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Yasmin F, Moeed A, Ur Rahman HA, Ali Fahim MA, Salman A, Shaharyar M, Ochani RK, Shaik AA, Asghar MS, Alraies MC. Trends and disparities in the prevalence of circulatory disease risk factors among U.S. adults from the National Health Interview Survey database (2019-2022). INTERNATIONAL JOURNAL OF CARDIOLOGY. CARDIOVASCULAR RISK AND PREVENTION 2025; 25:200393. [PMID: 40160700 PMCID: PMC11951206 DOI: 10.1016/j.ijcrp.2025.200393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Revised: 01/11/2025] [Accepted: 03/05/2025] [Indexed: 04/02/2025]
Abstract
Introduction Circulatory diseases are the leading cause of mortality in the United States (U.S)., making it crucial to understand trends and disparities in the prevalence of cardiovascular risk factors including diabetes, obesity, smoking, and hyperlipidemia. Methods Data from the Centers for Disease Control and Prevention (CDC)'s National Health Interview Survey (NHIS) database was analyzed for adults aged 18 and older from 2019 to 2022. Prevalence percentages and Annual Percentage Changes (APCs) were calculated using regression analysis with Joinpoint, with 95 % confidence intervals (CI). The data was stratified by year, gender, age, race, nativity, veteran status, social vulnerability, employment status, and geographic distribution. Results Among circulatory disease risk factors, obesity had the highest prevalence remaining consistent across all years. The highest obesity rates were observed amongst females, those aged 45-64, and Black or African American adults, with regional peaks in the South and Midwest. High Cholesterol, the second most prevalent risk factor, rose significantly from 20.1 % to 22 % [APC: 3.3175∗ (95 % CI: 1.1417 to 5.5416)] with males [APC: 3.3175∗ (95 % CI: 1.1417 to 5.5416)] and females [APC: 3.1315∗ (95 % CI: 3.0191 to 3.2428)] both showing significant increases over time. Furthermore, those aged >65 yrs and White adults in addition to those residing in the Northeast and South revealed the highest rates. Smoking rates remained steady, with a higher male prevalence which showed a significant decrease [APC: -5.0336∗ (95 % CI: -9.156 to -0.6731)] over time. Diabetes prevalence was stable, with males, adults aged 64 and above, American Indians and Black or African American adults and those residing in the southern region consistently showing the highest rates of incidence. Conclusion Significant disparities and increasing trends in risk factors for circulatory diseases have been identified, highlighting the need for targeted interventions, particularly for high-risk groups such as males, older adults, veterans, and the unemployed.
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Affiliation(s)
| | - Abdul Moeed
- Dow University of Health Sciences, Karachi, Pakistan
| | | | | | - Afia Salman
- Dow University of Health Sciences, Karachi, Pakistan
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Nehme M, Uppal A, Zimmerman O, Lamour J, Mechoullam S, Guessous I. Twenty years population-based trends in prevalence, awareness, treatment, and control of hypertension in Geneva, Switzerland. Prev Med Rep 2025; 53:103055. [PMID: 40235578 PMCID: PMC11999646 DOI: 10.1016/j.pmedr.2025.103055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2024] [Revised: 03/27/2025] [Accepted: 03/28/2025] [Indexed: 04/17/2025] Open
Abstract
Objective Hypertension is a leading cause of cardiovascular disease and affects about 1.3 billion adults worldwide. Despite interventions, awareness and control remain suboptimal and might have worsened due to the COVID-19 pandemic. This population-based study examines 20-year trends in hypertension prevalence, awareness, treatment, and control in Geneva, Switzerland (2005-2023). Methods This is a year-trends population-based study (Bus Sante) ongoing in Geneva, Switzerland. Data collected in this study were between 2005 and 2023. Hypertension trends and prevalence were stratified by sex, age, education, and income. Multivariable regression models adjusted for sociodemographic and health factors identified determinants of outcomes. Results Overall, 11,278 individuals participated. Hypertension prevalence decreased from 38.9 % to 35.2 %, with greater reductions in individuals with primary education (-6.1 %) and low income (-6.1 %). Awareness remained stable with time. Uncontrolled hypertension decreased (44.9 % to 42.2 %, p = 0.01), with improvements in lower socioeconomic groups, and individuals with diabetes. Older women were more likely to have untreated (+16.1 %) and uncontrolled hypertension, while younger men exhibited higher unawareness rates (57.7 %). Having a doctor visit in the past 12 months was not associated with increased awareness. Conclusions Hypertension prevalence and control improved overall, with reduced socioeconomic disparities. However, some groups remain at risk and primary care is essential for better screening, awareness, treatment, and control of hypertension.
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Affiliation(s)
- Mayssam Nehme
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Anshu Uppal
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Ophelia Zimmerman
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Lamour
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Shannon Mechoullam
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Idris Guessous
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
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Khera R, Sawano M, Warner F, Coppi A, Pedroso AF, Spatz ES, Yu H, Gottlieb M, Saydah S, Stephens KA, Rising KL, Elmore JG, Hill MJ, Idris AH, Montoy JCC, O’Laughlin KN, Weinstein RA, Venkatesh A. Assessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study. J Am Med Inform Assoc 2025; 32:784-794. [PMID: 40036551 PMCID: PMC12012333 DOI: 10.1093/jamia/ocaf027] [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: 09/09/2024] [Revised: 01/17/2025] [Accepted: 02/07/2025] [Indexed: 03/06/2025] Open
Abstract
OBJECTIVES Direct electronic access to multiple electronic health record (EHR) systems through patient portals offers a novel avenue for decentralized research. Given the critical value of patient characterization, we sought to compare computable evaluation of health conditions from patient-portal EHR against the traditional self-report. MATERIALS AND METHODS In the nationwide Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study, which linked self-reported questionnaires with multiplatform patient-portal EHR data, we compared self-reported health conditions across different clinical domains against computable definitions based on diagnosis codes, medications, vital signs, and laboratory testing. We assessed their concordance using Cohen's Kappa and the prognostic significance of differentially captured features as predictors of 1-year all-cause hospitalization risk. RESULTS Among 1683 participants (mean age 41 ± 15 years, 67% female, 63% non-Hispanic Whites), the prevalence of conditions varied substantially between EHR and self-report (-13.2% to +11.6% across definitions). Compared with comprehensive EHR phenotypes, self-report under-captured all conditions, including hypertension (27.9% vs 16.2%), diabetes (10.1% vs 6.2%), and heart disease (8.5% vs 4.3%). However, diagnosis codes alone were insufficient. The risk for 1-year hospitalization was better defined by the same features from patient-portal EHR (area under the receiver operating curve [AUROC] 0.79) than from self-report (AUROC 0.68). DISCUSSION EHR-derived computable phenotypes identified a higher prevalence of comorbidities than self-report, with prognostic value of additionally identified features. However, definitions based solely on diagnosis codes often undercaptured self-reported conditions, suggesting a role of broader EHR elements. CONCLUSION In this nationwide study, patient-portal-derived EHR data enabled extensive capture of patient characteristics across multiple EHR platforms, allowing better disease phenotyping compared with self-report.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- The Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, United States
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, United States
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, United States
| | - Mitsuaki Sawano
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
| | - Frederick Warner
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
| | - Aline F Pedroso
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- The Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, United States
| | - Erica S Spatz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
- Department of Epidemiology, Yale School of Public Health, New Haven, CT 06510, United States
| | - Huihui Yu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
| | - Michael Gottlieb
- Department of Emergency Medicine, Rush University Medical Center, Chicago, IL 60612, United States
| | - Sharon Saydah
- National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30329, United States
| | - Kari A Stephens
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, United States
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195-6560, United States
| | - Kristin L Rising
- Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, United States
- Center for Connected Care, Thomas Jefferson University, Philadelphia, PA 19107, United States
| | - Joann G Elmore
- Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA 90024, United States
| | - Mandy J Hill
- Department of Emergency Medicine, UTHealth Houston, Houston, TX 77030, United States
| | - Ahamed H Idris
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | - Juan Carlos C Montoy
- Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA 94143, United States
| | - Kelli N O’Laughlin
- Department of Emergency Medicine, University of Washington, Seattle, WA 98195, United States
- Department of Global Health, University of Washington, Seattle, WA 98195, United States
| | - Robert A Weinstein
- Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, United States
- Department of Medicine, Cook County Hospital, Chicago, IL 60612, United States
| | - Arjun Venkatesh
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT 06510, United States
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT 06520, United States
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Tanglai W, Jeamjitvibool T, Chen P, Lockwood MB, Cajita M. Gender and Sex-Based Differences in Hypertension Risk Factors Among Non-Hispanic Asian Adults in the United States. J Cardiovasc Nurs 2025; 40:280-289. [PMID: 39330764 DOI: 10.1097/jcn.0000000000001147] [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] [Indexed: 09/28/2024]
Abstract
INTRODUCTION The prevalence of hypertension (HTN) is rising at an accelerated rate, and it remains the primary factor contributing to cardiovascular illnesses. Sex can serve as an influencing factor, leading to variations in the factors affecting HTN. OBJECTIVE This study aimed to investigate gender and sex differences in the prevalence of HTN and explore the associations between HTN and 4 categories of risk factors: demographics, habits or lifestyle, body measurement, and laboratory blood results among non-Hispanic Asians in the United States. METHODS This secondary analysis included non-Hispanic Asian adults aged 18 years or older from the 2017 to 2018 National Health and Nutrition Examination Surveys. RESULTS Among the 815 participants, 35% of men (140 of 399) and 37% (154 of 416) of women had HTN ( P = .610). The mean age for men is 46.03 ± 16.9 years, whereas the mean age for women is 49.24 ± 16.8 years. After regression analysis, advancing age, increased body mass index, and increased serum uric acid were significant predictors of HTN in both sexes. However, men developed HTN earlier compared with women. Marital status and increased fasting glucose were only significant in men. Compared with their never-married counterparts, men who were currently married or living with a partner had lower odds of having HTN (odds ratio, 0.28; P = .034). CONCLUSIONS There was no significant difference in the prevalence of HTN between the sexes. Age, body mass index, and serum uric acid were significant risk factors in both men and women. Meanwhile, marital status and fasting glucose were only significant in men.
