BACKGROUND:Previous studies on the reproducibility of 7-day accelerometer measurements have been limited by small sample sizes and short follow-up periods. We aimed to assess the long-term reproducibility of accelerometer-derived physical activity and sleep, and to illustrate the impact of regression dilution bias on the association between daily step count and coronary heart disease (CHD) in UK Biobank. METHODS:We analysed data from 3138 UK Biobank participants in the main accelerometry sub-study with up to four repeat accelerometer measurements after 3-4 years. Nine physical activity and sleep phenotypes were extracted to capture different movement behaviours. Reproducibility was assessed by using intraclass correlation coefficients (ICCs). The impact on disease associations was illustrated by considering daily step count and incident CHD using Cox regression (87 038 participants; 3879 CHD events), before and after correction for regression dilution. RESULTS:Among the 3138 participants, 51% were women and the mean (SD) age was 63.1 (9.4) years. Reproducibility was good for overall activity, with an ICC (95% confidence interval) of 0.75 (0.74-0.76), and moderate for other phenotypes, with ICCs ranging from 0.58 (0.56-0.59) for sleep efficiency to 0.69 (0.68-0.70) for sedentary behaviour. In our example, the inverse association between daily step count and CHD showed a 20% lower risk of CHD per usual 4000 steps after correcting for regression dilution compared with 13% before correction. CONCLUSION:Accelerometer measurements are moderately reproducible and comparable to measures such as blood pressure. Correction for regression dilution bias is crucial to quantify associations of usual physical activity and sleep with disease risk.

AIMS:Electronic healthcare records (EHR) are at the forefront of advances in epidemiological research emerging from large-scale population biobanks and clinical studies. Hospital admissions, diagnoses, and procedures (HADP) data are often used to identify disease cases. However, this may result in incomplete ascertainment of chronic conditions such as atrial fibrillation (AF), which are principally managed in primary care (PC). We examined the relevance of EHR sources for AF ascertainment, and the implications for risk factor associations, patient management, and outcomes in UK Biobank. METHODS AND RESULTS:UK Biobank is a prospective study, with HADP and PC records available for 230 000 participants (to 2016). AF cases were ascertained in three groups: from PC records only (PC-only), HADP only (HADP-only), or both (PC + HADP). Conventional statistical methods were used to describe differences between groups in terms of characteristics, risk factor associations, ascertainment timing, rates of anticoagulation, and post-AF stroke and death. A total of 7136 incident AF cases were identified during 7 years median follow-up (PC-only: 22%, PC + HADP: 49%, HADP-only: 29%). There was a median lag of 1.3 years between cases ascertained in PC and subsequently in HADP. AF cases in each of the ascertainment groups had comparable baseline demographic characteristics. However, AF cases identified in hospital data alone had a higher prevalence of cardiometabolic comorbidities and lower rates of subsequent anticoagulation (PC-only: 44%, PC + HADP: 48%, HADP-only: 10%, P < 0.0001) than other groups. HADP-only cases also had higher rates of death [PC-only: 9.3 (6.8, 12.7), PC + HADP: 23.4 (20.5, 26.6), HADP-only: 81.2 (73.8, 89.2) events per 1000 person-years, P < 0.0001] compared to other groups. CONCLUSION:Integration of data from primary care with that from hospital records has a substantial impact on AF ascertainment, identifying a third more cases than hospital records alone. However, about a third of AF cases recorded in hospital were not present in the primary care records, and these cases had lower rates of anticoagulation, as well as higher mortality from both cardiovascular and non-cardiovascular causes. Initiatives aimed at enhancing information exchange of clinically confirmed AF between healthcare settings have the potential to benefit patient management and AF-related outcomes at an individual and population level. This research underscores the importance of access and integration of de-identified comprehensive EHR data for a definitive understanding of patient trajectories, and for robust epidemiological and translational research into AF.

