The advantages of UK Biobank's open-access strategy for health research

Corneal hysteresis as a biomarker in glaucoma development among patients with myopia: a prospective study based on the UK Biobank

BACKGROUND/AIMS

We investigated the impact of potential risk factors on the development of incident glaucoma in individuals with myopia.

METHODS

This prospective cohort study investigated participants aged 40-69 years from the UK Biobank. Participants were classified based on the degree of myopia: non-myopic (spherical equivalent (SE)≤-0.5D), mild myopia (-3.0D

RESULTS

Of 105 548 participants who did not have prior glaucoma, 35 870 were classified as myopic and 69 678 as non-myopic. A total of 1877 incident glaucoma cases (1.78%) were documented during a median 11.1 years of follow-up. Those who developed glaucoma had lower SE, lower corneal hysteresis (CH) and higher intraocular pressure. Systemic factors such as type 2 diabetes, dyslipidaemia, baseline fasting blood sugar and haemoglobin A1c were significantly higher in those who developed glaucoma, but only in the non-myopia and mild myopia subgroups. Degree of myopia and low CH (≤10.1 mm Hg) were significantly associated with increased glaucoma incidence. When we explored the impact of CH on the risk of incident glaucoma according to myopia grade, consistent elevation of risk with low CH was observed, with the highest risk being exhibited in the high myopia population (HR 1.67 (95% CI 1.10 to 2.57)).

CONCLUSIONS

Our results demonstrated a significant joint association between CH and myopia regarding glaucoma development, suggesting that altered ocular biomechanical properties could play a pronounced role in glaucoma development, particularly within the highly myopic population.

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Key Words

• Cornea
• Epidemiology
• Glaucoma

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Affiliation(s)

Jae-Seung Yun Department of Internal Medicine, The Catholic University of Korea St Vincent's Hospital, Suwon, Korea (the Republic of)
Sang-Hyuk Jung Department of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, Korea (the Republic of)
Seung Min Jung The Catholic University of Korea College of Medicine, Seocho-gu, Seoul, Korea (the Republic of)
Hong-Hee Won Samsung Biomedical Research Institute, Samsung Medical Center, Seoul, Korea (the Republic of)
Dokyoon Kim University of Pennsylvania, Philadelphia, Pennsylvania, USA
Jin A Choi Ophthalmology, The Catholic University of Korea St Vincent's Hospital, Suwon, Korea (the Republic of)

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Sleep characteristics and intervertebral disc degeneration risk: an observational and Mendelian randomization study

OBJECTIVES

Sleep disorders are considered a risk factor for aging and skeletal degeneration, but their impact on intervertebral disc degeneration (IDD) remains unclear. The aim of this study was to assess associations between sleep characteristics and IDD, and to identify potential causal relationships.

METHODS

Exposure factors included six unhealthy sleep characteristics: insomnia, short sleep duration (< 7 h), long sleep duration (≥ 9 h), evening chronotype, daytime sleepiness, and snoring. The primary outcomes included cervical disc degeneration (CDD) and lumbar disc degeneration (LDD). Firstly, we examined the associations between sleep characteristics and IDD risk in 368,348 participants from the UK Biobank using Cox proportional hazards model. Two-sample Mendelian randomization (MR) analyses were conducted to validate associations found in observational analyses, using genome-wide association data from the UK Biobank and FinnGen consortia.

RESULTS

During a median follow-up time of 13.8 years, a total of 1,637 cases of CDD and 7,654 cases of LDD were identified. Observational analyses found that almost all unhealthy sleep characteristics were associated with an elevated risk of IDD, except snoring. Conversely, the risk of IDD decreased linearly with an increasing number of healthy sleep characteristics. MR analyses supported a causal association between genetically determined insomnia and increased risk of LDD (OR 1.25 [1.07-1.47]), and between short sleep duration and increased risk of both IDD phenotypes (OR 5.41 [1.95-15.01] for CDD; OR 3.48 [1.76-6.89] for LDD). However, long sleep duration was causally associated with a reduced risk of LDD (OR 0.13 [0.03-0.53]), which contrasts with the observational findings.

CONCLUSION

We found associations between multiple sleep characteristics and IDD risk and confirmed that insomnia and short sleep duration increased IDD risk. Although more research is needed to confirm the underlying mechanisms, prioritizing interventions to improve sleep quality and ensure adequate sleep could help mitigate IDD.

Defining a High-Quality Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cohort in UK Biobank

Background

Progress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) research is being slowed by the relatively small-scale studies being performed whose results are often not replicated. Progress could be accelerated by analyses of large population-scale projects, such as UK Biobank (UKB), which provide extensive phenotype and genotype data linked to both ME/CFS cases and controls.

Methods

Here, we analysed the overlap and discordance among four UKB-defined ME/CFS cohorts, and additional questionnaire data when available.

Results

A total of 5,354 UKB individuals were linked to at least one piece of evidence of MECFS, a higher proportion (1.1%) than most prevalence estimates. Only a third (36%; n=1,922) had 2 or more pieces of evidence for MECFS, in part due to data missingness. For the same UKB participant, ME/CFS status defined by ICD-10 (International Classification of Diseases, Tenth Revision) code G93.3 (Post-viral fatigue syndrome) was most likely to be supported by another data type (72%); ME/CFS status defined by Pain Questionnaire responses is least likely to be supported (43%), in part due to data missingness.

Conclusions

We conclude that ME/CFS status in UKB, and potentially other biobanks, is best supported by multiple, and not single, lines of evidence. Finally, we raise the estimated ME/CFS prevalence in the UK to 410,000 using the most consistent evidence for ME/CFS status, and accounting for those who had no opportunity to participate in UKB due to being bed- or house-bound.

Assessment of physical activity patterns in patients with rheumatoid arthritis using the UK Biobank

Measures of physical activity patterns that may characterize rheumatoid arthritis status were investigated, using actigraphy data from a large, prospective database study (UK Biobank). Population characterization identified 1080 individuals with rheumatoid arthritis who participated in accelerometer-measured physical activity data collection and met the eligibility criteria; these individuals were subsequently matched with 2160 non-rheumatoid arthritis controls. Raw actigraphy data were pre-processed to interpretable acceleration magnitude and general signal-based features were used to derive activity labels from a human activity recognition model. Qualitative assessment of average activity profiles indicated small differences between groups for activity in the first 5 hours of the day, engagement in moderate-to-vigorous activity, and evening sleep patterns. Of 145 metrics capturing different aspects of physical activity, 57 showed an ability to differentiate between participants with rheumatoid arthritis and non-rheumatoid arthritis controls, most notably activities related to moderate-to-vigorous activity, sleep and the ability to perform sustained activity, which remained different when adjusting for baseline imbalances. Objective measures derived from wrist-worn accelerometer data may be used to assess and quantify the impact of rheumatoid arthritis on daily activity and may reflect rheumatoid arthritis symptoms. This work represents an initial step towards the characterization of such impact. Importantly, this study offers a glimpse of the potential use of large-scale datasets to support the analysis of smaller clinical study datasets.

