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Kong N, Sakhuja S, Colantonio LD, Levitan EB, Lloyd-Jones DM, Cushman M, Muntner P, Polonsky TS. Atherosclerotic cardiovascular disease events among adults with high predicted risk without established risk factors. Am J Prev Cardiol 2024; 17:100612. [PMID: 38125204 PMCID: PMC10730342 DOI: 10.1016/j.ajpc.2023.100612] [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: 07/16/2023] [Revised: 10/27/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Objective Age is the strongest contributor to 10-year predicted atherosclerotic cardiovascular disease (ASCVD) risk. Some older adults have a predicted ASCVD risk ≥7.5 %, without established risk factors. We sought to compare ASCVD incidence among adults with predicted ASCVD risk ≥7.5 %, with and without established ASCVD risk factors, to adults with predicted risk <7.5 %. Methods We analyzed data from REasons for Geographic and Racial Differences in Stroke study participants, 45-79 years old, without ASCVD or diabetes, not taking statins and with low-density lipoprotein cholesterol 70-189 mg/dL. Participants were categorized into 3 groups based on their 10-year predicted ASCVD risk and presence of established risk factors: <7.5 %, ≥7.5 % with established risk factors and ≥7.5 % without established risk factors. Established risk factors included smoking, systolic blood pressure ≥130 mmHg or antihypertensive medication use, total cholesterol ≥200 mg/dL, or high-density lipoprotein cholesterol <50 mg/dL for women (<40 mg/dL for men). Participants were followed for ASCVD events. Results Among 11,115 participants, 911 incident ASCVD events occurred over a median of 11.1 years. ASCVD incidence rates were 3.6, 12.8, and 9.8 per 1,000 person-years for participants with predicted risk <7.5 %, predicted risk ≥7.5 % with established risk factors and predicted risk ≥7.5 % without established risk factors, respectively. Compared to adults with predicted risk <7.5 %, hazard ratios for incident ASCVD in participants with risk ≥7.5 % with and without established risk factors were 3.58 (95 %CI 3.03 - 4.21) and 2.72 (95 %CI 1.91-3.88), respectively. Conclusions Adults with a 10-year predicted ASCVD risk ≥7.5 % but without established risk factors had a high ASCVD incidence.
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Affiliation(s)
- Nathan Kong
- Department of Medicine, University of Chicago, Chicago, IL, United States
| | - Swati Sakhuja
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Lisandro D. Colantonio
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Emily B. Levitan
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Donald M. Lloyd-Jones
- Feinberg School of Medicine, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Mary Cushman
- Department of Medicine, Larner College of Medicine, The University of Vermont, Burlington, VT, United States
| | - Paul Muntner
- Office of Science, Center for Disease Control and Prevention, Atlanta, GA, United States
| | - Tamar S. Polonsky
- Department of Medicine, University of Chicago, Chicago, IL, United States
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Bai P, Shao X, Ning X, Jiang X, Liu H, Lin Y, Hou F, Zhang Y, Zhou S, Yu P. Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study. BMC Geriatr 2024; 24:193. [PMID: 38408910 PMCID: PMC10898137 DOI: 10.1186/s12877-024-04760-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 01/30/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD). OBJECTIVE This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD. METHODS In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models. RESULTS Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend < 0.001). Comparing the high-stable with the low-stable group, the risk of CKD decreased by 46%. All sensitivity analyses, including adjusting for baseline CVH and removing each CVH component from the total CVH, produced consistent results. Furthermore, the likelihood ratio test revealed that the model established by the CVH trajectory fit better than the baseline CVH and Δ CVH. CONCLUSION The higher CVH metrics trajectory and improvement of CVH metrics were associated with decreased risk of CKD. This study emphasized the importance of improving CVH to achieve primary prevention of CKD in older people.
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Affiliation(s)
- Pufei Bai
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China
| | - Xian Shao
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China
| | - Xiaoqun Ning
- Special Medical Service Center, Zhujiang Hospital, Southern Medical University, No. 253, Middle Industrial Avenue, Haizhu District, Guangzhou, Guangdong, China
| | - Xi Jiang
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China
| | - Hongyan Liu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China
| | - Yao Lin
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China
| | - Fang Hou
- Community Health Service Center, Jiefang Road, Tanggu Street, Binhai New District, Tianjin, China
| | - Yourui Zhang
- Community Health Service Center, Jiefang Road, Tanggu Street, Binhai New District, Tianjin, China
| | - Saijun Zhou
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China.
| | - Pei Yu
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134, China.
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Armstrong ND, Srinivasasainagendra V, Ammous F, Assimes TL, Beitelshees AL, Brody J, Cade BE, Ida Chen YD, Chen H, de Vries PS, Floyd JS, Franceschini N, Guo X, Hellwege JN, House JS, Hwu CM, Kardia SLR, Lange EM, Lange LA, McDonough CW, Montasser ME, O’Connell JR, Shuey MM, Sun X, Tanner RM, Wang Z, Zhao W, Carson AP, Edwards TL, Kelly TN, Kenny EE, Kooperberg C, Loos RJF, Morrison AC, Motsinger-Reif A, Psaty BM, Rao DC, Redline S, Rich SS, Rotter JI, Smith JA, Smith AV, Irvin MR, Arnett DK. Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program. Front Genet 2023; 14:1278215. [PMID: 38162683 PMCID: PMC10755672 DOI: 10.3389/fgene.2023.1278215] [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: 08/15/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
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Affiliation(s)
- Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Themistocles L. Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Amber L. Beitelshees
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jennifer Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - 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, United States
| | - Han Chen
- 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, United States
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Paul S. de Vries
- 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, United States
| | - James S. Floyd
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - 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, United States
| | - Jacklyn N. Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John S. House
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - May E. Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | - Megan M. Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Rikki M. Tanner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States
| | - Todd L. Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Tanika N. Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, United States
| | - Eimear E. Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - 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, United States
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Dabeeru C. Rao
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Stephen S. Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - 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, United States
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Albert V. Smith
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Donna K. Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, United States
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