Background and aims In the primary prevention setting, low-dose aspirin reduces major vascular events (MVEs) by approximately 11% but increases major bleeding (MB) by 40–50%, implying that net benefit will be most evident when the MVE-to-MB ratio is >4. This study aimed to derive cross-validated risk scores for MB and MVE and use the MVE-to-MB ratio to identify groups who may derive differing net benefits from treatment. Methods 431 167 UK Biobank participants without known atherosclerotic cardiovascular disease at baseline were followed through record linkage for incident MVEs (myocardial infarction, non-haemorrhagic stroke, transient ischaemic attack, arterial revascularisation or vascular death) and MB (gastrointestinal and intracranial bleeds with hospital admission for ≥2 days). Risk scores were derived for MVE and MB using Cox proportional hazards models with cross-validation. Ratios of observed MVE-to-MB rates were calculated across risk categories. Results During a median follow-up of 12 years, 18 310 participants suffered an MVE and 5352 an MB. MB risk was highest among participants with frailty, prior bleeds, cancer, liver disease or renal dysfunction, with a 4.3-fold difference in risk between the highest and lowest fifths of MB risk (HR 4.3, 95% CI 3.87 to 4.77). The MVE-to-MB ratio was ≤2.6 in the highest MB risk groups and ≥4 in lower MB risk categories. Conclusions The derived models using routinely available disease history and laboratory measurements improved distinction of the MVE-to-MB ratio compared with using conventional models for MB risk including vascular risk factors. Such models can help identify those with moderate MVE risk but low MB risk who may benefit from low-dose aspirin.

BACKGROUND:Genome-wide association studies have clustered candidate genes associated with atrial fibrillation (AF) into biological pathways reflecting different pathophysiological mechanisms. We investigated whether these pathways associate with distinct intermediate phenotypes and confer differing risks of cardioembolic stroke. METHODS:Three distinct subsets of AF-associated genetic variants, each representing a different mechanistic pathway, that is, the cardiac muscle function and integrity pathway (15 variants), the cardiac developmental pathway (25 variants), and the cardiac ion channels pathway (12 variants), were identified from previous AF genome-wide association studies. Using genetic epidemiological methods and large-scale datasets such as UK Biobank, deCODE, and GIGASTROKE, we investigated the associations of these pathways with AF-related cardiac intermediate phenotypes, which included electrocardiogram parameters (≈16 500 electrocardiograms), left atrial and ventricular size and function (≈36 000 cardiac magnetic resonance imaging scans), and relevant plasma biomarkers (N-terminal pro-B-type natriuretic peptide, ≈70 000 samples; high-sensitivity troponin I and T, ≈87 000 samples), as well as with subtypes of ischemic stroke (≈11 000 cases). RESULTS:Genetic variants representing distinct AF-related mechanistic pathways had significantly different effects on several AF-related phenotypes. In particular, the muscle pathway was associated with a longer PR interval (P for heterogeneity between pathways [Phet]=1×10-10), lower left atrial emptying fraction (Phet=5×10-5), and higher N-terminal pro-B-type natriuretic peptide (Phet=2×10-3) per log-odds higher risk of AF compared with the developmental and ion-channel pathways. In contrast, the ion-channel pathway was associated with a lower risk of cardioembolic stroke (Phet=0.04 in European, and 7×10-3 in multiancestry populations) compared with the other pathways. CONCLUSIONS:Genetic variants representing specific mechanistic pathways for AF are associated with distinct intermediate cardiac phenotypes and a different risk of cardioembolic stroke. These findings provide a better understanding of the etiological heterogeneity underlying the development of AF and its downstream impact on disease and may offer a route to more targeted treatment strategies.

