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Bourdillon MT, Song RJ, Musa Yola I, Xanthakis V, Vasan RS. Prevalence, Predictors, Progression, and Prognosis of Hypertension Subtypes in the Framingham Heart Study. J Am Heart Assoc 2022; 11:e024202. [PMID: 35261291 PMCID: PMC9075287 DOI: 10.1161/jaha.121.024202] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022]
Abstract
Background The epidemiology of hypertension subtypes has not been well characterized in the recent era. Methods and Results We delineated the prevalence, predictors, progression, and prognostic significance of hypertension subtypes in 8198 Framingham Heart Study participants (mean age, 46.5 years; 54% women). The prevalence of hypertension subtypes was as follows: nonhypertensive (systolic blood pressure [SBP] <140 mm Hg and diastolic blood pressure [DBP] <90 mm Hg), 79%; isolated systolic hypertension (ISH; SBP ≥140 mm Hg and DBP <90 mm Hg), 8%; isolated diastolic hypertension (SBP <140 mm Hg and DBP ≥90 mm Hg), 4%; and systolic-diastolic hypertension (SDH; SBP ≥140 mm Hg and DBP ≥90 mm Hg), 9%. The prevalence of ISH and SDH increased with age. Analysis of a subsample of nonhypertensive participants demonstrated that increasing age, female sex, higher heart rate, left ventricular mass, and greater left ventricular concentricity were predictors of incident ISH and SDH. Higher baseline DBP was associated with the risk of developing isolated diastolic hypertension and SDH, whereas higher SBP was associated with all 3 hypertension subtypes. On follow-up (median, 5.5 years), isolated diastolic hypertension often reverted to nonhypertensive BP (in 42% of participants) and ISH progressed to SDH (in 26% of participants), whereas SDH frequently transitioned to ISH (in 20% of participants). During follow-up (median, 14.6 years), 889 participants developed cardiovascular disease. Compared with the nonhypertensive group (referent), ISH (adjusted hazard ratio [HR], 1.57; 95% CI, 1.30-1.90) and SDH (HR, 1.66; 95% CI, 1.36-2.01) were associated with increased cardiovascular disease risk, whereas isolated diastolic hypertension was not (HR, 1.03; 95% CI, 0.68-1.57). Conclusions Hypertension subtypes vary in prevalence with age, are dynamic during short-term follow-up, and exhibit distinctive prognoses, underscoring the importance of blood pressure subphenotyping.
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Affiliation(s)
| | - Rebecca J. Song
- Department of EpidemiologyBoston University School of Public HealthBostonMA
| | - Ibrahim Musa Yola
- Section of Preventive Medicine and EpidemiologyDepartment of MedicineBoston University School of MedicineBostonMA
| | - Vanessa Xanthakis
- Section of Preventive Medicine and EpidemiologyDepartment of MedicineBoston University School of MedicineBostonMA
- Framingham Heart StudyFraminghamMA
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Ramachandran S. Vasan
- Department of EpidemiologyBoston University School of Public HealthBostonMA
- Section of Preventive Medicine and EpidemiologyDepartment of MedicineBoston University School of MedicineBostonMA
- Framingham Heart StudyFraminghamMA
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Vasan RS, Song RJ, Xanthakis V, Beiser A, DeCarli C, Mitchell GF, Seshadri S. Hypertension-Mediated Organ Damage: Prevalence, Correlates, and Prognosis in the Community. Hypertension 2022; 79:505-515. [PMID: 35138872 PMCID: PMC8849561 DOI: 10.1161/hypertensionaha.121.18502] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 11/21/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Guidelines emphasize screening people with elevated BP for the presence of end-organ damage. METHODS We characterized the prevalence, correlates, and prognosis of hypertension-mediated organ damage (HMOD) in the community-based Framingham Study. 7898 participants (mean age 51.6 years, 54% women) underwent assessment for the following HMOD: electrocardiographic and echocardiographic left ventricular hypertrophy, abnormal brain imaging findings consistent with vascular injury, increased carotid intima-media thickness, elevated carotid-femoral pulse wave velocity, reduced kidney function, microalbuminuria, and low ankle-brachial index. We characterized HMOD prevalence according to blood pressure (BP) categories defined by four international BP guidelines. Participants were followed up for incidence of cardiovascular disease. RESULTS The prevalence of HMOD varied positively with systolic BP and pulse pressure but negatively with diastolic BP; it increased with age, was similar in both sexes, and varied across BP guidelines based on their thresholds defining hypertension. Among participants with hypertension, elevated carotid-femoral pulse wave velocity was the most prevalent HMOD (40%-60%), whereas low ankle-brachial index was the least prevalent (<5%). Left ventricular hypertrophy, reduced kidney function, microalbuminuria, increased carotid intima-media thickness, and abnormal brain imaging findings had an intermediate prevalence (20%-40%). HMOD frequently clustered within individuals. On follow-up (median, 14.1 years), there were 384 cardiovascular disease events among 5865 participants with concurrent assessment of left ventricular mass, carotid-femoral pulse wave velocity, kidney function, and microalbuminuria. For every BP category above optimal (referent group), the presence of HMOD increased cardiovascular disease risk compared with its absence. CONCLUSIONS The prevalence of HMOD varies across international BP guidelines based on their different thresholds for defining hypertension. The presence of HMOD confers incremental prognostic information regarding cardiovascular disease risk at every BP category.
