Genetic Predisposition to High Blood Pressure and Lifestyle Factors
- is corrected by
- Other version(s) of this article
You are viewing the most recent version of this article. Previous versions:
Abstract
Background:
High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVDs), the leading cause of mortality worldwide. Both heritable and lifestyle risk factors contribute to elevated BP levels. We aimed to investigate the extent to which lifestyle factors could offset the effect of an adverse BP genetic profile and its effect on CVD risk.
Methods:
We constructed a genetic risk score for high BP by using 314 published BP loci in 277 005 individuals without previous CVD from the UK Biobank study, a prospective cohort of individuals aged 40 to 69 years, with a median of 6.11 years of follow-up. We scored participants according to their lifestyle factors including body mass index, healthy diet, sedentary lifestyle, alcohol consumption, smoking, and urinary sodium excretion levels measured at recruitment. We examined the association between tertiles of genetic risk and tertiles of lifestyle score with BP levels and incident CVD by using linear regression and Cox regression models, respectively.
Results:
Healthy lifestyle score was strongly associated with BP (P<10–320) for systolic and diastolic BP and CVD events regardless of the underlying BP genetic risk. Participants with a favorable in comparison with an unfavorable lifestyle (bottom versus top tertile lifestyle score) had 4.9, 4.3, and 4.1 mm Hg lower systolic BP in low, middle, and high genetic risk groups, respectively (P for interaction=0.0006). Similarly, favorable in comparison with unfavorable lifestyle showed 30%, 33%, and 31% lower risk of CVD among participants in low, middle, and high genetic risk groups, respectively (P for interaction=0.99).
Conclusions:
Our data further support population-wide efforts to lower BP in the population via lifestyle modification. The advantages and disadvantages of disclosing genetic predisposition to high BP for risk stratification needs careful evaluation.
Introduction
Clinical Perspective
What Is New?
We show that adherence to a healthy lifestyle (including healthy diet, limited alcohol consumption, low urinary sodium excretion, low body mass index, and increased physical activity) is associated with lower blood pressure regardless of the underlying blood pressure genetic risk.
Adherence to a healthy lifestyle is also associated with lower risk of myocardial infarction, stroke, and composite cardiovascular disease at all levels of underlying blood pressure genetic risk.
Healthy compared with unhealthy lifestyle showed 30%, 31%, and 33% lower risk of cardiovascular disease among participants at low, middle, and high genetic risk groups, respectively.
What Are the Clinical Implications?
Genetically predetermined rise in blood pressure and its complications can be offset at least to some extent by healthy lifestyle.
Our results further support population-wide efforts to lower blood pressure and subsequent cardiovascular disease risk through lifestyle modification.
High blood pressure (BP) is the leading single risk factor for mortality and global burden of disease (9.4 million deaths in 2010).1 There is a strong graded relationship between BP and cardiovascular disease (CVD) with even small increments in BP associated with an increased risk of CVD, the leading cause of death and disease burden worldwide.2
In the past 4 decades, efforts to detect and treat elevated BP have been vigorous. However, there is a need for improved primary prevention of high BP. Heritable and environmental/lifestyle risk factors both contribute to elevated BP levels.3,4 There is well-established evidence for independent unfavorable additive effects on BP of excess sodium intake, unhealthy dietary patterns or adverse calorie balance (body mass), excess alcohol use, and physical inactivity.5–11 At the same time, >314 genetic variants are known to affect BP levels and could have a cumulative effect of up to 10 mm Hg higher systolic BP level by age 50.3 The mechanism through which these genetic variants affect BP levels is largely unknown.3
We aimed to investigate the associations between healthy lifestyle adherence and blood pressure levels and cardiovascular risk in different subgroups of genetic BP risk. We examined the role of lifestyle in individuals predetermined to have higher BP levels based on their genetic profile in relation to both BP and future CVD events within UK Biobank.
Methods
Additional material is provided in the online-only Data Supplement. Approval for this research was obtained from the UK Biobank Research Ethics Committee and Human Tissue Authority, and the participants gave informed consent. The calculated genetic risk score will be made available to other researchers on the UK Biobank website for purposes of reproducing the results or replicating the procedure. These data can be obtained by contacting UK Biobank access team.
Study Population
The UK Biobank is a national long-term cohort in the United Kingdom that recruited 502 638 individuals aged between 40 and 69 years. The present study is based on a subset of unrelated individuals with genome-wide association study data12–14 and of European ancestry following quality measures and exclusions (sex discordance, high missingness/heterozygosity, first-, second-, and third-degree relatives, and non-European ancestry excluded) (Figure I in the online-only Data Supplement).