BACKGROUND:Inflammation has been implicated in the pathogenesis of coronary heart disease, but the relevance and independence of individual inflammatory proteins is uncertain. OBJECTIVE:To examine the relationships between a spectrum of inflammatory proteins and myocardial infarction (MI). METHODS AND RESULTS:A panel of 92 inflammatory proteins was assessed using an OLINK multiplex immunoassay among 432 MI cases (diagnosed < 66 years) and 323 controls. Logistic regression was used to estimate associations between individual proteins and MI, after adjustment for established cardiovascular risk factors and medication use, and stepwise regression to identify proteins with independent effects. Machine learning techniques (Boruta analysis and LASSO regression) and bioinformatic resources were used to examine the concordance of results with those obtained by conventional methods and explore the underlying biological processes to inform the validity of the associations. Among the 92 proteins studied, 62 (67%) had plasma concentrations above the lower limit of detection in at least 50% of samples. Of these, 15 individual proteins were significantly associated with MI after covariate adjustment and correction for multiple testing. Five of these 15 proteins (CDCP1, CD6, IL1-8R1, IL-6, and CXCL1) were independently associated with MI, with up to three-fold higher risks of MI per doubling in plasma concentrations. Findings were further validated using machine learning techniques and biologically focused analyses. CONCLUSIONS:This study, demonstrating independent relationships between five inflammatory proteins and MI, provides important novel insights into the inflammatory hypothesis of MI and the potential utility of proteomic analyses in precision medicine.

Abstract Background Atrial fibrillation (AF) is strongly associated with older age. However, it is unclear whether, in addition to chronological age, more advanced biological ageing confers AF risk. Previous studies suggest AF and peripheral leukocyte telomere length (LTL, a measure of biological ageing) are associated, but this relationship and the causal relevance remains controversial. Purpose This study aims to explore the role of LTL in risk of AF through observational analyses and Mendelian randomisation (MR). Methods UK Biobank (UKB) is a large prospective study of individuals 40 to 69 years of age in which 393,018 participants have measured LTL and genetic data available. Observational associations between LTL and incident AF (ascertained in hospital admission data) were estimated using Cox proportional hazards models adjusted for potential confounders (age at LTL measurement, body mass index, physical activity, smoking status, alcohol intake, ethnicity, Townsend deprivation index, and sex). MR analyses included 195 SNPS previously shown to have conditionally independent genome-wide significant associations with measured LTL. In UKB, causal effects of genetically-predicted LTL for AF risk were estimated using the inverse-variance weighted approach. Validation was undertaken in independent data from the AFGen Consortium (Nielsen et al. 2015). Sensitivity analyses to consider the potential effects of pleiotropy were also conducted (weighted median MR, MR-Egger and Radial MR). Results Among 385,851 participants with available LTL measurements and without AF at baseline, 26,639 (6.9%) developed AF during follow-up. Longer measured LTL was associated with lower risk of AF (HR=0.98, 95%CI=0.97-0.99, p=0.002), with the cumulative incidence of AF by LTL quartile shown in Figure 1. However, MR analyses did not support a causal association between genetically-predicted LTL and AF (OR=1.04, 95%CI=0.96-1.14, p=0.36). Sensitivity analyses were consistent with the primary MR results, and did not suggest unbalanced pleiotropy (MR Egger intercept, p=0.54). Radial MR identified 4 outlier SNPs (associated with height, adiposity, and blood cell traits) but results remained consistent after exclusion of these variants (OR 1.00, 95%CI 0.93-1.08, p=0.90). Validation analyses in independent data were consistent with the non-significant association observed in UKB (OR=1.08, 95%CI=0.99-1.18, p=0.07; Figure 2). Conclusion This study combines conventional and genetic epidemiological approaches to explore the role of LTL in risk of AF. Large-scale observational analyses in UKB suggest an inverse association between LTL and AF. However, despite previous studies reporting associations between genetically-determined LTL and AF risk factors, our validated MR results do not support a causal relationship between LTL and AF, and potentially suggest observational associations are due to residual confounding.Figure 1Figure 2

