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Research Article
Originally Published 15 April 2021
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Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease

Michael G. Levin https://orcid.org/0000-0002-9937-9932, Derek Klarin https://orcid.org/0000-0002-4636-5780, Venexia M. Walker, Dipender Gill https://orcid.org/0000-0001-7312-7078, Julie Lynch, Jacklyn N. Hellwege https://orcid.org/0000-0001-7479-0920, Jacob M. Keaton https://orcid.org/0000-0002-6371-7530, … Show All … , Kyung M. Lee, Themistocles L. Assimes https://orcid.org/0000-0003-2349-0009, Pradeep Natarajan https://orcid.org/0000-0001-8402-7435, Adriana M. Hung https://orcid.org/0000-0002-3203-1608, Todd L. Edwards, Daniel J. Rader https://orcid.org/0000-0002-9245-9876, J. Michael Gaziano, Neil M. Davies, Philip S. Tsao https://orcid.org/0000-0001-7274-9318, Kyong-Mi Chang, Benjamin F. Voight https://orcid.org/0000-0002-6205-9994, and Scott M. Damrauer https://orcid.org/0000-0001-8009-1632 [email protected] on behalf of the VA Million Veteran ProgramAuthor Info & Affiliations
Arteriosclerosis, Thrombosis, and Vascular Biology
Volume 41, Number 6
https://doi.org/10.1161/ATVBAHA.120.315482
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Graphical Abstract

Abstract

Objective:

We aimed to estimate the effect of blood pressure (BP) traits and BP-lowering medications (via genetic proxies) on peripheral artery disease.

Approach and Results:

Genome-wide association studies summary statistics were obtained for BP, peripheral artery disease (PAD), and coronary artery disease. Causal effects of BP on PAD were estimated by 2-sample Mendelian randomization using a range of pleiotropy-robust methods. Increased systolic BP (SBP), diastolic BP, mean arterial pressure (MAP), and pulse pressure each significantly increased risk of PAD (SBP odds ratio [OR], 1.20 [1.16–1.25] per 10 mm Hg increase, P=1×10−24; diastolic BP OR, 1.27 [1.18–1.35], P=4×10−11; MAP OR, 1.26 [1.19–1.33], P=6×10−16; pulse pressure OR, 1.31 [1.24–1.39], P=9×10−23). The effects of SBP, diastolic BP, and MAP were greater for coronary artery disease than PAD (SBP ratio of OR [ROR], 1.06 [1.0–1.12], P=0.04; MAP ratio of OR, 1.15 [1.06–1.26], P=8.6×10−4; diastolic BP ratio of OR, 1.21 [1.08–1.35], P=6.9×10−4). Considered jointly, both pulse pressure and MAP directly increased risk of PAD (pulse pressure OR, 1.26 [1.17–1.35], P=3×10−10; MAP OR, 1.14 [1.06–1.23], P=2×10−4). The effects of antihypertensive medications were estimated using genetic instruments. SBP-lowering via β-blocker (OR, 0.74 per 10 mm Hg decrease in SBP [95% CI, 0.65–0.84]; P=5×10−6), loop diuretic (OR, 0.66 [0.48–0.91], P=0.01), and thiazide diuretic (OR, 0.57 [0.41–0.79], P=6×10−4) associated variants were protective of PAD.

Conclusions:

Higher BP is likely to cause PAD. BP-lowering through β blockers, loop diuretics, and thiazide diuretics (as proxied by genetic variants) was associated with decreased risk of PAD. Future study is needed to clarify the specific mechanisms by which BP influences PAD.

Highlights

•
Although peripheral artery disease (PAD) is a common manifestation of atherosclerotic cardiovascular disease, the causal impact of blood pressure on risk of PAD has remained uncertain.
•
In this Mendelian randomization study, increases in blood pressure were robustly associated with increased risk of PAD.
•
Genetic proxies of several antihypertensive medication classes were associated with decreased risk of PAD.
•
Overall, this study provides evidence consistent with a causal association between blood pressure and PAD and prioritizes medications for future studies that consider PAD-specific outcomes.

Introduction

Peripheral artery disease (PAD) is a common manifestation of atherosclerotic cardiovascular disease (ASCVD), estimated to affect >12 million individuals in the United States and >120 million individuals worldwide.1,2 PAD shares a number of risk factors with other forms ASCVD like coronary artery disease (CAD) and ischemic stroke.3 These risk factors include smoking, diabetes, hypertension, hyperlipidemia, and obesity.2–4 Observational studies have identified hypertension as one of the strongest risk factors for incident and prevalent PAD,5–11 although these studies may be limited by residual environmental confounding or reverse-causality. While randomized controlled trials of antihypertensive medications have demonstrated broad protection from CAD and death from cardiovascular causes, whether lower blood pressure (BP) reduces risk of PAD specifically has not been reliably established. Similarly, the relative effect of BP on PAD has not been fully investigated.
Recent genome-wide association studies (GWAS) of PAD and BP including >700 000 individuals have identified hundreds of genetic variants associated with these traits.12,13 The Mendelian randomization (MR) framework (under certain assumptions) can leverage this genetic variation (which is randomly assorted during meiosis, mimicking a randomized trial), to provide unconfounded causal estimates of the relationship between traits.14 MR assumes that genetic variants are likely to be independent of many confounders of the exposure-outcome relationship. This assumption is plausible because genetic variants are randomly inherited by offspring from parents during meiosis and conception, analogous to treatment allocation in a randomized trial. Because large, randomized trials evaluating the relationship between treatment of hypertension and PAD outcomes may be unfeasible, other study designs are needed to fill this evidence gap. Here, we leverage population-scale genetic variation within the MR framework to (1) establish the relationship between BP and risk of PAD, (2) quantify differences in the effect of BP on CAD and PAD risk, and (3) estimate the effect of BP lowering (using genetic proxies of antihypertensive medications) on PAD risk.

