The DOI matches its Crossref record.
AN INVESTIGATION OF CORONARY HEART DISEASE IN FAMILIES
Abstract
The Framingham Heart Study (FHS) was started in 1948 as a prospective investigation of cardiovascular disease in a cohort of adult men and women. Continuous surveillance of this sample of 5209 subjects has been maintained through biennial physical examinations. In 1971 examinations were begun on the children of the FHS cohort. This study, called the Framingham Offspring Study (FOS), was undertaken to expand upon knowledge of cardiovascular disease, particularly in the area of familial clustering of the disease and its risk factors. This report reviews the sampling design of the FHS and describes the nature of the FOS sample. The FOS families appear to be of typical size and age structure for families with parents born in the late 19th or early 20th century. In addition, there is little evidence that coronary heart disease (CHD) experience and CHD risk factors differ in parents of those who volunteered for this study and the parents of those who did not volunteer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Items 101–149 of 149
Association of parental heart failure with risk of heart failure in offspring
Lee, D. S., Pencina, M. J., Benjamin, E. J., Wang, T. J., Levy, D., O'Donnell, C. J., Nam, B.-H., Larson, M. G., D'Agostino, R. B., & Vasan, R. S. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa052948 · Cited by 191
Correlated genotypes in friendship networks
Fowler, J. H., Settle, J. E., & Christakis, N. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011687108 · Cited by 188
Ma, J., Hennein, R., Liu, C., Long, M. T., Hoffmann, U., Jacques, P. F., Lichtenstein, A. H., Hu, F. B., & Levy, D. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.03.038 · Cited by 187
Eaker, E. D., Sullivan, L. M., Kelly-Hayes, M., D’Agostino, R. B., & Benjamin, E. J. (2004) · Circulation · doi:10.1161/01.cir.0000118535.15205.8f · Cited by 184
Cardiovascular risk assessment scores for people with diabetes: a systematic review
Chamnan, P., Simmons, R. K., Sharp, S. J., Griffin, S. J., & Wareham, N. J. (2009) · Diabetologia · doi:10.1007/s00125-009-1454-0 · Cited by 176
LDL particle size distribution. Results from the Framingham Offspring Study.
Campos, H., Blijlevens, E., McNamara, J. R., Ordovas, J. M., Posner, B. M., Wilson, P. W., Castelli, W. P., & Schaefer, E. J. (1992) · Arteriosclerosis and Thrombosis: A Journal of Vascular Biology · doi:10.1161/01.atv.12.12.1410 · Cited by 158
Self-citationBiomarkers of oxidative stress are associated with frailty: the Framingham Offspring Study
Liu, C. K., Lyass, A., Larson, M. G., Massaro, J. M., Wang, N., D’Agostino, R. B., Benjamin, E. J., & Murabito, J. M. (2016) · AGE · doi:10.1007/s11357-015-9864-z · Cited by 157
Krasnoff, J. B., Basaria, S., Pencina, M. J., Jasuja, G. K., Vasan, R. S., Ulloor, J., Zhang, A., Coviello, A., Kelly-Hayes, M., D'Agostino, R. B., et al. (2010) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-2680 · Cited by 152
Parikh, N. I., Gona, P., Larson, M. G., Fox, C. S., Benjamin, E. J., Murabito, J. M., O'Donnell, C. J., Vasan, R. S., & Levy, D. (2009) · Circulation · doi:10.1161/circulationaha.108.825364 · Cited by 149
Genes, economics, and happiness.