Evaluating Health and Well-Being Returns on Investment in a Cancer Biobank

Objectives: To evaluate the population health returns from investment in the Victorian Cancer Biobank (VCB), a research consortium including five hospital-integrated sample repositories located in Melbourne, Australia. Methods: This economic evaluation assigned monetary values to the health gains attributable to VCB-supported research. These were then compared with the total investment in VCB infrastructure since inception (2006-2022) to determine the return on investment (ROI). A time lag of 40 years was incorporated, recognizing the delay from investment to impact in scientific research. Health gains were therefore measured for the years 2046-2066, with a 3% discount rate applied. Health gains were measured in terms of disability-adjusted life years (DALYs) attributable to VCB-associated research, with monetary cost assigned via the standardized value of a statistical life year (AU$227,000). The age-standardized DALY rate attributable to cancer was modeled for two standpoints (1) extrapolating the current decreasing trajectory and (2) assuming nil future improvement from current rates, with 33% of the difference attributed to scientific innovation. The proportion of the aggregate health gain attributable to VCB-supported research was estimated from the number of VCB-credited scientific publications as a proportion of total oncology publications over the same period. Results: The AU$32,628,016 of public funding invested in VCB activities over the years 2006-2022 is projected to generate AU$84,561,373 in total (discounted) savings. ROI was AU$1.59 for each AU$1 invested. Conclusions: The VCB offers a strong ROI in terms of impacts on health, justifying the expenditure of public funds and supporting the use of biobanks to advance scientific research.

Having two children might be best for women's mental health: Evidence from UK Biobank

BACKGROUND

Limited research has examined the association between the number of live births and bipolar disorder and major depression (BDMD). This study aims to investigate the relationship between the number of live births and BDMD among women in the UK, providing a theoretical basis for reproductive decision-making.

METHODS

This cross-sectional study utilized data from >55,000 women in the UK Biobank. Inverse probability treatment weighting (IPTW) logistic regression models were employed to reduce bias and confounding. Multivariate logistic regression was applied to examine the relationship between live births and BDMD, while restricted cubic spline regression was utilized to analyze the nonlinear association between the number of live births and BDMD. Subgroup analyses were conducted, stratifying by age, BMI, spontaneous abortion, induced abortion, and spontaneous abortion.

RESULTS

The IPTW-multivariate logistic regression analysis indicated that live births serve as an independent protective factor against BDMD. In IPTW-multivariate Model 4, the odds ratio (OR) for the live births group was 0.71 (95 % CI 0.69-0.73, P < 0.001). A significant nonlinear relationship was identified between the number of live births and the risk of BDMD. Additionally, it was found that women with two live births exhibited the lowest risk of BDMD.

CONCLUSION

Our study demonstrated that live births are an independent protective factor against BDMD. This research holds particular significance in the context of globally declining fertility rates and the increasing prevalence of mental disorders. It provides guidance for women on fertility decisions amid declining global fertility rates and increasing mental health issues.

Implementation of a genotyped African population cohort, with virtual follow-up: A feasibility study in the Western Cape Province, South Africa

Background

There is limited knowledge regarding African genetic drivers of disease due to prohibitive costs of large-scale genomic research in Africa.

Methods

We piloted a scalable virtual genotyped cohort in South Africa that was affordable in this resource-limited context, cost-effective, scalable virtual genotyped cohort in South Africa, with participant recruitment using a tiered informed consent model and DNA collection by buccal swab. Genotype data was generated using the H3Africa Illumina micro-array, and phenotype data was derived from routine health data of participants. We demonstrated feasibility of nested case control genome wide association studies using these data for phenotypes type 2 diabetes mellitus (T2DM) and severe COVID-19.

Results

2267346 variants were analysed in 459 participant samples, of which 229 (66.8%) are female. 78.6% of SNPs and 74% of samples passed quality control (QC). Principal component analysis showed extensive ancestry admixture in study participants. Of the 343 samples that passed QC, 93 participants had T2DM and 63 had severe COVID-19. For 1780 previously published COVID-19-associated variants, 3 SNPs in the pre-imputation data and 23 SNPS in the imputed data were significantly associated with severe COVID-19 cases compared to controls (p<0.05). For 2755 published T2DM associated variants, 69 SNPs in the pre-imputation data and 419 SNPs in the imputed data were significantly associated with T2DM cases when compared to controls (p<0.05).

Conclusions

The results shown here are illustrative of what will be possible as the cohort expands in the future. Here we demonstrate the feasibility of this approach, recognising that the findings presented here are preliminary and require further validation once we have a sufficient sample size to improve statistical significance of findings.We implemented a genotyped population cohort with virtual follow up data in a resource-constrained African environment, demonstrating feasibility for scale up and novel health discoveries through nested case-control studies.

Long-term Associations Between Time-varying Exposure to Ambient PM 2.5 and Mortality: An Analysis of the UK Biobank

BACKGROUND

Evidence for long-term mortality risks of PM 2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM 2.5 -mortality associations in the UK Biobank cohort using detailed information on confounders and exposure.

METHODS

We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM 2.5 concentrations from spatiotemporal machine-learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual- and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions.

RESULTS

In fully adjusted models, an increase of 10 μg/m³ in PM 2.5 was associated with hazard ratios of 1.27 (95% confidence interval: 1.06, 1.53) for all-cause, 1.24 (1.03, 1.50) for nonaccidental, 2.07 (1.04, 4.10) for respiratory, and 1.66 (0.86, 3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (hazard ratio = 0.88, 95% confidence interval: 0.59, 1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates.

CONCLUSIONS

We found associations of long-term PM 2.5 exposure with all-cause, nonaccidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM 2.5 and mortality.

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.

Circulating fatty acids, genetic susceptibility and hypertension: a prospective cohort study

Background

Combining genetic risk factors and plasma fatty acids (FAs) can be used as an effective method of precision medicine to prevent hypertension risk.

Methods

A total of 195,250 participants in the UK Biobank cohort were included in this study from 2006-2010. Polygenic risk scores (PRSs) were calculated for hypertension using single-nucleotide polymorphisms (SNPs). Concentrations of plasma FAs, including polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs) and saturated fatty acids (SFAs), were tested by nuclear magnetic resonance. The Cox model was used to test for the main effects of PRS, different plasma FAs and their joint effects on hypertension. Relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP) were used to test the additive interaction.

Results

Plasma PUFAs, n-3 PUFAs, MUFAs and SFAs were related to the risk of hypertension (PUFAs: HR, 0.878; 95% CI, 0.868-0.888; MUFAs: HR, 1.13; 95% CI, 1.123-1.150; SFAs: HR, 1.086; 95% CI, 1.074-1.098; n-3 PUFAs: HR, 0.984; 95% CI, 0.973-0.995). Moreover, an additive interaction was found between PRS and plasma FAs, which could contribute to an approximately 10-18% risk of hypertension, and the associations between high plasma MUFAs and a high PRS of hypertension were the strongest positive [RERI: 0.178 (95% CI: 0.062, 0.294), AP: 0.079 (95% CI: 0.027, 0.130)].

Conclusion

Increased plasma MUFAs or SFAs and decreased plasma PUFAs or n-3 PUFAs were associated with hypertension risk, especially among people at high genetic risk.