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Study Exposures

Trans-ancestry BP GWAS of systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) were obtained from the Pan UK Biobank resource (https://pan.ukbb.broadinstitute.org/). These studies included up to 483 729 participants (262 223 male and 221 506 female; 420 136 European, 8842 Central/South Asian, 6614 African, 2705 East Asian, 1590 Middle Eastern, and 980 American [Hispanic/Latino]). BP measurements included both automated and manual measurements and were adjusted for antihypertensive medication use. Details of genotyping and quality control, as well as links to download summary statistics can be found at https://pan.ukbb.broadinstitute.org/.
Ancestry-specific BP effect estimates were obtained by first identifying genetic variants associated with each BP trait in the trans-ancestry Pan UK Biobank BP GWAS studies, and then extracting the effect estimates and standard errors for the corresponding variants in European and African ancestry-specific analyses. European ancestry-specific BP effect estimates were obtained by first identifying from the 2018 Evangelou et al.15 International Consortium for Blood Pressure+UK Biobank GWAS, which included measurements of SBP, DBP, and PP in up to 757 601 individuals. Full GWAS summary statistics for the European ancestry BP genome-wide association study are publicly available and may be downloaded from the National Heart, Lung, and Blood Institute GRASP catalog (https://grasp.nhlbi.nih.gov/FullResults.aspx). African ancestry-specific BP effect estimates were obtained from BioVU (Vanderbilt University) and UK Biobank and combined using fixed-effects inverse variance-weighted meta-analysis, including up to 16 784 individuals across both studies (Methods in the Data Supplement).

Study Outcomes

The 2019 Million Veteran Program genome-wide association study of PAD by Klarin et al12 identified 31 307 PAD cases (24 009 European; 7373 African; and 1925 Hispanic) and 211 753 PAD-free controls. This study defined cases and controls based on electronic health record phenotyping within the Veterans Affairs Healthcare System and was validated against ankle brachial index measurement and manual chart review. The current analyses included trans-ancestry, European, and African-specific GWAS results. Million Veteran Program PAD genome-wide association study summary statistics are available on dbGAP (Accession phs001672.v2.p1).
We considered CAD (as another manifestation of atherosclerosis traditionally associated with elevations in BP) for comparison. GWAS summary statistics for CAD were obtained from the Nikpay et al13 2015 CARDIoGRAMplusC4D 1000 genomes-based GWAS. This study was a meta-analysis including 60 801 CAD cases and 123 504 controls, with genotypes imputed using the 1000 genomes phase 1 version 3 reference. Summary statistics were downloaded from www.cardiogramplusc4d.org/data-downloads/.

Mendelian Randomization

Two-sample MR analyses were performed in R using the TwoSampleMR package (https://github.com/MRCIEU/TwoSampleMR).16 Genetic instruments for BP traits were constructed using variants that were in linkage equilibrium, physically separate (r2<0.001, distance=10 000 kb; 1000 genomes reference panel), and associated with each trait at genome-wide significance (P<5×10−8). For bidirectional MR analysis, additional instruments were constructed for CAD and PAD using the same procedure. F statistics were calculated for each variant using the formula F=β2/SE2.The primary MR analyses used inverse-variance weighting with random effects. The MR-Egger intercept test was used to evaluate for evidence of horizontal pleiotropy. Leave-one-out, single-SNP, and funnel-plot diagnostic MR analyses were performed. Sensitivity analyses were performed using MR methods that make different assumptions about the presence of pleiotropy (weighted median, penalized weighted median, and weighted mode).17 Multivariable MR was used in additional sensitivity analyses to jointly estimate the direct effects of BP traits, again using genetic instruments based on variants that were in linkage equilibrium, physically separate (r2<0.001, distance=10 000 kb), and associated with any exposure at genome-wide significance (P<5×10−8), weighted by the effect of each SNP on each exposure.18 MR-Steiger was performed to test the correct direction of effect.19 Effect estimates were scaled to correspond to a 10 mm Hg change in BP.

Antihypertensive Drug MR

MR analyses were performed to estimate the effect of 10 mm Hg lowering of BP by antihypertensive drugs. Genetic instruments consisted of variants that were associated with each BP trait at genome-wide significance and located near (±200 kb) or within genes encoding protein targets of 12 antihypertensive medication classes, with effect estimates for each genetic variant derived for each BP trait from the trans-ancestry BP GWAS.20,21 The primary analysis focused on the SBP-lowering effect, with sensitivity analyses considering the remaining BP traits (DBP, PP, MAP). Inverse-variance weighted, weighted median, penalized weighted median, and weighted mode 2-sample MR was performed, with MR-Egger intercept test used to assess for horizontal pleiotropy. For instruments with only 1 variant, Wald-ratio MR was performed.

Statistical Analysis

The primary analysis of the effect of BP on PAD was performed using 2-sample MR considering trans-ancestry BP exposures and trans-ancestry outcomes. We performed 2 additional ancestry-specific sensitivity analyses. First, we performed an analysis considering trans-ancestry BP exposures and ancestry-specific PAD outcomes. Second, due to the lack of genetic variants associated with BP traits as genome-wide significance in African-specific BP GWAS, we also performed a 3-sample MR analysis. Here, genetic variants associated with BP were obtained from the trans-ancestry BP GWAS, with corresponding effect estimates and standard errors obtained from the European- and African-specific BP GWAS. Instruments were then filtered to include only those with F statistic >10 to minimize weak instrument bias.14 Heterogeneity across ancestries was assessed using I2 and Cochran Q. The ratio of odds ratios (ROR) was used to compare effects of each BP trait on PAD and CAD.22 For all analyses we used Bonferroni adjustment for 4 BP traits, with P values <0.05/4=0.0125 considered significant. All statistical analyses were performed using R version 3.6.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Effects of Genetic Variation in BP on PAD: MR

