2515 diseases and phenotypes evaluated in both All of Us and UK Biobank, 85.6% (N = 2152) were more common in All of Us (PR >1.1, P < 2 × 10-5). The Pearson correlation coefficients of effect sizes from PheWAS between All of Us and UK Biobank were 0.61, 0.50, 0.60, 0.57, 0.40, 0.53, 0.46, 0.47, and 0.24 for ischemic heart diseases, lung cancer, chronic obstructive pulmonary disease, dementia, colorectal cancer, lower back pain, multiple sclerosis, lupus, and cystic fibrosis, respectively., Discussion: Despite the differences in prevalence of diseases in All of Us compared to the US general population or the UK Biobank, our study supports that All of Us can facilitate rapid investigation of a broad range of diseases., Conclusion: Most diseases were more common in All of Us than in the general US population or the UK Biobank. Results of disease-disease association tests from All of Us are comparable to those estimated in another well-studied national cohort., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Background: Poor self-rated health (SRH) is a well-established risk factor for premature employment exit through unemployment, work disability, and early retirement. However, it is unclear whether the premature employment exit risk associated with underlying cardio-metabolic health conditions is fully captured by poor SRH. This study examines the metabolic syndrome (MetS), an early-stage risk factor for cardiovascular disease and type two diabetes mellitus, as a risk factor for premature employment exit while controlling for poor SRH., Methods: We analyzed data from N = 55 016 Dutch workers (40-64 years) from five waves of the Lifelines Cohort Study and Biobank. MetS components were based on physical measures, blood markers, and medication use. SRH and employment states were self-reported. The associations between MetS, SRH, and premature employment exit types were analyzed using competing risk regression analysis., Results: During 4.3 years of follow-up, MetS remained an independent risk factor for unemployment [adjusted subdistribution hazard ratio (SHR): 1.14, 95% CI: 1.03, 1.25] and work disability (adjusted SHR: 1.33, 95% CI: 1.11, 1.58) when adjusted for poor SRH, common chronic diseases related to labor market participation (i.e., cancer, musculoskeletal-, pulmonary-, and psychiatric diseases), and sociodemographic factors. MetS was not associated with early retirement., Conclusions: Poor SRH did not fully capture the risk for unemployment and work disability associated with MetS. More awareness about MetS as a 'hidden' cardio-metabolic risk factor for premature employment exit is needed among workers, employers, and occupational health professionals. Regular health check-ups including MetS assessment and MetS prevention might help to prolong healthy working lives., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Public Health Association.)

Objectives: To investigate the association between periodontal disease and the development of inflammatory arthritides in the general population., Methods: In total, 489 125 participants from the UK Biobank without a previous history of RA, AS and PsA were enrolled. The primary outcome was the incidence of inflammatory arthritides, which was a composite of RA, AS and PsA according to the presence of periodontal disease based on self-reported oral health indicators. Multivariate Cox proportional hazard regression analyses using four different models were performed to assess the association between periodontal disease and inflammatory arthritides development., Results: In all, 86 905 and 402 220 individuals were categorized as with and without periodontal disease, respectively. Cox hazard analysis indicated that the presence of periodontal disease was an independent predictor of the occurrence of composite outcomes of inflammatory arthritides, which was also consistent for RA and AS. Significant associations were found to be consistent in the four Cox models and were replicated even when different criteria were used to define periodontal disease. Subgroup analyses indicated that periodontal disease was associated with an increased RA risk in those aged <60 years, and this risk was persistent for both male and female patients and for patients with seropositive/seronegative RA., Conclusion: Self-reported periodontal disease is associated