In detail, we excluded participants who were pregnant or unsure of their pregnancy at baseline (n=372), and those who had withdrawn consent, as well (n=19). Genetic data were available for 487 409 individuals. After merging genetic and phenotype data, 487 048 individuals remained. We excluded 147 637 individuals being related to at least 1 individual in the genotype data. We further excluded 30 464 individuals of non-European ancestry, 21 134 individuals with CVD events at or before baseline, and 10 808 individuals with missing values on the main covariables of the current study. The final sample for analysis comprised 277 005 participants.
Lifestyle Factors and Physical Measurements
Following informed consent, participants completed a standardized questionnaire on life course exposures, medical history, and treatments, and had a range of physical measurements. We assessed diet based on a self-completed food frequency questionnaire. Participants ranked their daily intake of dietary consumption including alcoholic products, fruits and vegetables, oily and nonoily fish, processed and unprocessed meat, by using touch-screen multiple choice questions.14 We defined smoking based on self-reported information on current (most of days and occasional smokers), past smokers, and never smokers. Sedentary behavior was defined by the sum of 3 questions about the hours per day participants spent (1) driving, (2) using a computer, and (3) watching television. We calculated alcohol intake from the self-reported alcohol drinking information on the touch-screen questionnaire. The quantity of each type of drink (red wine, white wine, beer/cider, fortified wine, and spirits) was multiplied by its standard drink size and reference alcohol content. Drink-specific alcohol intake during the reported drinking period (a week for frequent drinkers or a month for occasional drinkers) was summed up and converted to grams per day for participants with complete response to the quantitative drinking questions. Grams per day of alcohol consumption for participants with incomplete response was imputed by bootstrap resampling from the complete responses, stratified by drinking frequency (occasional or frequent), and sex.
Two BP measurements were taken seated after 2 minutes rest using an appropriate cuff and an Omron HEM-7015IT digital BP monitor. Systolic (SBP) and diastolic blood pressure (DBP) were analyzed. We calculated mean SBP and DBP from 2 automated (n=253 419) or 2 manual reading (n=15 454) BP measurements. For individuals with 1 manual and 1 automated BP reading (n=7886), we used the mean of these 2 values. For individuals with a single BP measurement (1 manual or 1 automated BP reading, n=246), we used that single measurement. For individuals reported to be taking BP-lowering medication (n=47 438 individuals), we added 15 and 10 mm Hg to SBP and DBP, respectively.15
Standing height was measured using a Seca 202 device. Body mass index (BMI) was calculated as weight divided by height squared (m2) with weight measured by using electronic weighing scales (Tanita BC-418).13
Sodium and potassium concentrations were measured in stored urine samples by the Ion Selective Electrode method (potentiometric method) using Beckman Coulter AU5400, UK Ltd. Analytic range for sodium was 2 to 200 mmol/L, and the analytic range for potassium was 10 to 400 mmol/L. Details of quality control and sample preparation have been published previously.16
Healthy Lifestyle Score
A composite healthy lifestyle score adapted from American Heart Association cardiovascular health recommendations was constructed.17 A healthy lifestyle score was defined by BMI below median, sedentary hours below median, alcohol intake below median, meat intake (processed and unprocessed) below median, urinary sodium excretion below median, fruit and vegetable intake above median, fish intake (oily fish and nonoily) above median, and never smoking. One point was given for each favorable lifestyle factor (range, 0–8).
In addition, we performed a sensitivity analysis using cutoffs of the American Heart Association for healthy lifestyle18,19 presented in Table I in the online-only Data Supplement.
Cardiovascular Events
For all participants, retrospective and prospective linkage to electronic health data is available, including Hospital Episode Statistics (HES) and Office for National Statistics cause of death data. HES provide detailed information for participants admitted to the hospital and include coded data on diagnoses and procedures. CVD was defined as an event of coronary artery disease, or stroke or myocardial infarction classified by using an in-house algorithm comprising codes from the International Classification of Diseases, Ninth and Tenth Revision codes and Classification of Interventions and Procedures codes (Table II in the online-only Data Supplement).
The recorded episode date was considered as the date of the event. For individuals with a missing episode date, the admission date was used as the date of event. For individuals with multiple CVD hospitalizations in HES, the date of the earliest event was used as the date of event. Fatal CVD events were searched in mortality data and were added to the main CVD variable. In addition, we used the algorithmically derived definitions of myocardial infarction (MI) and stroke as coded by UK Biobank.20 History of CVD at baseline was defined based on self-reported data and on HES data with episode date preceding the date of the visit in the study assessment center. CVD events occurring before the assessment date and self-reported CVD events were considered as existing CVD events at baseline. Individuals with CVD events occurring before assessment were excluded. Follow-up time was calculated as the time starting from the assessment date for each individual until March 31, 2015. Individuals who died during follow-up were censored.