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Prendergast H, Kitsiou S, Petzel Gimbar R, Freels S, Sanders A, Daviglus M, Kotini-Shah P, Carter B, Del Rios M, Heinert S, Khosla S. Emergency Department-Based Education and mHealth Empowerment Intervention for Hypertension: The TOUCHED Randomized Clinical Trial. JAMA Cardiol 2025:2832858. [PMID: 40266598 PMCID: PMC12019670 DOI: 10.1001/jamacardio.2025.0675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 02/23/2025] [Indexed: 04/24/2025]
Abstract
Importance Hypertension is a leading risk factor for cardiovascular diseases and is often undiagnosed. Emergency department (ED) visits serve as critical access points within health care and present a unique opportunity for hypertension screening and intervention. Objective To evaluate the effectiveness of an Education and mHealth Empowerment (E2) intervention compared with usual care in reducing systolic blood pressure (SBP) among patients with elevated BP discharged from the ED. Design, Setting, and Participants This randomized clinical trial enrolled participants who presented to an urban academic medical center ED for any indication and had elevated blood pressure (≥140/90 mm Hg and ≤180/110 mm Hg). Eligible participants who were discharged from the ED were enrolled between February 12, 2019, and March 31, 2023, and were randomized to receive either usual care or the intervention with follow-up visits at 3 and 6 months. Interventions Usual care involved standard hypertension discharge instructions with a referral for outpatient follow-up. The E2 intervention involved a 3-prong approach, which included a brief Post-Acute Care Hypertension consultation (PACHT-c) with a clinical pharmacist or an advanced practice nurse, a smartphone-enabled BP monitoring kit (Withings device and mobile app) for daily self-monitoring along with behavior change text messages, and primary care referral. Main Outcomes and Measures The primary outcome was the mean change in SBP (mm Hg) from baseline to 6 months. Results Of the 574 participants enrolled, mean (SD) age was 51.1 (12.5) years, and 323 (56%) were female; 413 were Black (72%), 115 were Hispanic or Latino (20%), 27 were White (5%), and 19 were other race and ethnicity (3%), which included Asian, American Indian, and other racial or ethnic groups. Of the 413 patients with BP data at 6 months, the E2 intervention group (n = 210) showed a greater mean reduction in SBP (mean difference, 4.9 mm Hg; 95% CI, 0.8-9.0 mm Hg; P = .02) compared with the usual-care group (n = 203). A similar proportion of patients achieved BP less than or equal to 140/90 mm Hg at 6 months in the intervention arm (42.9% [90 of 210]) and the control arm (36.9% [75 of 203]; P = .22). Conclusions and Relevance In this single-center randomized clinical trial, a multicomponent intervention directed at patients in the ED who have elevated BP was associated with greater reduction in SBP at 6 months. Identifying patients who present to the ED with hypertension may be a viable strategy to improve BP management. Trial Registration ClinicalTrials.gov Identifier: NCT03749499.
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Affiliation(s)
| | - Spyros Kitsiou
- Department of Health Information Sciences, University of Illinois Chicago
| | - Renee Petzel Gimbar
- Department of Emergency Medicine, University of Illinois Chicago
- College of Pharmacy - Pharmacy Practice, University of Illinois Chicago
| | - Sally Freels
- School of Public Health, University of Illinois Chicago
| | - Anissa Sanders
- Department of Emergency Medicine, University of Illinois Chicago
| | | | | | - Barry Carter
- Pharmacy Practice and Science, University of Iowa, Iowa City
| | - Marina Del Rios
- Department of Emergency Medicine, University of Iowa, Iowa City
| | - Sara Heinert
- Department of Emergency Medicine, Rutgers–Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Shaveta Khosla
- Department of Emergency Medicine, University of Illinois Chicago
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Tang KS, Jones JE, Fan W, Wong ND. Prevalence and Mortality Trends of Hypertension Subtypes Among US Adults: An Analysis of the National Health and Nutrition Examination Survey. Am J Hypertens 2025; 38:303-312. [PMID: 39891307 DOI: 10.1093/ajh/hpaf010] [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: 09/30/2024] [Revised: 12/10/2024] [Accepted: 01/13/2025] [Indexed: 02/03/2025] Open
Abstract
BACKGROUND Hypertension (HTN) has been demonstrated as one of the leading risk factors for development of cardiovascular disease (CVD) and CVD mortality. METHODS This study examines the prevalence and distribution of HTN subtypes (isolated diastolic hypertension [IDH], isolated systolic hypertension [ISH], and systolic-diastolic hypertension [SDH]) across age, sex, and race/ethnicity per the nationally representative National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020 based on the updated 2017 ACC/AHA HTN definition. We further examined for associations of each subtype with CVD and all-cause mortality using Cox regression analysis. RESULTS Among US adults, the overall prevalence of HTN is 47.4%. Across increasing age, the prevalence of IDH decreased, ISH increased, and SDH increased and peaked in the 6th decade of life after which SDH prevalence decreased. By age 80, over 80% of persons with HTN demonstrated ISH. A subcohort from NHANES 1999-2008 with follow-up until 2018 showed that ISH and SDH were most strongly associated with increased risk for CVD (HR = 1.18, 95% CI, 1.01-1.38; HR = 1.31, 95% CI, 1.07-1.60, respectively) and all-cause mortality (HR = 1.17, 95% CI, 1.06-1.28; HR = 1.21, 95% CI, 1.08-1.37, respectively). CONCLUSIONS Our data demonstrate the continuing importance of HTN subtype transitions across age and their differences in predicting future CVD and total mortality.
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Affiliation(s)
- Kevin S Tang
- Heart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Irvine, CA, USA
| | - Jeffrey E Jones
- Heart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Irvine, CA, USA
| | - Wenjun Fan
- Heart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Irvine, CA, USA
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA
| | - Nathan D Wong
- Heart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Irvine, CA, USA
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA
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Liu SQ, Ji XY, Liang HY, Zhao SH, Yang FY, Tang Y, Shi S. A Bibliometric Analysis of hypertension and anxiety from 2004 to 2022. Medicine (Baltimore) 2025; 104:e41859. [PMID: 40153757 PMCID: PMC11957653 DOI: 10.1097/md.0000000000041859] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 02/25/2025] [Indexed: 03/30/2025] Open
Abstract
BACKGROUND A growing body of clinical evidence points to an association between hypertension and anxiety, but the mechanisms by which the two occur are unclear. This article aims to explore possible common influences and associations between hypertension and anxiety. METHODS We searched for publications on hypertension and anxiety from January 01, 2004 to December 31, 2022 in Web of Science and performed bibliometrics using CiteSpace, VOSviewer, Scimago Graphica and Gephi. RESULTS A total of 3216 related articles were retrieved from the Web of Science database. After screening, 3051 articles were included. The number of published articles has increased over the past 19 years. The United States has more researches in this area and has strong collaborative relationships with other countries, which gives it some credibility and authority. The words that appear in the burst keywords are gender, age, obesity, depression, panic disorder, pregnancy induced hypertension, coronary heart disease, chronic kidney disease, and pituitary adrenal axi, which are co-related with hypertension and anxiety. CONCLUSION There is a link between hypertension and anxiety, and the 2 influence each other, usually in a positive way. Common influences on hypertension and anxiety include age, gender, obesity, depression, panic attacks, pregnancy, coronary heart disease and chronic kidney disease. Recent research hotspots have focused on population aging and comorbidities. Future research hotspots are likely continue to focus on influencing factors, clinical research and prognosis.