with inflammatory arthritides incidence in participants included in the UK Biobank, particularly for RA and AS. Higher clinical attention and optimal dental care in patients with signs of periodontal disease may be recommended for early disease detection and for reducing this risk., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Cannabis use has increased in recent years. However, the long-term implications of cannabis use on brain health remain unknown. We explored the associations of cannabis use with volumetric brain magnetic resonance imaging (MRI) measures in dementia-free older adults., Methods: This cross-sectional and longitudinal study included dementia-free participants of the UK Biobank aged ≥60 years. Linear regression models were used to evaluate the association of cannabis use and patterns of use with volumetric brain MRI measures. The association between cannabis use and change in brain MRI measures over time was also tested. All models were adjusted for potential confounders., Results: The sample included 19,932 participants (mean age 68 ± 5 years, 48% men), 3,800 (19%) reported lifetime use of cannabis. Cannabis use was associated with smaller total, white, grey and peripheral cortical grey matter volumes (B = -6,690 ± 1,157; P < 0.001, B = -4,396 ± 766; P < 0.001, B = -2,140 ± 690; P = 0.002 and B = -2,451 ± 606; P < 0.001, respectively). Among cannabis users, longer duration of use was associated with smaller total brain, grey and cortical grey matter volumes (B = -7,878 ± 2,396; P = 0.001, B = -5,411 ± 1,430; P < 0.001, B = -5,396 ± 1,254; P < 0.001, respectively), and with increased white matter hyperintensity volume (B = 0.09 ± 0.03; P = 0.008). Additionally, current vs. former users (B = -10,432 ± 4,395; P = 0.020) and frequent versus non-frequent users (B = -2,274 ± 1,125; P = 0.043) had smaller grey and cortical grey matter volumes, respectively. No significant associations were observed between cannabis use and change in brain MRI measures., Discussion: Our findings suggest that cannabis use, particularly longer duration and frequent use, may be related to smaller grey and white matter volumes in older ages, but not to late-life changes in these measures over time., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Motivation: The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis., Results: To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable., Availability and Implementation: https://github.com/vicruiser/3Dmapper., (© The Author(s) 2024. Published by Oxford University Press.)

Study Objectives: To examine the associations between sleep duration, continuity, timing, and mortality using actigraphy among adults., Methods: Data were from a cohort of 88 282 adults (40-69 years) in UK Biobank that wore a wrist-worn triaxial accelerometer for 7 days. Actigraphy data were processed to generate estimates of sleep duration and other sleep characteristics including wake after sleep onset (WASO), number of 5-minute awakenings, and midpoint for sleep onset/wake-up and the least active 5 hours (L5). Data were linked to mortality outcomes with follow-up to October 31, 2021. We implemented Cox models (hazard ratio, confidence intervals [HR, 95% CI]) to quantify sleep associations with mortality. Models were adjusted for demographics, lifestyle factors, and medical conditions., Results: Over an average of 6.8 years 2973 deaths occurred (1700 cancer, 586 CVD deaths). Overall sleep duration was significantly associated with risk for all-cause (p < 0.01), cancer (p < 0.01), and CVD (p = 0.03) mortality. For example, when compared to sleep durations of 7.0 hrs/d, durations of 5 hrs/d were associated with a 29% higher risk for all-cause mortality (HR: 1.29 [1.09, 1.52]). WASO and number of awakenings were not associated with mortality. Individuals with L5 early or late midpoints (<2:30 or ≥ 3:30) had a ~20% higher risk for all-cause mortality, compared to those with intermediate L5 midpoints (3:00-3:29; p ≤ 0.01; e.g. HR ≥ 3:30: 1.19 [1.07, 1.32])., Conclusions: Shorter sleep duration and both early and late sleep timing were associated with a higher mortality risk. These findings reinforce the importance of public health efforts to promote healthy sleep patterns in adults., (Published by Oxford University Press on behalf of Sleep Research Society (SRS) 2023.)