Our primary traits and outcomes were (1) SBP and DBP measured at baseline and (2) the composite measure of CVD events (see Table II in the online-only Data Supplement), and separately MI and stroke (nonfatal and fatal) during follow-up (2006–2016). The main exposures studied included: (1) genetic risk score for BP (tertiles), (2) lifestyle score (tertiles), and (3) the 3×3 matrix of the genetic risk and lifestyle scores.
Genotyping and Imputation
Detailed information about genotyping and imputation in the UK Biobank study has been provided elsewhere.21,22 In brief, DNA samples of the UK Biobank study participants were genotyped by using a custom Affymetrix UK Biobank Axiom array (designed to optimize imputation performance). Genotype imputation used a special reference panel comprising a merged sample of UK10K sequencing and 1000 Genomes imputation reference panels to maximize the use of haplotypes with British and European ancestry for imputation. Imputation was performed centrally by the UK Biobank using an algorithm implemented in the IMPUTE2 program. Genetic principal components to account for population stratification were computed centrally by the UK Biobank.
Genetic Risk Score for BP
A weighted genetic risk score was calculated based on previously reported genetic variants (references 1 and 2 in the online-only Data Supplement) for SBP, DBP, and pulse pressure: (1) 267 single-nucleotide polymorphismswith weights (β-coefficients) reported by Warren et al3 (we extracted weights from replication cohorts for novel signals and from UK Biobank for known signals as described in Warren et al3) and (2) 47 single-nucleotide polymorphisms identified and replicated from Hoffmann et al23 with weights (β-coefficients) from the International Consortium for Blood Pressure+Genetic Epidemiology Research on Adult Health and Aging meta-analysis. We calculated a standardized BP genetic risk score for each of SBP, DBP, and pulse pressure loci and combined them into a single BP genetic risk score by averaging the 3 standardized SBP, DBP, and pulse pressure genetic risks. Pairwise-independent, linkage disequilibrium–filtered (r2<0.2) variants were used for the analysis (Table III in the online-only Data Supplement).
Statistical Analysis
Genetic risk and lifestyle scores (and their combination) were analyzed with respect to (1) SBP and DBP considered separately using multiple linear regression and (2) CVD events using Cox proportional hazards regression with the duration of follow-up as the time metric. Proportional hazards assumptions were tested using Schoenfeld residuals implemented in R Package Survival, and risk estimates were presented as hazard ratio (HR). Analyses were adjusted for age, sex and, when genetic risk was included in the model, for the first 10 genetic principal components. For the genetic risk analyses and analyses of individuals lifestyle risk factors, we also adjusted for BMI, sedentary lifestyle, smoking status, and healthy diet score (defined as consumption of fruits and vegetables each ≥3 servings a day; fish ≥2 servings a week; processed meat ≤1 servings a week; and unprocessed meat ≤1.5 servings a week17). We tested for interaction between the genetic risk and lifestyle scores using a likelihood ratio test comparing a model with and without interaction terms (categorized in tertiles).
We calculated standardized 5-year cumulative incidence rates for CVD, MI, and stroke. Standardization was performed using the World Health Organization Standard population24 and the European Standard population.25
As sensitivity analyses, we excluded participants receiving BP-lowering (n= 47 438) or lipid-lowering (n=35 155) medication and those with a diabetes diagnosis at baseline (n=10 958), because they may have changed their lifestyle habits because of the diagnosis of their condition (total exclusion, n=64 555). In addition, we examined the association between genetic risk score and each lifestyle variable separately in relation to SBP, DBP, and CVD events. Lifestyle variables included BMI (tertiles), healthy diet score (as above), sedentary lifestyle (tertiles), smoking status (present, past, or never smoker), urinary sodium and potassium excretion, and alcohol intake (tertiles). For sedentary lifestyle, we performed additional sensitivity analysis, excluding individuals with an event during the first 2 years of follow-up, because low physical activity could be a marker of subclinical or undiagnosed disease.
All statistical analyses were performed in R software, version 3.3 (R Project for Statistical Computing).
Results
The sample of 277 005 individuals (Figure I in the online-only Data Supplement) comprised 152 121 (55%) women and 124 884 (45%) men (Table). Median follow-up for CVD was 6.11 years. During follow-up, 9278 CVD events occurred (incidence rate 5.55 per 1000 person-years), of which 2984 were MI (incidence rate 1.53 per 1000 person-years) and 1919 were stroke (incidence rate 1.00 per 1000 person-years). Table IV in the online-only Data Supplement shows the distribution of various risk factors of CVD per subgroup of genetic risk and healthy lifestyle.