Metabolic Syndrome and Risk of Amyotrophic Lateral Sclerosis: Insights from a Large-Scale Prospective Study

OBJECTIVE

Although metabolic abnormalities are implicated in the etiology of neurodegenerative diseases, their role in the development of amyotrophic lateral sclerosis (ALS) remains a subject of controversy. We aimed to identify the association between metabolic syndrome (MetS) and the risk of ALS.

METHODS

This study included 395,987 participants from the UK Biobank to investigate the relationship between MetS and ALS. Cox regression model was used to estimate hazard ratios (HR). Stratified analyses were performed based on gender, body mass index (BMI), smoking status, and education level. Mediation analysis was conducted to explore potential mechanisms.

RESULTS

In this study, a total of 539 cases of ALS were recorded after a median follow-up of 13.7 years. Patients with MetS (defined harmonized) had a higher risk of developing ALS after adjusting for confounding factors (HR: 1.50, 95% CI: 1.19-1.89). Specifically, hypertension and high triglycerides were linked to a higher risk of ALS (HR: 1.53, 95% CI: 1.19-1.95; HR: 1.31, 95% CI: 1.06-1.61, respectively). Moreover, the quantity of metabolic abnormalities showed significant results. Stratified analysis revealed that these associations are particularly significant in individuals with a BMI <25. These findings remained stable after sensitivity analysis. Notably, mediation analysis identified potential metabolites and metabolomic mediators, including alkaline phosphatase, cystatin C, γ-glutamyl transferase, saturated fatty acids to total fatty acids percentage, and omega-6 fatty acids to omega-3 fatty acids ratio.

INTERPRETATION

MetS exhibits a robust association with an increased susceptibility to ALS, particularly in individuals with a lower BMI. Furthermore, metabolites and metabolomics, as potential mediators, provide invaluable insights into the intricate biological mechanisms. ANN NEUROL 2024;96:788-801.

Challenges and recommendations for the translation of biomarkers of aging

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

Association of peripheral inflammatory indicators with osteoarthritis risk

Objectives

Numerous studies have established the role of inflammation in osteoarthritis (OA) progression, yet limited research explores the association between systemic inflammatory indicators and pre-diagnosis OA risk. This study aimed to investigate the association between peripheral inflammatory indicators and the risk of OA using data from the UK Biobank.

Methods

The study analyzed data from 417,507 participants in the UK Biobank, including neutrophil count, lymphocyte count, monocyte count, platelet count, and C-reactive protein meter. Additionally, derived ratios such as NLR(neutrophils-lymphocytes ratio), PLR(Platelets-lymphocytes ratio), SII(systemic immune-inflammation index), and LMR (lymphocytes-monocytes ratio) were examined. Cox proportional hazards models and restricted cubic spline models were used to assess both linear and nonlinear associations.

Results

Over a mean follow-up period of 12.7 years, a total of 49,509 OA events were identified. The findings revealed that CRP (HR:1.06, 95%CI:1.05-1.07), NLR (HR:1.02, 95%CI:1.01-1.03), PLR (HR:1.02, 95%CI:1.01-1.03), and SII (HR:1.03, 95%CI:1.01-1.04) were associated with an increased risk of OA, while LMR (HR:0.97, 95%CI:0.96-0.99) showed a significant negative correlation with OA risk. Subgroup analyses further emphasized that these associations were significant across most of the population. Although neutrophils, lymphocytes, monocytes, and platelets showed a nominal association with the risk of OA, the results were unreliable, especially for specific joint OA.

Conclusion

The study provides evidence of a significant association between elevated peripheral inflammatory indicators and OA risk. These findings underscore the importance of low-grade chronic inflammation in OA development. The potential clinical utility of these indicators as early predictors of OA is suggested, warranting further exploration.

Long COVID science, research and policy

Long COVID represents the constellation of post-acute and long-term health effects caused by SARS-CoV-2 infection; it is a complex, multisystem disorder that can affect nearly every organ system and can be severely disabling. The cumulative global incidence of long COVID is around 400 million individuals, which is estimated to have an annual economic impact of approximately $1 trillion-equivalent to about 1% of the global economy. Several mechanistic pathways are implicated in long COVID, including viral persistence, immune dysregulation, mitochondrial dysfunction, complement dysregulation, endothelial inflammation and microbiome dysbiosis. Long COVID can have devastating impacts on individual lives and, due to its complexity and prevalence, it also has major ramifications for health systems and economies, even threatening progress toward achieving the Sustainable Development Goals. Addressing the challenge of long COVID requires an ambitious and coordinated-but so far absent-global research and policy response strategy. In this interdisciplinary review, we provide a synthesis of the state of scientific evidence on long COVID, assess the impacts of long COVID on human health, health systems, the economy and global health metrics, and provide a forward-looking research and policy roadmap.

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

The Effects of Estrogen on the Risk of Developing Dementia: A Cohort Study Using the UK Biobank Data

OBJECTIVES

The protective role of estrogen in the development of dementia remains uncertain. We investigated the role of lifetime cumulative exposure to estrogen in dementia in the UK Biobank.

METHODS

Reproductive characteristics, including estrogen length and history of surgery (hysterectomy/oophorectomy), were used as exposure variables. Cox Proportional Hazard models were used to estimate hazard ratios (HR) for the development of dementia.

RESULTS

A total of 273,260 female participants were included in this study. Compared to women with the shortest estrogen length, women with the longer estrogen length (38-42) had a 28% decreased risk of dementia (HR = 0.718, 95% confidence interval [CI] = 0.651-0.793). Women with later last age at estrogen exposure (50-52) had a 24% decreased risk for dementia (HR = 0.763, 95% CI = 0.695-0.839) compared to women with younger age at last estrogen exposure (≤45). Later age at menarche (≥15) was associated with a 12% increased risk for dementia (HR = 1.121, 95% CI = 1.018-1.234) compared to women with earlier age at menarche (≤12). Women with a history of surgery had an 8% increased risk of dementia (HR = 1.079, 95% CI = 1.002-1.164) compared to women without a history of surgery.

CONCLUSION

This study found that more prolonged exposure to estrogen (longer estrogen length and later age at last estrogen exposure) had a decreased risk for dementia, and shorter exposure to estrogen (later age at menarche and history of reproductive surgery) had an increased risk for dementia. Based on the results of this study, estrogen might have a protective role in women in the development of dementia.

Polygenic risk scores for mood and related disorders and environmental factors: Interaction effects on wellbeing in the UK biobank

Mood disorders have a genetic and environmental component and interactions (GxE) on the risk of psychiatric diseases have been investigated. The same GxE interactions may affect wellbeing measures, which go beyond categorical diagnoses and reflect the health-disease continuum. We evaluated GxE effects in the UK Biobank, considering as outcomes subjective wellbeing (feeling good and functioning well) and objective measures (education and income). We estimated the polygenic risk scores (PRSs) of major depressive disorder, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder. Stressful/traumatic events during adulthood or childhood were considered as E variables, as well as social support. The addition of the PRSxE interaction to PRS and E variables was tested in linear or multinomial regression models, adjusting for confounders. We included 33 k-380 k participants, depending on the variables considered. Most PRSs and E factors showed additive effects on outcomes, with effect sizes generally 3-5 times larger for E variables than PRSs. We found some interaction effects, particularly when considering recent stress, history of a long illness/disability/infirmity, and social support. Higher PRSs increased the negative effects of stress on wellbeing, but they also increased the positive effects of social support, with interaction effects particularly for the outcomes health satisfaction, loneliness, and income (p < Bonferroni corrected threshold of 1.92e-4). PRSxE terms usually added ∼0.01-0.02% variance explained to the corresponding additive model. PRSxE effects on wellbeing involve both positive and negative E factors. Despite small variance explained at the population level, preventive/therapeutic interventions that modify E factors could be beneficial at the individual level.