Background: Studies of sleep and prostate cancer are almost entirely based on self-report, with limited research using actigraphy. Our goal was to evaluate actigraphy-measured sleep and prostate cancer and to expand on findings from prior studies of self-reported sleep., Methods: We prospectively examined 34 260 men without a history of prostate cancer in the UK Biobank. Sleep characteristics were measured over 7 days using actigraphy. We calculated sleep duration, onset, midpoint, wake-up time, social jetlag (difference in weekend-weekday sleep midpoints), sleep efficiency (percentage of time spent asleep between onset and wake-up time), and wakefulness after sleep onset. Cox proportional hazards models were used to estimate covariate-adjusted hazards ratios (HRs) and 95% confidence intervals (CIs)., Results: Over 7.6 years, 1152 men were diagnosed with prostate cancer. Sleep duration was not associated with prostate cancer risk. Sleep midpoint earlier than 4:00 am was not associated with prostate cancer risk, though sleep midpoint of 5:00 am or later was suggestively associated with lower prostate cancer risk but had limited precision (earlier than 4:00 am vs 4:00-4:59 am HR = 1.00, 95% CI = 0.87 to 1.16; 5:00 am or later vs 4:00-4:59 am HR = 0.79, 95% CI = 0.57 to 1.10). Social jetlag was not associated with greater prostate cancer risk (1 to <2 hours vs <1 hour HR = 1.06, 95% CI = 0.89 to 1.25; ≥2 hours vs <1 hour HR = 0.90, 95% CI = 0.65 to 1.26). Compared with men who averaged less than 30 minutes of wakefulness after sleep onset per day, men with 60 minutes or more had a higher risk of prostate cancer (HR = 1.20, 95% CI = 1.00 to 1.43)., Conclusions: Of the sleep characteristics studied, higher wakefulness after sleep onset-a measure of poor sleep quality-was associated with greater prostate cancer risk. Replication of our findings between wakefulness after sleep onset and prostate cancer are warranted., (Published by Oxford University Press 2023.)

Aim: This study examined sex-based differences in associations of vascular risk factors with incident cardiovascular events in the UK Biobank., Methods: Baseline participant demographic, clinical, laboratory, anthropometric, and imaging characteristics were collected. Multivariable Cox regression was used to estimate independent associations of vascular risk factors with incident myocardial infarction (MI) and ischaemic stroke for men and women. Women-to-men ratios of hazard ratios (RHRs), and related 95% confidence intervals, represent the relative effect-size magnitude by sex., Results: Among the 363 313 participants (53.5% women), 8470 experienced MI (29.9% women) and 7705 experienced stroke (40.1% women) over 12.66 [11.93, 13.38] years of prospective follow-up. Men had greater risk factor burden and higher arterial stiffness index at baseline. Women had greater age-related decline in aortic distensibility. Older age [RHR: 1.02 (1.01-1.03)], greater deprivation [RHR: 1.02 (1.00-1.03)], hypertension [RHR: 1.14 (1.02-1.27)], and current smoking [RHR: 1.45 (1.27-1.66)] were associated with a greater excess risk of MI in women than men. Low-density lipoprotein cholesterol was associated with excess MI risk in men [RHR: 0.90 (0.84-0.95)] and apolipoprotein A (ApoA) was less protective for MI in women [RHR: 1.65 (1.01-2.71)]. Older age was associated with excess risk of stroke [RHR: 1.01 (1.00-1.02)] and ApoA was less protective for stroke in women [RHR: 2.55 (1.58-4.14)]., Conclusion: Older age, hypertension, and smoking appeared stronger drivers of cardiovascular disease in women, whereas lipid metrics appeared stronger risk determinants for men. These findings highlight the importance of sex-specific preventive strategies and suggest priority targets for intervention in men and women., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables., Methods: We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers., Results: Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group., Conclusion: Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone., (© The Author(s) 2024. Published by Oxford University Press.)

Background: The 2018 (WCRF)/American Institute for Cancer Research (AICR) Cancer Prevention Recommendations are evidence-based lifestyle recommendations which aim to reduce the risk of cancer worldwide. Sociodemographic factors modulate lifestyle behaviours, and both cancer incidence and survival are socio-economically patterned. We investigated adherence to these recommendations and examined patterns of adherence across sociodemographic subgroups in the UK Biobank cohort., Methods: We included 158 415 UK Biobank participants (mean age 56 years, 53% female). Total adherence scores were derived from dietary, physical activity and anthropometric data using the 2018 WCRF/AICR standardized scoring system. One-Way analysis of variance (ANOVA) was used to test for differences in total scores and in values for individual score components according to sociodemographic factors and Pearson's Χ2 test to investigate associations between sociodemographic factors according to tertiles of adherence score., Results: Mean total adherence score was 3.85 points (SD 1.05, range 0-7 points). Higher total scores were observed in females, and older (>57 years), Chinese or South Asian, and more educated participants. We found significant variations in adherence to individual recommendations by sociodemographic factors including education, Townsend deprivation index and ethnicity., Conclusions: Identifying and understanding lifestyle and dietary patterns according to sociodemographic factors could help to guide public health strategies for the prevention of cancers and other non-communicable diseases., (© The Author(s) 2023. Published by Oxford University Press on behalf of Faculty of Public Health.)