| Characteristics | All Participants (N=277 005) | Males (n=124 884) | Females (n=152 121) |
|---|---|---|---|
| Age, mean (SD), y | 56.3 (8) | 56.4 (8.1) | 56.3 (7.9) |
| Males, n (%) | 124 884 (45) | NA | NA |
| High blood pressure,* n (%) | 133 786 (52) | 69 098 (59) | 64 688 (46) |
| Antihypertensive medication, n (%) | 47 438 (17) | 24 332 (19) | 23 106 (15) |
| Lipid treatment, n (%) | 35 155 (13) | 20 120 (16) | 15 035 (10) |
| Diabetes mellitus, diagnosed by doctor, n (%) | 10 958 (4) | 6462 (5) | 4496 (3) |
| Body mass index, mean (SD), kg/m2 | 27.2 (4.7) | 27.7 (4.1) | 26.8 (5.1) |
| Sedentary lifestyle, mean (SD), hours/d | 4.4 (2.5) | 4.9 (2.7) | 4 (2.2) |
| Smoking, n (%) | |||
| Current | 27 788 (10) | 14 797 (12) | 12 991 (9) |
| Past | 138 168 (50) | 65 703 (53) | 72 465 (48) |
| Never | 111 049 (40) | 44 384 (36) | 66 665 (44) |
| Alcohol intake, mean (SD), g/d | 17.6 (20.9) | 25.5 (25.5) | 11.2 (12.9) |
| Fruit pieces (dry or fresh) daily consumption, mean (SD) | 4.8 (3.2) | 4.5 (3.2) | 5 (3.1) |
| Vegetable (cooked or raw) consumption, mean (SD) | 3 (2.5) | 2.6 (2.5) | 3.3 (2.5) |
| Unprocessed meat consumption frequency, mean (SD) | 3.7 (1.7) | 3.9 (1.7) | 3.5 (1.8) |
| Processed meat consumption frequency, mean (SD) | 1.9 (1.1) | 2.2 (1) | 1.6 (1) |
| Fish consumption, mean (SD) | 3.4 (1.4) | 3.4 (1.4) | 3.5 (1.4) |
| Healthy (DASH) diet score, median [IQR] | 3 [2–3] | 2 [2–3] | 3 [2–4] |
| Healthy lifestyle score, mean (SD) | 6.1 (2.2) | 5.3 (2.1) | 6.8 (2.1) |
| Genetic risk category, mean (SD) | 0 (0.9) | 0 (0.9) | 0 (0.9) |
| Systolic blood pressure, mean (SD), mm Hg | 140.5 (20.5) | 144.2 (19.3) | 137.4 (21) |
| Diastolic blood pressure, mean (SD), mm Hg | 84.1 (11.2) | 86.4 (11) | 82.2 (11) |
| Incident outcomes (nonfatal and fatal) | |||
| Composite cardiovascular disease, n (%) | 9278 (3.3) | 6068 (4.9) | 3210 (2.1) |
| Myocardial infarction, n (%) | 2984 (1.1) | 2155 (1.7) | 829 (0.5) |
| Stroke, n (%) | 1919 (0.7) | 1081 (0.9) | 838 (0.6) |
Genetic risk score as a continuous variable (Table V in the online-only Data Supplement) was significantly associated with all outcomes examined (HR CVD, 1.11; 95% CI, 1.09–1.14; P<10–320) per unit increase in the genetic risk score. Healthy lifestyle score (per unit) as a continuous variable was also strongly and inversely associated with both SBP (β=–0.88 mm Hg; 95% CI, –0.92 to –0.85, P<10–320) and DBP (β=–0.71 mm Hg; 95% CI, –0.73 to –0.69, P<10–320) (Table V in the online-only Data Supplement). Similarly, strong inverse associations were observed between lifestyle score (per unit increase) and incident CVD (HR, 0.92; 95% CI, 0.92–0.93; P<10–320). Figures 1 and 2 show significant risk gradients between tertiles of healthy lifestyle or tertiles of genetic risk score in relation to SBP levels and incident CVD.
Figure 1. Cumulative hazard rates according to genetic and lifestyle risk tertiles in the UK Biobank study. The graphs compare different tertiles of genetic risk and lifestyle risk for hazard of CVD (left-hand graphs), myocardial infarction (middle graphs), and stroke (right-hand graphs) (see Table II in online-only Data Supplement for definition of CVD). Cox regression models were adjusted for age and sex. CVD indicates cardiovascular disease; and MI, myocardial infarction.
Figure 2. Predicted values and 95% confidence intervals of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank cohort according to genetic and lifestyle risk tertiles. Predicted values come from linear regression models adjusted for age and sex.
With the exception of alcohol, which was associated with BP, MI, and stroke but not with CVD (Table V in the online-only Data Supplement), all other individual healthy lifestyle factors (diet, BMI, and sedentary lifestyle) were associated with both BP and all CVD events.