Real-world impact of continuous positive airway pressure on sleepiness in patients with obstructive sleep apnea in a national registry

OBJECTIVE

Excessive daytime sleepiness (EDS) persists in some patients with obstructive sleep apnea (OSA) despite continuous positive airway pressure (CPAP) treatment. This study characterized response to CPAP and factors associated with residual EDS.

METHODS

Danish National Patient Registry data were analyzed. Patients with OSA diagnosis (1994-2016), Epworth Sleepiness Scale (ESS) scores and apnea-hypopnea index recorded before beginning CPAP (baseline) and after 1-13 months of CPAP use, and CPAP adherence were included. Odds ratios (OR) for residual EDS after CPAP treatment were estimated using multivariate logistic regression.

RESULTS

Of 1174 patients (mean age, 57 years; 75.5% male), 41.1% had baseline EDS (mild, 13.2%; moderate, 14.0%; severe, 13.9%); 58.9% did not. After CPAP treatment, follow-up mean ESS scores were normal (≤10) for all baseline EDS subgroups; however, 15.6% (n = 183) of patients had residual EDS (mild, 6.7%; moderate, 5.5%; severe, 3.4%). Odds of residual EDS were higher for patients with mild (OR, 5.2; 95% confidence interval [CI], 3.2-8.6), moderate (OR, 4.5; 95% CI, 2.7-7.4), and severe (OR, 13.0; 95% CI, 8.0-21.2) EDS at baseline compared with those with normal daytime sleepiness at baseline. Patients adherent with CPAP use were 38.2% less likely to have residual EDS compared with nonadherent patients (OR, 0.62; 95% CI, 0.43-0.88).

CONCLUSIONS

EDS was common in this cohort of Danish patients with OSA. Baseline EDS severity predicted higher odds of residual EDS. After CPAP treatment, adherence was associated with reduced odds of residual EDS, but EDS persisted in a subgroup of patients.

[Hypothetical Alcohol Consumption Interventions and Hepatic Steatosis: A Longitudinal Study in a Large Cohort]

Objective

Non-alcoholic fatty liver disease (NAFLD) and alcohol-associated fatty liver disease (ALD) are the most common chronic liver diseases. Hepatic steatosis is an early histological subtype of both NAFLD and ALD. Excessive alcohol consumption is widely known to lead to hepatic steatosis and subsequent liver damage. However, reported findings concerning the association between moderate alcohol consumption and hepatic steatosis remain inconsistent. Notably, alcohol consumption as a modifiable lifestyle behavior is likely to change over time, but most previous studies covered alcohol intake only once at baseline. These inconsistent findings from existing studies do not inform decision-making concerning policies and clinical guidelines, which are of greater interest to health policymakers and clinician-scientists. Additionally, recommendations on the types of alcoholic beverages are not available. Usually, assessing the effects of two or more hypothetical alcohol consumption interventions on hepatic steatosis provides answers to questions concerning the population risk of hepatic steatosis if everyone changes from heavy drinking to abstinence, or if everyone keeps on drinking moderately, or if everyone of the drinking population switches from red wine to beer? Thus, we simulated a target trial to estimate the effects of several hypothetical interventions, including changes in the amount of alcohol consumption or the types of alcoholic beverages consumed, on hepatic steatosis using longitudinal data, to inform decisions about alcohol-related policymaking and clinical care.

Methods

This longitudinal study included 12687 participants from the UK Biobank (UKB), all of whom participated in both baseline and repeat surveys. We excluded participants with missing data related to components of alcohol consumption and fatty liver index (FLI) in the baseline and the repeat surveys, as well as those who had reported liver diseases or cancer at the baseline survey. We used FLI as an outcome indicator and divided the participants into non-, moderate, and heavy drinkers. The surrogate marker FLI has been endorsed by many international organizations' guidelines, such as the European Association for the Study of the Liver. The calculation of FLI was based on laboratory and anthropometric data, including triglyceride, gamma-glutamyl transferase, body mass index, and waist circumference. Participants responded to questions about the types of alcoholic beverages, which were defined in 5 categories, including red wine, white wine/fortified wine/champagne, beer or cider, spirits, and mixed liqueurs, along with the average weekly or monthly amounts of alcohol consumed. Alcohol consumption was defined as pure alcohol consumed per week and was calculated according to the amount of alcoholic beverages consumed per week and the average ethanol content by volume in each alcoholic beverage. Participants were categorized as non-drinkers, moderate drinkers, and heavy drinkers according to the amount of their alcohol consumption. Moderate drinking was defined as consuming no more than 210 g of alcohol per week for men and 140 g of alcohol per week for women. We defined the following hypothetical interventions for the amount of alcohol consumed: sustaining a certain level of alcohol consumption from baseline to the repeat survey (e.g., none to none, moderate to moderate, heavy to heavy) and changing from one alcohol consumption level to another (e.g., none to moderate, moderate to heavy). The hypothetical interventions for the types of alcoholic beverages were defined in a similar way to those for the amount of alcohol consumed (e.g., red wine to red wine, red wine to beer/cider). We applied the parametric g-formula to estimate the effect of each hypothetical alcohol consumption intervention on the FLI. To implement the parametric g-formula, we first modeled the probability of time-varying confounders and FLI conditional on covariates. We then used these conditional probabilities to estimate the FLI value if the alcohol consumption level of each participant was under a specific hypothetical intervention. The confidence interval was obtained by 200 bootstrap samples.

Results

For the alcohol consumption from baseline to the repeat surveys, 6.65% of the participants were sustained non-drinkers, 63.68% were sustained moderate drinkers, and 14.74% were sustained heavy drinkers, while 8.39% changed from heavy drinking to moderate drinking. Regarding the types of alcoholic beverages from baseline to the repeat surveys, 27.06% of the drinkers sustained their intake of red wine. Whatever the baseline alcohol consumption level, the hypothetical interventions for increasing alcohol consumption from the baseline alcohol consumption were associated with a higher FLI than that of the sustained baseline alcohol consumption level. When comparing sustained non-drinking with the hypothetical intervention of changing from non-drinking to moderate drinking, the mean ratio of FLI was 1.027 (95% confidence interval [CI]: 0.997-1.057). When comparing sustained non-drinking with the hypothetical intervention of changing from non-drinking to heavy drinking, the mean ratio of FLI was 1.075 (95% CI: 1.042-1.108). When comparing sustained heavy drinking with the hypothetical intervention of changing from heavy drinking to moderate drinking, the mean ratio of FLI was 0.953 (95% CI: 0.938-0.968). The hypothetical intervention of changing to red wine in the UKB was associated with lower FLI levels, compared with sustained consumption of other types of alcoholic beverages. For example, when comparing sustaining spirits with the hypothetical intervention of changing from spirits to red wine, the mean ratio of FLI was 0.981 (95% CI: 0.948-1.014).

