UK Biobank

Abstract Background Long-term electrocardiogram (ECG) monitoring with wearable devices enables large-scale characterisation of cardiac rhythms, but population-based evidence remains limited. The UK Biobank Cardiac Monitoring Study integrates 14-day patch-based ECG monitoring with accelerometry and detailed phenotypic and lifestyle data. Here, we report the acquisition protocol, data processing, and initial findings from 27,658 participants. Methods Participants in the UK Biobank imaging study were invited to undergo 14-day cardiac monitoring using a Zio XT (pilot phase; 2015–18) or BodyGuardian MINI (main phase; 2019– ongoing) monitor. ECGs were analysed by certified technicians and automated algorithms to identify atrial, ventricular, and conduction arrhythmias. In parallel, beat-to-beat RR intervals were derived using in-house algorithms, and physical activity from calibrated triaxial accelerometer data. Analyses assessed wear time, arrhythmia prevalence, circadian patterns, and repeatability. Findings In total, 27,658 participants (mean age 71 years; 49.9% women) were analysed, including 7,795 from the pilot phase and 21,141 from the main phase; 1,353 (4.9%) had repeat recordings. In the main phase, median wear time was 13.2 days (IQR 11.9–13.9), and undiagnosed atrial fibrillation (AF) was detected more frequently in men than women (3.2% vs 1.7%; p<0.001); 68% was paroxysmal, with 27.4% detected during week two. Ventricular tachycardia occurred in 12.1% (8.4% in women), with sustained episodes rare (0.4%) but observed. Arrhythmia timing varied markedly with activity, with AF peaking during nocturnal inactivity and ventricular ectopy increasing during activity, peaking at midday. Repeat assessments showed strong reproducibility of diurnal heart rate and activity profiles, with more modest arrhythmia consistency. Interpretation Extended ECG monitoring enables detection of subclinical arrhythmias and long-term physiological rhythms in older adults. Linkage to imaging, multi-omics, and clinical outcomes in UK Biobank will enable unprecedented evaluation of the natural history of asymptomatic rhythm disturbances and their impact on brain health. Funding British Heart Foundation and Wellcome Trust.

Abstract Study Objectives Our study introduced the 2023 UK Biobank sleep questionnaire and described variation in sleep health dimensions and prevalence of disordered sleep. Methods A questionnaire comprising validated measures and bespoke items was developed to capture key self-reported domains of sleep health and symptoms of sleep disorders. We quantified cohort prevalence of operationally defined sleep disorders and assessed patterning of sleep health dimensions across key sociodemographic and clinically relevant variables. Results 327,752 individuals were invited of whom 185,056 (56.5%) completed at least one module and were included in the analysis. Respondents were predominately from a White ethnic background (96.8%), had a mean age of 69.9 (SD, 7.5) years, 57.9% were female, and 25.5% were in employment. Compared to non-respondents, respondents were more likely to be female, tended to be better educated, healthier, and exhibit lower levels of socioeconomic deprivation, although baseline sleep variables were similar between respondents and non-respondents. Around 40% of respondents reported sleep duration less than 7 hours and 49% reported poor sleep quality (Pittsburgh Sleep Quality Index > 5). Approximately one-quarter (25.2%) met criteria for at least one operationally defined sleep disorder, with insomnia being the most common (14.4%) followed by obstructive sleep apnoea (8.0%), restless legs syndrome (4.1%), and frequent nightmares (3.7%). Sleep disorders were associated with higher levels of anxiety, depression, fatigue, and cognitive complaints. Conclusions Poor sleep quality and operationally defined sleep disorders are common in the UK Biobank cohort. Sleep questionnaire data can now be integrated with a range of biomedical information to advance understanding of sleep. Statement of significance A comprehensive sleep questionnaire was introduced to the UK Biobank, with over 185,000 participants providing data. Overall, respondents reported relatively poor sleep quality; 40% reported sleep duration less than 7 hours, and 25% met criteria for at least one sleep disorder. Enhanced assessment of sleep in UK Biobank now enables integration with extensive biomedical data, including genetic, wearable, imaging, lifestyle, biomarker, and electronic health record data, offering opportunities to investigate the biological and environmental factors that influence sleep and circadian systems, and their impact on health.

