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Cheddani L, Lelong H, Goldberg M, Zins M, Blacher J, Kab S. Prediction of incidence of hypertension in France and associated factors: results from the CONSTANCES cohort. J Hypertens 2025; 43:976-985. [PMID: 40008529 PMCID: PMC12052061 DOI: 10.1097/hjh.0000000000003989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 12/01/2024] [Accepted: 12/16/2024] [Indexed: 02/27/2025]
Abstract
INTRODUCTION Estimating hypertension incidence and improving screening in general population could enhance blood pressure control and decrease cardiometabolic risks. Identifying those likely to develop hypertension is essential. Our study focused on predicting onset hypertension and its incidence based on initial characteristics. METHODS We utilized data from the French prospective CONSTANCES cohort, including volunteers assessed twice over 5 years up to 31 December 2019, who were initially free from hypertension. Hypertension was defined as having a SBP at least 140 mmHg or DBP at least 90 mmHg during the second checkup or if antihypertensive medication was prescribed. We calculated annual incidence rates among subgroups and used machine learning models to identify predictors of hypertension. The impact of changes in BMI was analyzed using logistic regression. RESULTS Of the 11 112 participants (average age 47.5 ± 12 years), 1929 (17.4%) developed hypertension within an average of 5.2 years, with 383 on medication. The incidence rate was 3.4 new cases per 100 person-years, rising with age and consistently higher in men (4.3 vs. 2.8). A blood pressure (BP) threshold of 130 mmHg predicted 70% of new cases. One-point BMI reduction significantly reduced hypertension risk by 16%, regardless of initial BMI and SBP levels. CONCLUSION The study reports a notable hypertension incidence of 3.4 new cases per 100 person-years, particularly among those with SBP over 130 mmHg, highlighting the need for regular screening. Early diagnosis and control can mitigate hypertension's adverse effects, emphasizing the crucial role of preventive measures like BMI reduction.
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Affiliation(s)
- Lynda Cheddani
- Unité hypertension artérielle, prévention et thérapeutiques cardiovasculaires, Centre de diagnostic et de thérapeutique, Hôtel-Dieu, Assistance Publique Hôpitaux de Paris
- Université Paris Cité, Paris Saclay University, Université de Versailles Saint-Quentin-en-Yvelines, INSERM, UMS 011 « Population-based Cohorts Unit », Paris, France
| | - Hélène Lelong
- Unité hypertension artérielle, prévention et thérapeutiques cardiovasculaires, Centre de diagnostic et de thérapeutique, Hôtel-Dieu, Assistance Publique Hôpitaux de Paris
| | - Marcel Goldberg
- Université Paris Cité, Paris Saclay University, Université de Versailles Saint-Quentin-en-Yvelines, INSERM, UMS 011 « Population-based Cohorts Unit », Paris, France
| | - Marie Zins
- Université Paris Cité, Paris Saclay University, Université de Versailles Saint-Quentin-en-Yvelines, INSERM, UMS 011 « Population-based Cohorts Unit », Paris, France
| | - Jacques Blacher
- Unité hypertension artérielle, prévention et thérapeutiques cardiovasculaires, Centre de diagnostic et de thérapeutique, Hôtel-Dieu, Assistance Publique Hôpitaux de Paris
- Université Paris Cité, Paris Saclay University, Université de Versailles Saint-Quentin-en-Yvelines, INSERM, UMS 011 « Population-based Cohorts Unit », Paris, France
| | - Sofiane Kab
- Université Paris Cité, Paris Saclay University, Université de Versailles Saint-Quentin-en-Yvelines, INSERM, UMS 011 « Population-based Cohorts Unit », Paris, France
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Joensuu L, Koivunen K, Tynkkynen NP, Palviainen T, Kaprio J, Klevjer M, Øvretveit K, Wisløff U, Bye A, Ekelund U, Sillanpää E. Genetic liability to sedentary behaviour and cardiovascular disease incidence in the FinnGen and HUNT cohorts. Br J Sports Med 2025:bjsports-2024-109491. [PMID: 40139721 DOI: 10.1136/bjsports-2024-109491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2025] [Indexed: 03/29/2025]
Abstract
OBJECTIVE Energy-saving sedentary behaviour may be an evolutionarily selected trait that is no longer advantageous. We investigated the associations between genetic liability to sedentary behaviour and the incidence of the most common cardiovascular disease (CVD). METHODS We constructed and validated a genome-wide polygenic score for leisure screen time (PGS LST) as a measure of genetic liability to sedentary behaviour. We performed survival analyses between higher PGS LST and register-based CVDs using the FinnGen cohort (N=293 250-333 012). Replication and exploratory analyses were conducted in an independent Norwegian Trøndelag Health Study (HUNT) cohort (N=35 289). RESULTS In FinnGen, each SD increase in PGS LST was associated with a higher risk of incident CVD (HR: 1.05 (95% CI 1.05 to 1.06)) (168 770 cases over 17 101 133 person-years). The magnitudes of association for the three most common CVDs were 1.09 ((95% CI 1.08 to 1.09), 1.06 ((95% CI 1.05 to 1.07) and 1.05 ((95% CI 1.04 to 1.06) for hypertensive disease, ischaemic heart disease and cerebrovascular disease, respectively. Those in the top decile of PGS LST had 21%, 35%, 26% and 19% higher risk of any CVD, hypertensive disease, ischaemic heart disease and cerebrovascular disease, respectively, than those in the bottom decile. Associations were replicated in HUNT and remained independent of covariates (socioeconomic status, body mass index and smoking) except for cerebrovascular disease. Besides direct effects, reduced physical activity served as a potential mediating pathway for the observed associations. CONCLUSIONS We found that genetic liability to sedentary behaviour is associated with incident CVD, although effect sizes with current PGS remained small. These findings suggest that genetic liability to sedentary behaviour is an under-recognised driver of common CVDs.
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Affiliation(s)
- Laura Joensuu
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Kaisa Koivunen
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Niko Paavo Tynkkynen
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Marie Klevjer
- Cardiac Exercise Research Group, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology, St. Olav's Hospital, Trondheim, Norway
| | - Karsten Øvretveit
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ulrik Wisløff
- Cardiac Exercise Research Group, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anja Bye
- Cardiac Exercise Research Group, Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Cardiology, St. Olav's Hospital, Trondheim, Norway
| | - Ulf Ekelund
- Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
- Department for Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Elina Sillanpää
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
- Wellbeing Services County of Central Finland, Jyväskylä, Finland
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Armoundas AA, Ahmad FS, Attia ZI, Doudesis D, Khera R, Kyriakoulis KG, Stergiou GS, Tang WHW. Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management. Hypertension 2025. [PMID: 40091745 DOI: 10.1161/hypertensionaha.124.22349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Abstract
Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.
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Affiliation(s)
- Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital and Broad Institute, Massachusetts Institute of Technology, Boston (A.A.A.)
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL (F.S.A.)
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (Z.I.A.)
| | - Dimitrios Doudesis
- British Heart Foundation (BHF) Centre for Cardiovascular Science, University of Edinburgh, United Kingdom (D.D.)
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine (R.K.)
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT (R.K.)
| | - Konstantinos G Kyriakoulis
- Hypertension Center STRIDE-7, National and Kapodistrian University of Athens, School of Medicine, Third Department of Medicine, Athens, Greece (K.G.K., G.S.S.)
| | - George S Stergiou
- Hypertension Center STRIDE-7, National and Kapodistrian University of Athens, School of Medicine, Third Department of Medicine, Athens, Greece (K.G.K., G.S.S.)
| | - W H Wilson Tang
- Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH (W.H.W.T.)
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Hiie L, Kolde A, Pervjakova N, Reigo A, Abner E, Võsa U, Esko T, Fischer K, Palta P, Kronberg J. Pathway level metabolomics analysis identifies carbon metabolism as a key factor of incident hypertension in the Estonian Biobank. Sci Rep 2025; 15:8470. [PMID: 40069276 PMCID: PMC11897224 DOI: 10.1038/s41598-025-92840-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2025] [Accepted: 03/03/2025] [Indexed: 03/14/2025] Open
Abstract
The purpose of this study was to find metabolic changes associated with incident hypertension in the volunteer-based Estonian Biobank. We used a subcohort of the Estonian Biobank where metabolite levels had been measured by mass-spectrometry (LC-MS, Metabolon platform). We divided annotated metabolites of 989 individuals into KEGG pathways, followed by principal component analysis of metabolites in each pathway, resulting in a dataset of 91 pathway components. Next, we defined incident hypertension cases and controls based on electronic health records, resulting in a dataset of 101 incident hypertension cases and 450 controls. We used Cox proportional hazards models and replicated the results in a separate cohort of the Estonian Biobank, assayed with LC-MS dataset of the Broad platform and including 582 individuals. Our results show that body mass index and a component of the carbon metabolism KEGG pathway are associated with incident hypertension in both discovery and replication cohorts. We demonstrate that a high-dimensional dataset can be meaningfully reduced into informative pathway components that can subsequently be analysed in an interpretable way, and replicated in a metabolomics dataset from a different platform.
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Affiliation(s)
- Liis Hiie
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva 18, 51009, Tartu, Estonia
| | - Anastassia Kolde
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva 18, 51009, Tartu, Estonia
| | - Natalia Pervjakova
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
| | - Anu Reigo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
| | - Erik Abner
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva 18, 51009, Tartu, Estonia
| | - Priit Palta
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia
| | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010, Tartu, Estonia.
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Liu Y, Hou W, Gao T, Yan Y, Wang T, Zheng C, Zeng P. Influence and role of polygenic risk score in the development of 32 complex diseases. J Glob Health 2025; 15:04071. [PMID: 40063714 PMCID: PMC11893022 DOI: 10.7189/jogh.15.04071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2025] Open
Abstract
Background The polygenic risk score (PRS) has been perceived as advantageous in predicting the risk of complex diseases compared to other measures. We aimed to systematically evaluate the influence of PRS on disease outcome and to explore its predictive value. Methods We comprehensively assessed the relationship between PRS and 32 complex diseases in the UK Biobank. We used Cox models to estimate the effects of PRS on the incidence risk. Then, we constructed prediction models to assess the clinical utility of PRS in risk prediction. For 16 diseases, we further compared the disease risk and prediction capability of PRS across early and late-onset cases. Results Higher PRS led to greater incident risk, with hazard ratio (HR) ranging from 1.07 (95% confidence interval (CI) = 1.06-1.08) for panic/anxiety disorder to 4.17 (95% CI = 4.03-4.31) for acute pancreatitis. This effect was more pronounced in early-onset cases for 12 diseases, increasing by 52.8% on average. Particularly, the early-onset risk of heart failure associated with PRS (HR = 3.02; 95% CI = 2.53-3.59) was roughly twice compared to the late-onset risk (HR = 1.48; 95% CI = 1.46-1.51). Compared to average PRS (20-80%), individuals positioned within the top 2.5% of the PRS distribution exhibited varying degrees of elevated risk, corresponding to a more than five times greater risk on average. PRS showed additional value in clinical risk prediction, causing an average improvement of 6.1% in prediction accuracy. Further, PRS demonstrated higher predictive accuracy for early-onset cases of 11 diseases, with heart failure displaying the most significant (37.5%) improvement when incorporating PRS into the prediction model (concordance index (C-index) = 0.546; standard error (SE) = 0.011 vs. C-index = 0.751; SE = 0.010, P = 2.47 × 10-12). Conclusions As a valuable complement to traditional clinical risk tools, PRS is closely related to disease risk and can further enhance prediction accuracy, especially for early-onset cases, underscoring its potential role in targeted prevention for high-risk groups.
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Affiliation(s)
- Yuxin Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Wenyan Hou
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Tongyu Gao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yu Yan
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Chu Zheng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Centre of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Centre of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
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Demarais ZS, Conlon C, Rivier CA, Clocchiatti-Tuozzo S, Renedo D, Torres-Lopez V, Sheth KN, Meeker D, Zhao H, Ohno-Machado L, Acosta JN, Huo S, Falcone GJ. Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors. Neurology 2025; 104:e210276. [PMID: 39889253 DOI: 10.1212/wnl.0000000000210276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2024] [Accepted: 11/16/2024] [Indexed: 02/02/2025] Open
Abstract
BACKGROUND AND OBJECTIVES Type 2 diabetes mellitus (T2DM) is highly genetically determined, and polygenic susceptibility to T2DM (PSD) increases the risk of worse glycemic control and adverse vascular outcomes. Its role in stroke patients remains unknown. We aim to determine whether higher PSD is associated with worse glycemic control in stroke survivors. METHODS We conducted a 2-stage genetic association study. In a cross-sectional design, we selected stroke survivors from the UK Biobank (enrollment between 2006 and 2010) to evaluate the relationship between PSD and glycemic control. Second, we replicated the results using data from All of Us (enrollment between 2018 and 2022). Exposures were low, intermediate, and high PSD, modeled through percentiles (<20, 20-80, >80) of a polygenic risk score of 2,522 independent risk variants associated with T2DM at genome-wide levels (p < 5 × 10-8). Outcomes were hemoglobin A1c (HbA1c) levels, uncontrolled diabetes (HbA1c ≥7.0%), and resistant diabetes (uncontrolled despite antidiabetic treatment). RESULTS Stage 1 included 6,908 stroke survivors (mean age 61 years, 42% female), including 977 (14%) with diabetes. Compared with low PSD, participants with high PSD had an increase of 0.49 in HbA1c (β = 0.49, standard error 0.03, p-trend <0.001), 6.9 times the odds of uncontrolled diabetes (odds ratio [OR] 6.92, 95% CI 4.71-10.52), and 7.8 times the odds of resistant diabetes (OR 7.76, 95% CI 4.92-12.89). Stage 2 (replication) confirmed the association with HbA1c in 4,451 stroke survivors, including 2,163 (49%) with diabetes (mean age 64 years, 53% female, p-trend <0.05 for all tests). DISCUSSION Among stroke survivors, a higher PSD was associated with poorer glycemic control. Further research should determine precision medicine strategies using PSD to improve clinical management of stroke patients.