Within combined subgroups of healthy lifestyle and genetic risk, healthy lifestyle was associated with lower SBP and DBP within each tertile of genetic risk (Figure 2). Among participants at low genetic risk, the estimated mean SBP was 140 mm Hg (95% CI, 102–177) for participants with unfavorable lifestyle and 134 mm Hg (95% CI, 95–172) with a favorable lifestyle (Figure 2). For those at high genetic risk, the estimated mean SBP was 146 mm Hg (95% CI, 106–186) among those with an unfavorable lifestyle and 142 mm Hg (95% CI, 100–184) among those with a favorable lifestyle. An unfavorable lifestyle in comparison with a favorable lifestyle was associated with 4.9 mm Hg higher SBP among participants at low genetic risk, 4.3 mm Hg higher SBP among participants at intermediate genetic risk, and 4.1 mm Hg higher SBP among participants at high genetic risk (Table VI in the online-only Data Supplement). There was modest statistical evidence for interaction between genetic risk and healthy lifestyle scores in relation to SBP (P for interaction=0.0006) but not for DBP (P for interaction=0.8).
Regarding CVD events, participants with favorable in comparison with unfavorable lifestyle showed 30%, 33%, and 31% lower relative risk of CVD than participants at low, intermediate, and high genetic risk, respectively (Table VII in the online-only Data Supplement). Participants with less favorable genetic and lifestyle profiles (top tertile of BP genetic risk, bottom tertile of healthy lifestyle score) had a nearly 2-fold greater risk of CVD (HR, 1.75; 95% CI,1.59–1.93) than participants with favorable genetic and lifestyle profiles (the reference group; Figure 3; Table VII in the online-only Data Supplement). Genetic risk and healthy lifestyle score were statistically independent of each other, and there was no statistically significant interaction (P for interaction=0.99). Among participants at low genetic risk, the standardized 5-year CVD rates based on the World Health Organization world and Europe standard populations were 2.77% and 3.22%, respectively, among those with an unfavorable lifestyle, and they were 1.46% and 1.75%, respectively, among those with a favorable lifestyle. Among participants at high genetic risk, the standardized 5-year coronary event rates were 3.53% and 4.11%, respectively, among those with an unfavorable lifestyle and 1.76% and 2.09%, respectively, among those with a favorable lifestyle. A similar pattern of associations was observed for MI and stroke (Table VIII in the online-only Data Supplement).
Figure 3. Risk of cardiovascular events (CVD), myocardial infarction (MI), stroke events in the UK Biobank cohort according to tertiles of genetic risk score (GRS) and lifestyle score. Low GRS corresponds to lowest tertile of the GRS. Favorable lifestyle corresponds to top tertile of lifestyle score. Adjusted HRs and 95% CI were derived from Cox regression models adjusted for age and sex. CI indicates confidence interval; CVD, cardiovascular disease; and HR, hazard ratio.
Sensitivity analyses with exclusion of participants with self-reported diabetes mellitus or taking blood pressure– and lipid-lowering medication, or the individual lifestyle components, showed similar associations for BP and CVD events (Tables VI and VII in the online-only Data Supplement, Figure II in the online-only Data Supplement). Sensitivity analysis for healthy lifestyle score using American Heart Association cutoffs, and sensitivity analysis for sedentary lifestyle, as well, excluding participants with an event within the first 2 years of follow-up, did not materially change the results (Tables IX and X in the online-only Data Supplement).
Discussion
In this large study of >277 000 individuals, we observed that unhealthy lifestyle and genetic susceptibility to high BP were associated with higher levels of BP and a greater risk of subsequent incident CVD events (11% higher risk of incident CVD per unit increase of genetic risk). The favorable association of lifestyle with BP and CVD was found across all genetic risk categories, suggesting that the genetically predetermined rise in BP and its complications can be offset at least to some extent by healthy lifestyle. Our results further support population-wide efforts to lower BP and subsequent CVD risk through lifestyle modification.
Consistent with a well-established causal effect of BP on CVD26 and with a lifelong effect of BP variants on BP levels,1,27 we confirmed that a genetic risk score with 314 BP-associated genetic variants is a strong predictor of CVD events, including MI and stroke. The association was independent of lifestyle risk factors related to BP and CVD. Indeed, BP is measured with large measurement error because of BP variability, whereas BP genotypes can be precisely measured, are constant over time, and therefore capture a fixed component of lifetime BP exposure. Evidence from other areas has shown that, for example, genetic risk scores using lipid or diabetes mellitus genetic variants predict CVD and diabetes mellitus, respectively, more consistently over time than standard clinical biomarkers.26,28
We showed that the detrimental effect of genes on BP and on subsequent CVD risk can be largely offset by a healthy lifestyle in support of previous observations on CVD and obesity.29,30 This observation challenges the deterministic interpretation of the genetic risk in individual-based risk assessment. Genetic risk can be known from birth, whereas the other conventional CVD risk factors usually appear in midlife, and, given that genetic risk is nonmodifiable, it is aligned with lifelong risk prediction. Our observations raise the possibility of targeting individuals at high genetic risk early in life for lifestyle or pharmacological modification and primordial prevention strategies.31 Yet, more evidence is needed on the effect of disclosing genetic information to individuals and risk communication to find the most appropriate means to achieve risk modification through lifestyle.