BackgroundAssociations of coronary heart disease (CHD) with plasma lipids are well described, but the associations with characteristics of lipoproteins (which transport lipids) remain unclear.Methods and ResultsUK Biobank is a prospective study of 0.5 million adults. Analyses were restricted to 89 422 participants with plasma lipoprotein and apolipoprotein measures from Nightingale nuclear magnetic resonance spectroscopy and without CHD at baseline. CHD risk was positively associated with concentrations of very‐low‐density lipoproteins, intermediate‐density lipoproteins, and low‐density lipoproteins (LDL), and inversely associated with high‐density lipoproteins. Hazard ratios (99% CIs) per SD were 1.22 (1.17–1.28), 1.16 (1.11–1.21), 1.20 (1.15–1.25), and 0.90 (0.86–0.95), respectively. Larger subclasses of very‐low‐density lipoproteins were less strongly associated with CHD risk, but associations did not materially vary by size of LDL or high‐density lipoprotein. Given lipoprotein particle concentrations, lipid composition (including cholesterol) was not strongly related to CHD risk, except for triglyceride in LDL particles. Apolipoprotein B was highly correlated with LDL concentration (r=0.99), but after adjustment for apolipoprotein B, concentrations of very‐low‐density lipoprotein and high‐density lipoprotein particles remained strongly related to CHD risk.ConclusionsThis large‐scale study reliably quantifies the associations of nuclear magnetic resonance–defined lipoprotein characteristics with CHD risk. CHD risk was most strongly related to particle concentrations, and separate measurements of lipoprotein concentrations may be of greater value than the measurement by apolipoprotein B, which was largely determined by LDL concentration alone. Furthermore, there was strong evidence of positive association with mean triglyceride molecules per LDL particle but little evidence of associations with total triglycerides or other lipid and lipoprotein fractions after accounting for lipoprotein concentrations.

UK Biobank (UKB) is an international health resource enabling research into the genetic and lifestyle determinants of common diseases of middle and older age. It comprises 500 000 participants. Public Health England’s Second Generation Surveillance System is a centralized microbiology database covering English clinical diagnostics laboratories that provides national surveillance of legally notifiable infections, bacterial isolations and antimicrobial resistance. We previously developed secure, pseudonymized, individual-level linkage of these systems. In this study, we implemented rapid dynamic linkage, which allows us to provide a regular feed of new COVID-19 (SARS-CoV-2) test results to UKB to facilitate rapid and urgent research into the epidemiological and human genetic risk factors for severe infection in the cohort. Here, we have characterized the first 1352 cases of COVID-19 in UKB participants, of whom 895 met our working definition of severe COVID-19 as inpatients hospitalized on or after 16 March 2020. We found that the incidence of severe COVID-19 among UKB cases was 27.4 % lower than the general population in England, although this difference varied significantly by age and sex. The total number of UKB cases could be estimated as 0.6 % of the publicly announced number of cases in England. We considered how increasing case numbers will affect the power of genome-wide association studies. This new dynamic linkage system has further potential to facilitate the investigation of other infections and the prospective collection of microbiological cultures to create a microbiological biobank (bugbank) for studying the interaction of environment, human and microbial genetics on infection in the UKB cohort.

Ready access to health research studies is becoming more important as researchers, and their funders, seek to maximize the opportunities for scientific innovation and health improvements. Large‐scale population‐based prospective studies are particularly useful for multidisciplinary research into the causes, treatment and prevention of many different diseases. UK Biobank has been established as an open‐access resource for public health research, with the intention of making the data as widely available as possible in an equitable and transparent manner. Access to UK Biobank's unique breadth of phenotypic and genetic data has attracted researchers worldwide from across academia and industry. As a consequence, it has enabled scientists to perform world‐leading collaborative research. Moreover, open access to an already deeply characterized cohort has encouraged both public and private sector investment in further enhancements to make UK Biobank an unparalleled resource for public health research and an exemplar for the development of open‐access approaches for other studies.