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Affiliation(s)
- Si-Qi Liu
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Xin-Yu Ji
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Hai-Yi Liang
- Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Shu-Han Zhao
- Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Fu-Yi Yang
- Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Yang Tang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Shuai Shi
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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Chaitoff A, Shimbo D, Bress AP. Epigenetic Aging: A Mechanism by Which Social Determinants Increase the Risk of Hypertension? Hypertension 2025; 82:e25-e27. [PMID: 39970252 DOI: 10.1161/hypertensionaha.124.24434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Affiliation(s)
- Alexander Chaitoff
- Veterans Affairs Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, MI (A.C.)
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor (A.C.)
| | - Daichi Shimbo
- Columbia Hypertension Center and Lab, Division of Cardiology, Columbia University Irving Medical Center, New York, NY (D.S.)
| | - Adam P Bress
- Intermountain Healthcare Department of Population Health Sciences, Divisions of Health System Innovation and Research and Biostatistics, Spencer Fox-Eccles School of Medicine, University of Utah, Salt Lake City (A.P.B.)
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT (A.P.B.)
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Rosenfeld EB, Sagaram D, Lee R, Sadural E, Miller RC, Lin R, Jenkins D, Blackledge K, Nikodijevic I, Rizzo A, Martinez V, Daggett EE, McGeough O, Ananth CV, Rosen T. Management of Postpartum Preeclampsia and Hypertensive Disorders (MOPP): Postpartum Tight vs Standard Blood Pressure Control. JACC. ADVANCES 2025; 4:101617. [PMID: 39983612 PMCID: PMC11891668 DOI: 10.1016/j.jacadv.2025.101617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Revised: 12/30/2024] [Accepted: 12/31/2024] [Indexed: 02/23/2025]
Abstract
BACKGROUND It is unknown whether tightly controlled blood pressure in the postpartum period will improve outcomes. OBJECTIVES The purpose of this study was to assess the effect of a lower treatment threshold (≥130/80 mm Hg) for initiating and titrating antihypertensive medication on reducing emergency department visits in postpartum patients with hypertension. METHODS A prospective cohort of postpartum patients was recruited in a multicenter study between March 2023 and March 2024 and treated to maintain blood pressure <130/80 mm Hg using remote blood pressure monitoring. These patients were compared to a propensity score-matched retrospective cohort from February 2021 to February 2023 who were treated to maintain blood pressures <150/100 mm Hg. Eligible patients were 18 or older with a diagnosis of hypertensive disorder. The primary outcome was an emergency department visit for hypertension. RESULTS There were 392 patients enrolled in the interventional cohort and 1,204 patients identified in the retrospective cohort. After the propensity score match, 276 and 429 patients remained in the prospective and retrospective groups, respectively. Emergency department visits for hypertensive disorders occurred in 10 patients (3.6%) in the intervention and 36 patients (8.4%) in the retrospective cohort (risk difference -4.8; 95% CI: -8.2 to -1.3; doubly robust OR: 0.32; 95% CI: 0.10-1.01). At 6 weeks postpartum, compared to the retrospective group, the intervention group had systolic and diastolic blood pressure that was 4.4 mm Hg (95% CI: -6.8 to -2.0) and 3.1 mm Hg (95% CI: -4.9 to -1.2) lower, respectively. CONCLUSIONS Tighter blood pressure control was associated with reduced postpartum emergency department visits for hypertensive disorders.
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Affiliation(s)
- Emily B Rosenfeld
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Division of Epidemiology and Biostatistics, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
| | - Deepika Sagaram
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Rachel Lee
- Division of Epidemiology and Biostatistics, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Ernani Sadural
- Department of Obstetrics and Gynecology, Cooperman Barnabas Medical Center, RWJBarnabas Health, Livingston, New Jersey, USA
| | - Richard C Miller
- Department of Obstetrics and Gynecology, Cooperman Barnabas Medical Center, RWJBarnabas Health, Livingston, New Jersey, USA
| | - Ruby Lin
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Deshae Jenkins
- Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Kristin Blackledge
- Department of Obstetrics and Gynecology, Cooperman Barnabas Medical Center, RWJBarnabas Health, Livingston, New Jersey, USA; New Jersey Medical School, Newark, New Jersey, USA
| | | | - Alex Rizzo
- Department of Obstetrics and Gynecology, Cooperman Barnabas Medical Center, RWJBarnabas Health, Livingston, New Jersey, USA; New Jersey Medical School, Newark, New Jersey, USA
| | - Vanessa Martinez
- Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Emily E Daggett
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Olivia McGeough
- Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Cande V Ananth
- Division of Epidemiology and Biostatistics, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Cardiovascular Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, New Jersey, USA; Environmental and Occupational Health Sciences Institute, Rutgers Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
| | - Todd Rosen
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
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Lee S, Miller CL, Bentley AR, Brown MR, Nagarajan P, Noordam R, Morrison J, Schwander K, Westerman K, Kho M, Kraja AT, de Vries PS, Ammous F, Aschard H, Bartz TM, Do A, Dupont CT, Feitosa MF, Gudmundsdottir V, Guo X, Harris SE, Hikino K, Huang Z, Lefevre C, Lyytikäinen LP, Milaneschi Y, Nardone GG, Santin A, Schmidt H, Shen B, Sofer T, Sun Q, Tan YA, Tang J, Thériault S, van der Most PJ, Ware EB, Weiss S, Ya Xing W, Yu C, Zhao W, Ansari MAY, Anugu P, Attia JR, Bazzano LA, Bis JC, Breyer M, Cade B, Chen G, Collins S, Corley J, Davies G, Dörr M, Du J, Edwards TL, Faquih T, Faul JD, Fohner AE, Fretts AM, Gangireddy S, Gepner A, Graff M, Hofer E, Homuth G, Hood MM, Jie X, Kähönen M, Kardia SL, Karvonen-Gutierrez CA, Launer LJ, Levy D, Maheshwari M, Martin LW, Matsuda K, McNeil JJ, Nolte IM, Okochi T, Raffield LM, Raitakari OT, Risch L, Risch M, Roux AD, Ruiz-Narvaez EA, Russ TC, Saito T, Schreiner PJ, Scott RJ, Shikany J, Smith JA, Snieder H, Spedicati B, Tai ES, Taylor AM, Taylor KD, Tesolin P, van Dam RM, Wang R, Wenbin W, Xie T, Yao J, et alLee S, Miller CL, Bentley AR, Brown MR, Nagarajan P, Noordam R, Morrison J, Schwander K, Westerman K, Kho M, Kraja AT, de Vries PS, Ammous F, Aschard H, Bartz TM, Do A, Dupont CT, Feitosa MF, Gudmundsdottir V, Guo X, Harris SE, Hikino K, Huang Z, Lefevre C, Lyytikäinen LP, Milaneschi Y, Nardone GG, Santin A, Schmidt H, Shen B, Sofer T, Sun Q, Tan YA, Tang J, Thériault S, van der Most PJ, Ware EB, Weiss S, Ya Xing W, Yu C, Zhao W, Ansari MAY, Anugu P, Attia JR, Bazzano LA, Bis JC, Breyer M, Cade B, Chen G, Collins S, Corley J, Davies G, Dörr M, Du J, Edwards TL, Faquih T, Faul JD, Fohner AE, Fretts AM, Gangireddy S, Gepner A, Graff M, Hofer E, Homuth G, Hood MM, Jie X, Kähönen M, Kardia SL, Karvonen-Gutierrez CA, Launer LJ, Levy D, Maheshwari M, Martin LW, Matsuda K, McNeil JJ, Nolte IM, Okochi T, Raffield LM, Raitakari OT, Risch L, Risch M, Roux AD, Ruiz-Narvaez EA, Russ TC, Saito T, Schreiner PJ, Scott RJ, Shikany J, Smith JA, Snieder H, Spedicati B, Tai ES, Taylor AM, Taylor KD, Tesolin P, van Dam RM, Wang R, Wenbin W, Xie T, Yao J, Young KL, Zhang R, Zonderman AB, Concas MP, Conen D, Cox SR, Evans MK, Fox ER, de Las Fuentes L, Giri A, Girotto G, Grabe HJ, Gu C, Gudnason V, Harlow SD, Holliday E, Jost JB, Lacaze P, Lee S, Lehtimäki T, Li C, Liu CT, Morrison AC, North KE, Penninx BW, Peyser PA, Province MM, Psaty BM, Redline S, Rosendaal FR, Rotimi CN, Rotter JI, Schmidt R, Sim X, Terao C, Weir DR, Zhu X, Franceschini N, O'Connell JR, Jaquish CE, Wang H, Manning A, Munroe PB, Rao DC, Chen H, Gauderman WJ, Bierut L, Winkler TW, Fornage M. A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations. RESEARCH SQUARE 2025:rs.3.rs-6025759. [PMID: 40034430 PMCID: PMC11875294 DOI: 10.21203/rs.3.rs-6025759/v1] [Show More Authors] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
Background Gene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample. Results Our study included 564,680 adults aged 18 years or older from 67 cohorts and 4 population backgrounds (African (5%), Asian (7%), European (85%), and Hispanic (3%)). We discovered seven novel gene-DEPR interaction loci for BP traits. These loci mapped to genes implicated in neurogenesis (TGFA, CASP3), lipid metabolism (ACSL1), neuronal apoptosis (CASP3), and synaptic activity (CNTN6, DBI). We also identified evidence for gene-DEPR interaction at nine known BP loci, further suggesting links between mood disturbance and BP regulation. Of the 16 identified loci, 11 loci were derived from African, Asian, or Hispanic populations. Post-GWAS analyses prioritized 36 genes, including genes involved in synaptic functions (DOCK4, MAGI2) and neuronal signaling (CCK, UGDH, SLC01A2). Integrative druggability analyses identified 11 druggable candidate gene targets, including genes implicated in pathways linked to mood disorders as well as gene products targeted by known antihypertensive drugs. Conclusions Our findings emphasize the importance of considering gene-DEPR interactions on BP, particularly in non-European populations. Our prioritized genes and druggable targets highlight biological pathways connecting mood disorders and hypertension and suggest opportunities for BP drug repurposing and risk factor prevention, especially in individuals with DEPR.