Our study has several strengths, including a large sample size and large number of incident CVD events, up to ≈8 years follow-up, and rich baseline phenotyping according to a well-defined and standardized protocol.12 We used an updated genetic risk score for BP by using the latest published results, including data on 314 validated BP loci.3,23 Limitations include the fact that lifestyle is poorly measured in contradistinction to the genetic information, such that the associations between lifestyle outcome measures may have been underestimated (regression dilution32–36). Also, we limited our analyses to food frequency questionnaire data that lacked information on dairy products, energy intake, and fat consumption. In addition, lifestyle may be influenced by preexisting conditions, and our results may therefore be subject to reverse causality. To account for these limitations, we excluded individuals with a history of CVD and performed sensitivity analyses excluding individuals on treatment for CVD risk factors.
In our study of 277 005 individuals, we showed that a healthy lifestyle is associated with low BP levels and a lower risk of subsequent cardiovascular events (ie, CVD, MI, and stroke) within each category of BP genetic profile. A high genetic risk was largely offset by a favorable lifestyle, but, in addition, people with low genetic risk could lose their inherent protection if they had an unhealthy lifestyle. Although it is possible to modify lifestyle, it is not possible to alter the genetic makeup, stressing the importance of population-wide lifestyle approaches to address the pressing BP problem. Our findings highlight the need for timely lifestyle interventions to offset the lifetime risk of future high BP and CVD. Given the importance of population-wide lifestyle modification, the use of genetic information for risk stratification merits careful evaluation before it is routinely implemented in clinical practice.
Sources of Funding
This research has been conducted using the UK Biobank Resource under applications number 10035 and 236 granting access to the corresponding UK Biobank genetic and phenotype data (released November 17, 2016). The research was supported by the British Heart Foundation (grant SP/13/2/30111) for Large-scale comprehensive genotyping of UK Biobank for cardiometabolic traits and diseases: UK CardioMetabolic Consortium. Dr Elliott is Director of the Medical Research Council-Public Health England Center for Environment and Health and acknowledges support from the Medical Research Council and Public Health England (MR/L01341X/1). He also acknowledges support from the National Institute of Health Research Biomedical Research Center at Imperial College Healthcare National Health Service Trust and Imperial College London, and the National Institute of Health Research Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012–10141). Dr Elliott is a UK Dementia Research Institute Professor, UK Dementia Research Institute at Imperial College London. This work was supported by the UK Dementia Research Institute which receives its funding from UK Dementia Research Institute Ltd funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. This work used the computing resources of the UK MEDical BIOinformatics partnership (UK MED-BIO) which is supported by the Medical Research Council (MR/L01632X/1).
Disclosures
None.
Footnotes
References
- 1.
Ezzati M, Obermeyer Z, Tzoulaki I, Mayosi BM, Elliott P, Leon DA . Contributions of risk factors and medical care to cardiovascular mortality trends.Nat Rev Cardiol. 2015; 12:508–530. doi: 10.1038/nrcardio.2015.82.CrossrefMedlineGoogle Scholar - 2.
Tzoulaki I, Elliott P, Kontis V, Ezzati M . Worldwide exposures to cardiovascular risk factors and associated health effects: current knowledge and data gaps.Circulation. 2016; 133:2314–2333. doi: 10.1161/CIRCULATIONAHA.115.008718.LinkGoogle Scholar - 3.
Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Lepe MP, O’Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M, Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, Mägi R, Stanton A, Connell J, Bakker SJ, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JM, Tobin MD, Munroe PB, Ehret GB, Wain LV ; International Consortium of Blood Pressure (ICBP) 1000G Analyses; BIOS Consortium; Lifelines Cohort Study; Understanding Society Scientific group; CHD Exome+ Consortium; ExomeBP Consortium; T2D-GENES Consortium; GoT2DGenes Consortium; Cohorts for Heart and Ageing Research in Genome Epidemiology (CHARGE) BP Exome Consortium; International Genomics of Blood Pressure (iGEN-BP) Consortium; UK Biobank CardioMetabolic Consortium BP working group. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk.Nat Genet. 2017; 49:403–415. doi: 10.1038/ng.3768.CrossrefMedlineGoogle Scholar - 4.
Chan Q, Stamler J, Griep LM, Daviglus ML, Horn LV, Elliott P . An update on nutrients and blood pressure.J Atheroscler Thromb. 2016; 23:276–289. doi: 10.5551/jat.30000.CrossrefMedlineGoogle Scholar - 5.