We performed 2-sample MR using summary statistics from trans-ancestry GWAS to estimate the effect of genetic variation in BP traits on PAD. Genetic instruments for BP contained between 259 and 333 independent genetic variants, with F statistics ranging from 29 to 938 (consistent with low risk of weak-instrument bias; Tables I and II in the Data Supplement).
In inverse-variance weighted analyses, each genetically proxied 10 mm Hg increase in SBP, DBP, MAP, and PP significantly increased the risk of PAD (SBP OR, 1.20 [1.16–1.25] per 10 mm Hg increase, P=1×10−24; DBP OR, 1.27 [1.18–1.35], P=4×10−11; MAP OR, 1.26 [1.19–1.33], P=6×10−16; PP OR, 1.31 [1.24–1.39], P=9×10−23; Figure 1 and Table III in the Data Supplement). The MR-Egger bias intercept term was P>0.05 for all trait-outcome pairs (Table III in the Data Supplement). The results remained robust in sensitivity analyses using MR methods that make different assumptions about the presence of pleiotropy (Table III in the Data Supplement). MR-Steiger confirmed the directionality of all associations.
Figure 1. Effect of blood pressure (BP) traits on peripheral artery disease (PAD). In inverse variance-weighted Mendelian randomization analyses, elevations in each BP trait increased risk of PAD. Results scaled to reflect odds of outcome per 10 mm Hg increase in BP. DBP indicates diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; and SBP, systolic BP.
When considering trans-ancestry genetic instruments for BP and ancestry-specific PAD outcomes, SBP, DBP, MAP, and PP were significantly associated with PAD in a European-specific population, while only SBP and PP were associated with PAD in an African-specific population (Figure IA and IB and Tables IV and V in the Data Supplement). In these ancestry-specific analyses, we detected heterogenous effects of BP on PAD across all BP measures (I2 ranging from 87% to 90%, Cochran P<0.05). Effects remained heterogeneous when considering both ancestry-specific BP effects and ancestry-specific PAD outcomes (Figure 2 and Tables VI and VII in the Data Supplement).
For comparison, we estimated the effects of SBP, DBP, MAP, and PP on CAD. As with PAD, each BP trait was significantly associated with CAD (Tables I through III in the Data Supplement). The effects of SBP, DBP, and MAP were greater for CAD than PAD (SBP ratio of ORs, 1.06 [1.0–1.12], P=0.04; MAP ROR, 1.15 [1.06–1.26], P=8.6×10−4; DBP ROR, 1.21 [1.08–1.35], P=6.9×10−4; Figure 2).
Figure 2. Effects of blood pressure (BP) traits on peripheral artery disease (PAD) vs coronary artery disease (CAD). Comparison of the effects of BP traits on CAD and PAD. A, Log-odds effect estimates for CAD and PAD, with dotted line representing equal effects on both atherosclerotic cardiovascular disease outcomes, and crosshairs representing 95% CIs for each effect estimate. B, Comparison of effect of each BP trait on PAD vs CAD using the ratio of odds ratios test, with ratio of odds ratio (ROR) >1 representing greater effect on CAD and ROR <1 representing greater effect on PAD. DBP indicates diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; and SBP, systolic BP.

Effects of Genetic Liability to PAD on BP: MR

Because stiffening of peripheral vessels may affect BP, the possibility of reverse-causation exists in assessment of the relationship between BP and PAD. To test for the presence of reverse-causation, we next performed bidirectional MR analyses. Genetic instruments for PAD were selected and used to estimate the effect of genetic liability to PAD on BP traits (Figure 3; Tables VIII and IX in the Data Supplement). In inverse-variance weighted analysis, genetic liability to PAD increased SBP (β=0.28 mm Hg per 1 log-odds increase in risk of PAD [0.16–0.41], P=8×10−4), MAP (β=0.13 [0.027–0.24], P=0.01), and PP (β=0.22 [0.13–0.32], P=7×10−6). The MR-Egger bias intercept term was P>0.05 for all analyses, indicating no positive evidence for bias. Results were consistent in sensitivity analysis applying MR methods making different assumptions about the presence of pleiotropy (Table IX in the Data Supplement). For comparison, genetic liability CAD was not associated with BP traits after accounting for multiple testing (Figure 2 and Tables VIII and IX in the Data Supplement). MR-Steiger confirmed the direction of effect for all associations.
Figure 3. Effects of peripheral artery disease (PAD) and coronary artery disease (CAD) on blood pressure (BP) traits. The effect of liability to PAD and CAD on each BP trait was estimated using inverse variance-weighted 2-sample Mendelian randomization. DBP indicates diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; and SBP, systolic BP.

Multivariable MR

Because BP traits are highly correlated and unlikely to affect cardiovascular outcomes in isolation, we performed multivariable MR to jointly estimate the direct effects of BP (as reflected by MAP), and arterial stiffness (as reflected by PP) trait on PAD. Considered jointly, each 10 mm Hg increase in both PP and MAP increased risk of PAD (PP OR, 1.26 [1.17–1.35], P=3×10−10; MAP OR, 1.14 [1.06–1.23], P=2×10−4; Figure III in the Data Supplement).

Antihypertensive Drug MR

In MR analyses designed to proxy the BP-lowering effects of antihypertensive medications, we identified several medications with protective effects on PAD. Genetically proxied SBP-lowering via β-blockers (OR, 0.74 per 10 mm Hg decrease in SBP; [95% CI, 0.65–0.84]; P=5×10−6), loop diuretics (OR, 0.66 [0.48–0.91], P=0.01), and thiazide diuretics (OR, 0.57 [0.41–0.79], P=6×10−4) were associated with decreased risk of PAD (Figure 4; Tables X and XI in the Data Supplement). The MR-Egger bias intercept term was P>0.05 for all analyses, indicating no positive evidence for bias. Results were similar using MR methods making different assumptions about the presence of pleiotropy (Tables X and XI in the Data Supplement). When considering genetically proxied DBP and MAP-lowering effects we detected an additional protective association for renin-inhibitors (DBP OR, 0.34 [0.17–0.67], P=0.001; MAP OR, 0.52 [0.29–0.91], P=0.02; Tables X and XI in the Data Supplement). We did not detect any significant associations between genetically proxied drug effects on PP-lowering and PAD.
Figure 4. Effect of systolic blood pressure (SBP)-lowering via genetic proxies of antihypertensive medications. Inverse variance weighted Mendelian randomization was performed to estimate the SBP-lowering effect of genetic proxies of antihypertensive medications on peripheral artery disease (PAD).