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Affiliation(s)
| | - Carolyn Conlon
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT
| | - Cyprien A Rivier
- Department of Neurology, Yale School of Medicine, New Haven, CT
- Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT
| | - Santiago Clocchiatti-Tuozzo
- Department of Neurology, Yale School of Medicine, New Haven, CT
- Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT
| | - Daniela Renedo
- Department of Neurology, Yale School of Medicine, New Haven, CT
- Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT
| | | | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Daniella Meeker
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT; and
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
| | - Julian N Acosta
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Shufan Huo
- Department of Neurology, Yale School of Medicine, New Haven, CT
- Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT
- Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT
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Wang M, Collings PJ, Jang H, Chen Z, Luo S, Au Yeung SL, Sharp SJ, Brage S, Kim Y. Prospective associations of genetic susceptibility to high blood pressure and muscle strength with incident cardiovascular disease outcomes. J Hypertens 2025; 43:280-289. [PMID: 39445587 DOI: 10.1097/hjh.0000000000003900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 09/28/2024] [Indexed: 10/25/2024]
Abstract
BACKGROUND This study explored the prospective associations of genetic susceptibility to high blood pressure (BP) and muscle strength with cardiovascular disease (CVD) mortality, incident coronary heart disease (CHD) and incident stroke. METHODS This study included 349 085 white British individuals from the UK Biobank study. Genetic risk of high BP was estimated using a weighted polygenic risk score that incorporated 136 and 135 nonoverlapping single-nucleotide polymorphisms for systolic BP and diastolic BP, respectively. Muscle strength was assessed using a hand dynamometer and expressed relative to fat-free mass. Sex- and age-specific tertiles were used to classify muscle strength into three categories. Cox regressions with age as the underlying timescale were fit for CVD mortality ( n = 8275), incident CHD ( n = 14 503), and stroke ( n = 7518). RESULTS Compared with the lowest genetic risk of high BP (bottom 20%), the highest (top 20%) had greater hazards of each outcome. Low muscle strength was associated with higher hazards of CVD mortality [hazard ratio (HR): 1.51, 95% confidence interval (CI): 1.43-1.59], incident CHD (HR: 1.16, 95% CI: 1.11-1.21), and stroke (HR: 1.20, 95% CI: 1.14-1.27), independently of confounders and genetic predisposition to high BP, compared with high muscle strength. Joint analyses revealed that the estimated 10-year absolute risks of each outcome were lower for high muscle strength combined with high genetic risk, compared with low muscle strength combined with low or medium genetic risk. CONCLUSION Individuals who are genetically predisposed to high BP but have high muscle strength could have lower risk of major CVD events, compared with those who have low or medium genetic risk but low muscle strength.
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Affiliation(s)
- Mengyao Wang
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
| | - Paul James Collings
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
| | - Haeyoon Jang
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
| | - Ziyuan Chen
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
| | - Shiu Lun Au Yeung
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, Cambridgeshire, UK
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, Cambridgeshire, UK
| | - Youngwon Kim
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, Cambridgeshire, UK
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Pausova Z, Tremblay J, Hamet P. Genetics of Hypertension: Additive and Interactive Effects. Hypertension 2025; 82:3-7. [PMID: 39523998 DOI: 10.1161/hypertensionaha.124.21724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Affiliation(s)
- Zdenka Pausova
- Centre hospitalier universitaire Sainte-Justine and Department of Pediatrics, University of Montreal, QC, Canada (Z.P.)
- Departments of Physiology and Nutritional Sciences, The Hospital for Sick Children, University of Toronto, ON, Canada (Z.P.)
| | - Johanne Tremblay
- Centre de recherche du Centre hospitalier de l'Université de Montréal, QC, Canada (J.T., P.H.)
| | - Pavel Hamet
- Centre de recherche du Centre hospitalier de l'Université de Montréal, QC, Canada (J.T., P.H.)
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Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Matsuda K, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, Yamamoto M. Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population. J Atheroscler Thromb 2024; 31:1717-1732. [PMID: 38910120 PMCID: PMC11620841 DOI: 10.5551/jat.64906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/22/2024] [Indexed: 06/25/2024] Open
Abstract
AIM This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes. METHODS This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC). RESULT Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732]). CONCLUSION Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.
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Affiliation(s)
- Masato Takase
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Kyoto Women fs University, Kyoto, Japan
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Miyagi, Japan
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Akira Narita
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Taku Obara
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Miyagi, Japan
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Miyagi, Japan
| | - Yohei Hamanaka
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Miyagi, Japan
- Suzuki Memorial Hospital, Miyagi, Japan
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Miyagi, Japan
| | - BioBank Japan Project
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Kyoto Women fs University, Kyoto, Japan
- Tohoku University Hospital, Tohoku University, Miyagi, Japan
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
- International Research Institute of Disaster Science, Tohoku University, Miyagi, Japan
- Suzuki Memorial Hospital, Miyagi, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of
Frontier Sciences, the University of Tokyo, Tokyo, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of
Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yoko Izumi
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Kengo Kinoshita
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - ToMMo investigators
- Graduate School of Medicine, Tohoku University, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
- Kyoto Women fs University, Kyoto, Japan
- Tohoku University Hospital, Tohoku University, Miyagi, Japan
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
- International Research Institute of Disaster Science, Tohoku University, Miyagi, Japan
- Suzuki Memorial Hospital, Miyagi, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of
Frontier Sciences, the University of Tokyo, Tokyo, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
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10
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Armstrong ND, Srinivasasainagendra V, Patki A, Jones AC, Parcha V, Pampana A, Broeckel U, Lange LA, Arora P, Limdi NA, Tiwari HK, Irvin MR. Utility of a Systolic Blood Pressure Polygenic Risk Score With Chlorthalidone Response. JAMA Cardiol 2024; 9:1134-1141. [PMID: 39441603 PMCID: PMC11581630 DOI: 10.1001/jamacardio.2024.3649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 08/30/2024] [Indexed: 10/25/2024]
Abstract
Importance The clinical utility of polygenic risk scores (PRS) for blood pressure (BP) response to antihypertensive treatment (AHT) has not been elucidated. Objective To investigate the ability of a systolic BP (SBP) PRS to predict AHT response and apparent treatment-resistant hypertension (aTRH). Design, Setting, and Participants The Genetics of Hypertension Associated Treatments (GenHAT) study was an ancillary pharmacogenomic study to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ALLHAT, which enrolled participants aged 55 years or older with hypertension (HTN) starting in February 1994, completed follow-up in March 2002. The current study was conducted from a subset of Black GenHAT participants randomized to the treatment groups of either chlorthalidone (n = 3745) or lisinopril (n = 2294), with genetic data available from a prior genetic association study. The current study's objective was to examine the association of the SBP PRS to AHT response over 6 months, as well as to examine the predictive accuracy of the SBP PRS with aTRH. The current analysis took place in February 2023, with additional analyses conducted in July 2024. Exposure An SBP PRS (comprising 1 084 157 genetic variants) stratified as quintiles and per SD. Main Outcomes and Measures The primary outcome was change in SBP (ΔSBP) and diastolic BP (ΔDBP) over 6 months. aTRH was defined as the use of 3 AHTs with uncontrolled HTN at year 3 of follow-up or taking 4 or more AHTs at year 3 of follow-up, regardless of BP. Baseline demographics were compared across PRS quintiles using Kruskal-Wallis or χ2 tests as appropriate. The least-square means of BP response were calculated through multivariable adjusted linear regression, and multivariable adjusted logistic regression was used to calculate the odds ratios and 95% confidence intervals for aTRH. Results Among 3745 Black GenHAT participants randomized to chlorthalidone treatment, median (IQR) participant age was 65 (60-71) years, and 2064 participants (55.1%) were female. Each increasing quintile of the SBP PRS from 1 to 5 was associated with a reduced BP response to treatment over 6 months. Participants in the lowest quintile experienced a mean ΔSBP of -10.01 mm Hg (95% CI, -11.11 to -8.90) compared to -6.57 mm Hg (95% CI, -7.67 to -5.48) for participants in the median quintile. No associations were observed between the SBP PRS and BP response to lisinopril. Participants in the highest PRS quintile had 67% higher odds of aTRH compared to those in the median quintile (odds ratio, 1.67; 95% CI, 1.19-2.36). These associations were independently validated. Conclusions and Relevance In this genetic association study, Black individuals with HTN at a lower genetic risk of elevated BP experienced an approximately 3.5 mm Hg-greater response to chlorthalidone compared with those at an intermediate genetic risk of elevated BP. SBP PRS may also identify individuals with HTN harboring a higher risk of treatment-resistant HTN. Overall, SBP PRS demonstrates potential to identify those who may have greater benefit from chlorthalidone, but future research is needed to determine if PRS can inform initiation and choice of treatment among individuals with HTN.
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Affiliation(s)
| | | | - Amit Patki
- Department of Biostatistics, University of Alabama at Birmingham
| | - Alana C. Jones
- Department of Epidemiology, University of Alabama at Birmingham
| | - Vibhu Parcha
- Division of Cardiovascular Disease, University of Alabama at Birmingham
| | - Akhil Pampana
- Division of Cardiovascular Disease, University of Alabama at Birmingham
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Children’s Research Institute, Department of Pediatrics, The Medical College of Wisconsin, Milwaukee
- RPRD Diagnostics, Milwaukee, Wisconsin
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Denver
| | - Pankaj Arora
- Division of Cardiovascular Disease, University of Alabama at Birmingham
- Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, Alabama
| | - Nita A. Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham
| | - Hemant K. Tiwari
- Department of Biostatistics, University of Alabama at Birmingham
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11
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Nurkkala J, Vaura F, Toivonen J, Niiranen T. Genetics of hypertension-related sex differences and hypertensive disorders of pregnancy. Blood Press 2024; 33:2408574. [PMID: 39371034 DOI: 10.1080/08037051.2024.2408574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 09/16/2024] [Accepted: 09/19/2024] [Indexed: 10/08/2024]
Abstract
Background: Hypertension and hypertensive disorders of pregnancy (HDP) cause a significant burden of disease on societies and individuals by increasing cardiovascular disease risk. Environmental risk factors alone do not explain the observed sexual dimorphism in lifetime blood pressure (BP) trajectories nor inter-individual variation in HDP risk. Methods: In this short review, we focus on the genetics of hypertension-related sex differences and HDP and discuss the importance of genetics utilization for sex-specific hypertension risk prediction. Results: Population and twin studies estimate that 28-66% of variation in BP levels and HDP is explained by genetic variation, while genomic wide association studies suggest that BP traits and HDP partly share a common genetic background. Moreover, environmental and epigenetic regulation of these genes differ by sex and oestrogen receptors in particular are shown to convey cardio- and vasculoprotective effects through epigenetic regulation of DNA. The majority of known genetic variation in hypertension and HDP is polygenic. Polygenic risk scores for BP display stronger associations with hypertension risk in women than in men and are associated with sex-specific age of hypertension onset. Monogenic forms of hypertension are rare and mostly present equally in both sexes. Conclusion: Despite recent genetic discoveries providing new insights into HDP and sex differences in BP traits, further research is needed to elucidate the underlying biology. Emphasis should be placed on demonstrating the added clinical value of these genetic discoveries, which may eventually facilitate genomics-based personalized treatments for hypertension and HDP.
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Affiliation(s)
- Jouko Nurkkala
- Division of Perioperative Services, Intensive Care and Pain Medicine, Turku University Hospital, Turku, Finland
- Department of Anesthesiology and Intensive Care, University of Turku, Turku, Finland
| | - Felix Vaura
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Jenni Toivonen
- Division of Perioperative Services, Intensive Care and Pain Medicine, Turku University Hospital, Turku, Finland
- Department of Anesthesiology and Intensive Care, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Turku, Finland
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12
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Teixeira SK, Rossi FPN, Patane JL, Neyra JM, Jensen AVV, Horta BL, Pereira AC, Krieger JE. Assessing the predictive efficacy of European-based systolic blood pressure polygenic risk scores in diverse Brazilian cohorts. Sci Rep 2024; 14:28123. [PMID: 39548300 PMCID: PMC11568199 DOI: 10.1038/s41598-024-79683-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 11/11/2024] [Indexed: 11/17/2024] Open
Abstract
Despite the identification of numerous genetic variants affecting SBP in European populations, their applicability in admixed populations remains unclear. This study evaluates the predictive efficacy of a systolic blood pressure (SBP) polygenic risk score (PRS), derived from the UK Biobank data, in two Brazilian cohorts. We analyzed 944 K genetic variants consistent across an independent UK Biobank dataset, Brazilian cohorts, and HapMap database. Results show a significant association between increased PRS and SBP, as well as hypertension, in each study groups analyzed. An increase of one standard deviation in the PRS showed a significant association with SBP (β [95% CI] (mmHg) = 5.2 [5.1-5.3], 2.8 [2.1-3.5] and 2.6 [2.2-3.0]) and hypertension (odds ratio (OR) [95% CI] = 1.56 [1.54-1.56], 1.28 [1.2-1.4] and 1.47 [1.3-1.6]) in an independent UKB dataset, Baependi, and Pelotas, respectively. The associations were weaker in the Brazilian samples and the reduced association was noticeable in the Pelotas vs. the UK comparison for hypertension stages 1 and 2 (OR [95% CI] = 2.1 [1.5-3.1] and 3.0 [1.9-4.7] vs. 2.5 [2.2-2.8] and 4.9 [4.4-5.6]), whereas the Baependi data showed no significance for stage 1 hypertension. This trend mirrors findings in homogeneous African and Asian populations with diverse genetic architecture, highlighting the limitations of European-based PRS also in admixed populations. These insights are crucial for developing tailored disease prevention and management strategies in ethnically diverse groups.