Conclusions

Regardless of the current level of alcohol consumption, interventions that increase alcohol consumption could raise the risk of hepatic steatosis in Western populations. The findings of this study could inform the formulation of future practice guidelines and health policies. If quitting drinking is challenging, red wine may be a better option than other types of alcoholic beverages in Western populations.

Polygenic risk score-based phenome-wide association for glaucoma and its impact on disease susceptibility in two large biobanks

BACKGROUND

Glaucoma is a leading cause of worldwide irreversible blindness. Considerable uncertainty remains regarding the association between a variety of phenotypes and the genetic risk of glaucoma, as well as the impact they exert on the glaucoma development.

METHODS

We investigated the associations of genetic liability for primary open angle glaucoma (POAG) with a wide range of potential risk factors and to assess its impact on the risk of incident glaucoma. The phenome-wide association study (PheWAS) approach was applied to determine the association of POAG polygenic risk score (PRS) with a wide range of phenotypes in 377, 852 participants from the UK Biobank study and 43,623 participants from the Penn Medicine Biobank study, all of European ancestry. Participants were stratified into four risk tiers: low, intermediate, high, and very high-risk. Cox proportional hazard models assessed the relationship of POAG PRS and ocular factors with new glaucoma events.

RESULTS

In both discovery and replication set in the PheWAS, a higher genetic predisposition to POAG was specifically correlated with ocular disease phenotypes. The POAG PRS exhibited correlations with low corneal hysteresis, refractive error, and ocular hypertension, demonstrating a strong association with the onset of glaucoma. Individuals carrying a high genetic burden exhibited a 9.20-fold, 11.88-fold, and 28.85-fold increase in glaucoma incidence when associated with low corneal hysteresis, high myopia, and elevated intraocular pressure, respectively.

CONCLUSION

Genetic susceptibility to POAG primarily influences ocular conditions, with limited systemic associations. Notably, the baseline polygenic risk for POAG robustly associates with new glaucoma events, revealing a large combined effect of genetic and ocular risk factors on glaucoma incidents.

Presence of periodontal disease and the incidence of inflammatory arthritides in the general population: data from the UK Biobank

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.

Evidence for Genetic Causal Relationships Between Multiple Immune-Mediated Inflammatory Diseases and Age-Related Macular Degeneration: A Univariable and Multivariable Mendelian Randomization Study

INTRODUCTION

With the global aging population on the rise, age-related macular degeneration (AMD) poses a growing healthcare burden. Prior research hints at immune-mediated inflammatory diseases (IMIDs) potentially elevating AMD risk via diverse mechanisms. However, causality remains disputed as a result of confounding factors. Hence, our Mendelian randomization (MR) study aims to untangle this link, mitigating confounding effects to explore the IMID-AMD causal relationship. This study aims to investigate the causal relationship between IMIDs and AMD, providing new strategies for the prevention and treatment of AMD in clinical practice.

METHODS

This study was registered with PROSPERO, CRD42023469815. We obtained data on IMIDs and AMD from Genome-Wide Association Studies (GWAS) summary statistics and the FinnGen consortium. Rigorous selection steps were applied to screen for eligible instrumental single nucleotide polymorphisms (SNPs). We conducted univariate Mendelian randomization, inverse variance-weighted (IVW), weighted median, Mendelian randomization-Egger (MR-Egger), and multivariate Mendelian randomization (MVMR) analyses. Various sensitivity analysis methods were employed to assess pleiotropy and heterogeneity. The aim was to explore the causal relationships between IMIDs and AMD.

RESULTS

The MR analysis revealed that Crohn's disease (CD) (IVW: odd ratios (OR) 1.05, 95% CI (confidence interval) 1.01-1.10, p = 0.007), rheumatoid arthritis (RA) (IVW: OR 1.09, 95% CI 1.04-1.15, p = 0.0001), and type 1 diabetes (T1D) (IVW: OR 1.05, 95% CI 1.02-1.09, p = 0.001) were correlated with an elevated risk of AMD, while multiple sclerosis (MS) (IVW: OR 2.78E-18, 95% CI 2.23E-31 to 3.48E-05, p = 0.008) appeared to be protective against AMD. These findings were supported by an array of MR analysis methodologies and the MVMR approach.

CONCLUSION

Our study results, based on MR, provide genetic evidence indicating a causal relationship between specific IMIDs and AMD. CD, RA, and T1D are factors increasing the risk of AMD, while MS may have a protective effect.

Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group

OBJECTIVE

To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field.

COMPOSITION OF THE COMMITTEE

Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders.

METHODS

This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future.

REPORT

The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm.

CONCLUSION

Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.

Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank

BACKGROUND

Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them.

METHODS

We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD.

RESULTS

The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve.

LIMITATIONS

The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent.

CONCLUSIONS

Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.

Maternal smoking, nutritional factors at different life stage, and the risk of incident type 2 diabetes: a prospective study of the UK Biobank

BACKGROUND

This study aims to investigate potential interactions between maternal smoking around birth (MSAB) and type 2 diabetes (T2D) pathway-specific genetic risks in relation to the development of T2D in offspring. Additionally, it seeks to determine whether and how nutritional factors during different life stages may modify the association between MSAB and risk of T2D.

METHODS

This study included 460,234 participants aged 40 to 69 years, who were initially free of T2D from the UK Biobank. MSAB and breastfeeding were collected by questionnaire. The Alternative health eating index(AHEI) and dietary inflammation index(DII) were calculated. The polygenic risk scores(PRS) of T2D and pathway-specific were established, including β-cell function, proinsulin, obesity, lipodystrophy, liver function and glycated haemoglobin(HbA1c). Cox proportion hazards models were performed to evaluate the gene/diet-MSAB interaction on T2D. The relative excess risk due to additive interaction (RERI) were calculated.

RESULTS

During a median follow-up period of 12.7 years, we identified 27,342 cases of incident T2D. After adjustment for potential confounders, participants exposed to MSAB had an increased risk of T2D (HR=1.11, 95%CI:1.08-1.14), and this association remained significant among the participants with breastfeeding (HR= HR=1.10, 95%CI: 1.06-1.14). Moreover, among the participants in the highest quartile of AHEI or in the lowest quartile of DII, the association between MSAB and the increased risk of T2D become non-significant (HR=0.94, 95%CI: 0.79-1.13 for AHEI; HR=1.09, 95%CI:0.99-1.20 for DII). Additionally, the association between MSAB and risk of T2D became non-significant among the participants with lower genetic risk of lipodystrophy (HR=1.06, 95%CI:0.99-1.14), and exposed to MSAB with a higher genetic risk for β-cell dysfunction or lipodystrophy additively elevated the risk of T2D(RERI=0.18, 95%CI:0.06-0.30 for β-cell function; RERI=0.16, 95%CI:0.04-0.28 for lipodystrophy).