Background Despite optimized risk factor control, people with prior cardiovascular disease remain at high cardiovascular disease risk. We assess the immediate‐ and longer‐term impacts of new vascular and nonvascular events on quality of life (QoL) and hospital costs among participants in the REVEAL (Randomized Evaluation of the Effects of Anacetrapib Through Lipid Modification) trial in secondary prevention. Methods and Results Data on demographic and clinical characteristics, health‐related quality of life (QoL: EuroQoL 5‐Dimension‐5‐Level), adverse events, and hospital admissions during the 4‐year follow‐up of the 21 820 participants recruited in Europe and North America informed assessments of the impacts of new adverse events on QoL and hospital costs from the UK and US health systems' perspectives using generalized linear regression models. Reductions in QoL were estimated in the years of event occurrence for nonhemorrhagic stroke (−0.067 [United Kingdom], −0.069 [US]), heart failure admission (−0.072 [United Kingdom], −0.103 [US]), incident cancer (−0.064 [United Kingdom], −0.068 [US]), and noncoronary revascularization (−0.071 [United Kingdom], −0.061 [US]), as well as in subsequent years following these events. Myocardial infarction and coronary revascularization (CRV) procedures were not found to affect QoL. All adverse events were associated with additional hospital costs in the years of events and in subsequent years, with the highest additional costs in the years of noncoronary revascularization (£5830 [United Kingdom], $14 133 [US Medicare]), of myocardial infarction with urgent CRV procedure (£5614, $24722), and of urgent/nonurgent CRV procedure without myocardial infarction (£4674/£4651 and $15 251/$17 539). Conclusions Stroke, heart failure, and noncoronary revascularization procedures substantially reduce QoL, and all cardiovascular disease events increase hospital costs. These estimates are useful in informing cost‐effectiveness of interventions to reduce cardiovascular disease risk in secondary prevention. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01252953; https://www.Isrctn.com . Unique identifier: ISRCTN48678192; https://www.clinicaltrialsregister.eu . Unique identifier: 2010‐023467‐18.

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

Abstract Background Cardiac and systemic inflammation have been associated with a higher risk of atrial fibrillation (AF) in both animal models and human studies. Causality, however, has been questioned due to inconsistent efficacy of anti-inflammatory treatments in preventing incident or recurrent AF, and potentially confounding associations with AF risk factors such as ischaemic heart disease. Inflammatory processes are highly complex and involve a large number of proteins, but the individual relevance and causal role of these for AF remains unclear. Purpose To explore the nature of the relationship between the inflammatory proteome and risk of AF. Methods Associations between 26 inflammatory proteins and AF were assessed in a two-sample Mendelian randomization (MR) framework, with primary MR causal estimates with AF estimated among 339,214 White British UK Biobank (UKB) participants (AF cases=30,630). MR analyses for each inflammatory protein included independent variants previously associated with the relevant protein at genome-wide significance (p<5x10-8) across the genome (trans-protein quantitative trait loci [pQTLs]), or restricted to at least 50kb from the closest upstream gene and 50 kb downstream from the target gene (cis-pQTLs). Sensitivity analyses to assess potential pleiotropy were conducted (MR-Egger, weighted median, and weighted mode). Associations passing multiple testing threshold (p<0.002) in UKB were further validated in meta-analyses with independent datasets including over 50,000 AF cases (FinnGen and AFGen). Research undertaken using UKB application 14568. Results This inflammatory protein-wide study identified three proteins that have support for causal associations with AF. Genetically-predicted levels of IL6R, CD40, and Chitinase-3-like protein 1 (YKL40) each showed a negative association with risk of AF, with up to a 5% lower AF risk per standard deviation higher genetically-predicted levels of these proteins (Figure 1). These findings were further confirmed in meta-analysis with data from AFGen and FinnGen. No evidence of pleiotropy was identified, and results were also confirmed in cis-pQTL analyses, which were fully consistent with the trans-pQTL analyses (Table 1). Analyses stratified by various baseline characteristics highlighted stronger causal estimates in those with higher levels of C-reactive protein (with interaction p-values <0.001). Conclusions Genetic approaches support the relevance of lifelong differences in the inflammatory proteome for AF, and causal relationships between specific inflammatory markers and risk of AF. Greater soluble IL6R and CD40 as measured in proteomic analyses are both associated with lower levels of inflammation. As previous work indicates that genetically upregulated CD40 and IL6R are similarly associated with a lower risk of ischemic stroke, the risk-benefit of directly targeting these pathways needs further consideration.Figure 1Table 1