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Affiliation(s)
- Samantha K Teixeira
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil.
| | - Fernando P N Rossi
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - José L Patane
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Jennifer M Neyra
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Ana Vitória V Jensen
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Bernardo L Horta
- Programa de Pós-Graduação em Epidemiologia, Universidade Federal de Pelotas, Pelotas, RS, Brazil
| | - Alexandre C Pereira
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Jose E Krieger
- Laboratório de Genética e Cardiologia Molecular, Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, SP, Brazil.
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13
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Roberts GHL, Fain PR, Santorico SA, Spritz RA. Inverse relationship between polygenic risk burden and age of onset of autoimmune vitiligo. Am J Hum Genet 2024; 111:2561-2565. [PMID: 39419028 PMCID: PMC11568747 DOI: 10.1016/j.ajhg.2024.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 09/25/2024] [Accepted: 09/25/2024] [Indexed: 10/19/2024] Open
Abstract
Vitiligo is a common autoimmune disease characterized by patches of depigmented skin and overlying hair due to destruction of melanocytes in the involved regions. We investigated the relationship between vitiligo risk and vitiligo age of onset (AOO) using a vitiligo polygenic risk score that incorporated the most significant SNPs from genome-wide association studies. We find that vitiligo genetic risk and AOO are strongly inversely correlated; subjects with higher common-variant polygenic risk tend to develop vitiligo at an earlier age. Nevertheless, the correlation is not simple. In individuals who carry a single high-risk major histocompatibility complex class II haplotype, the effect of additional polygenic risk on vitiligo AOO is reduced. Particularly among those with early-AOO vitiligo (onset ≤12 years of age), genetic risk can reflect contributions from high common-variant burden but also rare variants of high effect and sometimes both. While the heritability of vitiligo is relatively high, and we here show that genetic risk factors predict vitiligo AOO, vitiligo is never congenital, and thus environmental triggers also play an important role in disease onset.
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Affiliation(s)
- Genevieve H L Roberts
- Human Medical Genetics and Genomics Program, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Pamela R Fain
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Stephanie A Santorico
- Human Medical Genetics and Genomics Program, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80217, USA
| | - Richard A Spritz
- Human Medical Genetics and Genomics Program, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA.
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14
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Fujii R. En route to conquer the silent killer "hypertension": Integration of polygenic risk score with non-genetic determinants. Hypertens Res 2024; 47:3079-3081. [PMID: 39090181 DOI: 10.1038/s41440-024-01826-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024]
Affiliation(s)
- Ryosuke Fujii
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, Toyoake, 470-1192, Japan.
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15
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Bae E, Ji Y, Jo J, Kim Y, Lee JP, Won S, Lee J. Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study. Hypertens Res 2024; 47:3045-3055. [PMID: 38982292 PMCID: PMC11534693 DOI: 10.1038/s41440-024-01784-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 07/11/2024]
Abstract
Genetic factors, lifestyle, and diet have been shown to play important roles in the development of hypertension. Increased salt intake is an important risk factor for hypertension. However, research on the involvement of genetic factors in the relationship between salt intake and hypertension in Asians is lacking. We aimed to investigate the risk of hypertension in relation to sodium and potassium intake and the effects of genetic factors on their interactions. We used Korean Genome and Epidemiology Study data and calculated the polygenic risk score (PRS) for the effect of systolic and diastolic blood pressure (SBP and DBP). We also conducted multivariable logistic modeling to evaluate associations among incident hypertension, PRSSBP, PRSDBP, and sodium and potassium intake. In total, 41,351 subjects were included in the test set. The top 10% PRSSBP group was the youngest of the three groups (bottom 10%, middle, top 10%), had the highest proportion of women, and had the highest body mass index, baseline BP, red meat intake, and alcohol consumption. The multivariable logistic regression model revealed the risk of hypertension was significantly associated with higher PRSSBP, higher sodium intake, and lower potassium intake. There was significant interaction between sodium intake and PRSSBP for incident hypertension especially in sodium intake ≥2.0 g/day and PRSSBP top 10% group (OR 1.27 (1.07-1.51), P = 0.007). Among patients at a high risk of incident hypertension due to sodium intake, lifestyle modifications and sodium restriction were especially important to prevent hypertension.
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Affiliation(s)
- Eunjin Bae
- Department of Internal Medicine, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
- Department of Internal Medicine, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
- Institute of Medical Science, College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
| | - Yunmi Ji
- College of Natural Sciences, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea
| | - Yaerim Kim
- Department of Internal Medicine, College of Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Department of Public Health Sciences, Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea.
- RexSoft Corps, Seoul, Republic of Korea.
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
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16
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Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Thomas LF, Steinsland I, Brumpton BM, Gudbjartsson DF, Holm H, Stefansson K, Wisløff U, Hveem K. Polygenic Interactions With Environmental Exposures in Blood Pressure Regulation: The HUNT Study. J Am Heart Assoc 2024; 13:e034612. [PMID: 39291479 DOI: 10.1161/jaha.123.034612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 07/10/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND The essential hypertension phenotype results from an interplay between genetic and environmental factors. The influence of lifestyle exposures such as excess adiposity, alcohol consumption, tobacco use, diet, and activity patterns on blood pressure (BP) is well established. Additionally, polygenic risk scores for BP traits are associated with clinically significant phenotypic variation. However, interactions between genetic and environmental risk factors in hypertension morbidity and mortality are poorly characterized. METHODS AND RESULTS We used genotype and phenotype data from up to 49 234 participants from the HUNT (Trøndelag Health Study) to model gene-environment interactions between genome-wide polygenic risk scores for systolic BP and diastolic BP and 125 environmental exposures. Among the 125 environmental exposures assessed, 108 and 100 were independently associated with SBP and DBP, respectively. Of these, 12 interactions were identified for genome-wide PRSs for systolic BP and 4 for genome-wide polygenic risk scores for diastolic BP, 2 of which were overlapping (P < 2 × 10-4). We found evidence for gene-dependent influence of lifestyle factors such as cardiorespiratory fitness, dietary patterns, and tobacco exposure, as well as biomarkers such as serum cholesterol, creatinine, and alkaline phosphatase on BP. CONCLUSIONS Individuals that are genetically susceptible to high BP may be more vulnerable to common acquired risk factors for hypertension, but these effects appear to be modifiable. The gene-dependent influence of several common acquired risk factors indicates the potential of genetic data combined with lifestyle assessments in risk stratification, and gene-environment-informed risk modeling in the prevention and management of hypertension.
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Affiliation(s)
- Karsten Øvretveit
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Emma M L Ingeström
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Michail Spitieris
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- Department of Mathematical Sciences Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | | | - Laurent F Thomas
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- Department of Clinical and Molecular Medicine Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Ingelin Steinsland
- Department of Mathematical Sciences Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Ben M Brumpton
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- HUNT Research Centre, Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Levanger Norway
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen Inc. Reykjavik Iceland
- School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland
| | - Hilma Holm
- deCODE Genetics/Amgen Inc. Reykjavik Iceland
| | - Kari Stefansson
- deCODE Genetics/Amgen Inc. Reykjavik Iceland
- Faculty of Medicine University of Iceland Reykjavik Iceland
| | - Ulrik Wisløff
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Kristian Hveem
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- Department of Innovation and Research, St. Olav's Hospital Trondheim Norway
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17
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Fujii R, Hishida A, Nakatochi M, Okumiyama H, Takashima N, Tsuboi Y, Suzuki K, Ikezaki H, Shimanoe C, Kato Y, Tamura T, Ito H, Michihata N, Tanoue S, Suzuki S, Kuriki K, Kadota A, Watanabe T, Momozawa Y, Wakai K, Matsuo K. Polygenic risk score for blood pressure and lifestyle factors with overall and CVD mortality: a prospective cohort study in a Japanese population. Hypertens Res 2024; 47:2284-2294. [PMID: 38961281 DOI: 10.1038/s41440-024-01766-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/29/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
Although previous polygenic risk score (PRS) studies for cardiovascular disease (CVD) focused on incidence, few studies addressed CVD mortality and quantified risks by environmental exposures in different genetic liability groups. This prospective study aimed to examine the associations of blood pressure PRS with all-cause and CVD mortality and to quantify the attributable risk by modifiable lifestyles across different PRS strata. 9,296 participants in the Japan Multi-Institutional Collaborative Cohort Study without hypertension at baseline were analyzed in this analysis. PRS for systolic blood pressure and diastolic blood pressure (PRSSBP and PRSDBP) were developed using publicly available Biobank Japan GWAS summary statistics. CVD-related mortality was defined by the International Classification of Diseases 10th version (I00-I99). Cox-proportional hazard model was used to examine associations of PRSs and lifestyle variables (smoking, drinking, and dietary sodium intake) with mortality. During a median 12.6-year follow-up period, we observed 273 all-cause and 41 CVD mortality cases. Compared to the middle PRS group (20-80th percentile), adjusted hazard ratios for CVD mortality at the top PRS group ( > 90th percentile) were 3.67 for PRSSBP and 2.92 for PRSDBP. Attributable risks of CVD mortality by modifiable lifestyles were higher in the high PRS group ( > 80th percentile) compared with the low PRS group (0-80th percentile). In summary, blood pressure PRS is associated with CVD mortality in the general Japanese population. Our study implies that integrating PRS with lifestyle could contribute to identify target populations for lifestyle intervention even though improvement of discriminatory ability by PRS alone is limited.
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Affiliation(s)
- Ryosuke Fujii
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan.
- Institute for Biomedicine, Eurac Research, Via A.Volta 21, Bolzano/Bozen, 39100, Italy.
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daikominami, Higashi-ku, Nagoya, 461-8673, Japan
| | - Hiroshi Okumiyama
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Naoyuki Takashima
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto, 602-8566, Japan
- NCD Epidemiology Research Center, Shiga University of Medical Science, Tsukiwacho, Seta, Otsu, 520-2192, Japan
| | - Yoshiki Tsuboi
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Koji Suzuki
- Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan
| | - Hiroaki Ikezaki
- Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
- Department of General Internal Medicine, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Chisato Shimanoe
- Department of Pharmacy, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Yasufumi Kato
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Hidemi Ito
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Nobuaki Michihata
- Cancer Prevention Center, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chuo-ku, Chiba, 260-8717, Japan
| | - Shiroh Tanoue
- Department of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka, 422-8526, Japan
| | - Aya Kadota
- NCD Epidemiology Research Center, Shiga University of Medical Science, Tsukiwacho, Seta, Otsu, 520-2192, Japan
- Faculty of Nursing Science, Tsuruga Nursing University, 78-2 Kizaki, Tsuruga, 914-0814, Japan
| | - Takeshi Watanabe
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Keitaro Matsuo
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
- Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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18
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Shetty NS, Gaonkar M, Pampana A, Patel N, Irvin MR, Lin HJ, Guo X, Rich SS, Rotter JI, Budoff MJ, Li P, Arora G, Arora P. Genetic Risk and Coronary Artery Calcium in Personalizing Antihypertensive Treatment: A Pooled Cohort Analysis. Mayo Clin Proc 2024; 99:1422-1434. [PMID: 39115511 DOI: 10.1016/j.mayocp.2023.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/29/2023] [Accepted: 12/27/2023] [Indexed: 09/06/2024]
Abstract
OBJECTIVE To assess the role of the systolic blood pressure polygenic risk score (SBP-PRS) in antihypertensive treatment initiation and its comparative efficacy with coronary artery calcium (CAC) scores. PATIENTS AND METHODS This retrospective cohort study included participants with whole genome sequencing data who underwent CAC scanning between 1971 and 2008, were free of prevalent cardiovascular disease (CVD), and were not taking antihypertensive medications. The cohort was stratified by blood pressure (BP) treatment group and SBP-PRS (low/intermediate, first and second tertiles; high, third tertile) and CAC score (0 vs >0) subgroups. The primary outcome was the first occurence of adjudicated coronary heart disease, heart failure, or stroke during 10-year follow-up. The 10-year number needed to treat (NNT) to prevent 1 event of the primary outcome was estimated. A relative risk reduction of 25% for the primary outcome based on the treatment effect of intensive control (SBP <120 mm Hg) of hypertension in SPRINT (Systolic Blood Pressure Intervention Trial) was used for estimating the NNT. RESULTS Among the 5267 study participants, the median age was 59 years (interquartile range, 51-68 years); 2817 (53.5%) were women and 2880 (54.7%) were non-White individuals. Among 1317 individuals with elevated BP/low-risk stage 1 hypertension not recommended treatment, the 10-year incidence rate of the primary outcome was 5.6% for low/intermediate SBP-PRS and 6.3% for high SBP-PRS with NNTs of 63 and 59, respectively. Similarly, the 10-year incidence rate of the primary outcome was 2.9% for CAC score 0 and 9.7% for CAC score greater than 0, with NNTs of 117 and 37, respectively. CONCLUSION Including genetic information in risk estimation of individuals with elevated BP/low-risk stage 1 hypertension has modest value in the initiation of antihypertensive therapy. Genetic risk and CAC both have efficacy in personalizing antihypertensive therapy.