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Affiliation(s)
- Ramachandran S. Vasan
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA
| | - Rebecca J. Song
- Department of Epidemiology, Boston University School of Public Health, Boston, MA
| | - Vanessa Xanthakis
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Alexa Beiser
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Department of Neurology, Boston University School of Medicine, Boston, MA
| | | | | | - Sudha Seshadri
- Biggs Institute for Alzheimer’s Disease, University of Texas Health Sciences Center at San Antonio, Texas
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Markovitz AA, Mack JA, Nallamothu BK, Ayanian JZ, Ryan AM. Incremental effects of antihypertensive drugs: instrumental variable analysis. BMJ 2017; 359:j5542. [PMID: 29273586 PMCID: PMC5736968 DOI: 10.1136/bmj.j5542] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVES To assess the incremental effects of adding extra antihypertensive drugs from a new class to a patient's regimen. DESIGN Instrumental variable analysis of data from SPRINT (Systolic Blood Pressure Intervention Trial). To account for confounding by indication-when treatments seem less effective if they are administered to sicker patients-randomization status was used as the instrumental variable. Patients' randomization status was either intensive (systolic blood pressure target <120 mm Hg) or standard (systolic blood pressure target <140 mm Hg) treatment. Results from instrumental variable models were compared with those from standard multivariable models. SETTING Secondary data analysis of a randomized clinical trial conducted at 102 sites in 2010-15. PARTICIPANTS 9092 SPRINT participants with hypertension and increased cardiovascular risk but no history of diabetes or stroke. MAIN OUTCOMES MEASURES Systolic blood pressure, major cardiovascular events, and serious adverse events. RESULTS In standard multivariable models not adjusted for confounding by indication, addition of an antihypertensive drug from a new class was associated with modestly lower systolic blood pressure (-1.3 mm Hg, 95% confidence interval -1.6 to -1.0) and no change in major cardiovascular events (absolute risk of events per 1000 patient years, 0.5, 95% confidence interval -1.5 to 2.3). In instrumental variable models, the addition of an antihypertensive drug from a new class led to clinically important reductions in systolic blood pressure (-14.4 mm Hg, -15.6 to -13.3) and fewer major cardiovascular events (absolute risk -6.2, -10.9 to -1.3). Incremental reductions in systolic blood pressure remained large and similar in magnitude for patients already taking drugs from zero, one, two, or three or more drug classes. This finding was consistent across all subgroups of patients. The addition of another antihypertensive drug class was not associated with adverse events in either standard or instrumental variable models. CONCLUSIONS After adjustment for confounding by indication, the addition of a new antihypertensive drug class led to large reductions in systolic blood pressure and major cardiovascular events among patients at high risk for cardiovascular events but without diabetes. Effects on systolic blood pressure persisted across all levels of baseline drug use and all subgroups of patients.
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Affiliation(s)
- Adam A Markovitz
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, USA
- University of Michigan Medical School, Ann Arbor, MI, USA
- University of Michigan Center for Evaluating Health Reform, Ann Arbor, MI, USA
- VA Center for Clinical Management Research, Ann Arbor, MI, USA
| | - Jacob A Mack
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Brahmajee K Nallamothu
- VA Center for Clinical Management Research, Ann Arbor, MI, USA
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, MI, USA
- Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John Z Ayanian
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, USA
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of General Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
- Gerald R Ford School of Public Policy, Ann Arbor, MI, USA
| | - Andrew M Ryan
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, USA
- University of Michigan Center for Evaluating Health Reform, Ann Arbor, MI, USA
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor, MI, USA
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Tanamas SK, Hanson RL, Nelson RG, Knowler WC. Effect of different methods of accounting for antihypertensive treatment when assessing the relationship between diabetes or obesity and systolic blood pressure. J Diabetes Complications 2017; 31:693-699. [PMID: 28139345 PMCID: PMC7293873 DOI: 10.1016/j.jdiacomp.2016.12.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 12/07/2016] [Accepted: 12/13/2016] [Indexed: 11/29/2022]
Abstract
BACKGROUND Underlying blood pressure is that observed in the absence of antihypertensive treatment or, among those treated, the estimate of that which would be observed without treatment. This study aims to examine the relationships between diabetes or obesity and underlying systolic blood pressure adjusted for antihypertensive treatment by several methods. METHODS Data from two population studies were analyzed-an American Indian community in Arizona and the National Health and Nutrition Examination Surveys. Antihypertensive treatment was accounted for using: no adjustment; antihypertensive use as a covariate; blood pressure dichotomized into normotension and hypertension; addition of a fixed treatment effect; non-parametric algorithm; and censored normal regression. RESULTS The magnitude of association at each time point differed by adjustment method particularly where there was a difference in prevalence of antihypertensive use between people with and without diabetes or obesity. The common methods of ignoring antihypertensive treatment or including it as a covariate in a regression model underestimated the effects of diabetes and obesity on underlying blood pressure, compared to the recommended method of the censored normal regression. CONCLUSION Proper accounting for antihypertensive treatment is needed in interpreting variables that affect blood pressure.