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Affiliation(s)
- Songmi Lee
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX
| | - Clint L Miller
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX
| | - Pavithra Nagarajan
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Kenneth Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA
| | - Minjung Kho
- Graduate School of Data Science, Seoul National University, Seoul
| | - Aldi T Kraja
- University of Mississippi Medical Center, Jackson, MS
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX
| | - Farah Ammous
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Hughes Aschard
- Department of Computational Biology, F-75015 Paris, France Institut Pasteur, Université Paris Cité, Paris
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Anh Do
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO
| | - Charles T Dupont
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | | | - 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
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh
| | - Keiko Hikino
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Christophe Lefevre
- Department of Data Sciences, Hunter Medical Research Institute, New Lambton Heights, NSW
| | - Leo-Pekka Lyytikäinen
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Health Technology, Tampere University, Tampere
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC/Vrije universiteit, Amsterdam
| | | | - Aurora Santin
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste
| | - Helena Schmidt
- Department of Molecular Biology and Biochemistry, Medical University Graz, Graz, Styria
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, Health Disparities Research Section, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Ye An Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Jingxian Tang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Sébastien Thériault
- Institut universitaire de cardiologie et de pneumologie de Québec-Université Laval, Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Quebec City, QC
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen
| | - Erin B Ware
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald
| | - Wang Ya Xing
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, Beijing
| | - Chenglong Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC
| | - Wei Zhao
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Md Abu Yusuf Ansari
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS
| | - Pramod Anugu
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS
| | - John R Attia
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW
| | - Lydia A Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Max Breyer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Stacey Collins
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Janie Corley
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh
| | - Marcus Dörr
- German Center for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald
| | - Jiawen Du
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Tariq Faquih
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Alison E Fohner
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Amanda M Fretts
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
| | - Srushti Gangireddy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Adam Gepner
- Cardiovascular Medicine, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - MariaElisa Graff
- Cardiovascular Disease (CVD) Genetic Epidemiology Laboratory, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Edith Hofer
- Department of Neurology, Medical University Graz, Graz, Styria
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald
| | - Michelle M Hood
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Xu Jie
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, Beijing
| | - Mika Kähönen
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere
| | - Sharon Lr Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | | | - Lisa W Martin
- Department of Cardiology, George Washington University, Washington, DC
| | - Koichi Matsuda
- Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo
| | - John J McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen
| | - Tomo Okochi
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC
| | - Olli T Raitakari
- Centre for Population Health Research, Department of Clinical Physiology and Nuclear Medicine, InFLAMES Research Flagship, Turku University Hospital and University of Turku, Turku
| | - Lorenz Risch
- Faculty of Medical Sciences , Institute for Laboratory Medicine, Private University in the Principality of Liechtenstein, Vaduz
| | - Martin Risch
- Central Laboratory, Cantonal Hospital Graubünden, Chur
| | - Ana Diez Roux
- Urban Health Collaborative, Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA
| | | | - Tom C Russ
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh
| | - Takeo Saito
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Aichi
| | - Pamela J Schreiner
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Rodney J Scott
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW
| | - James Shikany
- Division of General Internal Medicine and Population Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Adele M Taylor
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Paola Tesolin
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Rujia Wang
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen
| | - Wei Wenbin
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, Beijing
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Kristin L Young
- Cardiovascular Disease (CVD) Genetic Epidemiology Laboratory, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, Health Disparities Research Section, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Maria Pina Concas
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste
| | - David Conen
- Population Health Research Institute, Department of Medicine, McMaster University, Hamilton, ON
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, Health Disparities Research Section, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Ervin R Fox
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS
| | - Lisa de Las Fuentes
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO
| | - Ayush Giri
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Giorgia Girotto
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Mecklenburg-Western Pomerania
| | - Charles Gu
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO
| | | | - Sioban D Harlow
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Elizabeth Holliday
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW
| | - Jonas B Jost
- Rothschild Foundation Hospital, Institut Français de Myopie, Paris
| | - Paul Lacaze
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul
| | - Terho Lehtimäki
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Health Technology, Tampere University, Tampere
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX
| | - Kari E North
- Cardiovascular Disease (CVD) Genetic Epidemiology Laboratory, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Michael M Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - 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
| | | | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Chikashi Terao
- The Clinical Research Center at Shizuoka General Hospital, Shizuoka
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jeffrey R O'Connell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Cashell E Jaquish
- Division of Cardiovascular Science, Epidemiology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Alisa Manning
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Patricia B Munroe
- Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA
| | - Laura Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX
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Reaume M, Labossière MN, Batista R, Van Haute S, Tangri N, Rigatto C, Bohm C, Prud’homme D, Tanuseputro P, Lix LM. Patient-Physician Language Concordance and Cardiovascular Outcomes Among Patients With Hypertension. JAMA Netw Open 2025; 8:e2460551. [PMID: 39969882 PMCID: PMC11840650 DOI: 10.1001/jamanetworkopen.2024.60551] [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: 07/23/2024] [Accepted: 12/16/2024] [Indexed: 02/20/2025] Open
Abstract
Importance Patients who live in minority language communities often receive health care services of lower quality and safety compared with patients who speak the majority language. Yet the outcomes associated with care provided by physicians who speak a patient's primary language remain unknown. Objective To examine patient-physician language concordance and the risk of major adverse cardiovascular events (MACEs) among patients with hypertension. Design, Setting, and Participants This retrospective cohort study identified adults with self-reported hypertension in the Canadian Community Health Survey, a national survey that collects data from a representative sample of Canadians, from January 1, 2003, to December 31, 2014. Respondents (excluding those living in Quebec) had their hospitalization and mortality records linked to their survey responses. Data were analyzed from October 2023 to May 2024. Exposures Respondents' primary home language was defined using language spoken most often at home. Language spoken with a regular physician was used to measure patient-physician language concordance. Respondents who spoke to their regular physician in their primary home language were classified as having received language-concordant care, while all other respondents were classified as having received language-discordant care. Main Outcomes and Measures MACEs within 5 years of survey completion. Results Among the 124 583 patients included in this study, 114 239 (91.7%) spoke English, 4790 (3.8%) spoke French, 325 (0.3%) spoke an Indigenous language, and 5229 (4.2%) spoke an allophone (ie, other) language. The mean (SD) age of the cohort was 63.7 (14.8) years; 57.1% of the patients reported their sex as female. Very few respondents who spoke an Indigenous language at home (<4.6%) received language-concordant care. For French-speaking patients, there was no statistically significant difference in the risk of MACE between those who received language-concordant care and those who received language-discordant care (hazard ratio [HR], 1.09; 95% CI, 0.86-1.36). Allophone-speaking patients who received language-concordant care were 36% less likely to experience MACE (HR, 0.64; 95% CI, 0.51-0.80) compared with allophone-speaking patients who received language-discordant care. Conclusions and Relevance This retrospective cohort study found large disparities in both access to language-concordant care and risk of MACEs. These findings suggest that language-concordant care could potentially improve the health of individuals in minority language communities.