Discussion

This MR study leveraged natural genetic variation to examine the relationship between BP and both PAD. The principal findings were (1) lifetime exposure to elevated SBP, DBP, MAP, and PP all increased risk of PAD; (2) elevated BP more strongly increased risk of CAD compared with PAD; (3) PAD led to small but significant increases in SBP, MAP, and PP; and (4) based on genetic proxies, β-blockers, loop diuretics, thiazide diuretics, and renin-inhibitors were associated with decreased PAD risk. There are several implications from the results of this study.
First, this study supports observational findings that elevated BP is associated with increased risk of PAD. Multiple observational studies have identified elevated SBP and clinical diagnosis of hypertension as strong risk factors for PAD, while the relationship between DBP and PAD has remained less clear.5–11,23–25 Unlike other observational studies, our MR study leveraged genetic variants as instrumental variables for SBP, DBP, MAP, and PP. Because genetic variants are randomly inherited by offspring from their parents, mimicking a trial randomizing individuals to a lifetime of increased BP, the MR framework is less susceptible to residual environmental confounding than traditional observational studies.14 The finding of our MR analysis that elevated SBP increases risk of both PAD and CAD is consistent with prior studies. We also find a strong effect of DBP on both PAD and CAD, clarifying discrepant findings in prior observational studies. Similarly, our multivariable MR findings demonstrate that both MAP and PP, reflecting pressure and pulsatility/stiffness respectively, both influence PAD risk. Overall, the MR findings of our study are consistent with a causal relationship between BP traits and PAD.
Next, we found that elevated SBP, DBP, and MAP each increased risk of CAD more than PAD. These findings are in contrast to a prior observational analysis that found that SBP or DBP had similar effects on CAD and PAD.7 While broad recommendations for lifestyle modification and treatment of ASCVD risk factors are clearly important at both the population level and individual level, understanding the impact of interventions on specific ASCVD outcomes may further inform treatment and prevention guidelines and discussions with patients. Particularly in light of our recent finding that smoking more strongly increases risk of PAD in comparison to CAD or ischemic stroke,26 this study adds further nuance to the relationship between traditional ASCVD risk factors and specific ASCVD outcomes.
Our finding that increased PP increases PAD risk is consistent with findings from multiple prior observational studies.27–30 Because increased PP is a marker of increased arterial stiffness and may be caused by PAD, the observational studies investigating the relationship between these traits may have been limited by the possibility of reverse causality. Using bidirectional MR, we were able to overcome this limitation, finding elevated PP to be a risk factor for PAD, and PAD to be a risk factor for increased PP. Similarly, when jointly considering MAP and PP we detected direct effects of both traits on PAD, suggesting that both arterial pressure (as measured by MAP) and arterial stiffness (as reflected by PP) directly influence development of PAD. Further study is warranted to determine the specific mechanisms by which these traits impact PAD, though our findings suggest interventions targeting both traits may be useful in reducing the burden of PAD.
Finally, we used antihypertensive drug MR to estimate the effect of 10 mm Hg lowering of BP by different classes of medication. In this analysis, we identified protective effects of several antihypertensive medications. A Cochrane Review found poor evidence for the use of antihypertensive medications specifically for PAD, though recognized the large benefit of these medications for prevention of cardiovascular events and mortality more broadly.31 Our results provide a genetic basis for considering future trials focused on β-blockers, loop diuretics, and thiazide diuretics. While small beneficial genetic effects may compound over a lifetime leading to protection from ASCVD, the effects of antihypertensive medications occur on a much shorter timescale. Our findings do not exclude meaningful beneficial effects of other potent antihypertensive medications on risk of PAD, particularly given the strong overall causal effects of each BP trait on PAD. The optimal antihypertensive regimens for prevention/treatment of PAD remains unclear and may represent a focus for future effectiveness studies.
The overall findings of our study have implications for PAD prevention and treatment guidelines. The current 2016 American Heart Association/American College of Cardiology and 2017 European Society of Cardiology PAD guidelines make strong recommendations for the treatment of hypertension to prevent cardiovascular events.3,4 The trials cited to support these recommendations focused on cardiovascular events broadly, or differences in safety and efficacy between different antihypertensive classes, rather than PAD-specific outcomes.32–40 Our MR study provides strong evidence consistent with a casual effect of increased BP on PAD. In the absence of large, randomized trials of antihypertensive medications focused on PAD-specific outcomes, these results add support for current guideline recommendations, and suggest possible medication classes that warrant further study specifically for PAD. Recent studies like SPRINT and multi-society BP guidelines have suggested that aggressive BP-lowering may be associated with improved outcomes in individuals at high ASCVD risk.41,42 While our current analyses do not provide a specific BP-lowering target that minimizes risk of PAD, future analyses leveraging participant-level data may help identify treatment thresholds. Our current results may help calibrate the overall expected benefit that programs to treat hypertension may have on the global burden of PAD.
This study has several limitations. Although we considered trans-ancestry studies of BP and PAD, the underlying populations were primarily composed of individuals of European ancestry. We performed extensive ancestry-specific analyses, identifying heterogenous effects of BP on PAD. Whether these findings reflect biological differences in the pathogenesis of elevated BP and PAD that vary by ancestry or reflect limitations of our current understanding of the genetic basis of these traits remains uncertain. Further study of BP and ASCVD genetics in diverse ancestral populations is necessary to improve the generalizability of our findings. Similarly, the stronger associations between BP traits and CAD in comparison to PAD may reflect pathophysiological differences in the risk factors for atherosclerosis across diverse vascular beds or may be due to differences in sampling or ascertainment of the underlying PAD and CAD GWAS studies. MR relies on a number of assumptions in order for causal estimates to be valid.14 While we have employed multiple MR methods and sensitivity analyses to assess for and address potential violations of these assumptions, we cannot completely exclude the possibility of confounding. Future study on the role of hypertension treatment in the prevention and treatment of PAD focused on PAD-specific outcomes is warranted.
Overall, we find strong evidence consistent with a causal effect of BP traits on PAD, although find a stronger effect of SBP, DBP, and MAP on CAD in comparison to PAD. We identify genetic proxies of antihypertensive medications associated with decreased PAD risk, which may be prioritized for future study.