BACKGROUND:Recognition of the importance of conventional lipid measures and the advent of novel lipid-lowering medications have prompted the need for more comprehensive lipid panels to guide use of emerging treatments for the prevention of coronary heart disease (CHD). This report assessed the relevance of 13 apolipoproteins measured using a single mass-spectrometry assay for risk of CHD in the PROCARDIS case-control study of CHD (941 cases/975 controls). METHODS:The associations of apolipoproteins with CHD were assessed after adjustment for established risk factors and correction for statin use. Apolipoproteins were grouped into 4 lipid-related classes [lipoprotein(a), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides] and their associations with CHD were adjusted for established CHD risk factors and conventional lipids. Analyses of these apolipoproteins in a subset of the ASCOT trial (Anglo-Scandinavian Cardiac Outcomes Trial) were used to assess their within-person variability and to estimate a correction for statin use. The findings in the PROCARDIS study were compared with those for incident cardiovascular disease in the Bruneck prospective study (n=688), including new measurements of Apo(a). RESULTS:Triglyceride-carrying apolipoproteins (ApoC1, ApoC3, and ApoE) were most strongly associated with the risk of CHD (2- to 3-fold higher odds ratios for top versus bottom quintile) independent of conventional lipid measures. Likewise, ApoB was independently associated with a 2-fold higher odds ratios of CHD. Lipoprotein(a) was measured using peptides from the Apo(a)-kringle repeat and Apo(a)-constant regions, but neither of these associations differed from the association with conventionally measured lipoprotein(a). Among HDL-related apolipoproteins, ApoA4 and ApoM were inversely related to CHD, independent of conventional lipid measures. The disease associations with all apolipoproteins were directionally consistent in the PROCARDIS and Bruneck studies, with the exception of ApoM. CONCLUSIONS:Apolipoproteins were associated with CHD independent of conventional risk factors and lipids, suggesting apolipoproteins could help to identify patients with residual lipid-related risk and guide personalized approaches to CHD risk reduction.

Abstract Background Electronic health data have played a pivotal role in the development and success of large-scale biobanks, and have enabled a range of epidemiological studies with wide-ranging applications[1]. Hospital admissions (HA) data are often used to identify individuals with disease, but this may result in incomplete ascertainment for conditions such as atrial fibrillation (AF), which often does not require hospitalisation. Understanding ascertainment through primary care (PC) data is essential to determine the potential benefits and drawbacks of using difference data sources for AF ascertainment. Purpose To explore potential differences between AF cases identified from PC and HA data with respect to participant characteristics, timing of ascertainment, and AF-related sequelae. Methods UK Biobank is a large prospective study with PC and HA data available in 230,000 participants[2]. Incident AF cases were defined using a combination of Read clinical codes in PC data and ICD-10 diagnostic and OPCS-4 procedural codes in HA data. Individuals were divided into 3 groups representing whether AF was defined using PC records only (PC-only), hospital admissions only (HA-only), or both (PC+HA). Descriptive statistics and multinomial regression were used to describe differences between groups in terms of ascertainment timing, risk factor associations, and post-AF stroke and death. Results During a median follow-up of 7 years, 7,142 incident AF cases were identified: 22% through PC-only (1571 new cases), 30% through HA-only, and 48% via PC+HA. In the latter group, when AF was first identified through PC (3827), there was an average lag of 1.3 years before it was identified in HA data, but a 5-year lag in the opposite direction (Figure 1). The patterns of associations for baseline age, sex, BMI, smoking, blood pressure, and composite clinical risk (CHARGE-AF[3]) suggested little evidence of systematic differences between the groups (Figure 2). However, the associations of AF with baseline diabetes, heart failure, myocardial infarction, and an AF polygenic risk score[4] suggested HA-only cases had a higher prevalence of cardiometabolic comorbities, but lower overall genetic AF risk. Moreover, HA-only AF cases had similar rates of subsequent stroke but substantially higher rates of death (73.2 per 1000 person-years) compared to the PC-only and PC+HA groups (6.6 and 23.1, respectively). Conclusion PC data identifies 28% additional AF cases than HA alone in UK Biobank, with important implications for the power of epidemiological studies. The strength of associations between risk factors and AF varies by ascertainment source, as do the rates of AF-related sequelae, suggesting that further studies comparing treatment in different groups may be warranted. Furthermore, for AF identified in HA alone, improved consistency and timeliness of routine recording of AF in PC may have important implications for management and risk of sequelae.

