ObjectiveTo identify factors associated with hypertension control among treated middle-aged UK adults.MethodsA cross-sectional population-based study including 99 468 previously diagnosed, treated hypertensives enrolled in the UK Biobank. Hypertension control was defined as systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg.ResultsMedian age was 62.3 years (IQR 57.3 to 66.0), 45.9% female, 92.0% white, 40.1% obese, 9.3% current smokers and 19.4% had prior cardiovascular disease. 38.1% (95% CI 37.8% to 38.4%) were controlled. In multivariable logistic regression, associations with lack of hypertension control included: older age (OR 0.61, 95% CI 0.58 to 0.64 for 60–69 years compared with age 40–50 years), higher alcohol use (OR 0.61, 95% CI 0.58 to 0.64, for consuming >30 units per week compared with none), black ethnicity (OR 0.73, 95% CI 0.65 to 0.82 compared with white), obesity (OR 0.73, 95% CI 0.71 to 0.76 compared with normal body mass index). The strongest positive association with control was having ≥3 comorbidities (OR 2.09, 95% CI 1.95 to 2.23). Comorbidities associated with control included cardiovascular disease (OR 2.11, 95% CI 2.04 to 2.19), migraines (OR 1.68, 95% CI 1.56 to 1.81), diabetes (OR 1.32, 95% CI 1.27 to 1.36) and depression (OR 1.27, 95% CI 1.20 to 1.34).ConclusionsIn one of the largest population-based analyses of middle-aged adults with measured blood pressure, the majority of treated hypertensives were uncontrolled. Risk factors for hypertension were associated with a lower probability of control. Having a comorbidity was associated with higher probability of control, possibly due to more frequent interaction with the healthcare system and/or appropriate management of those at greater cardiovascular risk.

A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.

Polygenic risk scores (PRS) are proposed for use in clinical and research settings for risk stratification. However, there are limited investigations on how different PRS diverge from each other in risk prediction of individuals. We compared two recently published PRS for each of three conditions, breast cancer, hypertension and dementia, to assess the stability of using these algorithms for risk prediction in a single large population. We used imputed genotyping data from the UK Biobank prospective cohort, limited to the White British subset. We found that: (1) 20% or more of SNPs in the first PRS were not represented in the more recent PRS for all three diseases, by the same SNP or a surrogate with R2 > 0.8 by linkage disequilibrium (LD). (2) Although the difference in the area under the receiver operating characteristic curve (AUC) obtained using the two PRS is hardly appreciable for all three diseases, there were large differences in individual risk prediction between the two PRS. For instance, for each disease, of those classified in the top 5% of risk by the first PRS, over 60% were not so classified by the second PRS. We found substantial discordance between different PRS for the same disease, indicating that individuals could receive different medical advice depending on which PRS is used to assess their genetic susceptibility. It is desirable to resolve this uncertainty before using PRS for risk stratification in clinical settings.

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 Polygenic risk scores (PRS) are proposed to be used in clinical settings for risk stratification, with the public being accustomed to the concept of genetic predisposition to diseases. Much work has focused on developing PRSs for informing people about their risk of future health conditions; however, there are limited investigations on how different PRSs diverge from each other for risk prediction of individuals. Methods and Findings We compared recently published PRS for three conditions, breast cancer, hypertension and dementia, to assess the stability in practice of running these algorithms for risk prediction in a single large population. We used imputed genotyping data from the UK Biobank (UKB) prospective cohort, limited to the White British subset. We found that 1. Only 65%-79% of SNPs in the first PRS were represented in the more recent PRS for all three diseases, after having taken linkage disequilibrium (LD) into account (R2>0.8). 2. Although the difference in the area under the received operator curve (AUC) obtained using the two PRS is hardly appreciable for all three diseases, there were large differences in individual risk prediction between the two PRS. Conclusions We found substantial discordance between different PRS for the same disease, indicating that individuals could receive different medical advice depending on which PRS is used to assess their genetic susceptibility for these disorders. It is desirable to resolving this uncertainty before using PRS for risk stratification in clinical settings.