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Affiliation(s)
- Naman S Shetty
- Division of Cardiovascular Disease, The University of Alabama at Birmingham, Birmingham
| | - Mokshad Gaonkar
- Department of Biostatistics, The University of Alabama at Birmingham, Birmingham
| | - Akhil Pampana
- Division of Cardiovascular Disease, The University of Alabama at Birmingham, Birmingham
| | - Nirav Patel
- Division of Cardiovascular Disease, The University of Alabama at Birmingham, Birmingham
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, The University of Alabama at Birmingham, Birmingham
| | - Henry J Lin
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Stephen S Rich
- Center for Public Health, University of Virginia, Charlottesville
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Matthew J Budoff
- Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles
| | - Peng Li
- School of Nursing, The University of Alabama at Birmingham, Birmingham
| | - Garima Arora
- Division of Cardiovascular Disease, The University of Alabama at Birmingham, Birmingham
| | - Pankaj Arora
- Division of Cardiovascular Disease, The University of Alabama at Birmingham, Birmingham; Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, AL.
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19
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Takase M, Hirata T, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Narita A, Metoki H, Satoh M, Obara T, Ishikuro M, Ohseto H, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tamiya G, Hozawa A, Yamamoto M. Associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertens Res 2024; 47:2064-2074. [PMID: 38914703 PMCID: PMC11298407 DOI: 10.1038/s41440-024-01705-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 06/26/2024]
Abstract
No study, to our knowledge, has constructed a polygenic risk score based on clinical blood pressure and investigated the association of genetic and lifestyle risks with home hypertension. We examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. In a cross-sectional study of 7027 Japanese individuals aged ≥20 years, we developed a lifestyle score based on body mass index, alcohol consumption, physical activity, and sodium-to-potassium ratio, categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score was constructed with the target data (n = 1405) using publicly available genome-wide association study summary statistics from BioBank Japan. Using the test data (n = 5622), we evaluated polygenic risk score performance and examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertension and home hypertension were defined as blood pressure measured at a community-support center ≥140/90 mmHg or at home ≥135/85 mmHg, respectively, or self-reported treatment for hypertension. In the test data, 2294 and 2322 participants had hypertension and home hypertension, respectively. Both polygenic risk and lifestyle scores were independently associated with hypertension and home hypertension. Compared with those of participants with low genetic risk and an ideal lifestyle, the odds ratios for hypertension and home hypertension in the low genetic risk and poor lifestyle group were 1.94 (95% confidence interval, 1.34-2.80) and 2.15 (1.60-2.90), respectively. In summary, lifestyle is important to prevent hypertension; nevertheless, participants with high genetic risk should carefully monitor their blood pressure despite a healthy lifestyle.
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Affiliation(s)
- Masato Takase
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Kyoto Women's University, Kyoto, Japan
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Narita
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Hirohito Metoki
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical and Pharmaceutical University, Miyagino-ku, Sendai, Japan
| | - Michihiro Satoh
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical and Pharmaceutical University, Miyagino-ku, Sendai, Japan
| | - Taku Obara
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Hisashi Ohseto
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Yohei Hamanaka
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku University Hospital, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Suzuki Memorial Hospital, Satonomori, Iwanumashi, Miyagi, Japan
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- International Research Institute of Disaster Science, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan.
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Aoba-ku, Sendai, Miyagi, Japan
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20
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome wide association study and genomic risk prediction of age related macular degeneration in Israel. Sci Rep 2024; 14:13034. [PMID: 38844476 PMCID: PMC11156861 DOI: 10.1038/s41598-024-63065-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10-5 threshold, we discovered four suggestive associations in FAM189A1, IGDCC4, C7orf50, and CNTNAP4. Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79-0.85) and performed better than covariates-only model (P = 5.1 × 10-9). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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Affiliation(s)
- Michelle Grunin
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Daria Triffon
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
| | - Gala Beykin
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Regev Schweiger
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Genetics, University of Cambridge, CB21TN, Cambridge, UK
| | - Liran Tiosano
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Samer Khateb
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Shira Hagbi-Levi
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Batya Rinsky
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Refael Munitz
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Eran Halperin
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Anesthesiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel.
| | - Itay Chowers
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel.
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21
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MacCarthy G, Pazoki R. Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank. J Clin Med 2024; 13:2955. [PMID: 38792496 PMCID: PMC11122671 DOI: 10.3390/jcm13102955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/01/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.
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Affiliation(s)
- Gideon MacCarthy
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
| | - Raha Pazoki
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London UB8 3PH, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary’s Campus, Norfolk Place, Imperial College London, London W2 1PG, UK
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Keaton JM, Kamali Z, Xie T, Vaez A, Williams A, Goleva SB, Ani A, Evangelou E, Hellwege JN, Yengo L, Young WJ, Traylor M, Giri A, Zheng Z, Zeng J, Chasman DI, Morris AP, Caulfield MJ, Hwang SJ, Kooner JS, Conen D, Attia JR, Morrison AC, Loos RJF, Kristiansson K, Schmidt R, Hicks AA, Pramstaller PP, Nelson CP, Samani NJ, Risch L, Gyllensten U, Melander O, Riese H, Wilson JF, Campbell H, Rich SS, Psaty BM, Lu Y, Rotter JI, Guo X, Rice KM, Vollenweider P, Sundström J, Langenberg C, Tobin MD, Giedraitis V, Luan J, Tuomilehto J, Kutalik Z, Ripatti S, Salomaa V, Girotto G, Trompet S, Jukema JW, van der Harst P, Ridker PM, Giulianini F, Vitart V, Goel A, Watkins H, Harris SE, Deary IJ, van der Most PJ, Oldehinkel AJ, Keavney BD, Hayward C, Campbell A, Boehnke M, Scott LJ, Boutin T, Mamasoula C, Järvelin MR, Peters A, Gieger C, Lakatta EG, Cucca F, Hui J, Knekt P, Enroth S, De Borst MH, Polašek O, Concas MP, Catamo E, Cocca M, Li-Gao R, Hofer E, Schmidt H, Spedicati B, Waldenberger M, Strachan DP, Laan M, Teumer A, Dörr M, Gudnason V, Cook JP, Ruggiero D, Kolcic I, Boerwinkle E, Traglia M, et alKeaton JM, Kamali Z, Xie T, Vaez A, Williams A, Goleva SB, Ani A, Evangelou E, Hellwege JN, Yengo L, Young WJ, Traylor M, Giri A, Zheng Z, Zeng J, Chasman DI, Morris AP, Caulfield MJ, Hwang SJ, Kooner JS, Conen D, Attia JR, Morrison AC, Loos RJF, Kristiansson K, Schmidt R, Hicks AA, Pramstaller PP, Nelson CP, Samani NJ, Risch L, Gyllensten U, Melander O, Riese H, Wilson JF, Campbell H, Rich SS, Psaty BM, Lu Y, Rotter JI, Guo X, Rice KM, Vollenweider P, Sundström J, Langenberg C, Tobin MD, Giedraitis V, Luan J, Tuomilehto J, Kutalik Z, Ripatti S, Salomaa V, Girotto G, Trompet S, Jukema JW, van der Harst P, Ridker PM, Giulianini F, Vitart V, Goel A, Watkins H, Harris SE, Deary IJ, van der Most PJ, Oldehinkel AJ, Keavney BD, Hayward C, Campbell A, Boehnke M, Scott LJ, Boutin T, Mamasoula C, Järvelin MR, Peters A, Gieger C, Lakatta EG, Cucca F, Hui J, Knekt P, Enroth S, De Borst MH, Polašek O, Concas MP, Catamo E, Cocca M, Li-Gao R, Hofer E, Schmidt H, Spedicati B, Waldenberger M, Strachan DP, Laan M, Teumer A, Dörr M, Gudnason V, Cook JP, Ruggiero D, Kolcic I, Boerwinkle E, Traglia M, Lehtimäki T, Raitakari OT, Johnson AD, Newton-Cheh C, Brown MJ, Dominiczak AF, Sever PJ, Poulter N, Chambers JC, Elosua R, Siscovick D, Esko T, Metspalu A, Strawbridge RJ, Laakso M, Hamsten A, Hottenga JJ, de Geus E, Morris AD, Palmer CNA, Nolte IM, Milaneschi Y, Marten J, Wright A, Zeggini E, Howson JMM, O'Donnell CJ, Spector T, Nalls MA, Simonsick EM, Liu Y, van Duijn CM, Butterworth AS, Danesh JN, Menni C, Wareham NJ, Khaw KT, Sun YV, Wilson PWF, Cho K, Visscher PM, Denny JC, Levy D, Edwards TL, Munroe PB, Snieder H, Warren HR. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits. Nat Genet 2024; 56:778-791. [PMID: 38689001 PMCID: PMC11096100 DOI: 10.1038/s41588-024-01714-w] [Show More Authors] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 03/11/2024] [Indexed: 05/02/2024]
Abstract
Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.
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Affiliation(s)
- Jacob M Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ahmad Vaez
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Ariel Williams
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Slavina B Goleva
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Ioannina, Greece
| | - Jacklyn N Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - William J Young
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Matthew Traylor
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Ayush Giri
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Daniel I Chasman
- Division of Preventive Medicine Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Mark J Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Shih-Jen Hwang
- Population Sciences Branch, NHLBI Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Jaspal S Kooner
- National Heart and Lung Institute, Imperial College London, London, UK
| | - David Conen
- Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - John R Attia
- Faculty of Health and Medicine, University of Newcastle, New Lambton Heights, Newcastle, New South Wales, Australia
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kati Kristiansson
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Andrew A Hicks
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
- University of Lübeck, Lübeck, Germany
| | - Peter P Pramstaller
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
- University of Lübeck, Lübeck, Germany
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Lorenz Risch
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, Triesen, Liechtenstein
- Department of Laboratory Medicine, Dr. Risch Anstalt, Vaduz, Liechtenstein
| | - Ulf Gyllensten
- Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Olle Melander
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Harriette Riese
- Interdisciplinary Center Psychopathology and Emotional Regulation (ICPE), Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Yingchang Lu
- Vanderbilt Genetic Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Johan Sundström
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, UK
- Leicester NIHR Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Jaakko Tuomilehto
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Zoltan Kutalik
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Giorgia Girotto
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Stella Trompet
- Department Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Paul M Ridker
- Division of Preventive Medicine Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Franco Giulianini
- Division of Preventive Medicine Brigham and Women's Hospital, Boston, MA, USA
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Sarah E Harris
- Lothian Birth Cohorts Group, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts Group, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Manchester Heart Institute, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Michael Boehnke
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Laura J Scott
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Thibaud Boutin
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | | | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Lehrstuhl für Epidemiologie, Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie (IBE), Ludwig-Maximilians-Universität München, Neuherberg, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Edward G Lakatta
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Francesco Cucca
- Institute of Genetic and Biomedical Research, National Research Council (CNR), Monserrato, Italy
| | - Jennie Hui
- Busselton Population Medical Research Institute, Perth, Western Australia, Australia
- School of Population and Global Health, The University of Western Australia, Nedlands, Western Australia, Australia
| | - Paul Knekt
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Stefan Enroth
- Department of Immunology, Genetics, and Pathology, Biomedical Center, Science for Life Laboratory (SciLifeLab) Uppsala, Uppsala University, Uppsala, Sweden
| | - Martin H De Borst
- Department of Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Ozren Polašek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Maria Pina Concas
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Eulalia Catamo
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Massimiliano Cocca
- Institute for Maternal and Child Health - IRCCS, Burlo Garofolo, Trieste, Italy
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Helena Schmidt
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - David P Strachan
- Population Health Sciences Institute St George's, University of London, London, UK
| | - Maris Laan
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Kopavogur, Iceland
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Daniela Ruggiero
- IRCCS Neuromed, Pozzilli, Italy
- Institute of Genetics and Biophysics - 'A. Buzzati-Traverso', National Research Council of Italy, Naples, Italy
| | - Ivana Kolcic
- Algebra University College, Zagreb, Croatia
- Department of Public Health, University of Split School of Medicine, Split, Croatia
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Andrew D Johnson
- Population Sciences Branch, NHLBI Framingham Heart Study, Framingham, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Christopher Newton-Cheh
- Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Morris J Brown
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Anna F Dominiczak
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Peter J Sever
- International Centre for Circulatory Health, Imperial College London, London, UK
| | - Neil Poulter
- School of Public Health, Imperial College London, London, UK
| | - John C Chambers
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Roberto Elosua
- Hospital del Mar Research Institute (IMIM), Barcelona, Spain
- CIBER Enfermedades Cardiovasculares (CIBERCV), Barcelona, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia (UVic-UCC), Vic, Spain
| | | | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Rona J Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Health Data Research UK, Glasgow, UK
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Anders Hamsten
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Eco de Geus
- Department of Biological Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Amsterdam, the Netherlands
| | - Andrew D Morris
- Data Science, University of Edinburgh, Edinburgh, UK
- Health Data Research UK, London, UK
| | - Colin N A Palmer
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yuri Milaneschi
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jonathan Marten
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
| | - Alan Wright
- Centre for Genomic and Experimental Medicine, IGC, University of Edinburgh, Edinburgh, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - Joanna M M Howson
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Christopher J O'Donnell
- VA Boston Healthcare System, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tim Spector
- Department of Twin Research, King's College London, London, UK
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias, NIA/NINDS, NIH, Bethesda, MD, USA
- Laboratory of Neurogenetics, NIA, NIH, Bethesda, MD, USA
- DataTecnica LLC, Washington, DC, USA
| | - Eleanor M Simonsick
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yongmei Liu
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - John N Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Human Genetics, The Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, London, UK
| | | | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA
- VA Atlanta Healthcare System, Decatur, GA, USA
| | - Peter W F Wilson
- Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Joshua C Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Levy
- Population Sciences Branch, NHLBI Framingham Heart Study, Framingham, MA, USA
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Helen R Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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23
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Hezekiah C, Blakemore AI, Bailey DP, Pazoki R. Physical activity alters the effect of genetic determinants of adiposity on hypertension among individuals of European ancestry in the UKB. Scand J Med Sci Sports 2024; 34:e14636. [PMID: 38671551 PMCID: PMC11135603 DOI: 10.1111/sms.14636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/30/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024]
Abstract
Hypertension is a leading risk factor for cardiovascular disease and is modulated by genetic variants. This study aimed to assess the effect of obesity genetic liability and physical activity on hypertension among European and African ancestry individuals within the UK Biobank (UKB). Participants were 230 115 individuals of European ancestry and 3239 individuals of African ancestry from UKB. Genetic liability for obesity were estimated using previously published data including genetic variants and effect sizes for body mass index (BMI), waist-hip ratio (WHR) and waist circumference (WC) using Plink software. The outcome was defined as stage 2 hypertension (systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥90 mmHg, or the use of anti-hypertensive medications). The association between obesity genetic liability and the outcome was assessed across categories of self-reported physical activity using logistic regression. Among European ancestry participants, there was up to a 1.2 greater odds of hypertension in individuals with high genetic liability and low physical activity compared to individuals with low genetic liability and high physical activity (p < 0.001). In individuals engaging in low levels of physical activity compared with moderate/high physical activity, the effect of BMI genetic liability on hypertension was greater (p interaction = 0.04). There was no evidence of an association between obesity genetic liability and hypertension in individuals of African ancestry in the whole sample or within separate physical activity groups (p > 0.05). This study suggests that higher physical activity levels are associated with lower odds of stage 2 hypertension among European ancestry individuals who carry high genetic liability for obesity. This cannot be inferred for individuals of African ancestry, possibly due to the low African ancestry sample size within the UKB.