Ischemic heart disease (IHD) causes mortality and morbidity. High levels of occupational physical activity (OPA) increases IHD risk, and occupational lifting (OL) is suggested as a detrimental OPA exposure. This study investigated the association between accumulated OL throughout working life, and risk for IHD, and potential sex and hypertension differences. Data from Copenhagen Ageing and Midlife Biobank linked to register-based information on incident IHD during 9 years follow-up in the Danish National Patient Registry were included. The outcome was the odds of IHD from baseline (2009-2011) to end of follow-up (2018), among participants without IHD at baseline. Accumulated OL was assessed by linking occupational codes to a Job Exposure Matrix, creating a measure in ton-years (lifting 1,000 kg/day/year). Multivariable logistic regression tested associations between level of accumulated OL and IHD, among the 6,606 included individuals (68% men). During follow-up, 7.3% men and 3.6% women were hospitalized with IHD. Among all participants, the odds for IHD were 47% (OR 1.47, 95% CI 1.05-2.06) higher among those with ≥5 to <10 ton-years, 39% (OR 1.39, 95% CI 1.06-1.83) higher among those with ≥10 to <30 ton-years, and 62% (OR 1.62, 95% CI 1.18-2.22) higher among those with ≥30 ton-years, compared to no accumulated OL. However, these increased odds were in the same direction in the fully-adjusted model but statistically insignificant, ≥5 to <10 ton-years OR 1.28, 95% CI 0.88-1.88; ≥10 to <30 ton-years OR 1.20, 95% CI 0.85-1.69; and ≥30 ton-years OR 1.22, 95% CI 0.81-1.84. No statistically significant interactions, nor any associations, between OL and sex, or hypertension were seen., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Study Objectives: To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure., Methods: Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions., Results: Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen., Conclusions: Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health., (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society.)

Body mass index (BMI; weight (kg)/height (m)2) is commonly used to measure general adiposity. However, evidence of its appropriateness for males and females remains inconsistent. We aimed to identify the most appropriate sex-specific power value that height should be raised to in the formula and the value that would make it achieve height independency and body fatness dependency. We randomly assigned UK Biobank participants recruited in the United Kingdom between 2006 and 2010 (n = 489,873; mean age = 56.5 years; 94.2% White) to training and testing sets (80%:20%). Using height raised to the power of -50.00 to 50.00, we identified the optimal power value that either minimized correlation with height or maximized correlation with body fat percentage, using age-adjusted correlations. The optimal power values for height were 1.77 for males and 1.39 for females. The new formulas resulted in 4.5% of females and 2.4% of males being reclassified into a different BMI category. The formulas did not show significant improvement (in terms of area under the receiver operating characteristic curve, sensitivity, and specificity) in identifying individuals with excessive body fat percentage or in predicting risk of all-cause mortality. Therefore, the conventional BMI formula is still valuable in research and disease screening for both sexes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Context: Sex hormones have been identified as cardiovascular risk factors, whereas the relationship between sex hormones and the risk of arrhythmias in men has not yet been well studied in the prospective cohort study., Objective: To analyze associations of serum testosterone and SHBG concentrations and calculate free testosterone (cFT) with arrhythmias in men., Methods: Sex hormones were measured at baseline from UK Biobank. Main outcomes were incidence of atrial fibrillation/flutter (AF), ventricular arrhythmia (VA), and bradyarrhythmia (BA)., Results: Of 173 498 men (aged 37-73 years, followed for 11 years), 11 368 had incident AF, 1646 had incident VA, and 4788 had incident BA. Compared with the third quartiles, the lowest category of serum testosterone was associated with increased risks of AF (hazard ratio [HR], 1.06; 95% CI, 1.00-1.12) and BA (HR, 1.11; 95% CI, 1.02-1.20) after multivariable adjustment, but no VA. Likewise, similar associations were found between cFT values and AF and BA events. Furthermore, higher levels of cFT were associated with increased risks of AF (HR, 1.07; 95% CI, 1.02-1.13) and VA (HR, 1.18; 95% CI, 1.01-1.37). Higher SHBG concentrations were associated with increased risks of AF (HR, 1.44; 95% CI, 1.34-1.54), VA (HR, 1.27; 95% CI, 1.07-1.52), and BA (HR, 1.17; 95% CI ,1.05-1.29)., Conclusions: Lower levels of testosterone and cFT were associated with increased risk