BackgroundFeedback of potentially serious incidental findings (PSIFs) to imaging research participants generates clinical assessment in most cases. Understanding the factors associated with increased risks of PSIFs and of serious final diagnoses may influence individuals’ decisions to participate in imaging research and will inform the design of PSIFs protocols for future research studies. We aimed to determine whether, and to what extent, socio-demographic, lifestyle, other health-related factors and PSIFs protocol are associated with detection of both a PSIF and a final diagnosis of serious disease.Methods and findingsOur cohort consisted of all UK Biobank participants who underwent imaging up to December 2015 (n = 7334, median age 63, 51.9% women). Brain, cardiac and body magnetic resonance, and dual-energy x-ray absorptiometry images from the first 1000 participants were reviewed systematically by radiologists for PSIFs. Thereafter, radiographers flagged concerning images for radiologists’ review. We classified final diagnoses as serious or not using data from participant surveys and clinical correspondence from GPs up to six months following imaging (either participant or GP correspondence, or both, were available for 93% of participants with PSIFs). We used binomial logistic regression models to investigate associations between age, sex, ethnicity, socio-economic deprivation, private healthcare use, alcohol intake, diet, physical activity, smoking, body mass index and morbidity, with both PSIFs and serious final diagnoses. Systematic radiologist review generated 13 times more PSIFs than radiographer flagging (179/1000 [17.9%] versus 104/6334 [1.6%]; age- and sex-adjusted OR 13.3 [95% confidence interval (CI) 10.3–17.1] p<0.001) and proportionally fewer serious final diagnoses (21/179 [11.7%]; 33/104 [31.7%]). Risks of both PSIFs and of serious final diagnoses increased with age (sex-adjusted ORs [95% CI] for oldest [67–79 years] versus youngest [44–58 years] participants for PSIFs and serious final diagnoses respectively: 1.59 [1.07–2.38] and 2.79 [0.86 to 9.0] for systematic radiologist review; 1.88 [1.14–3.09] and 2.99 [1.09–8.19] for radiographer flagging). No other factor was significantly associated with either PSIFs or serious final diagnoses. Our study is the largest so far to investigate the factors associated with PSIFs and serious final diagnoses, but despite this, we still may have missed some associations due to sparsity of these outcomes within our cohort and small numbers within some exposure categories.ConclusionRisks of PSIFs and serious final diagnosis are substantially influenced by PSIFs protocol and to a lesser extent by age. As only 1/5 PSIFs represent serious disease, evidence-based PSIFs protocols are paramount to minimise over-investigation of healthy research participants and diversion of limited health services away from patients in need.

Little is known about the neural correlates of lower limbs position sense, despite the impact that proprioceptive deficits have on everyday life activities, such as posture and gait control. We used fMRI to investigate in 30 healthy right-handed and right-footed subjects the regional distribution of brain activity during position matching tasks performed with the right dominant and the left nondominant foot. Along with the brain activation, we assessed the performance during both ipsilateral and contralateral matching tasks. Subjects had lower errors when matching was performed by the left nondominant foot. The fMRI analysis suggested that the significant regions responsible for position sense are in the right parietal and frontal cortex, providing a first characterization of the neural correlates of foot position matching.

BackgroundMotor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases.MethodsWe performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy—positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies.ResultsThirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55–92% and sensitivities from 75–93%. The single (UK-based) study of primary care data reported a PPV of 85%.ConclusionsDiagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation.

IntroductionQuantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects.Materials and Methods3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics.ResultsOf the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view.Discussion and ConclusionsIn conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.