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Affiliation(s)
- Chukwueloka Hezekiah
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life SciencesBrunel University LondonLondonUK
- Department of Mental Health, Faculty of Health, Science, Social Care and EducationKingston UniversitySurreyUK
| | - Alexandra I. Blakemore
- Department of Life Sciences, Centre for Cognitive Neuroscience, College of Health, Medicine and Life SciencesBrunel University LondonLondonUK
- Department of MetabolismDigestion and Reproduction, Imperial College LondonLondonUK
- School of MedicineUniversity of IrelandGalwayIreland
| | - Daniel P. Bailey
- Centre for Physical Activity in Health and Disease, College of Health, Medicine and Life SciencesBrunel University LondonUxbridgeUK
- Division of Sport, Health and Exercise Sciences, Department of Life SciencesBrunel University LondonUxbridgeUK
| | - Raha Pazoki
- Cardiovascular and Metabolic Research Group, Division of Biomedical Sciences, Department of Life Sciences, College of Health, Medicine and Life SciencesBrunel University LondonLondonUK
- Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
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24
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Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Wade KH, Thomas LF, Wolford BN, Wisløff U, Gudbjartsson DF, Holm H, Stefansson K, Brumpton BM, Hveem K. Polygenic risk scores associate with blood pressure traits across the lifespan. Eur J Prev Cardiol 2024; 31:644-654. [PMID: 38007706 PMCID: PMC11025038 DOI: 10.1093/eurjpc/zwad365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 10/18/2023] [Accepted: 11/02/2023] [Indexed: 11/28/2023]
Abstract
AIMS Hypertension is a major modifiable cause of morbidity and mortality that affects over 1 billion people worldwide. Blood pressure (BP) traits have a strong genetic component that can be quantified with polygenic risk scores (PRSs). To date, the performance of BP PRSs has mainly been assessed in adults, and less is known about polygenic hypertension risk in childhood. METHODS AND RESULTS Multiple PRSs for systolic BP (SBP), diastolic BP (DBP), and pulse pressure were developed using either genome-wide significant weights, pruning and thresholding, or Bayesian regression. Among 87 total PRSs, the top performer for each trait was applied in independent cohorts of children and adult to assess genotype-phenotype associations and disease risk across the lifespan. Differences between those with low (1st decile), average (2nd-9th decile), and high (10th decile) PRS emerge in the first years of life and are maintained throughout adulthood. These diverging BP trajectories also seem to affect cardiovascular and renal disease risk, with increased risk observed among those in the top decile and reduced risk among those in the bottom decile of the polygenic risk distribution compared with the rest of the population. CONCLUSION Genetic risk factors are associated with BP traits across the lifespan, beginning in the first years of life. Given the importance of exposure time in disease pathogenesis and the early rise in BP levels among those genetically susceptible, PRSs may help identify high-risk individuals prior to hypertension onset, facilitate primordial prevention, and reduce the burden of this public health challenge.
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Affiliation(s)
- Karsten Øvretveit
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Emma M L Ingeström
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Michail Spitieris
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Kaitlin H Wade
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, Bristol BS8 1TH, UK
- Avon Longitudinal Study of Parents and Children, Bristol BS8 1TH, UK
| | - Laurent F Thomas
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Brooke N Wolford
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
| | - Ulrik Wisløff
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Ben M Brumpton
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, N-7491 Trondheim, Norway
- Department of Innovation and Research, St. Olavs Hospital, Trondheim, Norway
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25
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Türkmen D, Bowden J, Masoli JAH, Delgado J, Kuo CL, Melzer D, Pilling LC. Polygenic scores for cardiovascular risk factors improve estimation of clinical outcomes in CCB treatment compared to pharmacogenetic variants alone. THE PHARMACOGENOMICS JOURNAL 2024; 24:12. [PMID: 38632276 PMCID: PMC11023935 DOI: 10.1038/s41397-024-00333-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/19/2024]
Abstract
Pharmacogenetic variants are associated with clinical outcomes during Calcium Channel Blocker (CCB) treatment, yet whether the effects are modified by genetically predicted clinical risk factors is unknown. We analyzed 32,000 UK Biobank participants treated with dihydropiridine CCBs (mean 5.9 years), including 23 pharmacogenetic variants, and calculated polygenic scores for systolic and diastolic blood pressures, body fat mass, and other patient characteristics. Outcomes included treatment discontinuation and heart failure. Pharmacogenetic variant rs10898815-A (NUMA1) increased discontinuation rates, highest in those with high polygenic scores for fat mass. The RYR3 variant rs877087 T-allele alone modestly increased heart failure risks versus non-carriers (HR:1.13, p = 0.02); in patients with high polygenic scores for fat mass, lean mass, and lipoprotein A, risks were substantially elevated (HR:1.55, p = 4 × 10-5). Incorporating polygenic scores for adiposity and lipoprotein A may improve risk estimates of key clinical outcomes in CCB treatment such as treatment discontinuation and heart failure, compared to pharmacogenetic variants alone.
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Affiliation(s)
- Deniz Türkmen
- Epidemiology & Public Health Group, Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK
| | - Jack Bowden
- Exeter Diabetes Group (ExCEED), Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus, Roosevelt Drive, Oxford, UK
| | - Jane A H Masoli
- Epidemiology & Public Health Group, Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK
- Department of Healthcare for Older People, Royal Devon University Healthcare NHS Foundation Trust, Barrack Road, Exeter, UK
| | - João Delgado
- Epidemiology & Public Health Group, Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK
| | - Chia-Ling Kuo
- UConn Center on Aging, University of Connecticut, Farmington, CT, USA
- Connecticut Convergence Institute for Translation in Regenerative Engineering, University of Connecticut, Storrs, CT, USA
| | - David Melzer
- Epidemiology & Public Health Group, Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK
| | - Luke C Pilling
- Epidemiology & Public Health Group, Department of Clinical & Biomedical Science, Faculty of Health & Life Sciences, University of Exeter, Exeter, UK.
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26
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Sun TH, Wang CC, Liu TY, Lo SC, Huang YX, Chien SY, Chu YD, Tsai FJ, Hsu KC. Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling. Nat Commun 2024; 15:3168. [PMID: 38609356 PMCID: PMC11014845 DOI: 10.1038/s41467-024-47472-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Abstract
Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.
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Affiliation(s)
- Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Yuan Liu
- Million-person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shih-Chang Lo
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Yi-Xuan Huang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Yu-De Chu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.
- School of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan.
- Division of Pediatric Genetics, Children's Hospital of China Medical University, Taichung, 40447, Taiwan.
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, 41354, Taiwan.
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Medicine, China Medical University, Taichung, 40402, Taiwan.
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27
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Alkis T, Luo X, Wall K, Brody J, Bartz T, Chang PP, Norby FL, Hoogeveen RC, Morrison AC, Ballantyne CM, Coresh J, Boerwinkle E, Psaty BM, Shah AM, Yu B. A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure. ESC Heart Fail 2024; 11:1086-1096. [PMID: 38258344 PMCID: PMC10966276 DOI: 10.1002/ehf2.14665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/01/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
AIMS Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations. METHODS AND RESULTS Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF. CONCLUSIONS The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.
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Affiliation(s)
- Taryn Alkis
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Xi Luo
- Department of Biostatistics and Data Science, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Katherine Wall
- Department of Biostatistics and Data Science, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Jennifer Brody
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWAUSA
- Department of MedicineUniversity of WashingtonSeattleWAUSA
| | - Traci Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and BiostatisticsUniversity of WashingtonSeattleWAUSA
| | - Patricia P. Chang
- Division of CardiologyUniversity of North Carolina School of MedicineChapel HillNCUSA
| | - Faye L. Norby
- Division of Epidemiology and Community HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | | | - Josef Coresh
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Human Genome Sequencing CenterBaylor College of MedicineHoustonTXUSA
| | - Bruce M. Psaty
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWAUSA
- Department of MedicineUniversity of WashingtonSeattleWAUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWAUSA
- Department of Health Systems and Population HealthUniversity of WashingtonSeattleWAUSA
| | - Amil M. Shah
- Department of Internal MedicineUT Southwestern Medical CenterDallasTXUSA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
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28
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Dzau VJ, Hodgkinson CP. Precision Hypertension. Hypertension 2024; 81:702-708. [PMID: 38112080 DOI: 10.1161/hypertensionaha.123.21710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Hypertension affects >1 billion people worldwide. Complications of hypertension include stroke, renal failure, cardiac hypertrophy, myocardial infarction, and cardiac failure. Despite the development of various antihypertensive drugs, the number of people with uncontrolled hypertension continues to rise. While the lack of compliance associated with frequent side effects to medication is a contributory issue, there has been a failure to consider the diverse nature of hypertensive populations. Instead, we propose that hypertension can only be truly managed by precision. A precision medicine approach would consider each patient's unique factors. In this review, we discuss the progress toward precision medicine for hypertension with more predictiveness and individualization of treatment. We will highlight the advances in data science, omics (genomics, metabolomics, proteomics, etc), artificial intelligence, gene therapy, and gene editing and their application to precision hypertension.
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Affiliation(s)
- Victor J Dzau
- Mandel Center for Hypertension and Atherosclerosis, the Duke Cardiovascular Research Center, Duke University Medical Center, Durham, NC (V.J.D., C.P.H.)
- National Academy of Medicine, Washington, DC (V.J.D.)
| | - Conrad P Hodgkinson
- Mandel Center for Hypertension and Atherosclerosis, the Duke Cardiovascular Research Center, Duke University Medical Center, Durham, NC (V.J.D., C.P.H.)
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29
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Reay WR, Clarke E, Eslick S, Riveros C, Holliday EG, McEvoy MA, Peel R, Hancock S, Scott RJ, Attia JR, Collins CE, Cairns MJ. Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension. Circulation 2024; 149:1019-1032. [PMID: 38131187 PMCID: PMC10962430 DOI: 10.1161/circulationaha.123.065394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47-mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97-mm Hg systolic BP increase in the lowest 10% (P=1.95×10-3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.