of AF and BA. Higher cFT levels were associated with increased risk of AF and VA. Higher SHBG levels were associated with increased risk of AF, VA, and BA., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: Some evidence suggests gene-treatment interactions might cause persistent symptoms in individuals receiving levothyroxine (LT4) treatment., Objective: We investigated, as previously hypothesized, if single-nucleotide variations (SNVs; formerly single-nucleotide polymorphisms) in rs225014 (Thr92Ala), rs225015, or rs12885300 (ORFa-Gly3Asp) in the deiodinase 2 gene (DIO2), or rs17606253 in the monocarboxylate transporter 10 gene (MCT10) were associated with outcomes indicative of local tissue hypothyroidism in LT4-treated patients and controls., Methods: We included 18 761 LT4-treated patients and 360 534 controls in a population-based cross-sectional study in the UK Biobank. LT4 treatment was defined as a diagnosis of hypothyroidism and self-reported use of LT4 without use of 3,5,3'-triiodothyronine. Outcomes were psychological well-being, cognitive function, and cardiovascular risk factors. Associations were evaluated by linear, logistic, or ordinal logistic multiple regression. Adjustments included sex, age, sex-age interaction, and genetic principal components 1 to 10., Results: Compared to controls, LT4 treatment was adversely associated with almost all outcomes, most noteworthy: Increased frequency of tiredness (P < .001), decreased well-being factor score (P < .001), increased reaction-time (P < .001), and increased body mass index (P < .001). Except for a significant association between the minor rs225015 A allele and financial dissatisfaction, there was no association of rs225014, rs225015, rs12885300, or rs17606253 with any outcomes in LT4-treated patients. For all outcomes, carrying the risk allele at these 4 SNVs did not amplify symptoms associated with LT4 treatment compared to controls., Conclusion: rs225014, rs225015, rs12885300, and rs17606253 could not explain changed psychological well-being, cognitive function, or cardiovascular risk factors in LT4-treated patients. Our findings do not support a gene-treatment interaction between these SNVs and LT4 treatment., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: This study aimed to examine the interactions between ultraprocessed food (UPF) consumption and genetic predisposition with the risk of gout., Methods: This prospective cohort study analysed 181 559 individuals from the UK Biobank study who were free of gout at baseline. UPF was defined according to the NOVA classification. Assessment of genetic predisposition for gout was developed from a genetic risk score of 33 single nucleotide polymorphisms. Cox proportional hazards were used to estimate the associations between UPF consumption, genetic predisposition and the risk of gout., Results: Among the 181 559 individuals in the study, 1558 patients developed gout over 1 648 167 person-years of follow-up. In the multivariable adjustment model, compared with the lowest quartile of UPF consumption, the hazard ratio (HR) and 95% CI of the highest UPF consumption was 1.16 (1.01, 1.33) for gout risk, and there was a non-linear correlation between UPF consumption and the development of gout. In substitution analyses, replacing 20% of the weight of UPF in the daily intake with an equal amount of unprocessed or minimally processed food resulted in a 13% lower risk of gout (HR: 0.87; 95% CI: 0.79, 0.95). In the joint-effect analysis, the HR (95% CI) for gout was 1.90 (1.39, 2.60) in participants with high genetic predisposition and high UPF consumption, compared with those with low genetic predisposition and low UPF consumption., Conclusion: In summary, UPF consumption was found to be associated with a higher risk of gout, particularly in those participants with genetic predisposition to gout. Our study indicated that reducing UPF consumption is crucial for gout prevention., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Substantial research has been devoted to elucidating the role of extracellular vesicles (EVs) in the different hallmarks of cancer. Consequently, EVs are increasingly explored as a source of cancer biomarkers in body fluids. However, the heterogeneity in EVs, the complexity of body fluids, and the diversity in methods available for EV analysis, challenge the development and translation of EV-based biomarker assays., Content: Essential steps in EV-associated biomarker development are emphasized covering biobanking, biomarker discovery, verification and validation, and clinical implementation. A meticulous study design is essential and ideally results from close interactions between clinicians and EV researchers. A plethora of different EV preparation protocols exists which warrants quality control and transparency to ensure reproducibility and thus enable verification of EV-associated biomarker candidates identified in the discovery phase in subsequent independent cohorts. The development of an EV-associated biomarker assay requires thorough analytical and clinical validation. Finally, regulatory affairs must be considered for clinical implementation of EV-based biomarker assays., Summary: In this review, the current challenges that prevent us from exploiting the full potential of EV-based biomarker assays are identified. Guidelines and tools to overcome these hurdles are highlighted and are crucial to advance EV-based biomarker assays into clinical use., (© Association for Diagnostics & Laboratory Medicine 2024. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: To assess the association between fructose consumption and serum sex hormone-binding globulin (SHBG), (free) testosterone, and risk of hyperandrogenism in a population-based cohort., Design: An observational and genetic association study in participants of the UK Biobank (n = 136 384 and n = 383 392, respectively)., Methods: We assessed the relationship of (1) the intake of different sources of fructose (ie, total, fruit, fruit juice, and sugar-sweetened beverages [SSBs]) and (2) rs2304681 (a missense variant in the gene encoding ketohexokinase, used as an instrument of impaired fructose metabolism), with SHBG, total and free testosterone levels, and risk of hyperandrogenism (free androgen index >4.5)., Results: The intake of total fructose and fructose from fruit was associated with higher serum SHBG and lower free testosterone in men and women and lower risk of hyperandrogenism in women. In contrast, fructose intake from SSB (≥10 g/day) was associated with lower SHBG in men and women and with higher free testosterone levels and risk of hyperandrogenism in women (odds ratio [OR]: 1.018; 95% confidence interval [CI]: 1.010; 1.026). Carriers of the rs2304681 A allele were characterized by higher circulating SHBG (both men and women), lower serum free testosterone (women), and a lower risk of biochemical hyperandrogenism (OR: 0.997, 95% CI: 0.955; 0.999; women) and acne vulgaris (OR: 0.975, 95% CI: 0.952; 0.999; men and women combined)., Conclusions: The consumption of ≥10 g/day fructose from SSB, corresponding to ≥200 mL serving, is associated with a 2% higher risk of hyperandrogenism in women. These observational data are supported by our genetic data., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Endocrinology.)

Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Background: Evidence on body fat distribution shows opposing effects of waist circumference (WC) and hip circumference (HC) for coronary heart disease (CHD). We aimed to investigate the causality and the shape of such associations., Methods: UK Biobank is a prospective cohort study of 0.5 million adults aged 40-69 years recruited between 2006 and 2010. Adjusted hazard ratios (HRs) for the associations of measured and genetically predicted body mass index (BMI), WC, HC and waist-to-hip ratio with incident CHD were obtained from Cox models. Mendelian randomization (MR) was used to assess causality. The analysis included 456 495 participants (26 225 first-ever CHD events) without prior CHD., Results: All measures of adiposity demonstrated strong, positive and approximately log-linear associations with CHD risk over a median follow-up of 12.7 years. For HC, however, the association became inverse given the BMI and WC (HR per usual SD 0.95, 95% CI 0.93-0.97). Associations for BMI and WC remained independently positive after adjustment for other adiposity measures and were similar (1.14, 1.13-1.16 and 1.18, 1.15-1.20, respectively), with WC displaying stronger associations among women. Blood pressure, plasma lipids and dysglycaemia accounted for much of the observed excess risk. MR results were generally consistent with the observational, implying causality., Conclusions: Body fat distribution measures displayed similar associations with CHD risk as BMI except for HC, which was inversely associated with CHD risk (given WC and BMI). These findings suggest that different measures of body fat distribution likely influence CHD risk through both overlapping and independent mechanisms., (© The Author(s) 2023. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Context: Evidence linking glucose metabolism status with brain macro- and microstructure is limited and inconsistent., Objective: We aim to investigate the associations of glucose metabolism status with brain macrostructure and microstructure, including brain volumes, subcortical gray matter volumes, and white matter microstructural metrics., Methods: This study enrolled 29 251 participants from the UK Biobank. Glucose metabolism status was classified into normal glucose metabolism (NGM), prediabetes, type 2 diabetes (T2D) with HbA1c <7%, and T2D with HbA1c ≥7%. Brain macrostructural metrics included volumes of total and subcortical gray matter, white matter, white matter hyperintensity (WMH), cerebrospinal fluid, and brain stem. Brain microstructural metrics included fractional anisotropy (FA) and mean diffusivity in white matter tracts. Multivariable linear regression models were used to estimate β values and 95% CI., Results: After multivariable adjustment including demographic and lifestyle factors, medical history, and total intracranial volume, those with prediabetes had smaller total and subcortical gray matter volumes than participants with NGM, while atrophy of total and subcortical gray matter was more pronounced in those with T2D (all P trend < .05). Moreover, participants with T2D had larger volumes of white matter and WMH (both P trend < .05). For brain microstructure, participants with prediabetes had lower FA values in commissural fibers (β -0.04; 95% CI -0.08, -0.003). Global and tract-specific microstructural abnormalities of white matter were observed in participants with T2D, especially for T2D with HbA1c ≥ 7% (all P trend < .05), except for FA values in projection fibers., Conclusion: These findings suggest that interventions for hyperglycemia at an earlier stage may help protect brain health., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The anterior cingulate cortex (ACC) and right ventrolateral prefrontal cortex (VLPFC) are thought to have important roles in loneliness (feeling of social isolation/exclusion) experience or regulation and in the pathophysiology of their disturbance in major depressive disorder (MDD). However, the structural abnormalities of these regions and the correlates with loneliness in MDD across the healthy population have not fully been clarified. The study analyzed the link between loneliness and gray matter volumes (GMVs) in the ACC and right VLPFC among 1,005 patients with MDD and 7,247 healthy controls (HCs) using UK Biobank data. Significant reductions in GMV in the right VLPFC were found in MDD males compared to HCs. MDD males also showed a higher association between loneliness and reduced GMVs in the right VLPFC and bilateral ACC than HCs. No such associations were found in MDD females. The findings suggest that loneliness may influence brain structures crucial for emotion experience and regulation, particularly in middle-older aged men with MDD. This highlights the potential adverse effects of loneliness on brain structure in MDD and suggests that social engagement could have a positive impact., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Endometriosis is defined by the presence of extrauterine endometrial-like tissue, which can cause pain and infertility in 10% of reproductive-age women. To date, the pathogenesis is poorly understood resulting in significant diagnostic delays and poor therapeutic outcomes in many women. Small extracellular vesicles (sEVs) (<200 nm) are cell-derived vesicles containing molecules that can influence gene expression and behaviour in target cells. One such cargo are microRNAs (miRNAs), which are short, non-coding RNAs mostly 19-25 nucleotides in length that regulate post-transcriptional gene expression. This mini-review focuses on the role of sEV-miRNAs, which are conceivably better biomarkers for endometriosis than free miRNAs, which reflect the true pathophysiological state in the body, as sEV-encapsulated miRNAs are protected from degradation compared to free miRNA and provide direct cell-to-cell communication via sEV surface proteins. sEV-miRNAs have been implicated in the immunomodulation of macrophages, the proliferation, migration and invasion of endometrial cells, and angiogenesis, all hallmarks of endometriosis. The diagnostic potential of sEV-miRNA was investigated in one study that reported the sensitivity and specificity of two sEV-miRNAs (hsa-miR-22-3p and hsa-miR-320a-3p) in distinguishing endometriosis from non-endometriosis cases. Only three studies have explored the therapeutic potential of sEV-miRNAs in vivo in mice-two looked into the role of sEV-hsa-miR-214-3p in decreasing fibrosis, and one investigated sEV-hsa-miR-30c-5p in suppressing the invasive and migratory potential of endometriotic lesions. While early results are encouraging, studies need to further address the potential influence of factors such as the menstrual cycle as well as the location and extent of endometriotic lesions on miRNA expression in sEVs. Given these findings, and extrapolating from other conditions such as cancer, diabetes, and pre-eclampsia, sEV-miRNAs could present an attractive and urgently needed future diagnostic and therapeutic target for millions of women suffering from endometriosis. However, research in this area is hampered by lack of adherence to the International Society for Extracellular Vesicles 2018 guideline in separating and characterising sEVs, as well as the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonisation Project protocols., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology.)