CONCLUSIONS

This study indicates that maintaining a high dietary quality or lower dietary inflammation in diet may reduce the risk of T2D associated with MSAB, and the combination of higher genetic risk of β-cell dysfunction or lipodystrophy and MSAB significantly elevate the risk of T2D in offspring.

Adherence to a healthy sleep pattern and risk of urologic cancers: A large prospective cohort study

OBJECTIVE

A variety of unhealthy sleep behaviors have been shown to be associated with an increased risk of urologic cancers. However, little is known about the association between the overall sleep patterns and urologic cancers. To prospectively investigate the associations between a healthy sleep pattern and the risks of urologic cancers, including bladder cancer (BCa) and renal cell carcinoma (RCC).

METHODS

In this prospective cohort study, 377,144 participants free of cancer at baseline were recruited from the UK Biobank. Data on sleep behaviors were collected through questionnaires at recruitment. The incident urologic cancer cases were determined through linkage to national cancer and death registries. We established a healthy sleep score according to five sleep traits (sleep duration, chronotype, insomnia, snoring, and daytime sleepiness). Cox proportional hazard regression models were used to calculate the adjusted hazard ratios and 95% confidence intervals to assess the relationship between the healthy sleep score and the risk of urologic cancers.

RESULTS

During a median of ≥9 years of follow-up, we identified 1986 incident urologic cancer cases, including 1272 BCa cases and 706 RCC cases. Compared with the participants with a poor sleep pattern (score of 0-2), the multivariable-adjusted hazard ratio and 95% confidence interval were 0.85 (0.75 to 0.96) for urologic cancers, 0.80 (0.68 to 0.93) for BCa, and 0.91 (0.74, 1.12) for RCC, respectively, for those with the healthier sleep pattern (score of 4-5).

CONCLUSION

Our results indicate that a healthy sleep pattern is associated with lower risks of urologic cancers.

The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis

Background

Poor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain.

Methods

We used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted "brain age gap" (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis.

Results

We found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10-4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60-73 (p-value = 2.37 × 10-8).

Conclusion

Poor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60-73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.

Prospective study design and data analysis in UK Biobank

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

Fruit and vegetable intake and the risk of cataract: insights from the UK Biobank study

PURPOSE

A prospective cohort study to investigate the association between fruit and vegetable (F&V) intake and the risk of cataract.

METHODS

We included 72,160 participants who were free of cataract at baseline from the UK Biobank. Frequency and type of F&V intake were assessed using a web-based 24 h dietary questionnaire from 2009 to 2012. Development of cataract during the follow-up was defined by self-report or hospital inpatient records up to 2021. Cox proportional regression models were used to estimate the association between F&V intake and incident cataract.

RESULTS

During a mean follow-up of 9.1 years, 5753 participants developed cataract with a corresponding incidence of 8.0%. After adjusting for multiple demographic, medical and lifestyle covariates, higher intake of F&V were associated with a lower risk of cataract (≥6.5 vs. <2 servings/week: hazards ratio [HR]: 0.82, 95% CI: 0.76 to 0.89; P < 0.0001). Regarding specific types, significant reduced risk of cataract was found for higher intake of legumes (P = 0.0016), tomatoes (≥5.2 vs. <1.8 servings/week: HR: 0.94, 95% CI: 0.88 to 1.00), and apple and pear (>7 vs. <3.5 servings/week: 0.89, 95% CI: 0.83 to 0.94; P < 0.0001), but not for cruciferous vegetables, green leafy vegetables, berry, citrus fruit or melon. Smokers were found to benefit more from F&V intake than former and never smokers. Men also could benefit more from higher vegetable intake than women.

CONCLUSIONS

More F&V intake, especially legumes, tomatoes, apple, and pear, was associated with a lower risk of cataract in this UK Biobank cohort.

Plasma proteomic associations with genetics and health in the UK Biobank

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

A Bidirectional Mendelian Randomization Study of Sarcopenia-Related Traits and Knee Osteoarthritis

Background

With the development of population aging worldwide, sarcopenia and knee osteoarthritis (KOA), two age-related diseases, will continue to impose increasing medical and economic burdens on the society. Previous studies have discovered an association between the two, but the causality remains controversial, and it is difficult to eliminate confounding factors. Therefore, a Mendelian randomization (MR) study was conducted to overcome these confounding factors and investigate the causal relationship between sarcopenia and KOA.

Objective

The present work focused on assessing the causality between KOA and sarcopenia, so as to provide new strategies to prevent and treat these two conditions in clinic.

Methods

We registered the title with PROSPERO (ID: CRD42023421096). The two-sample bidirectional MR analysis was conducted in two steps, with sarcopenia being the exposure whereas KOA being the outcome in the first step, and vice versa in the second step. Genome-wide association studies (GWAS) data on low hand-grip strength (n=256,523), walking pace (n=459,915), appendicular lean mass (ALM, n=450,243), and KOA (n=403,124) were obtained from the UK Biobank. Methods such as the inverse variance weighted (IVW) and weighted median were utilized for assessing the causality of KOA with sarcopenia, and sensitivity analyses were also conducted.

Results

In the main MR analysis using the IVW method, evidence suggested that low hand-grip strength, walking pace, and ALM had adverse effects on KOA (p-value 0.0001, odds ratio (OR) 1.4569, 95% confidence interval (CI) 1.2007-1.7677 for low hand-grip strength; p-value 0.0003, OR 1.1500, 95% CI 1.050-1.183 for ALM; p-value 5.29E-19, OR 0.0932, 95% CI 0.0553-0.1572 for walking pace). However, there was no causality of KOA with sarcopenia in the opposite direction.

Conclusion

Our study suggests an obvious unidirectional causality of KOA with sarcopenia, and supports the notion that patients with sarcopenia are more susceptible to the development of KOA.

A Rapid Review on the Value of Biobanks Containing Genetic Information

OBJECTIVES

Increasing access to health data through biobanks containing genetic information has the potential to expand the knowledge base and thereby improve screening, diagnosis, and treatment options for many diseases. Nevertheless, although privacy concerns and risks surrounding genetic data sharing are well documented, direct evidence in favor of the hypothesized benefits of data integration is scarce, which complicates decision making in this area. Therefore, the objective of this study is to summarize the available evidence on the research and clinical impacts of biobanks containing genetic information, so as to better understand how to quantify the value of expanding genomic data access.

METHODS

Using a rapid review methodology, we performed a search of MEDLINE/PubMed and Embase databases; and websites of biobanks and genomic initiatives published from 2010 to 2022. We classified findings into 11 indicators including outputs (a direct product of the biobank activities) and outcomes (changes in scientific and clinical capacity).

RESULTS

Of 8479 abstracts and 101 gray literature sources were reviewed, 96 records were included. Although most records did not report key indicators systematically, the available evidence concentrated on research indicators such as publications and gene-disorder association discoveries (63% of studies), followed by research infrastructure (26%), and clinical indicators (11%) such as supporting the diagnosis of individual patients.

CONCLUSIONS

Existing evidence on the benefits of biobanks is skewed toward easily quantifiable research outputs. Measuring a comprehensive set of outputs and outcomes inspired by value frameworks is necessary to generate better evidence on the benefits of genomic data sharing.

Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants

Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.

Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank

BACKGROUND

Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes.

METHODS

Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC).

RESULTS

For MD vs. controls (n_(MD) = 24,229; n_(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n_(atypical) = 958; n_(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes.

LIMITATIONS

Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary.

DISCUSSION

Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Managing re-identification risks while providing access to the All of Us research program

OBJECTIVE

The All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels.

METHODS

At the time of the study, the resource consisted of 329 084 participants. Systematic amendments were applied to the data to mitigate re-identification risk (eg, generalization of geographic regions, suppression of public events, and randomization of dates). We computed the re-identification risk for each participant using a state-of-the-art adversarial model specifically assuming that it is known that someone is a participant in the program. We confirmed the expected risk is no greater than 0.09, a threshold that is consistent with guidelines from various US state and federal agencies. We further investigated how risk varied as a function of participant demographics.

RESULTS

The results indicated that 95th percentile of the re-identification risk of all the participants is below current thresholds. At the same time, we observed that risk levels were higher for certain race, ethnic, and genders.

CONCLUSIONS

While the re-identification risk was sufficiently low, this does not imply that the system is devoid of risk. Rather, All of Us uses a multipronged data protection strategy that includes strong authentication practices, active monitoring of data misuse, and penalization mechanisms for users who violate terms of service.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Influence of poor sleep on cardiovascular disease-free life expectancy: a multi-resource-based population cohort study

BACKGROUND

The complexity of sleep hinders the formulation of sleep guidelines. Recent studies suggest that different unhealthy sleep characteristics jointly increase the risks for cardiovascular disease (CVD). This study aimed to estimate the differences in CVD-free life expectancy between people with different sleep profiles.

METHODS

We included 308,683 middle-aged adults from the UK Biobank among whom 140,181 had primary care data linkage. We used an established composite sleep score comprising self-reported chronotype, duration, insomnia complaints, snoring, and daytime sleepiness to derive three sleep categories: poor, intermediate, and healthy. We also identified three clinical sleep disorders captured by primary care and inpatient records within 2 years before enrollment in the cohort: insomnia, sleep-related breathing disorders, and other sleep disorders. We estimated sex-specific CVD-free life expectancy with three-state Markov models conditioning on survival at age 40 across different sleep profiles and clinical disorders.

RESULTS

We observed a gradual loss in CVD-free life expectancy toward poor sleep such as, compared with healthy sleepers, poor sleepers lost 1.80 [95% CI 0.96-2.75] and 2.31 [1.46-3.29] CVD-free years in females and males, respectively, while intermediate sleepers lost 0.48 [0.41-0.55] and 0.55 [0.49-0.61] years. Among men, those with clinical insomnia or sleep-related breathing disorders lost CVD-free life by 3.84 [0.61-8.59] or 6.73 [5.31-8.48] years, respectively. Among women, sleep-related breathing disorders or other sleep disorders were associated with 7.32 [5.33-10.34] or 1.43 [0.20-3.29] years lost, respectively.

CONCLUSIONS

Both self-reported and doctor-diagnosed poor sleep are negatively associated with CVD-free life, especially pronounced in participants with sleep-related breathing disorders.

Association of glaucoma and lifestyle with incident cardiovascular disease: a longitudinal prospective study from UK Biobank

The shared pathophysiological features of the cerebrovascular disease (CVD) and glaucoma suggest an association between the two diseases. Using the prospective UK Biobank cohort, we examined the associations between glaucoma and incident CVD and assessed the extent to which a healthy lifestyle reduced the CVD risk in subjects with glaucoma, using a scoring system consisting of four factors: current smoking, obesity, regular physical activity, and a healthy diet. During a mean follow-up time of 8.9 years, 22,649 (4.9%) incident CVD cases were documented. Multivariable Cox regression analyses revealed that subjects with glaucoma were significantly more likely to exhibit incident CVD (hazard ratio [HR]:1.19, 95% confidence interval [CI] 1.03-1.37; p = 0.016) than controls. In the further subgroup analyses, glaucoma increased incident CVD risk both in the young (40-55 years) and the old (56-70 years) and in both sexes, with higher risk in the young (HR: 1.33, CI 1.02-1.74) and female subjects (HR: 1.32, CI 1.14-1.52). When we analyze the associations between glaucoma and incident CVD by lifestyle factors, the highest absolute risks were observed in individuals with both glaucoma and an unhealthy lifestyle (HR: 2.66, CI 2.22-3.19). In conclusion, glaucoma was an independent risk factor for incident CVD. A healthy lifestyle was associated with a substantially lower risk for CVD incidence among adults with glaucoma.

UK Biobank: a globally important resource for cancer research

UK Biobank is a large-scale prospective study with deep phenotyping and genomic data. Its open-access policy allows researchers worldwide, from academia or industry, to perform health research in the public interest. Between 2006 and 2010, the study recruited 502,000 adults aged 40-69 years from the general population of the United Kingdom. At enrolment, participants provided information on a wide range of factors, physical measurements were taken, and biological samples (blood, urine and saliva) were collected for long-term storage. Participants have now been followed up for over a decade with more than 52,000 incident cancer cases recorded. The study continues to be enhanced with repeat assessments, web-based questionnaires, multi-modal imaging, and conversion of the stored biological samples to genomic and other '-omic' data. The study has already demonstrated its value in enabling research into the determinants of cancer, and future planned enhancements will make the resource even more valuable to cancer researchers. Over 26,000 researchers worldwide are currently using the data, performing a wide range of cancer research. UK Biobank is uniquely placed to transform our understanding of the causes of cancer development and progression, and drive improvements in cancer treatment and prevention over the coming decades.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

The genetic architecture of changes in adiposity during adulthood

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.

Joint associations of social health and movement behaviours with mortality and cardiovascular disease: an analysis of 497,544 UK biobank participants

BACKGROUND

Poor physical activity and excessive sedentary behaviour are well-established risk factors for morbidity and mortality. In the presence of emerging social problems, including loneliness and social isolation, these risks may be even greater. We aimed to investigate the joint effects of social health and movement behaviours on mortality and cardiovascular disease (CVD).

METHODS

497,544 UK Biobank participants were followed for an average of 11 years. Loneliness and social isolation were measured via self-report. Physical activity was categorised around current World Health Organisation (WHO) guidelines as low (< 600 metabolic equivalent of task [MET]-mins/week), moderate (600 < 1200) and high (≥ 1200). Sedentary behaviour was classified as low (≤ 3.5 h/day), moderate (3.5 ≤ 5) and high (> 5.5). We derived 24 social health-movement behaviour combinations, accordingly. Mortality and hospitalisations were ascertained to May 2020 for all-cause and CVD mortality, and non-fatal cardiovascular events.

RESULTS

Social isolation amplified the risk of both all-cause and CVD death across all physical activity and sedentary levels (hazard ratio, 95% confidence interval [HR, 95% CIs] for all-cause mortality; 1.58 [1.49 to 1.68] for low active-isolated vs. 1.26 [1.22 to 1.30] for low active-not isolated). Loneliness was only found to amplify the risk of death from cardiovascular disease among the high active and low sedentary participants. Loneliness and social isolation did not add to the risk of non-fatal cardiovascular events across most activity levels.