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Affiliation(s)
- Stephanie K Tanamas
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ.
| | - Robert L Hanson
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ.
| | - Robert G Nelson
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ.
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ.
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Fennema-Notestine C, McEvoy LK, Notestine R, Panizzon MS, Yau WYW, Franz CE, Lyons MJ, Eyler LT, Neale MC, Xian H, McKenzie RE, Kremen WS. White matter disease in midlife is heritable, related to hypertension, and shares some genetic influence with systolic blood pressure. Neuroimage Clin 2016; 12:737-745. [PMID: 27790395 PMCID: PMC5071546 DOI: 10.1016/j.nicl.2016.10.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 09/20/2016] [Accepted: 10/03/2016] [Indexed: 12/12/2022]
Abstract
White matter disease in the brain increases with age and cardiovascular disease, emerging in midlife, and these associations may be influenced by both genetic and environmental factors. We examined the frequency, distribution, and heritability of abnormal white matter and its association with hypertension in 395 middle-aged male twins (61.9 ± 2.6 years) from the Vietnam Era Twin Study of Aging, 67% of whom were hypertensive. A multi-channel segmentation approach estimated abnormal regions within the white matter. Using multivariable regression models, we characterized the frequency distribution of abnormal white matter in midlife and investigated associations with hypertension and Apolipoprotein E-ε4 status and the impact of duration and control of hypertension. Then, using the classical twin design, we estimated abnormal white matter heritability and the extent of shared genetic overlap with blood pressure. Abnormal white matter was predominantly located in periventricular and deep parietal and frontal regions; associated with age (t = 1.9, p = 0.05) and hypertension (t = 2.9, p = 0.004), but not Apolipoprotein ε4 status; and was greater in those with uncontrolled hypertension relative to controlled (t = 3.0, p = 0.003) and normotensive (t = 4.0, p = 0.0001) groups, suggesting that abnormal white matter may reflect currently active cerebrovascular effects. Abnormal white matter was highly heritable (a2 = 0.81) and shared some genetic influences with systolic blood pressure (rA = 0.26), although there was evidence for distinct genetic contributions and unique environmental influences. Future longitudinal research will shed light on factors impacting white matter disease presentation, progression, and potential recovery.
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Key Words
- AWM, abnormal white matter
- ApoE, apolipoprotein E
- BMI, body mass index
- Blood pressure
- Brain
- CRP, C-Reactive protein
- DBP, diastolic blood pressure
- HDL, high-density lipoprotein
- HTN, hypertension
- Heritability
- Hypertension
- ICV, intracranial vault
- LDL, Low
- MRI
- SBP, systolic blood pressure
- White matter
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Affiliation(s)
- Christine Fennema-Notestine
- Department of Psychiatry at the University of California, San Diego, La Jolla, CA, USA
- Department of Radiology at the University of California, San Diego, La Jolla, CA, USA
| | - Linda K. McEvoy
- Department of Radiology at the University of California, San Diego, La Jolla, CA, USA
| | - Randy Notestine
- Department of Psychiatry at the University of California, San Diego, La Jolla, CA, USA
| | - Matthew S. Panizzon
- Department of Psychiatry at the University of California, San Diego, La Jolla, CA, USA
| | | | - Carol E. Franz
- Department of Psychiatry at the University of California, San Diego, La Jolla, CA, USA
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Lisa T. Eyler
- Department of Psychiatry at the University of California, San Diego, La Jolla, CA, USA
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - Hong Xian
- Department of Biostatistics, St. Louis University and St. Louis Veterans Affairs Medical Center, St. Louis, MO, USA
| | - Ruth E. McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S. Kremen
- Department of Psychiatry at the University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
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Petruski-Ivleva N, Viera AJ, Shimbo D, Muntner P, Avery CL, Schneider ALC, Couper D, Kucharska-Newton A. Longitudinal Patterns of Change in Systolic Blood Pressure and Incidence of Cardiovascular Disease: The Atherosclerosis Risk in Communities Study. Hypertension 2016; 67:1150-6. [PMID: 27045024 DOI: 10.1161/hypertensionaha.115.06769] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 03/09/2016] [Indexed: 11/16/2022]
Abstract