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Affiliation(s)
- Michael Reaume
- Department of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Mathieu N. Labossière
- Department of Département de Médecine, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Canada
| | - Ricardo Batista
- Akausivik Inuit Family Health Team, Ottawa, Ontario, Canada
- Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Stephanie Van Haute
- College of Nursing, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Manitoba Métis Federation, Winnipeg, Manitoba, Canada
| | - Navdeep Tangri
- Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada
| | - Claudio Rigatto
- Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada
| | - Clara Bohm
- Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Manitoba, Canada
| | - Denis Prud’homme
- Université de Moncton, Moncton, New Brunswick, Canada
- Institut du Savoir Montfort, Ottawa, Ontario, Canada
| | - Peter Tanuseputro
- Department of Family Medicine and Primary Care, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Lisa M. Lix
- Department of Community Health Sciences, Max Rady College of Medicine, Winnipeg, Manitoba, Canada
- George and Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada
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12
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Arabadjian M, Li Y, Jaeger BC, Colvin CL, Kalinowski J, Miles MA, Jones LM, Taylor JY, Butler KR, Muntner P, Spruill TM. Caregiving and Hypertension in Younger Black Women: The Jackson Heart Study. Hypertension 2025; 82:232-240. [PMID: 39601131 PMCID: PMC11735328 DOI: 10.1161/hypertensionaha.124.23721] [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: 07/23/2024] [Accepted: 11/11/2024] [Indexed: 11/29/2024]
Abstract
BACKGROUND Caregiving has been associated with high blood pressure in middle-aged and older women, but this relationship is understudied among younger Black women, a population at high risk for hypertension. We examined the associations of caregiving stress and caregiving for high-needs dependents with incident hypertension among reproductive-age women in the JHS (Jackson Heart Study), a cohort of community-dwelling Black adults. METHODS We included 453 participants, aged 21 to 44 years, with blood pressure <140/90 mm Hg, and not taking antihypertensive medication at baseline (2000-2004). Caregiving stress over the past 12 months was assessed via a single item in the global perceived stress scale. Caregiving for a high-needs dependent status was assessed via a question on hours per week spent caregiving for children (≤5 years or disabled) or older adults. Incident hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or self-report of taking antihypertensive medication at follow-up exams in 2005 to 2008 and 2009 to 2013. RESULTS Over a median follow-up of 7.4 years, 43.5% of participants developed hypertension. Participants with moderate/high versus no/low caregiving stress had a higher incidence of hypertension (51.7% versus 40.6%). Higher caregiving stress was associated with incident hypertension after adjustment for sociodemographic and clinical factors, health behaviors, and depressive symptoms (hazard ratio, 1.39 [95% CI, 1.01-1.94]). Being a caregiver for a high-needs dependent was not associated with incident hypertension (adjusted hazard ratio, 0.88 [95% CI, 0.64-1.21]). CONCLUSIONS Higher caregiving stress among reproductive-age Black women was associated with incident hypertension. Hypertension prevention approaches for this high-risk population may include caregiving stress management strategies.
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Affiliation(s)
- Milla Arabadjian
- New York University Grossman Long Island School of Medicine, Department of Foundations of Medicine, Mineola, NY
| | - Yiwei Li
- New York University Grossman School of Medicine, Department of Population Health, New York, NY
| | - Byron C. Jaeger
- Wake Forest University, Department of Biostatistics, Winston-Salem, NC
| | - Calvin L. Colvin
- Columbia University, Mailman School of Public Health, Department of Epidemiology, New York City, NY
| | - Jolaade Kalinowski
- University of Connecticut, Department of Human Development and Family Sciences, Storrs, CT
| | - Miriam A. Miles
- University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL
| | | | - Jacquelyn Y. Taylor
- Columbia University School of Nursing Center for Research on People of Color, New York, NY
| | | | - Paul Muntner
- University of Alabama at Birmingham, School of Public Health, Birmingham, AL
| | - Tanya M. Spruill
- New York University Grossman School of Medicine, Department of Population Health, New York, NY
- Institute for Excellence in Health Equity, NYU Langone Health, New York, NY
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13
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Chaitoff A, Zheutlin AR. Epidemiology of Hypertension in Older Adults. Clin Geriatr Med 2024; 40:515-528. [PMID: 39349028 DOI: 10.1016/j.cger.2024.04.007] [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: 10/02/2024]
Abstract
The exact definition of hypertension in older adults has changed over the decades, but the benefits of strict blood pressure control across the life span are being increasingly recognized by professional societies and guideline committees. This article discusses the prevalence of hypertension in older adults and describes the associations between hypertension and both clinical and nonclinical morbidity in that population.
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Affiliation(s)
- Alexander Chaitoff
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
| | - Alexander R Zheutlin
- Division of Cardiology, Feinberg School of Medicine, Northwestern University, 676 North St. Clair Street, Arkes Suite 2330, Chicago, IL 60611, USA
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14
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Spitz JA, Yang E, Blumenthal RS, Sharma G. Public Health Messaging to Older Adults About Hypertension. Clin Geriatr Med 2024; 40:669-683. [PMID: 39349039 DOI: 10.1016/j.cger.2024.04.006] [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: 10/02/2024]
Abstract
Hypertension is a major risk factor for cardiovascular disease, cognitive decline, and frailty. Given the increasing burden of hypertension in the aging population, it is imperative to improve hypertension management in that population. Apart from variations in treatment goals, challenges such as polypharmacy, medication side effects, and therapeutic inertia hinder adherence to guideline-directed medical therapies among older people. Effective public health messaging is essential for spreading evidence-based guidelines, raising awareness among clinicians, enhancing patient education and health literacy, and implementing community-based strategies to tackle hypertension. This review examines the current state of public messaging on hypertension in older adults.
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Affiliation(s)
- Jared A Spitz
- Inova Schar Heart and Vascular, Inova Health System, 8081 Innovation Park Drive, #700, Inova Specialty Center, Fairfax, VA 22031, USA.
| | - Eugene Yang
- Department of Medicine, Division of Cardiology, University of Washington School of Medicine, Seattle, WA, USA; UW Medicine Cardiovascular Wellness and Prevention Program, Medicine, UW Medicine - Eastside Specialty Center, Carl and Renée Behnke Endowed Professorship for Asian Health, 3100 Northup Way Box 356005 Bellevue, WA 98004, USA
| | - Roger S Blumenthal
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, 601 North Caroline Street, Suite 7200, Baltimore, MD 21287, USA
| | - Garima Sharma
- Inova Schar Heart and Vascular, Inova Health System, 8081 Innovation Park Drive, #700, Inova Specialty Center, Fairfax, VA 22031, USA; Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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15
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He Y, Chen H, Xiang P, Zhao M, Li Y, Liu Y, Wang T, Liang J, Lei J. Establishing an Evaluation Indicator System for User Satisfaction With Hypertension Management Apps: Combining User-Generated Content and Analytic Hierarchy Process. J Med Internet Res 2024; 26:e60773. [PMID: 39226103 PMCID: PMC11408894 DOI: 10.2196/60773] [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: 05/21/2024] [Revised: 06/24/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Hypertension management apps (HMAs) can be effective in controlling blood pressure, but their actual impact is often suboptimal. Establishing a user satisfaction evaluation indicator system for HMAs can assist app developers in enhancing app design and functionality, while also helping users identify apps that best meet their needs. This approach aims to improve the overall effectiveness of app usage. OBJECTIVE This study aims to systematically collect data on HMAs and their user reviews in the United States and China. It analyzes app usage patterns and functional characteristics, identifies factors influencing user satisfaction from existing research, and develops a satisfaction evaluation indicator system to provide more accurate recommendations for improving user satisfaction. METHODS We conducted a descriptive statistical analysis to assess the development status of HMAs in both countries and applied the task-technology fit model to evaluate whether the app functionalities align with business needs. We separately summarized the factors influencing user satisfaction in both countries from previous research, utilized the analytic hierarchy process to develop an evaluation indicator system for HMA user satisfaction, and calculated satisfaction levels. Based on these findings, we propose improvements to enhance app functionality and user satisfaction. RESULTS In terms of current development status, there were fewer HMAs and user reviews in China compared with the United States. Regarding app functional availability, fewer than 5% (4/91) of the apps achieved a demand fulfillment rate exceeding 80% (8/10). Overall, user satisfaction in both countries was low. CONCLUSIONS In the United States, user satisfaction was lowest for advertising distribution, data synchronization, and reliability. By contrast, Chinese apps need improvements in cost efficiency and compatibility.