Acknowledgments

We thank the participants of the VA Million Veteran Program and VA MVP collaborators (Acknowledgments in the Data Supplement). Summary statistics for blood pressure genome-wide association studies are available from https://pan.ukbb.broadinstitute.org/ and the National Heart, Lung, and Blood Institute GRASP (Genome-Wide Repository of Associations Between SNPs and Phenotypes) catalog (https://grasp.nhlbi.nih.gov/FullResults.aspx). MVP PAD genome-wide association study summary statistics are available from the National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes (dbGAP) (Accession phs001672.v2.p1). Data on coronary artery disease have been contributed by CARDIoGRAMplusC4D investigators and may been downloaded from www.cardiogramplusc4d.org/data-downloads/.

Footnote

Nonstandard Abbreviations and Acronyms

ASCVD
atherosclerotic cardiovascular disease
BP
blood pressure
CAD
coronary artery disease
DBP
diastolic BP
GWAS
genome-wide association studies
MAP
mean arterial pressure
MR
Mendelian randomization
OR
odds ratio
PAD
peripheral artery disease
PP
pulse pressure
SBP
systolic BP

Supplemental Material

File (atvb_atvb-2020-315482_supp1.pdf)
File (atvb_atvb-2020-315482_supp2.xlsx)

References

1.
James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, et al; GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–1858. doi: 10.1016/S0140-6736(18)32279-7
Crossref
PubMed
Google Scholar
2.
Criqui MH, Aboyans V. Epidemiology of peripheral artery disease. Circ Res. 2015;116:1509–1526. doi: 10.1161/CIRCRESAHA.116.303849
Crossref
PubMed
Google Scholar
3.
Gerhard-Herman MD, Gornik HL, Barrett C, et al. 2016 lower extremity peripheral arterial disease guidelines. J Am Coll Cardiol. 2017;69:e71–e126.
Crossref
PubMed
Google Scholar
4.
Aboyans V, Ricco JB, Bartelink MEL, Björck M, Brodmann M, Cohnert T, Collet JP, Czerny M, De Carlo M, Debus S, et al; ESC Scientific Document Group. 2017 ESC guidelines on the diagnosis and treatment of peripheral arterial diseases, in collaboration with the European Society for Vascular Surgery (ESVS): document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: the European Stroke Organization (ESO)The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J. 2018;39:763–816. doi: 10.1093/eurheartj/ehx095
Crossref
PubMed
Google Scholar
5.
Bainton D, Sweetnam P, Baker I, Elwood P. Peripheral vascular disease: consequence for survival and association with risk factors in the Speedwell prospective heart disease study. Br Heart J. 1994;72:128–132. doi: 10.1136/hrt.72.2.128
Crossref
PubMed
Google Scholar
6.
Criqui MH, Vargas V, Denenberg JO, Ho E, Allison M, Langer RD, Gamst A, Bundens WP, Fronek A. Ethnicity and peripheral arterial disease: the San Diego Population Study. Circulation. 2005;112:2703–2707. doi: 10.1161/CIRCULATIONAHA.105.546507
Crossref
PubMed
Google Scholar
7.
Fowkes FG, Housley E, Riemersma RA, Macintyre CC, Cawood EH, Prescott RJ, Ruckley CV. Smoking, lipids, glucose intolerance, and blood pressure as risk factors for peripheral atherosclerosis compared with ischemic heart disease in the Edinburgh Artery Study. Am J Epidemiol. 1992;135:331–340. doi: 10.1093/oxfordjournals.aje.a116294
Crossref
PubMed
Google Scholar
8.
Hooi JD, Kester AD, Stoffers HE, Overdijk MM, van Ree JW, Knottnerus JA. Incidence of and risk factors for asymptomatic peripheral arterial occlusive disease: a longitudinal study. Am J Epidemiol. 2001;153:666–672. doi: 10.1093/aje/153.7.666
Crossref
PubMed
Google Scholar
9.
Dagenais GR, Maurice S, Robitaille NM, Gingras S, Lupien PJ. Intermittent claudication in Quebec men from 1974-1986: the Quebec Cardiovascular Study. Clin Invest Med. 1991;14:93–100.
PubMed
Google Scholar
10.
Garg PK, Biggs ML, Carnethon M, Ix JH, Criqui MH, Britton KA, Djoussé L, Sutton-Tyrrell K, Newman AB, Cushman M, et al. Metabolic syndrome and risk of incident peripheral artery disease: the cardiovascular health study. Hypertension. 2014;63:413–419. doi: 10.1161/HYPERTENSIONAHA.113.01925
Crossref
PubMed
Google Scholar
11.
Howard DP, Banerjee A, Fairhead JF, Hands L, Silver LE, Rothwell PM; Oxford Vascular Study. Population-based study of incidence, risk factors, outcome, and prognosis of ischemic peripheral arterial events: implications for prevention. Circulation. 2015;132:1805–1815. doi: 10.1161/CIRCULATIONAHA.115.016424
Crossref
PubMed
Google Scholar
12.
Klarin D, Lynch J, Aragam K, Chaffin M, Assimes TL, Huang J, Lee KM, Shao Q, Huffman JE, Natarajan P, et al; VA Million Veteran Program. Genome-wide association study of peripheral artery disease in the Million Veteran Program. Nat Med. 2019;25:1274–1279. doi: 10.1038/s41591-019-0492-5
Crossref
PubMed
Google Scholar
13.
Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, Saleheen D, Kyriakou T, Nelson CP, Hopewell JC, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47:1121–1130. doi: 10.1038/ng.3396
Crossref
PubMed
Google Scholar
14.
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi: 10.1136/bmj.k601
Crossref
PubMed
Google Scholar
15.
Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, et al; Million Veteran Program. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–1425. doi: 10.1038/s41588-018-0205-x
Crossref
PubMed
Google Scholar
16.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. doi: 10.7554/eLife.34408
Crossref
PubMed
Google Scholar
17.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–314. doi: 10.1002/gepi.21965
Crossref
PubMed
Google Scholar
18.
Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019;48:713–727. doi: 10.1093/ije/dyy262
Crossref
PubMed
Google Scholar
19.
Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13:e1007081. doi: 10.1371/journal.pgen.1007081
Crossref
PubMed
Google Scholar
20.