Elevated blood pressure in midlife contributes significantly to the risk of cardiovascular disease. However, patterns of blood pressure increase may differ among individuals and may result in differential risk. Our goal was to examine the contribution of longitudinal patterns of blood pressure change to incidence of heart failure, coronary heart disease, stroke, and cardiovascular disease mortality. Latent class growth models were used to identify patterns of change in blood pressure across 4 clinical examinations (1987-1998) among 9845 Atherosclerosis Risk in Communities (ARIC) cohort participants (mean age, 53.7 [SD 5.7] years). Patterns of change in systolic blood pressure included slowly and steeply increasing, a decreasing and a sustained elevated blood pressure. Changes in diastolic and mid-blood pressure (½ systolic+½ diastolic) were less pronounced. The association of blood pressure pattern group membership with incidence of clinical outcomes was examined in follow-up from the fourth clinical examination (1996-1998) to December 31, 2011, using Poisson regression models adjusted for demographic and metabolic characteristics, and hypertension medication use. A gradient of rates of all events was observed across the identified patterns. Associations were attenuated after adjustment for covariates. Cumulative systolic blood pressure load, rather than the temporal pattern of change in systolic blood pressure itself, plays a role in determining the risk of cardiovascular disease, in particular, of heart failure and cardiovascular disease mortality, independent of blood pressure level measured at one point in time.
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Affiliation(s)
- Natalia Petruski-Ivleva
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.).
| | - Anthony J Viera
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
| | - Daichi Shimbo
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
| | - Paul Muntner
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
| | - Christy L Avery
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
| | - Andrea L C Schneider
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
| | - David Couper
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
| | - Anna Kucharska-Newton
- From the Department of Epidemiology (N.P.-I., C.L.A.), Department of Family Medicine and Hypertension Research Program (A.J.V.), Department of Biostatistics (D.C.), and Cecil G. Sheps Center for Health Services Research (A.K.-N.), University of North Carolina, Chapel Hill; Department of Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY (D.S.); Department of Epidemiology, University of Alabama Birmingham, Birmingham (P.M.); and Departments of Neurology and Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University School of Medicine and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (A.L.C.S.)
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Panizzon M, Hauger RL, Sailors M, Lyons MJ, Jacobson KC, Murray RE, Rana B, Vasilopoulos T, Vuoksimaa E, Xian H, Kremen WS, Franz CE. A new look at the genetic and environmental coherence of metabolic syndrome components. Obesity (Silver Spring) 2015; 23:2499-507. [PMID: 26524476 PMCID: PMC4701648 DOI: 10.1002/oby.21257] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Revised: 06/29/2015] [Accepted: 07/16/2015] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Metabolic syndrome, a clustering of risk factors including insulin resistance, dyslipidemia, central obesity, and hypertension, increases risk for cardiovascular disease and cognitive decline. The etiology of the risk factors' cohesion remains unclear. How genetic and environmental influences explained co-occurrence of metabolic syndrome components was examined. METHODS Continuous measures of body mass index (BMI), waist circumference, blood pressure (BP), fasting insulin and glucose, high-density lipoprotein cholesterol (HDL), and triglycerides from 1,193 middle-aged twin men participating in the Vietnam Era Twin Study of Aging at average age 62 (range 56-67) were analyzed using multivariate biometrical modeling. RESULTS Four heritable factors were found: adiposity (BMI, waist circumference), insulin resistance (glucose, insulin), lipids (HDL, triglycerides), and BP (systolic, diastolic). Heritabilities were 0.42-0.68. In the best-fitting model, insulin resistance, lipids, and adiposity comprised a higher-order latent genetic factor. Adiposity and BP shared genetic influences independent of the latent genetic factor. All factors aggregated on a latent unique environmental factor. CONCLUSIONS Metabolic syndrome components form the equivalent of two genetic factors. BP was genetically unrelated to insulin resistance and lipids. Adiposity was the only characteristic genetically and environmentally related to all other factors. These results inform strategies for gene discovery and prediction of health outcomes.
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Affiliation(s)
- Matthew Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
| | - Richard L. Hauger
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, USA
| | - Megan Sailors
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Kristen C. Jacobson
- Department of Psychiatry & Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Ruth E. Murray
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Brinda Rana
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
| | - Terrie Vasilopoulos
- Department of Psychiatry & Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Eero Vuoksimaa
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
- University of Helsinki, Finland
| | - Hong Xian
- Department of Public Health, St. Louis University, St. Louis, MO, USA
| | - William S. Kremen
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, USA
| | - Carol E. Franz
- Department of Psychiatry, University of California San Diego, La Jolla CA, USA
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