Models often need to include many health outcomes to capture the disease clinical pathway. Parametric survival regression models are commonly used to extrapolate beyond available follow-up for outcomes. The choice of parametric distribution to use for each outcome needs careful consideration. We illustrate the challenges of choosing the appropriate distributions when simultaneously extrapolating several outcomes. A cardiovascular microsimulation model with 10 outcomes illustrates our approach. We initially fitted parametric proportional hazards regression models for each outcome using the observed data, and used AIC and visual inspection to compare observed cumulative incidences with those predicted by the model to choose the best-fitting distributions. Due to interrelatedness between outcomes, this involved checking all possible combination of using an exponential, Weibull or Gompertz for relevant outcomes. We then checked the combinations against external longer-term data after extending to a lifetime horizon. Extrapolating over lifetime just using the combination that fitted the observed data well could lead to implausible results. e.g. for incident cancer there was little to choose between distributions over the observed period but over lifetime the cumulative incidences ranged from 38% - 58%. With the trend in increasing life expectancy due to declining cardiovascular morbidity/mortality, and in view of the trend in increasing cancer incidence, we chose a combination with 50% lifetime risk of cancer overall - with higher risk in females and by age. We also checked the plausibility of model predictions for the interlinked outcome non-vascular death and other outcomes against longer-term epidemiological evidence. In models with several outcomes, the distribution for a particular outcome should not be chosen in isolation, nor solely based on the observed short duration of follow-up. A balance is needed between a distribution that fits the data well and provides a plausible extrapolation.

BackgroundConflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity.MethodsWe undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase).ResultsThere were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1–9/day: OR 2.14, 95% CI 0.87 to 5.24; 10–19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72).InterpretationCongruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

Aims Many studies have investigated associations between polygenic risk scores (PRS) and the incidence of cardiovascular disease (CVD); few have examined whether risk factor-related PRS predict CVD outcomes among adults treated with risk-modifying therapies. We assessed whether PRS for systolic blood pressure (PRSSBP) and for low-density lipoprotein cholesterol (PRSLDL-C) were associated with achieving SBP and LDL-C-related targets, and with major adverse cardiovascular events (MACE: non-fatal stroke or myocardial infarction, CVD death, and revascularization procedures). Methods and results Using observational data from the UK Biobank (UKB), we calculated PRSSBP and PRSLDL-C and constructed two sub-cohorts of unrelated adults of White British ancestry aged 40-69 years and with no history of CVD, who reported taking medications used in the treatment of hypertension or hypercholesterolaemia. Treatment effectiveness in achieving adequate risk factor control was ascertained using on-treatment blood pressure (BP) or LDL-C levels measured at enrolment (uncontrolled hypertension: BP >= 140/90 mmHg; uncontrolled hypercholesterolaemia: LDL-C >= 3 mmol/L). We conducted multivariable logistic and Cox regression modelling for incident events, adjusting for socioeconomic characteristics, and CVD risk factors. There were 55 439 participants using BP lowering therapies (51.0% male, mean age 61.0 years, median follow-up 11.5 years) and 33 787 using LDL-C lowering therapies (58.5% male, mean age 61.7 years, median follow-up 11.4 years). PRSSBP was associated with uncontrolled hypertension (odds ratio 1.70; 95% confidence interval: 1.60-1.80) top vs. bottom quintile, equivalent to a 5.4 mmHg difference in SBP, and with MACE [hazard ratio (HR) 1.13; 1.04-1.23]. PRSLDL-C was associated with uncontrolled hypercholesterolaemia (HR 2.78; 2.58-3.00) but was not associated with subsequent MACE. Conclusion We extend previous findings in the UKB cohort to examine PRSSBP and PRSLDL-C with treatment effectiveness. Our results indicate that both PRSSBP and PRSLDL-C can help identify individuals who, despite being on treatment, have inadequately controlled SBP and LDL-C, and for SBP are at higher risk for CVD events. This extends the potential role of PRS in clinical practice from identifying patients who may need these interventions to identifying patients who may need more intensive intervention.