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Affiliation(s)
- William R. Reay
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
| | - Erin Clarke
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Shaun Eslick
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Elizabeth G. Holliday
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Mark A. McEvoy
- Rural Health School, La Trobe University, Bendigo, Victoria, Australia (M.A.M.)
| | - Roseanne Peel
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Stephen Hancock
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
| | - Rodney J. Scott
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Cancer Detection and Therapy Research Program (R.J.S.), New Lambton, NSW, Australia
| | - John R. Attia
- Medicine and Public Health (E.G.H., R.P., S.H., J.R.A.), The University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute (C.R., E.G.H., J.R.A.), New Lambton, NSW, Australia
| | - Clare E. Collins
- Health Sciences (E.C., S.E., C.E.C.), The University of Newcastle, Callaghan, NSW, Australia
- Food and Nutrition Research Program (E.C., C.E.C.), New Lambton, NSW, Australia
| | - Murray J. Cairns
- Schools of Biomedical Sciences and Pharmacy (W.R.R., R.J.S., M.J.C.), The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program (W.R.R., M.J.C.), New Lambton, NSW, Australia
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30
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Schjerven FE, Lindseth F, Steinsland I. Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis. PLoS One 2024; 19:e0294148. [PMID: 38466745 PMCID: PMC10927109 DOI: 10.1371/journal.pone.0294148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/26/2023] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic. METHODS A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions. RESULTS From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%). CONCLUSION Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.
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Affiliation(s)
- Filip Emil Schjerven
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Lindseth
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ingelin Steinsland
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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31
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Yang ML, Xu C, Gupte T, Hoffmann TJ, Iribarren C, Zhou X, Ganesh SK. Sex-specific genetic architecture of blood pressure. Nat Med 2024; 30:818-828. [PMID: 38459180 PMCID: PMC11797078 DOI: 10.1038/s41591-024-02858-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/05/2024] [Indexed: 03/10/2024]
Abstract
The genetic and genomic basis of sex differences in blood pressure (BP) traits remain unstudied at scale. Here, we conducted sex-stratified and combined-sex genome-wide association studies of BP traits using the UK Biobank resource, identifying 1,346 previously reported and 29 new BP trait-associated loci. Among associated loci, 412 were female-specific (Pfemale ≤ 5 × 10-8; Pmale > 5 × 10-8) and 142 were male-specific (Pmale ≤ 5 × 10-8; Pfemale > 5 × 10-8); these sex-specific loci were enriched for hormone-related transcription factors, in particular, estrogen receptor 1. Analyses of gene-by-sex interactions and sexually dimorphic effects identified four genomic regions, showing female-specific associations with diastolic BP or pulse pressure, including the chromosome 13q34-COL4A1/COL4A2 locus. Notably, female-specific pulse pressure-associated loci exhibited enriched acetylated histone H3 Lys27 modifications in arterial tissues and a female-specific association with fibromuscular dysplasia, a female-biased vascular disease; colocalization signals included Chr13q34: COL4A1/COL4A2, Chr9p21: CDKN2B-AS1 and Chr4q32.1: MAP9 regions. Sex-specific and sex-biased polygenic associations of BP traits were associated with multiple cardiovascular traits. These findings suggest potentially clinically significant and BP sex-specific pleiotropic effects on cardiovascular diseases.
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Affiliation(s)
- Min-Lee Yang
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chang Xu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Trisha Gupte
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Thomas J Hoffmann
- Department of Epidemiology & Biostatistics, and Institute for Human Genetics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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Woo K, Lim JE, Lee EY. Influence of blood pressure polygenic risk scores and environmental factors on coronary artery disease in the Korean Genome and Epidemiology Study. J Hum Hypertens 2024; 38:221-227. [PMID: 37985823 DOI: 10.1038/s41371-023-00878-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 10/19/2023] [Accepted: 11/02/2023] [Indexed: 11/22/2023]
Abstract
The present study aimed to investigate the association of blood pressure polygenic risk scores (BP PRSs) with coronary artery disease (CAD) in a Korean population and the interaction effects between PRSs and environmental factors on CAD. Data were derived from the Cardiovascular Disease Association Study (CAVAS; N = 5100) and the Health Examinee Study (HEXA; N = 58,623) within the Korean Genome and Epidemiology Study. PRSs for systolic and diastolic BP were calculated with the weighted allele sum of >200 single-nucleotide polymorphisms. Multivariable logistic regression models were used. BP PRSs were strongly associated with systolic BP (SBP), diastolic BP (DBP), and hypertension in both CAVAS and HEXA (p < 0.0001). PRSSBP was significantly associated with CAD in CAVAS, while PRSSBP and PRSDBP were significantly associated with CAD in HEXA. There was an interaction effect between the BP PRSs and environmental factors on CAD. The odds ratios (ORs) for CAD were 1.036 (95% confidence interval [CI], 1.016-1.055) for obesity, 1.028 (95% CI, 1.011-1.045) for abdominal obesity, 1.030 (95% CI, 1.009-1.050) for triglyceride, 1.024 (95% CI, 1.008-1.041) for high-density lipoprotein cholesterol, and 1.039 for smoking (95% CI, 1.003-1.077) in CAVAS. There was no significant interaction in HEXA, except between PRSDBP and triglyceride (OR, 1.012; 95% CI, 1.001-1.024). BP PRS was associated with an increased risk of hypertension and CAD. The interactions among PRSs and environmental risk factors increased the risk of CAD. Multi-component interventions to lower BP in the population via healthy behaviors are needed to prevent CAD regardless of genetic predisposition.
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Affiliation(s)
- Kyungsook Woo
- Institute of Health and Society, Hanyang University, Seoul, 04763, Republic of Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, 02447, Republic of Korea
| | - Eun Young Lee
- Department of Nursing, Catholic Kkottongnae University, Cheongju, 28211, Republic of Korea.
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Chappell E, Arbour L, Laksman Z. The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology. J Cardiovasc Dev Dis 2024; 11:56. [PMID: 38392270 PMCID: PMC10888590 DOI: 10.3390/jcdd11020056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
Novel genetic risk markers have helped us to advance the field of cardiovascular epidemiology and refine our current understanding and risk stratification paradigms. The discovery and analysis of variants can help us to tailor prognostication and management. However, populations underrepresented in cardiovascular epidemiology and cardiogenetics research may experience inequities in care if prediction tools are not applicable to them clinically. Therefore, the purpose of this article is to outline the barriers that underrepresented populations can face in participating in genetics research, to describe the current efforts to diversify cardiogenetics research, and to outline strategies that researchers in cardiovascular epidemiology can implement to include underrepresented populations. Mistrust, a lack of diverse research teams, the improper use of sensitive biodata, and the constraints of genetic analyses are all barriers for including diverse populations in genetics studies. The current work is beginning to address the paucity of ethnically diverse genetics research and has already begun to shed light on the potential benefits of including underrepresented and diverse populations. Reducing barriers for individuals, utilizing community-driven research processes, adopting novel recruitment strategies, and pushing for organizational support for diverse genetics research are key steps that clinicians and researchers can take to develop equitable risk stratification tools and improve patient care.
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Affiliation(s)
- Elias Chappell
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Laura Arbour
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada
| | - Zachary Laksman
- Department of Medicine and the School of Biomedical Engineering, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Kovacheva VP, Eberhard BW, Cohen RY, Maher M, Saxena R, Gray KJ. Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies. Hypertension 2024; 81:264-272. [PMID: 37901968 PMCID: PMC10842389 DOI: 10.1161/hypertensionaha.123.21053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 10/12/2023] [Indexed: 10/31/2023]
Abstract
BACKGROUND Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed. METHODS We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk. RESULTS Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score. CONCLUSIONS Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.
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Affiliation(s)
- Vesela P Kovacheva
- Department of Anesthesiology, Perioperative and Pain Medicine (V.P.K., B.W.E., R.Y.C.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Braden W Eberhard
- Department of Anesthesiology, Perioperative and Pain Medicine (V.P.K., B.W.E., R.Y.C.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Raphael Y Cohen
- Department of Anesthesiology, Perioperative and Pain Medicine (V.P.K., B.W.E., R.Y.C.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- PathAI, Boston, MA (R.Y.C.)
| | - Matthew Maher
- Department of Anesthesia, Critical Care and Pain Medicine, Center for Genomic Medicine, Massachusetts General Hospital, Boston (M.M., R.S., K.J.G.)
| | - Richa Saxena
- Department of Anesthesia, Critical Care and Pain Medicine, Center for Genomic Medicine, Massachusetts General Hospital, Boston (M.M., R.S., K.J.G.)
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (R.S.)
| | - Kathryn J Gray
- Division of Maternal-Fetal Medicine (K.J.G.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- Department of Anesthesia, Critical Care and Pain Medicine, Center for Genomic Medicine, Massachusetts General Hospital, Boston (M.M., R.S., K.J.G.)
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Kim GH. Primary Role of the Kidney in Pathogenesis of Hypertension. Life (Basel) 2024; 14:119. [PMID: 38255734 PMCID: PMC10817438 DOI: 10.3390/life14010119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
Previous transplantation studies and the concept of 'nephron underdosing' support the idea that the kidney plays a crucial role in the development of essential hypertension. This suggests that there are genetic factors in the kidney that can either elevate or decrease blood pressure. The kidney normally maintains arterial pressure within a narrow range by employing the mechanism of pressure-natriuresis. Hypertension is induced when the pressure-natriuresis mechanism fails due to both subtle and overt kidney abnormalities. The inheritance of hypertension is believed to be polygenic, and essential hypertension may result from a combination of genetic variants that code for renal tubular sodium transporters or proteins involved in regulatory pathways. The renin-angiotensin-aldosterone system (RAAS) and sympathetic nervous system (SNS) are the major regulators of renal sodium reabsorption. Hyperactivity of either the RAAS or SNS leads to a rightward shift in the pressure-natriuresis curve. In other words, hypertension is induced when the activity of RAAS and SNS is not suppressed despite increased salt intake. Sodium overload, caused by increased intake and/or reduced renal excretion, not only leads to an expansion of plasma volume but also to an increase in systemic vascular resistance. Endothelial dysfunction is caused by an increased intracellular Na+ concentration, which inhibits endothelial nitric oxide (NO) synthase and reduces NO production. The stiffness of vascular smooth muscle cells is increased by the accumulation of intracellular Na+ and subsequent elevation of cytoplasmic Ca++ concentration. In contrast to the hemodynamic effects of osmotically active Na+, osmotically inactive Na+ stimulates immune cells and produces proinflammatory cytokines, which contribute to hypertension. When this occurs in the gut, the microbiota may become imbalanced, leading to intestinal inflammation and systemic hypertension. In conclusion, the primary cause of hypertension is sodium overload resulting from kidney dysregulation.
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Affiliation(s)
- Gheun-Ho Kim
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
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Charchar FJ, Prestes PR, Mills C, Ching SM, Neupane D, Marques FZ, Sharman JE, Vogt L, Burrell LM, Korostovtseva L, Zec M, Patil M, Schultz MG, Wallen MP, Renna NF, Islam SMS, Hiremath S, Gyeltshen T, Chia YC, Gupta A, Schutte AE, Klein B, Borghi C, Browning CJ, Czesnikiewicz-Guzik M, Lee HY, Itoh H, Miura K, Brunström M, Campbell NR, Akinnibossun OA, Veerabhadrappa P, Wainford RD, Kruger R, Thomas SA, Komori T, Ralapanawa U, Cornelissen VA, Kapil V, Li Y, Zhang Y, Jafar TH, Khan N, Williams B, Stergiou G, Tomaszewski M. Lifestyle management of hypertension: International Society of Hypertension position paper endorsed by the World Hypertension League and European Society of Hypertension. J Hypertens 2024; 42:23-49. [PMID: 37712135 PMCID: PMC10713007 DOI: 10.1097/hjh.0000000000003563] [Citation(s) in RCA: 57] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/12/2023] [Accepted: 08/22/2023] [Indexed: 09/16/2023]
Abstract
Hypertension, defined as persistently elevated systolic blood pressure (SBP) >140 mmHg and/or diastolic blood pressure (DBP) at least 90 mmHg (International Society of Hypertension guidelines), affects over 1.5 billion people worldwide. Hypertension is associated with increased risk of cardiovascular disease (CVD) events (e.g. coronary heart disease, heart failure and stroke) and death. An international panel of experts convened by the International Society of Hypertension College of Experts compiled lifestyle management recommendations as first-line strategy to prevent and control hypertension in adulthood. We also recommend that lifestyle changes be continued even when blood pressure-lowering medications are prescribed. Specific recommendations based on literature evidence are summarized with advice to start these measures early in life, including maintaining a healthy body weight, increased levels of different types of physical activity, healthy eating and drinking, avoidance and cessation of smoking and alcohol use, management of stress and sleep levels. We also discuss the relevance of specific approaches including consumption of sodium, potassium, sugar, fibre, coffee, tea, intermittent fasting as well as integrated strategies to implement these recommendations using, for example, behaviour change-related technologies and digital tools.