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Affiliation(s)
- Yunfan He
- Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, China
| | - Han Chen
- Department of Cardiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Peng Xiang
- Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Intelligent Medical Research Center, Zhejiang University Institute of Computer Innovation Technology, Hangzhou, China
| | - Min Zhao
- IT Center, The First Affiliated Hospital of Xiamen University, XiaMen, China
- Department of Gynecology, The First Affiliated Hospital of Xiamen University, XiaMen, China
| | - Yingjun Li
- School of Public Health, Hangzhou Medical College, Hangzhou, China
| | | | - Tong Wang
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, China
- School of Basic Medical Sciences, Shandong University, Jinan, China
- Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Jun Liang
- Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, China
- Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- School of Public Health, Hangzhou Medical College, Hangzhou, China
- National Key Laboratory of Transvascular Implantable Devices, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianbo Lei
- Clinical Research Center, Affiliated Hospital of Southwest Medical University, Luzhou, China
- The First Affiliated Hospital, Hainan Medical University, Haikou, China
- Center for Medical Informatics, Health Science Center, Peking University, Beijing, China
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16
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Namkung EH, Kang SH. The Trend of Chronic Diseases Among Older Koreans, 2004-2020: Age-Period-Cohort Analysis. J Gerontol B Psychol Sci Soc Sci 2024; 79:gbae128. [PMID: 39051674 DOI: 10.1093/geronb/gbae128] [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: 02/22/2024] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVES This study aimed to examine age, period, and cohort effects contributing to the prevalence of diabetes and hypertension among older Koreans. Additionally, it sought to investigate how sociodemographic characteristics interact with period and cohort effects to influence the disease prevalence. METHODS Using the 2004-2020 data from the National Survey of Older Koreans, a nationally representative sample of older adults aged 65 or older, hierarchical age-period-cohort cross-classified random effects models (HAPC-CCREMs) were employed to estimate separate age, period, and cohort components of the recent trends in diabetes and hypertension. Sociodemographic characteristics were tested for their interactions with period and cohort effects. RESULTS Significant period effects were observed, indicating a steady increase in the likelihood of being diagnosed with diabetes and hypertension over time. Age effects revealed a quadratic trend, with disease risks generally increasing with age, but the rate of increase diminishing at older ages. Cohort effects exhibited an inverted U-shaped pattern, with higher risks observed in the 1930s and early 1940s cohorts compared to earlier and later cohorts. Gender and educational attainment emerged as significant moderators. Women than men born in the early 1930s exhibited higher risks of diabetes and hypertension, whereas individuals with lower educational attainment showed a steadily increasing risk of hypertension over time. DISCUSSION The results underscore the complex interplay of age, period, and cohort effects in shaping disease prevalence among older Koreans. Our findings highlight the importance of considering historical context and sociodemographic factors in understanding disease trends and designing targeted interventions to mitigate health disparities.
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Affiliation(s)
- Eun Ha Namkung
- Department of Social Welfare, Ewha Womans University, Seoul, South Korea
| | - Sung Hye Kang
- Department of Social Welfare, Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, California, USA
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17
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Sun Y, Zhang R, Tian L, Pan Y, Sun X, Huang Z, Fan J, Chen J, Zhang K, Li S, Chen W, Bazzano LA, Kelly TN, He J, Bundy JD, Li C. Novel Metabolites Associated With Blood Pressure After Dietary Interventions. Hypertension 2024; 81:1966-1975. [PMID: 39005213 PMCID: PMC11324412 DOI: 10.1161/hypertensionaha.124.22999] [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: 03/07/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND The blood pressure (BP) etiologic study is complex due to multifactorial influences, including genetic, environmental, lifestyle, and their intricate interplays. We used a metabolomics approach to capture internal pathways and external exposures and to study BP regulation mechanisms after well-controlled dietary interventions. METHODS In the ProBP trail (Protein and Blood Pressure), a double-blinded crossover randomized controlled trial, participants underwent dietary interventions of carbohydrate, soy protein, and milk protein, receiving 40 g daily for 8 weeks, with 3-week washout periods. We measured plasma samples collected at baseline and at the end of each dietary intervention. Multivariate linear models were used to evaluate the association between metabolites and systolic/diastolic BP. Nominally significant metabolites were examined for enriching biological pathways. Significant ProBP findings were evaluated for replication among 1311 participants of the BHS (Bogalusa Heart Study), a population-based study conducted in the same area as ProBP. RESULTS After Bonferroni correction for 77 independent metabolite clusters (α=6.49×10-4), 18 metabolites were significantly associated with BP at baseline or the end of a dietary intervention, of which 11 were replicated in BHS. Seven emerged as novel discoveries, which are as follows: 1-linoleoyl-GPE (18:2), 1-oleoyl-GPE (18:1), 1-stearoyl-2-linoleoyl-GPC (18:0/18:2), 1-palmitoyl-2-oleoyl-GPE (16:0/18:1), maltose, N-stearoyl-sphinganine (d18:0/18:0), and N6-carbamoylthreonyladenosine. Pathway enrichment analyses suggested dietary protein intervention might reduce BP through pathways related to G protein-coupled receptors, incretin function, selenium micronutrient network, and mitochondrial biogenesis. CONCLUSIONS Seven novel metabolites were identified to be associated with BP at the end of different dietary interventions. The beneficial effects of protein interventions might be mediated through specific metabolic pathways.
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Affiliation(s)
- Yixi Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Ling Tian
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago (Y.P., X.S., T.N.K.)
| | - Xiao Sun
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago (Y.P., X.S., T.N.K.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Jia Fan
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Jing Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Kai Zhang
- Department of Environmental Health Sciences, University of Albany, State University of New York, Rensselaer (K.Z.)
| | - Shengxu Li
- Children's Minnesota Research Institute, Children's Minnesota, Minneapolis (S.L.)
| | - Wei Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Lydia A Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago (Y.P., X.S., T.N.K.)
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Joshua D Bundy
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
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18
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Tsuyuki RT, Rader F. Pharmacist's Role in the Success of Blood Pressure Control Interventions: Evidence Isn't the Barrier…. Circ Cardiovasc Qual Outcomes 2024; 17:e011175. [PMID: 39027935 DOI: 10.1161/circoutcomes.124.011175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Affiliation(s)
- Ross T Tsuyuki
- EPICORE Centre, Division of Cardiology, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada (R.T.T.)
| | - Florian Rader
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA (F.R.)
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19
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Agwuegbo CC, Antia AU, Shamaki GR, Bob-Manuel T. Controversies related to renal artery denervation and devices. Curr Opin Cardiol 2024; 39:244-250. [PMID: 38567924 DOI: 10.1097/hco.0000000000001146] [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] [Indexed: 06/05/2024]
Abstract
PURPOSE OF REVIEW This review article discusses the controversies, strengths, and limitations of the current literature on renal artery denervation in the management of resistant hypertension, as well as the future directions of this intervention. RECENT FINDINGS There have been conflicting data from the different randomized control trials assessing the efficacy of renal artery denervation in the management of resistant hypertension. SUMMARY Renal artery denervation is achieved by ablating the sympathetic nerves surrounding the renal arteries using endovascular ultrasound, radiofrequency, or alcohol. Our review article highlights that renal artery denervation is generally effective in improving blood pressure in patients with resistant hypertension. The Food and Drug Administration (FDA) has recently approved the ReCor Medical Paradise system, and the Symplicity Spyral RDN systems for renal artery denervation.
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Affiliation(s)
| | | | | | - Tamunoinemi Bob-Manuel
- Division of Cardiovascular Diseases, University of Tennessee Health Science Center, Memphis, Tennessee, USA
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20
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Schiffrin EL, Fisher NDL. Diagnosis and management of resistant hypertension. BMJ 2024; 385:e079108. [PMID: 38897628 DOI: 10.1136/bmj-2023-079108] [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] [Indexed: 06/21/2024]
Abstract
Resistant hypertension is defined as blood pressure that remains above the therapeutic goal despite concurrent use of at least three antihypertensive agents of different classes, including a diuretic, with all agents administered at maximum or maximally tolerated doses. Resistant hypertension is also diagnosed if blood pressure control requires four or more antihypertensive drugs. Assessment requires the exclusion of apparent treatment resistant hypertension, which is most often the result of non-adherence to treatment. Resistant hypertension is associated with major cardiovascular events in the short and long term, including heart failure, ischemic heart disease, stroke, and renal failure. Guidelines from several professional organizations recommend lifestyle modification and antihypertensive drugs. Medications typically include an angiotensin converting enzyme inhibitor or angiotensin receptor blocker, a calcium channel blocker, and a long acting thiazide-type/like diuretic; if a fourth drug is needed, evidence supports addition of a mineralocorticoid receptor antagonist. After a long pause since 2007 when the last antihypertensive class was approved, several novel agents are now under active development. Some of these may provide potent blood pressure lowering in broad groups of patients, such as aldosterone synthase inhibitors and dual endothelin receptor antagonists, whereas others may provide benefit by allowing treatment of resistant hypertension in special populations, such as non-steroidal mineralocorticoid receptor antagonists in patients with chronic kidney disease. Several device based approaches have been tested, with renal denervation being the best supported and only approved interventional device treatment for resistant hypertension.