Marmot MG, Elliott P, Shipley MJ, Dyer AR, Ueshima H, Beevers DG, Stamler R, Kesteloot H, Rose G, Stamler J . Alcohol and blood pressure: the INTERSALT study.BMJ. 1994; 308:1263–1267.CrossrefMedlineGoogle Scholar - 6.
Marmot MG, Elliott P . Public health measures for blood pressure control in the whole community.Clin Exp Hypertens A. 1989; 11:1171–1186.MedlineGoogle Scholar - 7.
Asghari G, Yuzbashian E, Mirmiran P, Hooshmand F, Najafi R, Azizi F . Dietary Approaches to Stop Hypertension (DASH) dietary pattern is associated with reduced incidence of metabolic syndrome in children and adolescents.J Pediatr. 2016; 174:178–184.e171. doi: 10.1016/j.jpeds.2016.03.077.CrossrefMedlineGoogle Scholar - 8.
Charakida M, Khan T, Johnson W, Finer N, Woodside J, Whincup PH, Sattar N, Kuh D, Hardy R, Deanfield J . Lifelong patterns of BMI and cardiovascular phenotype in individuals aged 60-64 years in the 1946 British birth cohort study: an epidemiological study.Lancet Diabetes Endocrinol. 2014; 2:648–654. doi: 10.1016/S2213-8587(14)70103-2.CrossrefMedlineGoogle Scholar - 9.
Pouliou T, Ki M, Law C, Li L, Power C . Physical activity and sedentary behaviour at different life stages and adult blood pressure in the 1958 British cohort.J Hypertens. 2012; 30:275–283. doi: 10.1097/HJH.0b013e32834f1915.CrossrefMedlineGoogle Scholar - 10.
García-Ortiz L, Recio-Rodríguez JI, Puig-Ribera A, Lema-Bartolomé J, Ibáñez-Jalón E, González-Viejo N, Guenaga-Saenz N, Agudo-Conde C, Patino-Alonso MC, Gomez-Marcos MA ; EVIDENT Group. Blood pressure circadian pattern and physical exercise assessment by accelerometer and 7-day physical activity recall scale.Am J Hypertens. 2014; 27:665–673. doi: 10.1093/ajh/hpt159.CrossrefMedlineGoogle Scholar - 11.
Elliott P, Stamler J, Nichols R, Dyer AR, Stamler R, Kesteloot H, Marmot M . Intersalt revisited: further analyses of 24 hour sodium excretion and blood pressure within and across populations. Intersalt Cooperative Research Group.BMJ. 1996; 312:1249–1253.CrossrefMedlineGoogle Scholar - 12.
Elliott P, Peakman TC ; UK Biobank. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine.Int J Epidemiol. 2008; 37:234–244. doi: 10.1093/ije/dym276.CrossrefMedlineGoogle Scholar - 13. UK Biobank Coordinating Centre. UK Biobank: Protocol for a large-scale prospective epidemiological resource.Protocol No: UKBB-PROT-09-06 (Main Phase). March 21, 2007 (AMENDMENT ONE FINAL). http://www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf; Accessed May 15, 2017.Google Scholar
- 14.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R . UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12:e1001779. doi: 10.1371/journal.pmed.1001779.CrossrefMedlineGoogle Scholar - 15.
Tobin MD, Sheehan NA, Scurrah KJ, Burton PR . Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure.Stat Med. 2005; 24:2911–2935. doi: 10.1002/sim.2165.CrossrefMedlineGoogle Scholar - 16.
Fry D, Almond R, Gordon M, Moffat S . UK Biobank Biomarker Project Details of assays and quality control information for the urinary biomarker data.October 28, 2016. http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=34972. Accessed May 15, 2017.Google Scholar - 17.
Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD ; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar - 18.
Lichtenstein AH, Appel LJ, Brands M, Carnethon M, Daniels S, Franch HA, Franklin B, Kris-Etherton P, Harris WS, Howard B, Karanja N, Lefevre M, Rudel L, Sacks F, Van Horn L, Winston M, Wylie-Rosett J . Diet and lifestyle recommendations revision 2006; a scientific statement from the American Heart Association Nutrition Committee.Arterioscler Thromb Vasc Biol. 2006; 114:82–96.Google Scholar - 19.
Bhupathiraju SN, Lichtenstein AH, Dawson-Hughes B, Tucker KL . Adherence index based on the AHA 2006 diet and lifestyle recommendations is associated with select cardiovascular disease risk factors in older Puerto Ricans.J Nutr. 2011; 141:460–469. doi: 10.3945/jn.110.133603.CrossrefMedlineGoogle Scholar - 20.