CONCLUSION

The detrimental associations of poor physical activity and sedentary behaviour with mortality were consistently amplified by social isolation. Our study supports the need to target the socially isolated as a priority group in preventive public health strategies.

Stratification of individuals with lifetime depression and low wellbeing in the UK Biobank

BACKGROUND

Previous studies stratified patients with major depressive disorder (MDD) based on their clinical characteristics. This study used this approach in individuals with lifetime MDD who reported low wellbeing, a group of high clinical relevance.

METHODS

We selected participants in the UK Biobank (UKB) with lifetime MDD and a wellbeing score in the lowest 25 %. A wellbeing score was previously created considering happiness, belief that own life is meaningful, health satisfaction and functioning in relevant areas. In the selected group, we applied latent class analysis using mood-spectrum symptoms and personality traits as input variables, then we compared the clinical-demographic and genetic (polygenic risk scores, PRSs) characteristics of the identified classes.

RESULTS

A total of 13,896 individuals were included and a model with five classes showed the best performance. The most common class (31.25 %) was characterised by periods of irritable mood and trait irritability with high neuroticism. A rarer class (16.49 %) showed depressive-manic mood fluctuations and risk-taking personality, higher percentage of males, atypical depressive symptoms, lower socio-economic status, higher PRS for attention-deficit hyperactivity disorder and lower PRS for education. The second most common class (29.79 %) showed worry as main personality trait with low risk of manic/irritable manifestations. The remaining classes showed an anxious-irritable personality profile and a purely depressive profile (4.92 % and 17.55 %, respectively).

LIMITATIONS

Our results may reflect the characteristics of UKB participants.

CONCLUSIONS

Subthreshold manic/irritable mood fluctuations and personality traits irritability and neuroticism may distinguish the most common groups with poor wellbeing in lifetime MDD.

The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.

New Horizons: the value of UK Biobank to research on endocrine and metabolic disorders

UK Biobank is an intensively characterized prospective study of 500 000 men and women, aged 40 to 69 years when recruited, between 2006 and 2010, from the general population of the United Kingdom. Established as an open-access resource for researchers worldwide to perform health research that is in the public interest, UK Biobank has collected (and continues to collect) a vast amount of data on genetic, physiological, lifestyle, and environmental factors, with prolonged follow-up of heath conditions through linkage to administrative electronic health records. The study has already demonstrated its unique value in enabling research into the determinants of common endocrine and metabolic diseases. The importance of UK Biobank, heralded as a flagship project for UK health research, will only increase over time as the number of incident disease events accrue, and the study is enhanced with additional data from blood assays (such as whole-genome sequencing, metabolomics, and proteomics), wearable technologies (including physical activity and cardiac monitors), and body imaging (magnetic resonance imaging and dual-energy X-ray absorptiometry). This unique research resource is likely to transform our understanding of the causes, diagnosis, and treatment of many endocrine and metabolic disorders.

Estrogen-Related Receptor γ Maintains Pancreatic Acinar Cell Function and Identity by Regulating Cellular Metabolism

BACKGROUND & AIMS

Mitochondrial dysfunction disrupts the synthesis and secretion of digestive enzymes in pancreatic acinar cells and plays a primary role in the etiology of exocrine pancreas disorders. However, the transcriptional mechanisms that regulate mitochondrial function to support acinar cell physiology are poorly understood. Here, we aim to elucidate the function of estrogen-related receptor γ (ERRγ) in pancreatic acinar cell mitochondrial homeostasis and energy production.

METHODS

Two models of ERRγ inhibition, GSK5182-treated wild-type mice and ERRγ conditional knock-out (cKO) mice, were established to investigate ERRγ function in the exocrine pancreas. To identify the functional role of ERRγ in pancreatic acinar cells, we performed histologic and transcriptome analysis with the pancreas isolated from ERRγ cKO mice. To determine the relevance of these findings for human disease, we analyzed transcriptome data from multiple independent human cohorts and conducted genetic association studies for ESRRG variants in 2 distinct human pancreatitis cohorts.

RESULTS

Blocking ERRγ function in mice by genetic deletion or inverse agonist treatment results in striking pancreatitis-like phenotypes accompanied by inflammation, fibrosis, and cell death. Mechanistically, loss of ERRγ in primary acini abrogates messenger RNA expression and protein levels of mitochondrial oxidative phosphorylation complex genes, resulting in defective acinar cell energetics. Mitochondrial dysfunction due to ERRγ deletion further triggers autophagy dysfunction, endoplasmic reticulum stress, and production of reactive oxygen species, ultimately leading to cell death. Interestingly, ERRγ-deficient acinar cells that escape cell death acquire ductal cell characteristics, indicating a role for ERRγ in acinar-to-ductal metaplasia. Consistent with our findings in ERRγ cKO mice, ERRγ expression was significantly reduced in patients with chronic pancreatitis compared with normal subjects. Furthermore, candidate locus region genetic association studies revealed multiple single nucleotide variants for ERRγ that are associated with chronic pancreatitis.

CONCLUSIONS

Collectively, our findings highlight an essential role for ERRγ in maintaining the transcriptional program that supports acinar cell mitochondrial function and organellar homeostasis and provide a novel molecular link between ERRγ and exocrine pancreas disorders.

Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample

UK Biobank is a prospective cohort study of around half-a-million general population participants, recruited between 2006 and 2010, with baseline studies at recruitment and multiple assessments since. From 2014 to date, magnetic resonance imaging (MRI) has been pursued in a participant sub-sample, with the aim to scan around n = 100k. This sub-sample is studied widely and therefore understanding its relative characteristics is important for future reports. We aimed to quantify psychological and physical health in the UK Biobank imaging sub-sample, compared with the rest of the cohort. We used t-tests and χ2 for continuous/categorical variables, respectively, to estimate average differences on a range of cognitive, mental and physical health phenotypes. We contrasted baseline values of participants who attended imaging (versus had not), and compared their values at the imaging visit versus baseline values of participants who were not scanned. We also tested the hypothesis that the associations of established risk factors with worse cognition would be underestimated in the (hypothesized) healthier imaging group compared with the full cohort. We tested these interactions using linear regression models. On a range of cognitive, mental health, cardiometabolic, inflammatory and neurological phenotypes, we found that 47 920 participants who were scanned by January 2021 showed consistent statistically significant 'healthy' bias compared with the ∼450 000 who were not scanned. These effect sizes were small to moderate based on Cohen's d/Cramer's V metrics (range = 0.02 to -0.21 for Townsend, the largest effect size). We found evidence of interaction, where stratified analysis demonstrated that associations of established cognitive risk factors were smaller in the imaging sub-sample compared with the full cohort. Of the ∼100 000 participants who ultimately will undergo MRI assessment within UK Biobank, the first ∼50 000 showed some 'healthy' bias on a range of metrics at baseline. Those differences largely remained at the subsequent (first) imaging visit, and we provide evidence that testing associations in the imaging sub-sample alone could lead to potential underestimation of exposure/outcome estimates.