Gill D, Georgakis MK, Koskeridis F, Jiang L, Feng Q, Wei WQ, Theodoratou E, Elliott P, Denny JC, Malik R, et al. Use of genetic variants related to antihypertensive drugs to inform on efficacy and side effects. Circulation. 2019;140:270–279. doi: 10.1161/CIRCULATIONAHA.118.038814
Crossref
PubMed
Google Scholar
21.
Walker VM, Kehoe PG, Martin RM, Davies NM. Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. Int J Epidemiol. 2020;49:1132–1140. doi: 10.1093/ije/dyz155
Crossref
PubMed
Google Scholar
22.
Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ. 2003;326:219. doi: 10.1136/bmj.326.7382.219
Crossref
PubMed
Google Scholar
23.
Meijer WT, Grobbee DE, Hunink MG, Hofman A, Hoes AW. Determinants of peripheral arterial disease in the elderly: the Rotterdam study. Arch Intern Med. 2000;160:2934–2938. doi: 10.1001/archinte.160.19.2934
Crossref
PubMed
Google Scholar
24.
Newman AB, Siscovick DS, Manolio TA, Polak J, Fried LP, Borhani NO, Wolfson SK. Ankle-arm index as a marker of atherosclerosis in the Cardiovascular Health Study. Cardiovascular Heart Study (CHS) Collaborative Research Group. Circulation. 1993;88:837–845. doi: 10.1161/01.cir.88.3.837
Crossref
PubMed
Google Scholar
25.
McGee DL. Update on some epidemiologic features of intermittent claudication: the Framingham Study. J Am Geriatr Soc. 1985;33:13–18.
Crossref
PubMed
Google Scholar
26.
Levin MG, Klarin D, Assimes TL, Freiberg MS, Ingelsson E, Lynch J, Natarajan P, O’Donnell C, Rader DJ, Tsao PS, et al. Genetics of smoking and risk of atherosclerotic cardiovascular diseases: a Mendelian Randomization Study. medRxiv. Preprint posted online April 08, 2020. doi: 10.1001/jamanetworkopen.2020.34461
Crossref
Google Scholar
27.
Mao Y, Huang Y, Yu H, Xu P, Yu G, Yu J, Zhan Y. Incidence of peripheral arterial disease and its association with pulse pressure: a prospective cohort study. Front Endocrinol (Lausanne). 2017;8:333.
Crossref
PubMed
Google Scholar
28.
Zhan Y, Yu J, Chen R, Sun Y, Fu Y, Zhang L, Li S, Zhang F, Hu D. Prevalence of low ankle brachial index and its association with pulse pressure in an elderly Chinese population: a cross-sectional study. J Epidemiol. 2012;22:454–461. doi: 10.2188/jea.je20110140
Crossref
PubMed
Google Scholar
29.
Korhonen P, Kautiainen H, Aarnio P. Pulse pressure and subclinical peripheral artery disease. J Hum Hypertens. 2014;28:242–245. doi: 10.1038/jhh.2013.99
Crossref
PubMed
Google Scholar
30.
Kiuchi S, Hisatake S, Watanabe I, Toda M, Kabuki T, Oka T, Dobashi S, Ikeda T. Pulse pressure and upstroke time are useful parameters for the diagnosis of peripheral artery disease in patients with normal ankle brachial index. Cardiol Res. 2016;7:161–166. doi: 10.14740/cr508e
Crossref
PubMed
Google Scholar
31.
Lane DA, Lip GYH. Treatment of hypertension in peripheral arterial disease. Cochrane Database Syst Rev. 2013;2013:CD003075. doi: 10.1002/14651858.CD003075.pub2
Crossref
Google Scholar
32.
Ostergren J, Sleight P, Dagenais G, Danisa K, Bosch J, Qilong Y, Yusuf S; HOPE study investigators. Impact of ramipril in patients with evidence of clinical or subclinical peripheral arterial disease. Eur Heart J. 2004;25:17–24. doi: 10.1016/j.ehj.2003.10.033
Crossref
PubMed
Google Scholar
33.
Yusuf S. Effects of an angiotensin-converting-enzyme inhibitor, ramipril, on cardiovascular events in high-risk patients. N Engl J Med. 2000;342:145–153.
Crossref
PubMed
Google Scholar
34.
Yusuf S, Teo KK, Pogue J, Dyal L, Copland I, Schumacher H, Dagenais G, Sleight P, Anderson C. Telmisartan, ramipril, or both in patients at high risk for vascular events. N Engl J Med. 2008;358:1547–1559.
Crossref
PubMed
Google Scholar
35.
Bavry AA, Anderson RD, Gong Y, Denardo SJ, Cooper-Dehoff RM, Handberg EM, Pepine CJ. Outcomes Among hypertensive patients with concomitant peripheral and coronary artery disease: findings from the INternational VErapamil-SR/Trandolapril STudy. Hypertension. 2010;55:48–53. doi: 10.1161/HYPERTENSIONAHA.109.142240
Crossref
PubMed
Google Scholar
36.
Zanchetti A, Julius S, Kjeldsen S, McInnes GT, Hua T, Weber M, Laragh JH, Plat F, Battegay E, Calvo-Vargas C, et al. Outcomes in subgroups of hypertensive patients treated with regimens based on valsartan and amlodipine: an analysis of findings from the VALUE trial. J Hypertens. 2006;24:2163–2168. doi: 10.1097/01.hjh.0000249692.96488.46
Crossref
PubMed
Google Scholar
37.
Diehm C, Pittrow D, Lawall H. Effect of nebivolol vs. hydrochlorothiazide on the walking capacity in hypertensive patients with intermittent claudication. J Hypertens. 2011;29:1448–1456. doi: 10.1097/HJH.0b013e3283471151
Crossref
PubMed
Google Scholar
38.
Espinola-Klein C, Weisser G, Jagodzinski A, Savvidis S, Warnholtz A, Ostad MA, Gori T, Munzel T. β-Blockers in patients with intermittent claudication and arterial hypertension: results from the nebivolol or metoprolol in arterial occlusive disease trial. Hypertension. 2011;58:148–154. doi: 10.1161/HYPERTENSIONAHA.110.169169
Crossref
PubMed
Google Scholar
39.
Paravastu SCV, Mendonca DA, Da Silva A. Beta blockers for peripheral arterial disease. Cochrane database Syst Rev. 2013;2013:CD005508.
PubMed
Google Scholar
40.
ALLHAT Officers and Coordinators for the ALLHAT Collaborative Research Group. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial. Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic. JAMA J Am Med Assoc. 2002;288:2981–2997. doi: 10.1001/jama.288.23.2981
Crossref
PubMed
Google Scholar
41.
Wright JT, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, et al; SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373:2103–2116. doi: 10.1056/NEJMoa1511939
Crossref
PubMed
Google Scholar
42.
Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Himmelfarb CD, DePalma SM, Gidding S, Jamerson KA, Jones DW, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults. J Am Coll Cardiol. 2018;71:e127–e248. doi: 10.1016/j.jacc.2017.11.006
Crossref
PubMed
Google Scholar