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Affiliation(s)
- Fadi J. Charchar
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
- Department of Physiology, University of Melbourne, Melbourne, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Priscilla R. Prestes
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Charlotte Mills
- Department of Food and Nutritional Sciences, University of Reading, Reading, UK
| | - Siew Mooi Ching
- Department of Family Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang
- Department of Medical Sciences, School of Medical and Live Sciences, Sunway University, Bandar Sunway, Selangor, Malaysia
| | - Dinesh Neupane
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Francine Z. Marques
- Hypertension Research Laboratory, School of Biological Sciences, Monash University
- Heart Failure Research Group, Baker Heart and Diabetes Institute, Melbourne
| | - James E. Sharman
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Liffert Vogt
- Department of Internal Medicine, Section Nephrology, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Microcirculation, Amsterdam, The Netherlands
| | - Louise M. Burrell
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Australia
| | - Lyudmila Korostovtseva
- Department of Hypertension, Almazov National Medical Research Centre, St Petersburg, Russia
| | - Manja Zec
- School of Nutritional Sciences and Wellness, University of Arizona, Tucson, USA
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Mansi Patil
- Department of Nutrition and Dietetics, Asha Kiran JHC Hospital, Chinchwad
- Hypertension and Nutrition, Core Group of IAPEN India, India
| | - Martin G. Schultz
- Department of Internal Medicine, Section Nephrology, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Microcirculation, Amsterdam, The Netherlands
| | | | - Nicolás F. Renna
- Unit of Hypertension, Hospital Español de Mendoza, School of Medicine, National University of Cuyo, IMBECU-CONICET, Mendoza, Argentina
| | | | - Swapnil Hiremath
- Department of Medicine, University of Ottawa and the Ottawa Hospital, Ottawa, Canada
| | - Tshewang Gyeltshen
- Graduate School of Public Health, St. Luke's International University, Tokyo, Japan
| | - Yook-Chin Chia
- Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Selangor
- Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhinav Gupta
- Department of Medicine, Acharya Shri Chander College of Medical Sciences and Hospital, Jammu, India
| | - Aletta E. Schutte
- School of Population Health, University of New South Wales, The George Institute for Global Health, Sydney, New South Wales, Australia
- Hypertension in Africa Research Team, SAMRC Unit for Hypertension and Cardiovascular Disease, North-West University
- SAMRC Developmental Pathways for Health Research Unit, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Britt Klein
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, Faculty of Medicine, University of Bologna, Bologna, Italy
| | - Colette J. Browning
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Marta Czesnikiewicz-Guzik
- School of Medicine, Dentistry and Nursing-Dental School, University of Glasgow, UK
- Department of Periodontology, Prophylaxis and Oral Medicine; Jagiellonian University, Krakow, Poland
| | - Hae-Young Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Hiroshi Itoh
- Department of Internal Medicine (Nephrology, Endocrinology and Metabolism), Keio University, Tokyo
| | - Katsuyuki Miura
- NCD Epidemiology Research Center, Shiga University of Medical Science, Otsu, Japan
| | - Mattias Brunström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Norm R.C. Campbell
- Libin Cardiovascular Institute, Department of Medicine, University of Calgary, Calgary, Canada
| | | | - Praveen Veerabhadrappa
- Kinesiology, Division of Science, The Pennsylvania State University, Reading, Pennsylvania
| | - Richard D. Wainford
- Department of Pharmacology and Experimental Therapeutics, The Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston
- Division of Cardiology, Emory University, Atlanta, USA
| | - Ruan Kruger
- Hypertension in Africa Research Team (HART), North-West University, Potchefstroom
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, South Africa
| | - Shane A. Thomas
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Takahiro Komori
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Udaya Ralapanawa
- Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | | | - Vikas Kapil
- William Harvey Research Institute, Centre for Cardiovascular Medicine and Devices, NIHR Barts Biomedical Research Centre, BRC, Faculty of Medicine and Dentistry, Queen Mary University London
- Barts BP Centre of Excellence, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Yan Li
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai
| | - Yuqing Zhang
- Department of Cardiology, Fu Wai Hospital, Chinese Academy of Medical Sciences, Chinese Hypertension League, Beijing, China
| | - Tazeen H. Jafar
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Nadia Khan
- Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Bryan Williams
- University College London (UCL), Institute of Cardiovascular Science, National Institute for Health Research (NIHR), UCL Hospitals Biomedical Research Centre, London, UK
| | - George Stergiou
- Hypertension Centre STRIDE-7, School of Medicine, Third Department of Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester
- Manchester Academic Health Science Centre, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK
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Benincasa G, Suades R, Padró T, Badimon L, Napoli C. Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases. EUROPEAN HEART JOURNAL. CARDIOVASCULAR PHARMACOTHERAPY 2023; 9:758-769. [PMID: 37562936 DOI: 10.1093/ehjcvp/pvad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 08/12/2023]
Abstract
Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.
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Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
| | - Rosa Suades
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Teresa Padró
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Lina Badimon
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
- Cardiovascular Research Chair, Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy
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Kariis HM, Kasela S, Jürgenson T, Saar A, Lass J, Krebs K, Võsa U, Haan E, Milani L, Lehto K. The role of depression and antidepressant treatment in antihypertensive medication adherence and persistence: Utilising electronic health record data. J Psychiatr Res 2023; 168:269-278. [PMID: 37924579 DOI: 10.1016/j.jpsychires.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/16/2023] [Accepted: 10/13/2023] [Indexed: 11/06/2023]
Abstract
Higher blood pressure levels in patients with depression may be associated with lower adherence to antihypertensive medications (AHMs). Here, we use electronic health record (EHR) data from the Estonian Biobank (EstBB) to investigate the role of lifetime depression in AHM adherence and persistence. We also explore the relationship between antidepressant initiation and intraindividual change in AHM adherence among hypertension (HTN) patients with newly diagnosed depression. Diagnosis and pharmacy refill data were obtained from the National Health Insurance database. Adherence and persistence to AHMs were determined for hypertension (HTN) patients initiating treatment between 2009 and 2017 with a three-year follow-up period. Multivariable regression was used to explore the associations between depression and AHM adherence or persistence, adjusting for sociodemographic, genetic, and health-related factors. A linear mixed-effects model was used to estimate the effect of antidepressant treatment initiation on antihypertensive medication adherence, adjusting for age and sex. We identified 20,724 individuals with newly diagnosed HTN (6294 depression cases and 14,430 controls). Depression was associated with 6% lower probability of AHM adherence (OR = 0.943, 95%CI = 0.909-0.979) and 12% lower odds of AHM persistence (OR = 0.876, 95%CI = 0.821-0.936). Adjusting for sociodemographic, genetic, and health-related factors did not significantly influence these associations. AHM adherence increased 8% six months after initiating antidepressant therapy (N = 132; β = 0.078; 95%CI = 0.025-0.131). Based on the EHR data on EstBB participants, depression is associated with lower AHM adherence and persistence. Additionally, antidepressant therapy may help improve AHM adherence in patients with depression.
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Affiliation(s)
- Hanna Maria Kariis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Silva Kasela
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Tuuli Jürgenson
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Aet Saar
- North Estonia Medical Centre, J. Sütiste Street 19, Tallinn, 13419, Harjumaa, Estonia
| | - Jana Lass
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia; Tartu University Hospital, L. Puusepa 8, Tartu, 50406, Tartumaa, Estonia
| | - Kristi Krebs
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Elis Haan
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia
| | - Kelli Lehto
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23B, Tartu, 51010, Tartumaa, Estonia.
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Clemente-Suárez VJ, Martín-Rodríguez A, Redondo-Flórez L, Villanueva-Tobaldo CV, Yáñez-Sepúlveda R, Tornero-Aguilera JF. Epithelial Transport in Disease: An Overview of Pathophysiology and Treatment. Cells 2023; 12:2455. [PMID: 37887299 PMCID: PMC10605148 DOI: 10.3390/cells12202455] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
Epithelial transport is a multifaceted process crucial for maintaining normal physiological functions in the human body. This comprehensive review delves into the pathophysiological mechanisms underlying epithelial transport and its significance in disease pathogenesis. Beginning with an introduction to epithelial transport, it covers various forms, including ion, water, and nutrient transfer, followed by an exploration of the processes governing ion transport and hormonal regulation. The review then addresses genetic disorders, like cystic fibrosis and Bartter syndrome, that affect epithelial transport. Furthermore, it investigates the involvement of epithelial transport in the pathophysiology of conditions such as diarrhea, hypertension, and edema. Finally, the review analyzes the impact of renal disease on epithelial transport and highlights the potential for future research to uncover novel therapeutic interventions for conditions like cystic fibrosis, hypertension, and renal failure.
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Affiliation(s)
- Vicente Javier Clemente-Suárez
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain;
- Group de Investigación en Cultura, Educación y Sociedad, Universidad de la Costa, Barranquilla 080002, Colombia
| | | | - Laura Redondo-Flórez
- Department of Health Sciences, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, C/Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain; (L.R.-F.); (C.V.V.-T.)
| | - Carlota Valeria Villanueva-Tobaldo
- Department of Health Sciences, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, C/Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain; (L.R.-F.); (C.V.V.-T.)
| | - Rodrigo Yáñez-Sepúlveda
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar 2520000, Chile;
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Lennon MJ, Thalamuthu A, Lam BCP, Crawford JD, Sachdev PS. Genetically Predicted Blood Pressure and Cognition in Midlife: A UK Biobank Study. Hypertension 2023; 80:2112-2121. [PMID: 37589153 DOI: 10.1161/hypertensionaha.123.21612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 07/18/2023] [Indexed: 08/18/2023]
Abstract
BACKGROUND This UK Biobank study uses a mendelian randomization approach to mitigate the variability and confounding that has affected previous analyses of the relationship between measured blood pressure (BP) and cognition and thus delineate the true association between the two. METHODS Systolic BP (SBP) and diastolic BP polygenic risk scores (PRSs) were calculated using summary statistics from the International Consortium of Blood Pressure-Genome Wide Association Study (n=299 024). Adjusted nonlinear mixed-effects regression models were used, including a natural splines term for BP-PRS with outcomes of fluid intelligence, reaction time (RT), and composite attention score. Moderating effects of age, sex, and antihypertensive use were assessed in separate models. RESULTS There were 448 575 participants (mean age, 56.3 years; age range, 37-72 years) included in the analysis after genetic and neurological disease exclusions. Genetic propensity for high SBP had an approximately linear association with worsened fluid intelligence (P=0.0018). This relationship was significantly moderated by age (P<0.0001). By contrast, genetic propensity for high and low SBP and diastolic BP predicted worse attention function (P=0.0099 and P=0.0019), with high PRSs predicting worse function than low PRSs. Genetic propensity for low SBP and diastolic BP was associated with considerably worse RTs, while for high SBP-PRSs, the RT plateaued (P<0.0001). The relationships between RT and the PRSs were significantly moderated by sex (P<0.0001) and antihypertensive use (P<0.0001). CONCLUSIONS Genetic propensity for high and low BP impacts on midlife cognition in subtle ways and differentially affects cognitive domains. While a genetic propensity to low BP may preserve nontimed tests in midlife, it may come at a trade-off with worsened attention scores and RT.
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Affiliation(s)
- Matthew J Lennon
- Faculty of Medicine (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
| | - Anbupalam Thalamuthu
- Faculty of Medicine (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
| | - Ben Chun Pan Lam
- Faculty of Medicine (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- School of Psychology and Public Health, La Trobe University, VIC, Australia (B.C.P.L.)
| | - John D Crawford
- Faculty of Medicine (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
| | - Perminder S Sachdev
- Faculty of Medicine (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health (M.J.L., A.T., B.C.P.L., J.D.C., P.S.S.), University of New South Wales, Sydney, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia (P.S.S.)
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome-wide association study and genomic risk prediction of age-related macular degeneration in Israel. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.06.23295126. [PMID: 37732190 PMCID: PMC10508791 DOI: 10.1101/2023.09.06.23295126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Purpose The risk of developing age-related macular degeneration(AMD) is influenced by genetic background. In 2016, International AMD Genomics Consortium(IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score(PRS) based on these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish(AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study(GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Methods For our discovery set, we recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped all individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary statistics excluding our study and developed PRSs via either clumping/thresholding or LDpred2. Results In our discovery set, 31/34 loci previously reported by the IAMDGC were AMD associated with P<0.05. Of those, all effects were directionally consistent with the IAMDGC and 11 loci had a p-value under Bonferroni-corrected threshold(0.05/34=0.0015). At a threshold of 5x10 -5 , we discovered four suggestive associations in FAM189A1 , IGDCC4 , C7orf50 , and CNTNAP4 . However, only the FAM189A1 variant was AMD associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS and other covariates had an AUC of 0.82(95%CI:0.79-0.85) and performed better than a covariates-only model(P=5.1x10 -9 ). Conclusions Previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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Kanbay M, Copur S, Tanriover C, Ucku D, Laffin L. Future treatments in hypertension: Can we meet the unmet needs of patients? Eur J Intern Med 2023; 115:18-28. [PMID: 37330317 DOI: 10.1016/j.ejim.2023.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 06/19/2023]
Abstract
The prevalence of arterial hypertension is approximately 47% in the United States and 55% in Europe. Multiple different medical therapies are used to treat hypertension including diuretics, beta blockers, calcium channel blockers, angiotensin receptor blockers, angiotensin converting enzyme inhibitors, alpha blockers, central acting alpha receptor agonists, neprilysin inhibitors and vasodilators. However, despite the numerous number of medications, the prevalence of hypertension is on the rise, a considerable proportion of the hypertensive population is resistant to these therapeutic modalities and a definitive cure is not possible with the current treatment approaches. Therefore, there is a need for novel therapeutic strategies to provide better treatment and control of hypertension. In this review, our aim is to describe the latest developments in the treatment of hypertension including novel medication classes, gene therapies and RNA-based modalities.