Background: The relevance of tobacco smoking for infectious respiratory diseases (IRD) is uncertain. We investigated the associations of cigarette smoking with severe IRD resulting in hospitalization or death in UK adults., Methods: We conducted a prospective study of cigarette smoking and risk of severe IRD in UK Biobank. The outcomes included pneumonia, other acute lower respiratory tract infections (OA-LRTI) and influenza. Multivariable Cox regression analyses were used to estimate hazard ratios (HRs) of severe IRD associated with smoking habits after adjusting for confounding factors., Results: Among 341 352 participants with no prior history of major chronic diseases, there were 12 384 incident cases with pneumonia, 7054 with OA-LRTI and 795 with influenza during a 12-year follow-up. Compared with non-smokers, current smoking was associated with ⁓2-fold higher rates of severe IRD (HR 2.40 [2.27-2.53] for pneumonia, 1.99 [1.84-2.14] for OA-LRTI and 1.82 [95% confidence interval: 1.47-2.24] for influenza). Incidence of all severe IRDs were positively associated with amount of cigarettes smoked. The HRs for each IRD (except influenza) also declined with increasing duration since quitting., Conclusions: Current cigarette smoking was positively associated with higher rates of IRD and the findings extend indications for tobacco control measures and vaccination of current smokers for prevention of severe IRD., (© The Author(s) 2023. Published by Oxford University Press on behalf of Faculty of Public Health.)

Background: Previous studies of the UK Biobank have examined intake of single food items and their association with health outcomes. Our aim was to develop a dietary quality score and examine the relationship between this score and markers of cardiometabolic health., Methods: Principal component analysis was performed on dietary data from UK Biobank participants. Linear regression was used to analyse the relationship between diet and cardiometabolic health., Results: The first component explained 14% of the variation in the dietary data. It was characterised by high consumption of meat and low fibre carbohydrates, and a low intake of fruit and vegetables. A higher score, indicative of healthier diet, was associated with lower systolic and diastolic blood pressure (β -0.81, 95% CI -1.0, -0.62; β - .61, 95% CI -0.72, -0.5) and a healthier lipid profile (lower levels of cholesterol β -0.05, 95% CI -0.06, -0.04, triglycerides β -0.05, 95% CI -0.06, -0.03, and higher HDL cholesterol β 0.01, 95% CI 0, 0.01)., Conclusions: The dietary quality score was a good approximation of overall dietary quality. An unhealthy diet was associated with markers of poorer cardiometabolic health., (© The Author(s) 2023. Published by Oxford University Press on behalf of Faculty of Public Health.)

Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The discrete-time Wright-Fisher (DTWF) model and its diffusion limit are central to population genetics. These models can describe the forward-in-time evolution of allele frequencies in a population resulting from genetic drift, mutation, and selection. Computing likelihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large samples or in the presence of strong selection. Existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here, we present a scalable algorithm that approximates the DTWF model with provably bounded error. Our approach relies on two key observations about the DTWF model. The first is that transition probabilities under the model are approximately sparse. The second is that transition distributions for similar starting allele frequencies are extremely close as distributions. Together, these observations enable approximate matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the tens of millions, paving the way for rigorous biobank-scale inference. Finally, we use our results to estimate the impact of larger samples on estimating selection coefficients for loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects., Competing Interests: Conflicts of interest The author(s) declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)