Background: It is well established that decreased kidney function can increase blood pressure (BP), but it is unproven whether moderately elevated BP causes chronic kidney disease (CKD) or glomerular hyperfiltration. Methods: 311 119 White British UK Biobank participants were included in logistic regression analyses to estimate the odds of CKD (defined as long-term kidney replacement therapy, estimated glomerular filtration rate [eGFR]< 60mL/min/1.73m 2 , or urinary albumin:creatinine ratio ≥3 mg/mmol) associated with higher genetically predicted BP using genetic risk scores comprising 219 systolic and 223 diastolic BP loci. Analyses estimating associations with clinical categories of eGFR and urinary albumin:creatinine ratio were also conducted, with an eGFR ≥120 mL (min·1.73m 2 ) considered evidence of glomerular hyperfiltration. Results: 21 623 participants had CKD: 7781 with reduced eGFR and 15 500 with albuminuria. 1828 participants had an eGFR ≥120 mL/min/1.73m 2 . Each genetically predicted 10 mmHg higher systolic BP and 5 mmHg higher diastolic BP were associated with a 37% (95% CI, 1.29–1.45) and 19% (1.14–1.25) higher odds of CKD, respectively. Associations were evident for both the reduced eGFR and albuminuria components of the CKD outcome. The odds of hyperfiltration (versus an eGFR ≥60 and <90 mL/min/1.73m 2 were 49% higher (95% CI, 1.21–1.84) for each genetically predicted 10 mmHg higher systolic BP. Associations with CKD and hyperfiltration were similar irrespective of preexisting diabetes, vascular disease, or different levels of adiposity. Conclusions: In this general population, genetic epidemiological evidence supports a causal role of life-long differences in BP for decreased kidney function, glomerular hyperfiltration, and albuminuria. Physiological autoregulation may not afford complete renal protection against the moderate BP elevations.

Type 2 diabetes (T2D) is an important heritable risk factor for coronary artery disease (CAD), the risk of both diseases being increased by metabolic syndrome (MS). With the availability of large‐scale genome‐wide association data, we aimed to elucidate the genetic burden of CAD risk in T2D predisposed individuals within the context of MS and their shared genetic architecture. Mendelian randomization (MR) analyses supported a causal relationship between T2D and CAD [odds ratio (OR) = 1.13 per log‐odds unit 95% confidence interval (CI): 1.10–1.16; p = 1.59 × 10 −17 ]. Simultaneously adjusting MR analyses for the effects of the T2D instrument including blood pressure, dyslipidaemia, and obesity attenuated the association between T2D and CAD (OR = 1.07, 95% CI: 1.04–1.11). Bayesian locus‐overlap analysis identified 44 regions with the same causal variant underlying T2D and CAD genetic signals (FDR < 1%) at a posterior probability >0.7; five (MHC, LPL, ABO, RAI1 and MC4R ) of these regions contain genome‐wide significant ( p < 5 × 10 −8 ) associations for both traits. Given the small effect sizes observed in genome‐wide association studies for complex diseases, even with 44 potential target regions, this has implications for the likely magnitude of CAD risk reduction that might be achievable by pure T2D therapies.