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Go to Arteriosclerosis, Thrombosis, and Vascular Biology
Go to Arteriosclerosis, Thrombosis, and Vascular Biology
Arteriosclerosis, Thrombosis, and Vascular Biology
Volume 41 • Number 6 • June 2021
Pages: 2027 - 2034
PubMed: 33853351

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© 2021 American Heart Association, Inc.

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History

Received: 9 October 2020
Accepted: 30 March 2021
Published online: 15 April 2021
Published in print: June 2021

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Keywords

  1. atherosclerosis
  2. blood pressure
  3. coronary artery disease
  4. peripheral artery disease
  5. risk factors

Subjects

  1. Coronary Artery Disease
  2. Genetics
  3. High Blood Pressure
  4. Peripheral Vascular Disease

Authors

Affiliations

Michael G. Levin https://orcid.org/0000-0002-9937-9932
Division of Cardiovascular Medicine (M.G.L.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Department of Medicine (M.G. L., D.J.R., K.-M.C.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.).
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Derek Klarin https://orcid.org/0000-0002-4636-5780
Malcolm Randall VA Medical Center, Gainesville, FL (D.K.).
Department of Surgery, University of Florida, Gainesville (D.K.).
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Venexia M. Walker
Medical Research Council Integrative Epidemiology Unit, University of Bristol, United Kingdom (V.M.W., N.M.D.).
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Dipender Gill https://orcid.org/0000-0001-7312-7078
Department of Epidemiology and Biostatistics, School of Public Health (D.G.), Imperial College London, United Kingdom.
Department of Medicine, Centre for Pharmacology and Therapeutics, Hammersmith Campus (D.G.), Imperial College London, United Kingdom.
Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.).
Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, United Kingdom (D.G.).
Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom (D.G.).
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Julie Lynch
Edith Nourse VA Medical Center, Bedford, MA (J.L.).
VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City Health Care System, CT (J.L., K.M.L.).
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Jacklyn N. Hellwege https://orcid.org/0000-0001-7479-0920
Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center (J.N.H.), Vanderbilt University Medical Center, Nashville, TN.
Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN (J.N.H.).
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Jacob M. Keaton https://orcid.org/0000-0002-6371-7530
Division of Epidemiology, Department of Medicine (J.M.K.), Vanderbilt University Medical Center, Nashville, TN.
Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD (J.M.K.).
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Kyung M. Lee
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.).
VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City Health Care System, CT (J.L., K.M.L.).
Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA (K.M.L.).
Department of Health Law, Policy and Management, Boston University School of Public Health, MA (K.M.L.).
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Themistocles L. Assimes https://orcid.org/0000-0003-2349-0009
Palo Alto VA Healthcare System, CA (T.L.A., P.S.T.).
Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (T.L.A., P.S.T.).
Stanford Cardiovascular Institute, Stanford University, CA (T.L.A., P.S.T.).
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Pradeep Natarajan https://orcid.org/0000-0001-8402-7435
Cardiovascular Research Center, Massachusetts General Hospital, Boston (P.N.).
Broad Institute of Harvard and MIT, Cambridge, MA (P.N.).
Department of Medicine (P.N.)
VA Boston Healthcare System, MA (P.N., J.M.G.).
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Adriana M. Hung https://orcid.org/0000-0002-3203-1608
Division of Nephrology and Hypertension, Department of Medicine (A.M.H.), Vanderbilt University Medical Center, Nashville, TN.
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Todd L. Edwards
Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute (T.L.E.), Vanderbilt University Medical Center, Nashville, TN.
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Daniel J. Rader https://orcid.org/0000-0002-9245-9876
Department of Medicine (M.G. L., D.J.R., K.-M.C.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Department of Genetics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Institute for Translational Medicine and Therapeutics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
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J. Michael Gaziano
Division of Aging, Department of Preventive Medicine, Brigham and Women’s Hospital (J.M.G.), Harvard Medical School, Boston, MA.
VA Boston Healthcare System, MA (P.N., J.M.G.).
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Neil M. Davies
Medical Research Council Integrative Epidemiology Unit, University of Bristol, United Kingdom (V.M.W., N.M.D.).
K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway (N.M.D.).
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Philip S. Tsao https://orcid.org/0000-0001-7274-9318
Palo Alto VA Healthcare System, CA (T.L.A., P.S.T.).
Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (T.L.A., P.S.T.).
Stanford Cardiovascular Institute, Stanford University, CA (T.L.A., P.S.T.).
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Kyong-Mi Chang
Department of Medicine (M.G. L., D.J.R., K.-M.C.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
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Benjamin F. Voight https://orcid.org/0000-0002-6205-9994
Department of Genetics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Institute for Translational Medicine and Therapeutics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Department of Systems Pharmacology and Translational Therapeutics (B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.).
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Scott M. Damrauer https://orcid.org/0000-0001-8009-1632 [email protected]
Department of Surgery (S.M.D.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.).
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on behalf of the VA Million Veteran Program
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Notes