Schnier C, Sudlow C . Definitions of acute myocardial infarction (MI) and main MI pathological types for UK Biobank phase 1 outcomes adjudication.January 2017. http://biobank.ctsu.ox.ac.uk/crystal/docs/alg_outcome_mi.pdf. Accessed July 15, 2017.Google Scholar - 21. Genotype Imputation and Genetic Association Studies of UK Biobank: Interim Data Release.May 2015. http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/imputation_documentation_May2015.pdf. Accessed May 17, 2017.Google Scholar
- 22.
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, Cortes A, Welsh S, McVean G, Leslie S, Donnelly P, Marchini J . Genome-wide genetic data on ~500,000 UK Biobank participants [published online ahead of print July 20, 2017].bioRxiv. doi: 10.1101/166298.Google Scholar - 23.
Hoffmann TJ, Ehret GB, Nandakumar P, Ranatunga D, Schaefer C, Kwok PY, Iribarren C, Chakravarti A, Risch N . Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation.Nat Genet. 2017; 49:54–64. doi: 10.1038/ng.3715.CrossrefMedlineGoogle Scholar - 24.
Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M . Age Standardization of Rates: A New Who Standard. GPE Discussion Paper Series: No.31. EIP/GPE/EBD.World Health Organization2001. http://www.who.int/healthinfo/paper31.pdf. Accessed July 28, 2017.Google Scholar - 25. ISDScotland. GPD Support Population: Standard Population.http://www.isdscotland.org/Products-and-Services/GPD-Support/Population/Standard-Populations/. Accessed July 28, 2017.Google Scholar
- 26.
Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, Hirschhorn JN, Berglund G, Hedblad B, Groop L, Altshuler DM, Newton-Cheh C, Orho-Melander M . Polymorphisms associated with cholesterol and risk of cardiovascular events.N Engl J Med. 2008; 358:1240–1249. doi: 10.1056/NEJMoa0706728.CrossrefMedlineGoogle Scholar - 27.
Havulinna AS, Kettunen J, Ukkola O, Osmond C, Eriksson JG, Kesäniemi YA, Jula A, Peltonen L, Kontula K, Salomaa V, Newton-Cheh C . A blood pressure genetic risk score is a significant predictor of incident cardiovascular events in 32,669 individuals.Hypertension. 2013; 61:987–994. doi: 10.1161/HYPERTENSIONAHA.111.00649.LinkGoogle Scholar - 28.
Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, Berglund G, Altshuler D, Nilsson P, Groop L . Clinical risk factors, DNA variants, and the development of type 2 diabetes.N Engl J Med. 2008; 359:2220–2232. doi: 10.1056/NEJMoa0801869.CrossrefMedlineGoogle Scholar - 29.
Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, Chasman DI, Baber U, Mehran R, Rader DJ, Fuster V, Boerwinkle E, Melander O, Orho-Melander M, Ridker PM, Kathiresan S . Genetic risk, adherence to a healthy lifestyle, and coronary disease.N Engl J Med. 2016; 375:2349–2358. doi: 10.1056/NEJMoa1605086.CrossrefMedlineGoogle Scholar - 30.
Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN, Jones SE, Tuke MA, Ruth KS, Freathy RM, Davey Smith G, Joost S, Guessous I, Murray A, Strachan DP, Kutalik Z, Weedon MN, Frayling TM . Gene-obesogenic environment interactions in the UK Biobank study.Int J Epidemiol. 2017; 46:559–575. doi: 10.1093/ije/dyw337.MedlineGoogle Scholar - 31.
Padmanabhan S . Prospects for genetic risk prediction in hypertension.Hypertension. 2013; 61:961–963. doi: 10.1161/HYPERTENSIONAHA.113.00948.LinkGoogle Scholar - 32.
Berglund L . Regression dilution bias: tools for correction methods and sample size calculation.Ups J Med Sci. 2012; 117:279–283. doi: 10.3109/03009734.2012.668143.CrossrefMedlineGoogle Scholar - 33.
Berglund L, Garmo H, Lindbäck J, Zethelius B . Correction for regression dilution bias using replicates from subjects with extreme first measurements.Stat Med. 2007; 26:2246–2257. doi: 10.1002/sim.2698.CrossrefMedlineGoogle Scholar - 34.
Hutcheon JA, Chiolero A, Hanley JA . Random measurement error and regression dilution bias.BMJ. 2010; 340:c2289.CrossrefMedlineGoogle Scholar - 35.
Masudi S, Yavari P, Mehrabi Y, Khalili D, Azizi F . Regression dilution bias in blood pressure and body mass index in a longitudinal population-based cohort study.J Res Health Sci. 2015; 15:77–82.MedlineGoogle Scholar - 36.
Dyer AR, Elliott P, Shipley M . Urinary electrolyte excretion in 24 hours and blood pressure in the INTERSALT Study. II. Estimates of electrolyte-blood pressure associations corrected for regression dilution bias. The INTERSALT Cooperative Research Group.Am J Epidemiol. 1994; 139:940–951.CrossrefMedlineGoogle Scholar