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Affiliation(s)
- Ernesto L Schiffrin
- Lady Davis Institute for Medical Research and Department of Medicine, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, QC, Canada
| | - Naomi D L Fisher
- Department of Medicine, Brigham and Women's Hospital, Harvard University, Boston, MA, USA
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21
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD, Sofer T. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep 2024; 14:12436. [PMID: 38816422 PMCID: PMC11139858 DOI: 10.1038/s41598-024-62945-9] [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: 01/22/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 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
| | - Byron C Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 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
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 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
| | - Matthew Moll
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, West Roxbury, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Myriam Fornage
- Department of Epidemiology, School of Public Health, Human Genetics Center, 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
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O'Connor
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Health Equity and Population Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, 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
| | - 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
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Center for Life Sciences CLS-934, 3 Blackfan St., Boston, MA, 02115, USA.
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22
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Sales I, AlRuthia Y. Arabic translation and cultural adaptation of Hill-Bone compliance to high blood pressure therapy scale. Saudi Pharm J 2024; 32:102053. [PMID: 38590609 PMCID: PMC10999866 DOI: 10.1016/j.jsps.2024.102053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
Background Adherence to prescription medications is vital to the success of any treatment plan, especially for chronic health conditions, such as hypertension (HTN). Although there are different scales used in assessing adherence to prescription medications, most if not all, of those scales are not available in Arabic. The absence of essential assessment tools makes the appraisal of adherence to prescription medications very difficult for native Arabic speakers. Therefore, this study aimed to translate and validate the Hill-Bone Compliance to High Blood Pressure Therapy (CHBPT) scale, which is commonly used to assess adherence to antihypertensive medications, among a sample of Arabic-speaking patients with HTN. Methods This was a single-center cross-sectional study that took place at a university-affiliated hospital. It interviewed adult (≥18 years) patients with HTN who were visiting the primary care clinics between January and November 2020. Non-Arabic speakers, those under 18 years of age, individuals without a diagnosis of HTN, and patients without any previously filled prescription medications for HTN within the past three months were excluded. The forward-backward translation method was used after receiving permission from the originators of the questionnaire to translate their scale to Arabic. Test-retest and Cronbach alpha methods were used to assess the reliability. Principal component analysis with varimax rotation was used to examine the construct validity. Results One hundred and forty-one patients consented and participated in the study. Most of the patients were ≥ 50 years old (75 %), male (72 %), and had another chronic health condition besides HTN (99 %). The translated scale had good internal consistency (Cronbach alpha = 0.83) and reliability (intraclass correlation coefficient of 0.9). The Kaiser-Meyer-Oklin was 0.82 indicating adequate sampling to conduct factor analysis; hence, three factors (e.g., subscales) were extracted similar to the original scale. The mean scores for appointment keeping, medication taking, and reducing sodium intake subscales, as well as for the overall scale were 5.62 ± 1.39, 33.94 ± 3.87, 9.73 ± 2.1, and 49.29 ± 5.21, respectively. Conclusion The translated version of the Hill-Bone CHBPT scale has both good reliability and validity and will hopefully help healthcare providers assess and monitor HTN patients' adherence to their antihypertensive medication regimens. Multicenter studies should be conducted to verify the validity and reliability of the translated questionnaire among different Arabic-speaking patient populations with HTN.
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Affiliation(s)
- Ibrahim Sales
- Department of Clinical Pharmacy, College of Pharmacy, Riyadh, Saudi Arabia
| | - Yazed AlRuthia
- Department of Clinical Pharmacy, College of Pharmacy, Riyadh, Saudi Arabia
- Pharmacoeconomics Research Unit, Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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23
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Wang Q, Si K, Xing X, Ye X, Liu Z, Chen J, Tang X. Association between dietary magnesium intake and muscle mass among hypertensive population: evidence from the National Health and Nutrition Examination Survey. Nutr J 2024; 23:37. [PMID: 38509619 PMCID: PMC10956219 DOI: 10.1186/s12937-024-00940-6] [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: 08/28/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Magnesium is critical for musculoskeletal health. Hypertensive patients are at high risk for magnesium deficiency and muscle loss. This study aimed to explore the association between magnesium intake and muscle mass in patients with hypertension. METHODS In this population-based cross-sectional study, 10,279 U.S. hypertensive adults aged 20 years or older were derived from the National Health and Nutrition Examination Survey in 1999-2006 and 2011-2018. Magnesium (Mg) intake from diet and supplements was assessed using 24-hour diet recalls. Muscle mass was evaluated by appendicular skeletal muscle mass index (ASMI, total ASM in kilograms [kg] divided by square of height in meters [m2]). The association of Mg intake with ASMI was estimated using weighted multivariable-adjusted linear regression models and restricted cubic splines. RESULTS Dose-response analyses showed a positive linear correlation between dietary Mg intake and ASMI. Every additional 100 mg/day in dietary Mg was associated with 0.04 kg/m2 (95% confidence interval [CI] 0.02-0.06 kg/m2) higher ASMI. The ASMI in participants who met the recommended dietary allowance (RDA) for dietary Mg was 0.10 kg/m2 (95% CI 0.04-0.16 kg/m2) higher than those whose dietary Mg was below estimated average requirement (EAR). However, the relationship of Mg intake from supplements with ASMI was not identified. CONCLUSION Higher level of dietary Mg intake rather than Mg supplements was associated with more muscle mass in U.S. adults with hypertension, which highlights the importance of meeting the recommended levels for dietary Mg intake.
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Affiliation(s)
- Qin Wang
- Department of Health Management, Naval Medical University, Shanghai, China
| | - Keyi Si
- Department of Nutrition and Food Hygiene, School of Public Health, Fudan University, Shanghai, China
| | - Xiaohong Xing
- Department of Nephrology, Shanghai Changzheng Hospital, Naval Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Xiaofei Ye
- Department of Military Health Statistics, Naval Medical University, Shanghai, China
| | - Ziyu Liu
- Department of Nephrology, Shanghai Changzheng Hospital, Naval Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Jing Chen
- Department of Nephrology, Shanghai Changzheng Hospital, Naval Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
| | - Xiaojing Tang
- Department of Nephrology, Shanghai Changzheng Hospital, Naval Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
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24
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Schiffrin EL. RNA Injection Every 6 Months to Improve Adherence and Lower Blood Pressure in Patients With Hypertension. JAMA 2024; 331:733-735. [PMID: 38363578 DOI: 10.1001/jama.2023.26071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Affiliation(s)
- Ernesto L Schiffrin
- Lady Davis Institute for Medical Research, and Department of Medicine, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
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25
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Brown TR, Jansen MO, Rolin SA, Liu SA, Xu KY. The misuse of malingering diagnoses in individuals with sickle cell disease. Gen Hosp Psychiatry 2024; 87:157-158. [PMID: 38102021 PMCID: PMC10982992 DOI: 10.1016/j.genhosppsych.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023]
Affiliation(s)
- Tashalee R Brown
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, 760 Westwood Plaza, Los Angeles, CA 90095, United States of America; Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, 760 Westwood Plaza, Los Angeles, CA 90095, United States of America; Health and Behavior Research Center, Department of Psychiatry, Washington University School of Medicine, 4940 Children's Place, Saint Louis, MO 63110, United States of America.
| | - Madeline O Jansen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, 760 Westwood Plaza, Los Angeles, CA 90095, United States of America; Jane and Terry Semel Institute for Neuroscience and Human Behavior at UCLA, 760 Westwood Plaza, Los Angeles, CA 90095, United States of America
| | - Stephanie A Rolin
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, 1051 Riverside Drive, New York, NY 10032, United States of America
| | - Shiyuan Anabeth Liu
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, 1051 Riverside Drive, New York, NY 10032, United States of America
| | - Kevin Y Xu
- John T. Milliken Department of Medicine, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, United States of America; Health and Behavior Research Center, Department of Psychiatry, Washington University School of Medicine, 4940 Children's Place, Saint Louis, MO 63110, United States of America.