De Neve, J.-E., Christakis, N. A., Fowler, J. H., & Frey, B. S. (2012) · Journal of Neuroscience, Psychology, and Economics · doi:10.1037/a0030292 · Cited by 148
Lauer, M. S., Anderson, K. M., & Levy, D. (1991) · Journal of the American College of Cardiology · doi:10.1016/0735-1097(91)90549-o · Cited by 143
A new method for indexing left ventricular mass for differences in body size
Lauer, M. S., Anderson, K. M., Larson, M. G., & Levy, D. (1994) · The American Journal of Cardiology · doi:10.1016/0002-9149(94)90909-1 · Cited by 142
Circulating cortisol and cognitive and structural brain measures: The Framingham Heart Study
Echouffo-Tcheugui, J. B., Conner, S. C., Himali, J. J., Maillard, P., DeCarli, C. S., Beiser, A. S., Vasan, R. S., & Seshadri, S. (2018) · Neurology · doi:10.1212/wnl.0000000000006549 · Cited by 139
Flannick, J., Beer, N. L., Bick, A. G., Agarwala, V., Molnes, J., Gupta, N., Burtt, N. P., Florez, J. C., Meigs, J. B., Taylor, H., et al. (2013) · Nature Genetics · doi:10.1038/ng.2794 · Cited by 136
Lewis, G. D., Gona, P., Larson, M. G., Plehn, J. F., Benjamin, E. J., O'Donnell, C. J., Levy, D., Vasan, R. S., & Wang, T. J. (2008) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2008.01.046 · Cited by 133
DNA methylation networks underlying mammalian traits
Haghani, A., Li, C. Z., Robeck, T. R., Zhang, J., Lu, A. T., Ablaeva, J., Acosta-Rodríguez, V. A., Adams, D. M., Alagaili, A. N., Almunia, J., et al. (2023) · Science · doi:10.1126/science.abq5693 · Cited by 128
Niiranen, T. J., Lyass, A., Larson, M. G., Hamburg, N. M., Benjamin, E. J., Mitchell, G. F., & Vasan, R. S. (2017) · Hypertension · doi:10.1161/hypertensionaha.117.09026 · Cited by 126
Machine learning enables new insights into genetic contributions to liver fat accumulation
Haas, M. E., Pirruccello, J. P., Friedman, S. N., Wang, M., Emdin, C. A., Ajmera, V. H., Simon, T. G., Homburger, J. R., Guo, X., Budoff, M., et al. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100066 · Cited by 118
Thanassoulis, G., Massaro, J. M., Cury, R., Manders, E., Benjamin, E. J., Vasan, R. S., Cupple, L. A., Hoffmann, U., O'Donnell, C. J., & Kathiresan, S. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.03.019 · Cited by 114
McManus, D. D., Xanthakis, V., Sullivan, L. M., Zachariah, J., Aragam, J., Larson, M. G., Benjamin, E. J., & Vasan, R. S. (2010) · Circulation · doi:10.1161/circulationaha.109.885806 · Cited by 112
Association of Sex Hormones, Aging, and Atrial Fibrillation in Men: The Framingham Heart Study
Magnani, J. W., Moser, C. B., Murabito, J. M., Sullivan, L. M., Wang, N., Ellinor, P. T., Vasan, R. S., Benjamin, E. J., & Coviello, A. D. (2014) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.113.001322 · Cited by 112
Dhingra, R., Gona, P., Benjamin, E. J., Wang, T. J., Aragam, J., D'Agostino, R. B., Kannel, W. B., & Vasan, R. S. (2010) · European Journal of Heart Failure · doi:10.1093/eurjhf/hfq106 · Cited by 108
Self-citationAssociation of APOE genotype with carotid atherosclerosis in men and women
Elosua, R., Ordovas, J. M., Cupples, L. A., Fox, C. S., Polak, J. F., Wolf, P. A., D’Agostino, R. A., & O’Donnell, C. J. (2004) · Journal of Lipid Research · doi:10.1194/jlr.m400114-jlr200 · Cited by 101
Statistical workflow for feature selection in human metabolomics data
Antonelli, J., Claggett, B. L., Henglin, M., Kim, A., Ovsak, G., Kim, N., Deng, K., Rao, K., Tyagi, O., Watrous, J. D., et al. (2019) · Metabolites · doi:10.3390/metabo9070143 · Cited by 88
Sehgal, R., Markov, Y., Qin, C., Meer, M., Hadley, C., Shadyab, A. H., Casanova, R., Manson, J. E., Bhatti, P., Moore, A. Z., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00958-3 · Cited by 79
Intramuscular fat and physical performance at the Framingham Heart Study
Therkelsen, K. E., Pedley, A., Hoffmann, U., Fox, C. S., & Murabito, J. M. (2016) · AGE · doi:10.1007/s11357-016-9893-2 · Cited by 77
Brautbar, A., Pompeii, L. A., Dehghan, A., Ngwa, J. S., Nambi, V., Virani, S. S., Rivadeneira, F., Uitterlinden, A. G., Hofman, A., Witteman, J. C. M., et al. (2012) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.05.035 · Cited by 71