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Affiliation(s)
- Mehmet Kanbay
- Department of Medicine, Division of Nephrology, Koc University School of Medicine, Istanbul, Turkey.
| | - Sidar Copur
- Department of Medicine, Koc University School of Medicine, Istanbul, Turkey
| | - Cem Tanriover
- Department of Medicine, Koc University School of Medicine, Istanbul, Turkey
| | - Duygu Ucku
- Department of Medicine, Koc University School of Medicine, Istanbul, Turkey
| | - Luke Laffin
- Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA
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Rivier CA, Szejko N, Renedo D, Noche RB, Acosta JN, Both CP, Sharma R, Torres-Lopez VM, Payabvash S, de Havenon A, Sheth KN, Gill TM, Falcone GJ. Polygenic Susceptibility to Hypertension and Cognitive Performance in Middle-aged Persons Without Stroke or Dementia. Neurology 2023; 101:e512-e521. [PMID: 37295956 PMCID: PMC10401683 DOI: 10.1212/wnl.0000000000207427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/04/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Mounting evidence indicates that hypertension leads to a higher risk of dementia. Hypertension is a highly heritable trait, and a higher polygenic susceptibility to hypertension (PSH) is known to associate with a higher risk of dementia. We tested the hypothesis that a higher PSH leads to worse cognitive performance in middle-aged persons without dementia. Confirming this hypothesis would support follow-up research focused on using hypertension-related genomic information to risk-stratify middle-aged adults before hypertension develops. METHODS We conducted a nested cross-sectional genetic study within the UK Biobank (UKB). Study participants with a history of dementia or stroke were excluded. We categorized participants as having low (≤20th percentile), intermediate, or high (≥80th percentile) PSH according to results of 2 polygenic risk scores for systolic and diastolic blood pressure (BP) generated with data on 732 genetic risk variants. A general cognitive ability score was calculated as the first component of an analysis that included the results of 5 cognitive tests. Primary analyses focused on Europeans, and secondary analyses included all race/ethnic groups. RESULTS Of the 502,422 participants enrolled in the UKB, 48,118 (9.6%) completed the cognitive evaluation, including 42,011 (8.4%) of European ancestry. Multivariable regression models using systolic BP-related genetic variants indicated that compared with study participants with a low PSH, those with intermediate and high PSH had reductions of 3.9% (β -0.039, SE 0.012) and 6.6% (β -0.066, SE 0.014), respectively, in their general cognitive ability score (p < 0.001). Secondary analyses including all race/ethnic groups and using diastolic BP-related genetic variants yielded similar results (p < 0.05 for all tests). Analyses evaluating each cognitive test separately indicated that reaction time, numeric memory, and fluid intelligence drove the association between PSH and general cognitive ability score (all individual tests, p < 0.05). DISCUSSION Among nondemented, community-dwelling, middle-aged Britons, a higher PSH is associated with worse cognitive performance. These findings suggest that genetic predisposition to hypertension influences brain health in persons who have not yet developed dementia. Because information on genetic risk variants for elevated BP is available long before the development of hypertension, these results lay the foundation for further research focused on using genomic data for the early identification of high-risk middle-aged adults.
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Affiliation(s)
- Cyprien A Rivier
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT.
| | - Natalia Szejko
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Daniela Renedo
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Rommell B Noche
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Julian N Acosta
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Cameron P Both
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Victor M Torres-Lopez
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Sam Payabvash
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Kevin N Sheth
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Thomas M Gill
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- From the Department of Neurology (C.A.R., N.S., D.R., J.N.A., R.S., V.M.T.-L., A.d.H., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.S.), and Department of Bioethics (N.S.), Medical University of Warsaw, Poland; Department of Neurosurgery (D.R.), Yale School of Medicine, New Haven; Frank H. Netter MD School of Medicine (R.B.N.), Quinnipiac University, North Haven, CT; UMass Chan Medical School (C.P.B.), University of Massachusetts, Worcester; and Department of Radiology (S.P.), and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
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Chekanova V, Vaucher J, Marques-Vidal P. No association between genetic markers and hypertension control in multiple cross-sectional studies. Sci Rep 2023; 13:11811. [PMID: 37479854 PMCID: PMC10362004 DOI: 10.1038/s41598-023-39103-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 07/20/2023] [Indexed: 07/23/2023] Open
Abstract
We aimed to assess whether genetic markers are associated with hypertension control using two cross-sectional surveys conducted in Lausanne, Switzerland. Management of hypertension was assessed as per ESC guidelines using the 140/90 or the 130/80 mm Hg thresholds. One genetic risk score (GRS) for hypertension (18 SNPs) and 133 individual SNPs related to response to specific antihypertensive drugs were tested. We included 1073 (first) and 1157 (second survey) participants treated for hypertension. The prevalence of controlled participants using the 140/90 threshold was 58.8% and 63.6% in the first and second follow-up, respectively. On multivariable analysis, only older age was consistently and negatively associated with hypertension control. No consistent associations were found between GRS and hypertension control (140/90 threshold) for both surveys: Odds ratio and (95% confidence interval) for the highest vs. the lowest quartile of the GRS: 1.06 (0.71-1.58) p = 0.788, and 1.11 (0.71-1.72) p = 0.657, in the first and second survey, respectively. Similar findings were obtained using the 130/80 threshold: 1.23 (0.79-1.90) p = 0.360 and 1.09 (0.69-1.73) p = 0.717, in the first and second survey, respectively. No association between individual SNPs and hypertension control was found. We conclude that control of hypertension is poor in Switzerland. No association between GRS or SNPs and hypertension control was found.
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Affiliation(s)
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Office BH10-642, Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Office BH10-642, Rue du Bugnon 46, 1011, Lausanne, Switzerland.
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Ciochetti NP, Lugli-Moraes B, da Silva BS, Rovaris DL. Genome-wide association studies: utility and limitations for research in physiology. J Physiol 2023; 601:2771-2799. [PMID: 37208942 PMCID: PMC10527550 DOI: 10.1113/jp284241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/10/2023] [Indexed: 05/21/2023] Open
Abstract
Physiological systems are subject to interindividual variation encoded by genetics. Genome-wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans-ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non-European populations.
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Affiliation(s)
- Nicolas Pereira Ciochetti
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Beatriz Lugli-Moraes
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
| | - Bruna Santos da Silva
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Developmental Psychiatry, Center of Experimental Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
| | - Diego Luiz Rovaris
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
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Kurniansyah N, Goodman MO, Khan AT, Wang J, Feofanova E, Bis JC, Wiggins KL, Huffman JE, Kelly T, Elfassy T, Guo X, Palmas W, Lin HJ, Hwang SJ, Gao Y, Young K, Kinney GL, Smith JA, Yu B, Liu S, Wassertheil-Smoller S, Manson JE, Zhu X, Chen YDI, Lee IT, Gu CC, Lloyd-Jones DM, Zöllner S, Fornage M, Kooperberg C, Correa A, Psaty BM, Arnett DK, Isasi CR, Rich SS, Kaplan RC, Redline S, Mitchell BD, Franceschini N, Levy D, Rotter JI, Morrison AC, Sofer T. Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups. Nat Commun 2023; 14:3202. [PMID: 37268629 PMCID: PMC10238525 DOI: 10.1038/s41467-023-38990-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.
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Affiliation(s)
- Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Alyna T Khan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jiongming Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Elena Feofanova
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Tali Elfassy
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Walter Palmas
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Henry J Lin
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Shih-Jen Hwang
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Yan Gao
- The Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Kendra Young
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Gregory L Kinney
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Medicine, Brown University, Providence, RI, USA
| | - Sylvia Wassertheil-Smoller
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - JoAnn E Manson
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City, 40705, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - C Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Adolfo Correa
- Departments of Medicine and Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, USA
| | - Carmen R Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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47
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Phulka JS, Ashraf M, Bajwa BK, Pare G, Laksman Z. Current State and Future of Polygenic Risk Scores in Cardiometabolic Disease: A Scoping Review. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:286-313. [PMID: 37035923 DOI: 10.1161/circgen.122.003834] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
A polygenic risk score (PRS) is derived from a genome-wide association study and represents an aggregate of thousands of single-nucleotide polymorphisms that provide a baseline estimate of an individual's genetic risk for a specific disease or trait at birth. However, it remains unclear how PRSs can be used in clinical practice. We provide an overview of the PRSs related to cardiometabolic disease and discuss the evidence supporting their clinical applications and limitations. The Preferred Reporting Items For Systematic Reviews and Meta-Analysis Extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. Across the 4863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to coronary artery disease, followed by hypertension and cerebrovascular disease. Limited ancestral diversity was observed in the study sample populations. Most studies included only individuals of European ancestry. The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies reported an integrated risk model combining a derived PRS and clinical risk tools such as the Framingham Risk Score and Pooled Cohort Equations. The inclusion of a PRS into a clinical risk model tended to improve predictive accuracy consistently. This scoping review is the first of its kind and reports strong evidence for the clinical utility of PRSs in coronary artery disease, hypertension, cerebrovascular disease, and atrial fibrillation. However, most PRSs are generated in cohorts of European ancestry, which likely contributes to a lack of PRS transferability across different ancestral groups. Future prospective studies should focus on further establishing the clinical utility of PRSs and ensuring diversity is incorporated into genome-wide association study cohorts.
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Affiliation(s)
- Jobanjit S Phulka
- Heart Rhythm Services & Center for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver (J.S.P., M.A., B.K.B., Z.L.)
| | - Mishal Ashraf
- Heart Rhythm Services & Center for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver (J.S.P., M.A., B.K.B., Z.L.)
| | - Beenu K Bajwa
- Heart Rhythm Services & Center for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver (J.S.P., M.A., B.K.B., Z.L.)
| | - Guillaume Pare
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute; Thrombosis and Atherosclerosis Research Institute, Department of Health Research Methods, Evidence, and Impact, Department of Pathology & Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada (G.P.)
| | - Zachary Laksman
- Heart Rhythm Services & Center for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver (J.S.P., M.A., B.K.B., Z.L.)
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Aryal S, Manandhar I, Mei X, Yeoh BS, Tummala R, Saha P, Osman I, Zubcevic J, Durgan DJ, Vijay-Kumar M, Joe B. Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e26. [PMID: 38550938 PMCID: PMC10953772 DOI: 10.1017/pcm.2023.13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 03/15/2023] [Accepted: 04/04/2023] [Indexed: 11/03/2024]
Abstract
The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.
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Affiliation(s)
- Sachin Aryal
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Ishan Manandhar
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Xue Mei
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Beng S. Yeoh
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Ramakumar Tummala
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Piu Saha
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Islam Osman
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Jasenka Zubcevic
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - David J. Durgan
- Integrative Physiology & Anesthesiology, Baylor College of Medicine, Houston, TX, USA
| | - Matam Vijay-Kumar
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Bina Joe
- Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
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49
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Zucker R, Kovalerchik M, Linial M. Gene-based association study reveals a distinct female genetic signal in primary hypertension. Hum Genet 2023:10.1007/s00439-023-02567-9. [PMID: 37133573 DOI: 10.1007/s00439-023-02567-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023]
Abstract
Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility.
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Affiliation(s)
- Roei Zucker
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michael Kovalerchik
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel.
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50
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Acosta JN, Both CP, Demarais ZS, Conlon CJ, Leasure AC, Torres-Lopez VM, de Havenon A, Petersen NH, Gill TM, Sansing LH, Sheth KN, Falcone GJ. Polygenic Susceptibility to Hypertension and Blood Pressure Control in Stroke Survivors. Neurology 2023; 100:e1587-e1597. [PMID: 36690452 PMCID: PMC10103110 DOI: 10.1212/wnl.0000000000206763] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 11/16/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Blood pressure (BP) is often not at goal in stroke survivors, leaving individuals vulnerable to additional vascular events. Given that BP is a highly heritable trait, we hypothesize that a higher polygenic susceptibility to hypertension (PSH) leads to worse BP control in stroke survivors. METHODS We conducted a study within the UK Biobank evaluating persons of European ancestry who survived an ischemic or hemorrhagic stroke. To model the PSH, we created polygenic risk scores (PRSs) for systolic and diastolic BP using 732 genetic variants. We divided the PRSs into quintiles and used linear/logistic regression to test whether higher PSH led to higher observed BP, uncontrolled BP (systolic BP > 140 mm Hg or diastolic BP > 90 mm Hg), and resistant BP (uncontrolled BP despite being on ≥3 antihypertensive drugs). We conducted an independent replication using data from the Vitamin Intervention for Stroke Prevention (VISP) trial. RESULTS We analyzed 5,940 stroke survivors. When comparing stroke survivors with very low vs very high PSH, the mean systolic BP was 137 (SD 18) vs 143 (SD 20, p < 0.001), the mean diastolic BP was 81 (SD 10) vs 84 (SD 11, p < 0.001), the prevalence of uncontrolled BP was 42.8% vs 57.2% (p < 0.001), and the prevalence of resistant hypertension was 3.9% vs 11% (p < 0.001). Results remained significant using multivariable models (p < 0.001) and were replicated in the VISP study (all tests with p < 0.05). DISCUSSION A higher PSH is associated with worse BP control in stroke survivors. These findings point to genetic predisposition as an important determinant of poorly controlled BP in this population.
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Affiliation(s)
- Julián N Acosta
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Cameron P Both
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Zachariah S Demarais
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Carolyn J Conlon
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Audrey C Leasure
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Victor M Torres-Lopez
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Nils H Petersen
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Thomas M Gill
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Lauren H Sansing
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Kevin N Sheth
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- From the Division of Neurocritical Care & Emergency Neurology (J.N.A., C.P.B., A.C.L., V.M.T.-L., A.H., N.H.P., K.N.S., G.J.F.), Department of Neurology, Yale School of Medicine; Frank H. Netter MD School of Medicine (Z.S.D., C.J.C.); Division of Vascular Neurology (N.H.P., L.H.S.), Department of Neurology, Yale School of Medicine; and Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT.
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