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26
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Ansah G, Conaway M, Childress S, Slater K, Vellozo P. The Rise and Fall of Well-Controlled Blood Pressure: Labile Hypertension Following Repair of a Ruptured Abdominal Aortic Aneurysm. Cureus 2024; 16:e56880. [PMID: 38659514 PMCID: PMC11041856 DOI: 10.7759/cureus.56880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Hypertension is a common pathology with several etiologies. If left uncontrolled, severe and even fatal complications can develop, including heart disease, vascular damage, and stroke. Primary hypertension is most commonly seen without an underlying etiology; however, several contributing factors can lead to the development of hypertension. There have been limited cases reporting the effects of an abdominal aortic dissection treated with endovascular aortic repair (EVAR) on the development of labile hypertension. We report a case of uncontrolled, labile hypertension following an EVAR of an abdominal aortic aneurysm in a patient without prior medical history of hypertension.
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Affiliation(s)
- Grace Ansah
- Internal Medicine, New York Institute of Technology College of Osteopathic Medicine, Jonesboro, USA
| | - Madeline Conaway
- Internal Medicine, New York Institute of Technology College of Osteopathic Medicine, Jonesboro, USA
| | - Shana Childress
- Internal Medicine, New York Institute of Technology College of Osteopathic Medicine, Jonesboro, USA
| | - Kristin Slater
- Internal Medicine, Lincoln Memorial University, DeBusk College of Osteopathic Medicine, Harrogate, USA
| | - Paul Vellozo
- Internal Medicine, Lawrence Memorial Hospital Family Medical Center, Walnut Ridge, USA
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27
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Varghese JS, Curtis MG, Opara SCO, Patel SA, Sheth AN, Hussen SA. Concordance of high blood pressure among middle-aged and older same-sex couples in the USA. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24300695. [PMID: 38260296 PMCID: PMC10802658 DOI: 10.1101/2024.01.09.24300695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Heterosexual couples in romantic relationships are known to influence each other's hypertension risk. However, the role of partners on an individual's hypertension status in same-sex relationships is less understood. Our objective is to characterize the burden of high blood pressure among middle-aged and older couples consisting of men who have sex with men (MSM) and women who have sex with women (WSW) living in the US. Among 81 same-sex couples from the Health and Retirement Study 2006-18, 72.4% (95%CI: 23.4-95.7) MSM couples and 61.0% (95%CI: 30.4-84.8) WSW couples consisted of both partners with hypertension. Same-sex couples demonstrate high concordance of hypertension and related risk factors, suggesting a need to develop novel interventions targeting MSM and WSW couples.
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Affiliation(s)
- Jithin Sam Varghese
- Emory Global Diabetes Research Center of Woodruff Health Sciences Center, Emory University, Atlanta, GA, USA
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Michael G Curtis
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Samuel C O Opara
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Shivani A Patel
- Emory Global Diabetes Research Center of Woodruff Health Sciences Center, Emory University, Atlanta, GA, USA
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Anandi N Sheth
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Sophia A Hussen
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
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28
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Li G, Li J, Tian F, Ren J, Guo Z, Pan S, Liu D, Duan J, Liu Z. A 10-year retrospective cohort of diabetic patients in a large medical institution: Utilizing multiple machine learning models for diabetic kidney disease prediction. Digit Health 2024; 10:20552076241265220. [PMID: 39229465 PMCID: PMC11369867 DOI: 10.1177/20552076241265220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 06/13/2024] [Indexed: 09/05/2024] Open
Abstract
Objective As the prevalence of diabetes steadily increases, the burden of diabetic kidney disease (DKD) is also intensifying. In response, we have utilized a 10-year diabetes cohort from our medical center to train machine learning-based models for predicting DKD and interpreting relevant factors. Methods Employing a large dataset from 73,101 hospitalized type 2 diabetes patients at The First Affiliated Hospital of Zhengzhou University, we analyzed demographic and medication data. Machine learning models, including XGBoost, CatBoost, LightGBM, Random Forest, AdaBoost, GBDT (gradient boosting decision tree), and SGD (stochastic gradient descent), were trained on these data, focusing on interpretability by SHAP. SHAP explains the output of the models by assigning an importance value to each feature for a particular prediction, enabling a clear understanding of how individual features influence the prediction outcomes. Results The XGBoost model achieved an area under the curve (AUC) of 0.95 and an area under the precision-recall curve (AUPR) of 0.76, while CatBoost recorded an AUC of 0.97 and an AUPR of 0.84. These results underscore the effectiveness of these models in predicting DKD in patients with type 2 diabetes. Conclusions This study provides a comprehensive approach for predicting DKD in patients with type 2 diabetes, employing machine learning techniques. The findings are crucial for the early detection and intervention of DKD, offering a roadmap for future research and healthcare strategies in diabetes management. Additionally, the presence of non-diabetic kidney diseases and diabetes with complications was identified as significant factors in the development of DKD.
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Affiliation(s)
- Guangpu Li
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
| | - Jia Li
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
| | - Fei Tian
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingjing Ren
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zuishuang Guo
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaokang Pan
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongwei Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiayu Duan
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
| | - Zhangsuo Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
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29
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchel BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJ, Kardia SLR, Rich SS, Redline S, Kelly T, O’Connor T, Zhao W, Kim W, Guo X, Der Ida Chen Y, Sofer T. Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299909. [PMID: 38168328 PMCID: PMC10760279 DOI: 10.1101/2023.12.13.23299909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - 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
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D. Mitchel
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 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
| | - Byron C. Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 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
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 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
| | - Matthew Moll
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- VA Boston Healthcare System, West Roxbury, MA, 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
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ramon Casanova
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark, DK
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O’Connor
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital
| | - 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
| | - Yii Der Ida Chen
- 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
| | | | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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30
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Borghi A, De Giorgi A, Monti A, Cappadona R, Manfredini R, Corazza M. Investigating Chronotype and Sleep Quality in Psoriatic Patients: Results from an Observational, Web-Based Survey. J Pers Med 2023; 13:1604. [PMID: 38003919 PMCID: PMC10672655 DOI: 10.3390/jpm13111604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Psoriasis is an inflammatory disease for which the implications and repercussions go far beyond the skin. Psoriasis patients suffer not only due to its skin manifestations and related symptoms but also because of comorbidities and a huge emotional impact. OBJECTIVE The objective of this study was to investigate chronotype and sleep quality in a group of Italian psoriatic patients. MATERIALS AND METHODS An observational, cross-sectional, web-based study was set up by the Dermatology and Clinical Medicine Sections of the Department of Medical Sciences, University of Ferrara, Italy. The web questionnaire was sent to an email list of an Italian association of psoriatic patients with the aim of recording their main demographic, social, historical, and clinical data. The survey included two questionnaires: the Morningness-Eveningness Questionnaire (MEQ) and the Pittsburg Sleep Quality Index (PSQI). RESULTS Two hundred and forty-three psoriatic patients (mean age 52.9 ± 12.8 yrs., 32.5% males and 67.5% females) filled out the questionnaire. A good 63.8% of them were affected with psoriasis for more than 10 years, 25.9% reported having a diffuse psoriasis, and 66.7% were on treatment at the time they completed the questionnaire. With reference to chronotype, the mean MEQ score was 55.2 ± 10.7; furthermore, 44% of the patients were "morning-oriented types", M-types, or "larks", 44.5% were "intermediate-types" or I-types, and 11.5% were "evening-oriented types", E-types, or "owls". No correlations were found between chronotype and psoriasis extension. Based on the PSQI results, 72.8% of the study population was judged to have a low sleep quality. Sleep disturbance was significantly related to female sex, living alone, and the presence of comorbidities. CONCLUSIONS Sleep disturbance is very common in psoriatic patients, especially in those with comorbidities, in females, and in patients who live alone. The chronotype in psoriatic patients does not appear different when compared to the general population, nor does it seem to have any link with psoriasis severity.
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Affiliation(s)
- Alessandro Borghi
- Section of Dermatology and Infectious Diseases, Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Alfredo De Giorgi
- Clinical Medicine Unit, Department of Medicine, Azienda Ospedaliero-Universitaria S. Anna, 44124 Ferrara, Italy;
| | - Alberto Monti
- Section of Dermatology and Infectious Diseases, Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Rosaria Cappadona
- University Center for Studies on Gender Medicine, Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Roberto Manfredini
- Clinical Medicine Unit, Department of Medicine, Azienda Ospedaliero-Universitaria S. Anna, 44124 Ferrara, Italy;
- University Center for Studies on Gender Medicine, Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Monica Corazza
- Section of Dermatology and Infectious Diseases, Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
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