This manuscript was sent to William C. Sessa, Senior Consulting Editor, for review by expert referees, editorial decision, and final disposition.
The Data Supplement is available with this article at Supplemental Material.
For Sources of Funding and Disclosures, see page 2033.
Correspondence to: Scott M. Damrauer, MD, 3900 Woodland Ave, Philadelphia, PA 19104. Email [email protected]

Disclosures

Disclosures D. Gill is employed part-time by Novo Nordisk. S.M. Damrauer has received grants from RenalytixAI and personal fees from Calico Labs outside the submitted work. The other authors report no conflicts.

Sources of Funding

This work was supported by US Department of Veterans Affairs grants IK2-CX001780 (S.M. Damrauer), MVP-DOE2 (S.M. Damrauer/P.S. Tsao), and I01-BX003362 (P.S. Tsao/K.-M. Chang). This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration. This publication does not represent the views of the Department of Veterans Affairs or the United States government. This work was also supported by the National Institute of Diabetes and Digestive and Kidney Diseases DK101478 (B.F. Voight), and a Linda Pechenik Montague Investigator Award (B.F. Voight). D. Gill was supported by the British Heart Foundation Centre of Research Excellence (RE/18/4/34215) at Imperial College London and a National Institute for Health Research Clinical Lectureship at St. George’s, University of London (CL-2020-16-001). The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit [MC_UU_00011/1]. N.M. Davies is supported by a Norwegian Research Council Grant number 295989. J.N. Hellwege is supported by K12 HD04348. The data set(s) used for the African ancestry BP analyses were obtained from Vanderbilt University Medical Center’s BioVU which is supported by institutional funding, private agencies, and federal grants, including: National Institutes of Health funded Shared Instrumentation Grant S10RR025141; Clinical and Translational Science Award (CTSA) grants UL1TR002243, UL1TR000445, and UL1RR024975; investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/.

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    • Zixian Yang,
    • Jinshuai Li,
    • Peichu Huang,
    • Zhichang Li,
    • Jianfeng He,
    • Dongchun Cai,
    • Yuzheng Lai,
    The causal relationship between antihypertensive drugs and depression: a Mendelian randomization study of drug targets, Frontiers in Endocrinology, 15, (2024).https://doi.org/10.3389/fendo.2024.1411343
    Crossref
    • A. Kharawala,
    • S. Nagraj,
    • S. Pargaonkar,
    • J. Seo,
    • D.G. Kokkinidis,
    • S.E. Altin,
    Hypertension Management in Peripheral Artery Disease: A Mini Review, Current Hypertension Reviews, 20, 1, (1-9), (2024).https://doi.org/10.2174/0115734021267004231122061712
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    • Bohan Fan,
    • Junmeng Zhang,
    • Jie V. Zhao,
    Systematic review of Mendelian randomization studies on antihypertensive drugs, BMC Medicine, 22, 1, (2024).https://doi.org/10.1186/s12916-024-03760-x
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    • Jie V. Zhao,
    Genetic proxies for antihypertensive drugs and mental disorders: Mendelian randomization study in European and East Asian populations, BMC Medicine, 22, 1, (2024).https://doi.org/10.1186/s12916-023-03218-6
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    • Seth S. Martin,
    • Aaron W. Aday,
    • Zaid I. Almarzooq,
    • Cheryl A.M. Anderson,
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    • Carissa M. Baker-Smith,
    • Bethany Barone Gibbs,
    • Andrea Z. Beaton,
    • Amelia K. Boehme,
    • Yvonne Commodore-Mensah,
    • Maria E. Currie,
    • Mitchell S.V. Elkind,
    • Kelly R. Evenson,
    • Giuliano Generoso,
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    • Michelle C. Johansen,
    • Rizwan Kalani,
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    • Junxiu Liu,
    • Jared W. Magnani,
    • Erin D. Michos,
    • Michael E. Mussolino,
    • Sankar D. Navaneethan,
    • Nisha I. Parikh,
    • Sarah M. Perman,
    • Remy Poudel,
    • Mary Rezk-Hanna,
    • Gregory A. Roth,
    • Nilay S. Shah,
    • Marie-Pierre St-Onge,
    • Evan L. Thacker,
    • Connie W. Tsao,
    • Sarah M. Urbut,
    • Harriette G.C. Van Spall,
    • Jenifer H. Voeks,
    • Nae-Yuh Wang,
    • Nathan D. Wong,
    • Sally S. Wong,
    • Kristine Yaffe,
    • Latha P. Palaniappan,
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    Crossref
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    • Michael G. Levin,
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    Crossref
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Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease
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