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      Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease

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          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.

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          Most cited references42

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          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

          Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1–4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0–8·4) while the total sum of global YLDs increased from 562 million (421–723) to 853 million (642–1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6–9·2) for males and 6·5% (5·4–7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782–3252] per 100 000 in males vs s1400 [1279–1524] per 100 000 in females), transport injuries (3322 [3082–3583] vs 2336 [2154–2535]), and self-harm and interpersonal violence (3265 [2943–3630] vs 5643 [5057–6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Funding Bill & Melinda Gates Foundation.
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            Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

            ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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              The MR-Base platform supports systematic causal inference across the human phenome

              Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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                Author and article information

                Contributors
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                Journal
                Arteriosclerosis, Thrombosis, and Vascular Biology
                ATVB
                Ovid Technologies (Wolters Kluwer Health)
                1079-5642
                1524-4636
                June 2021
                June 2021
                : 41
                : 6
                : 2027-2034
                Affiliations
                [1 ]Division of Cardiovascular Medicine (M.G.L.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
                [2 ]Department of Medicine (M.G. L., D.J.R., K.-M.C.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
                [3 ]Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (M.G.L., K.-M.C., B.F.V., S.M.D.).
                [4 ]Malcolm Randall VA Medical Center, Gainesville, FL (D.K.).
                [5 ]Department of Surgery, University of Florida, Gainesville (D.K.).
                [6 ]Medical Research Council Integrative Epidemiology Unit, University of Bristol, United Kingdom (V.M.W., N.M.D.).
                [7 ]Department of Epidemiology and Biostatistics, School of Public Health (D.G.), Imperial College London, United Kingdom.
                [8 ]Department of Medicine, Centre for Pharmacology and Therapeutics, Hammersmith Campus (D.G.), Imperial College London, United Kingdom.
                [9 ]Novo Nordisk Research Centre Oxford, Old Road Campus, United Kingdom (D.G.).
                [10 ]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.).
                [11 ]Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom (D.G.).
                [12 ]Edith Nourse VA Medical Center, Bedford, MA (J.L.).
                [13 ]VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City Health Care System, CT (J.L., K.M.L.).
                [14 ]Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center (J.N.H.), Vanderbilt University Medical Center, Nashville, TN.
                [15 ]Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN (J.N.H.).
                [16 ]Division of Epidemiology, Department of Medicine (J.M.K.), Vanderbilt University Medical Center, Nashville, TN.
                [17 ]Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD (J.M.K.).
                [18 ]Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA (K.M.L.).
                [19 ]Department of Health Law, Policy and Management, Boston University School of Public Health, MA (K.M.L.).
                [20 ]Palo Alto VA Healthcare System, CA (T.L.A., P.S.T.).
                [21 ]Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (T.L.A., P.S.T.).
                [22 ]Stanford Cardiovascular Institute, Stanford University, CA (T.L.A., P.S.T.).
                [23 ]Cardiovascular Research Center, Massachusetts General Hospital, Boston (P.N.).
                [24 ]Broad Institute of Harvard and MIT, Cambridge, MA (P.N.).
                [25 ]Department of Medicine (P.N.)
                [26 ]VA Boston Healthcare System, MA (P.N., J.M.G.).
                [27 ]Division of Nephrology and Hypertension, Department of Medicine (A.M.H.), Vanderbilt University Medical Center, Nashville, TN.
                [28 ]Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute (T.L.E.), Vanderbilt University Medical Center, Nashville, TN.
                [29 ]Department of Genetics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
                [30 ]Institute for Translational Medicine and Therapeutics (D.J.R., B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
                [31 ]Division of Aging, Department of Preventive Medicine, Brigham and Women’s Hospital (J.M.G.), Harvard Medical School, Boston, MA.
                [32 ]K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norway (N.M.D.).
                [33 ]Department of Systems Pharmacology and Translational Therapeutics (B.F.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
                [34 ]Department of Surgery (S.M.D.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
                Article
                10.1161/ATVBAHA.120.315482
                33853351
                1c85dd78-8425-4eba-813a-8c343c5be0ef
                © 2021
                History

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