Aims REVEAL was the first randomized controlled trial to demonstrate that adding cholesteryl ester transfer protein inhibitor therapy to intensive statin therapy reduced the risk of major coronary events. We now report results from extended follow-up beyond the scheduled study treatment period. Methods and results A total of 30 449 adults with prior atherosclerotic vascular disease were randomly allocated to anacetrapib 100 mg daily or matching placebo, in addition to open-label atorvastatin therapy. After stopping the randomly allocated treatment, 26 129 survivors entered a post-trial follow-up period, blind to their original treatment allocation. The primary outcome was first post-randomization major coronary event (i.e. coronary death, myocardial infarction, or coronary revascularization) during the in-trial and post-trial treatment periods, with analysis by intention-to-treat. Allocation to anacetrapib conferred a 9% [95% confidence interval (CI) 3-15%; P = 0.004] proportional reduction in the incidence of major coronary events during the study treatment period (median 4.1 years). During extended follow-up (median 2.2 years), there was a further 20% (95% CI 10-29%; P < 0.001) reduction. Overall, there was a 12% (95% CI 7-17%, P < 0.001) proportional reduction in major coronary events during the overall follow-up period (median 6.3 years), corresponding to a 1.8% (95% CI 1.0-2.6%) absolute reduction. There were no significant effects on non-vascular mortality, site-specific cancer, or other serious adverse events. Morbidity follow-up was obtained for 25 784 (99%) participants. Conclusion The beneficial effects of anacetrapib on major coronary events increased with longer follow-up, and no adverse effects emerged on non-vascular mortality or morbidity. These findings illustrate the importance of sufficiently long treatment and follow-up duration in randomized trials of lipid-modifying agents to assess their full benefits and potential harms.

Background Taller adult height is associated with lower risks of ischemic heart disease in mendelian randomization (MR) studies, but little is known about the causal relevance of height for different subtypes of ischemic stroke. The present study examined the causal relevance of height for different subtypes of ischemic stroke. Methods and findings Height-associated genetic variants (up to 2,337) from previous genome-wide association studies (GWASs) were used to construct genetic instruments in different ancestral populations. Two-sample MR approaches were used to examine the associations of genetically determined height with ischemic stroke and its subtypes (cardioembolic stroke, large-artery stroke, and small-vessel stroke) in multiple ancestries (the MEGASTROKE consortium, which included genome-wide studies of stroke and stroke subtypes: 60,341 ischemic stroke cases) supported by additional cases in individuals of white British ancestry (UK Biobank [UKB]: 4,055 cases) and Chinese ancestry (China Kadoorie Biobank [CKB]: 10,297 cases). The associations of genetically determined height with established cardiovascular and other risk factors were examined in 336,750 participants from UKB and 58,277 participants from CKB. In MEGASTROKE, genetically determined height was associated with a 4% lower risk (odds ratio [OR] 0.96; 95% confidence interval [CI] 0.94, 0.99; p = 0.007) of ischemic stroke per 1 standard deviation (SD) taller height, but this masked a much stronger positive association of height with cardioembolic stroke (13% higher risk, OR 1.13 [95% CI 1.07, 1.19], p < 0.001) and stronger inverse associations with large-artery stroke (11% lower risk, OR 0.89 [0.84, 0.95], p < 0.001) and small-vessel stroke (13% lower risk, OR 0.87 [0.83, 0.92], p< 0.001). The findings in both UKB and CKB were directionally concordant with those observed in MEGASTROKE, but did not reach statistical significance: For presumed cardioembolic stroke, the ORs were 1.08 (95% CI 0.86, 1.35; p = 0.53) in UKB and 1.20 (0.77, 1.85; p = 0.43) in CKB; for other subtypes of ischemic stroke in UKB, the OR was 0.97 (95% CI 0.90, 1.05; p = 0.49); and for other nonlacunar stroke and lacunar stroke in CKB, the ORs were 0.89 (0.80, 1.00; p = 0.06) and 0.99 (0.88, 1.12; p= 0.85), respectively. In addition, genetically determined height was also positively associated with atrial fibrillation (available only in UKB), and with lean body mass and lung function, and inversely associated with low-density lipoprotein (LDL) cholesterol in both British and Chinese ancestries. Limitations of this study include potential bias from assortative mating or pleiotropic effects of genetic variants and incomplete generalizability of genetic instruments to different populations. Conclusions The findings provide support for a causal association of taller adult height with higher risk of cardioembolic stroke and lower risk of other ischemic stroke subtypes in diverse ancestries. Further research is needed to understand the shared biological and physical pathways underlying the associations between height and stroke risks, which could identify potential targets for treatments to prevent stroke.

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry 1,2 . Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated ( P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis 3 , and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN ) and variants (such as at GRK5 and NOS3 ). Using a three-pronged approach 4 , we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry 5 . Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.