Cited by 472Cardiovascular Health and Risk FactorsHealthcare Systems and Public HealthHealth Promotion and Cardiovascular Prevention

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Cohort Profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology

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Tsao, Connie W, Vasan, Ramachandran S·2015·International Journal of Epidemiology

DOI 10.1093/ije/dyv337PMC PMC5156338

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Abstract

The Framingham Heart Study (FHS) has conducted seminal research defining cardiovascular disease (CVD) risk factors and fundamentally shaping public health guidelines for CVD prevention over the past five decades. The success of the Original Cohort, initiated in 1948, paved the way for further epidemiological research in preventive cardiology. Due to the keen observations suggesting the role of shared familial factors in the development of CVD, in 1971 the FHS began enroling the second generation cohort, comprising the children of the Original Cohort and the spouses of the children. In 2002, the third generation cohort, comprising the grandchildren of the Original Cohort, was initiated to additionally explore genetic contributions to CVD in greater depth. Additionally, because of the predominance of White individuals of European descent in the three generations of FHS participants noted above, the Heart Study enrolled the OMNI1 and OMNI2 cohorts in 1994 and 2003, respectively, aimed to reflect the current greater racial and ethnic diversity of the town of Framingham. All FHS cohorts have been examined approximately every 2-4 years since the initiation of the study. At these periodic Heart Study examinations, we obtain a medical history and perform a cardiovascular-focused physical examination, 12-lead electrocardiography, blood and urine samples testing and other cardiovascular imaging studies reflecting subclinical disease burden.The FHS has continually evolved along the cutting edge of cardiovascular science and epidemiological research since its inception. Participant studies now additionally include study of cardiovascular imaging, serum and urine biomarkers, genetics/genomics, proteomics, metabolomics and social networks. Numerous ancillary studies have been established, expanding the phenotypes to encompass multiple organ systems including the lungs, brain, bone and fat depots, among others. Whereas the FHS was originally conceived and designed to study the epidemiology of cardiovascular disease, it has evolved over the years with staggering expanded breadth and depth that have far greater implications in the study of the epidemiology of a wide spectrum of human diseases. The FHS welcomes research collaborations using existing or new collection of data. Detailed information regarding the procedures for research application submission and review are available at [http://www.framinghamheartstudy.org/researchers/index.php].

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Atrial fibrillation as an independent risk factor for stroke: the Framingham study
Wolf, P. A., Abbott, R. D., & Kannel, W. B. (1991) · Stroke · doi:10.1161/01.str.22.8.983 · Cited by 7,388
Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart study
Levy, D., Garrison, R. J., Savage, D. D., Kannel, W. B., & Castelli, W. P. (1990) · New England Journal of Medicine · doi:10.1056/nejm199005313222203 · Cited by 5,699
High density lipoprotein as a protective factor against coronary heart disease
Gordon, T., Castelli, W. P., Hjortland, M. C., Kannel, W. B., & Dawber, T. R. (1977) · The American Journal of Medicine · doi:10.1016/0002-9343(77)90874-9 · Cited by 5,059
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Impact of Atrial Fibrillation on the Risk of Death: The Framingham Heart Study
Benjamin, E. J., Wolf, P. A., D’Agostino, R. B., Silbershatz, H., Kannel, W. B., & Levy, D. (1998) · Circulation · doi:10.1161/01.cir.98.10.946 · Cited by 4,575
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534
Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study.
Hubert, H. B., Feinleib, M., McNamara, P. M., & Castelli, W. P. (1983) · Circulation · doi:10.1161/01.cir.67.5.968 · Cited by 4,241
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
The natural history of congestive heart failure: the Framingham study
McKee, P. A., Castelli, W. P., McNamara, P. M., & Kannel, W. B. (1971) · New England Journal of Medicine · doi:10.1056/nejm197112232852601 · Cited by 3,448
Metabolite profiles and the risk of developing diabetes
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez, C., et al. (2011) · Nature Medicine · doi:10.1038/nm.2307 · Cited by 3,143
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Kagan, 1959, Blood pressure and its relation to coronary heart disease in the Framingham study, Hypertension, 7, 53
10.1001/jama.1970.03180020021004 · DOI · Paper page
10.1001/jama.1987.03390160062027 · DOI
10.1001/jama.1988.03410230074031 · DOI
10.1001/archinte.1979.03630450011006 · DOI
10.1001/jama.1971.03180230027005 · DOI
10.1001/jama.1988.03720070025028 · DOI

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Authors: Tsao, C. W., & Vasan, R. S.
Affiliations: Beth Israel Deaconess Medical Center (US)
Boston University (US)
Corresponding author: Tsao, Connie W
ORCID: Tsao, Connie W
Vasan, Ramachandran S
Venue: International Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 2015-12
Volume: 44
Issue: 6
Pages: 1800-1813
ISSN: 0300-5771 (ISSN-L)
1464-3685
DOI: 10.1093/ije/dyv337
PMC: PMC5156338
OpenAlex: W2207219039
Cited by: 457
References: 119
Citations by year: 2026: 49 · 2025: 59 · 2024: 68 · 2023: 52 · 2022: 63 · 2021: 50 · 2020: 58 · 2019: 26 · 2018: 14 · 2017: 12 · 2016: 4 · 2015: 2
FWCI: 7.76
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: International Journal of Epidemiology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (n01hc25195-41-0-1, 5r01hl080124-05)
National Institute on Aging (R01AG028321, AG-028321)
National Heart, Lung, and Blood Institute (R01-HL080124, HL-076784, 1K23 HL118259, N01‐HC25195, HHSN268201500001I, HL107385, HL 080124, HHSN268201500001C, HL077447, N01-HC-025195, R01HL118259, R01HL077447, R01HL107385, R01HL076784, HL070100, HL 060040, R01HL070100, HL071039, R01HL060040, R01HL071039)
National Institute of Neurological Disorders and Stroke (6R01NS17950, R01NS017950)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Health Sciences › Medicine › Epidemiology › Healthcare Systems and Public Health
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Keywords: Framingham Heart Study, cardiovascular risk factors, cardiovascular disease prevention, family-based cohort, racial and ethnic diversity, cardiovascular biomarkers, cardiovascular imaging, subclinical cardiovascular disease, ancillary studies, preventive cardiology
MeSH terms: Adolescent, Adult, Aged, Aged, 80 and over, Asian, Black or African American, Cardiovascular Diseases, Child, Child, Preschool, Diet, Echocardiography, Electrocardiography, Ethnicity, Female, Hispanic or Latino, Humans, Hypercholesterolemia, Hypertension, Longitudinal Studies, Male, Massachusetts, Middle Aged, Phenotype, Prospective Studies, Risk Factors, Tomography, X-Ray Computed, Cohort Studies, Biomarkers, Epidemiologic Studies, Hypertrophy, Left Ventricular, Age Distribution, Sex Distribution, Genetic Predisposition to Disease, White People, Overweight, Young Adult

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