Midlife lipid and glucose levels are associated with Alzheimer's disease
Zhang, X., Tong, T., Chang, A., Ang, T. F. A., Tao, Q., Auerbach, S., Devine, S., Qiu, W. Q., Mez, J., Massaro, J., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12641 · Cited by 65
DiCorpo, D., LeClair, J., Cole, J. B., Sarnowski, C., Ahmadizar, F., Bielak, L. F., Blokstra, A., Bottinger, E. P., Chaker, L., Chen, Y.-D. I., et al. (2022) · Diabetes Care · doi:10.2337/dc21-1395 · Cited by 66
Dietary Quality Predicts Adult Weight Gain: Findings from the Framingham Offspring Study
Quatromoni, P. A., Pencina, M., Cobain, M. R., Jacques, P. F., & D'Agostino, R. B. (2006) · Obesity · doi:10.1038/oby.2006.157 · Cited by 66
Rutter, M. K., Wilson, P. W. F., Sullivan, L. M., Fox, C. S., D’Agostino, R. B., & Meigs, J. B. (2008) · Circulation · doi:10.1161/circulationaha.107.727727 · Cited by 64
Mendelson, M. M., Lyass, A., O’Donnell, C. J., D’Agostino, R. B., & Levy, D. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2015.0304 · Cited by 60
Phenotypic extremes in rare variant study designs
Peloso, G. M., Rader, D. J., Gabriel, S., Kathiresan, S., Daly, M. J., & Neale, B. M. (2016) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.197 · Cited by 58
Sex-specific health deterioration and mortality: the morbidity–mortality paradox over age and time
Kulminski, A. M., Culminskaya, I. V., Ukraintseva, S. V., Arbeev, K. G., Land, K. C., & Yashin, A. I. (2008) · Experimental Gerontology · doi:10.1016/j.exger.2008.09.007 · Cited by 56
Choung, R. S., Branda, M. E., Chitkara, D., Shah, N. D., Katusic, S. K., Locke III, G. R., & Talley, N. J. (2011) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2010.04513.x · Cited by 53
Ramos-Cejudo, J., Johnson, A. D., Beiser, A., Seshadri, S., Salinas, J., Berger, J. S., Fillmore, N. R., Do, N., Zheng, C., Kovbasyuk, Z., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.773984 · Cited by 49
Park, W. Y., Yiannakou, I., Petersen, J. M., Hoffmann, U., Ma, J., & Long, M. T. (2022) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2021.11.001 · Cited by 53
O’Keefe, J. H., Tintle, N. L., Harris, W. S., O’Keefe, E. L., Sala-Vila, A., Attia, J., Garg, G. M., Hure, A., Bork, C. S., Schmidt, E. B., et al. (2024) · Stroke · doi:10.1161/strokeaha.123.044281 · Cited by 46
Patel, K. V., Segar, M. W., Klonoff, D. C., Khan, M. S., Usman, M. S., Lam, C. S. P., Verma, S., DeFilippis, A. P., Nasir, K., Bakker, S. J. L., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067530 · Cited by 38
Mitchell, G. F., Guo, C.-Y., Kathiresan, S., Vasan, R. S., Larson, M. G., Vita, J. A., Keyes, M. J., Vyas, M., Newton-Cheh, C., Musone, S. L., et al. (2007) · Hypertension · doi:10.1161/hypertensionaha.106.085266 · Cited by 38
Lee, J., Vasan, R. S., & Xanthakis, V. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.015554 · Cited by 35
Hwang, P. H., Ang, T. F. A., De Anda‐Duran, I., Liu, X., Liu, Y., Gurnani, A., Mez, J., Auerbach, S., Joshi, P., Yuan, J., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12940 · Cited by 28
Na, H., Yang, J. B., Zhang, Z., Gan, Q., Tian, H., Rajab, I. M., Potempa, L. A., Tao, Q., & Qiu, W. Q. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.02.013 · Cited by 27
Gene expression markers of age-related inflammation in two human cohorts
Pilling, L. C., Joehanes, R., Melzer, D., Harries, L. W., Henley, W., Dupuis, J., Lin, H., Mitchell, M., Hernandez, D., Ying, S.-X., et al. (2015) · Experimental Gerontology · doi:10.1016/j.exger.2015.05.012 · Cited by 24
Walker, M. E., O’Donnell, A. A., Himali, J. J., Rajendran, I., Melo van Lent, D., Ataklte, F., Jacques, P. F., Beiser, A. S., Seshadri, S., Vasan, R. S., et al. (2021) · British Journal of Nutrition · doi:10.1017/s0007114521000660 · Cited by 22
Haring, R., Xanthakis, V., Coviello, A., Sullivan, L., Bhasin, S., Wallaschofski, H., Murabito, J. M., & Vasan, R. S. (2012) · International Journal of Andrology · doi:10.1111/j.1365-2605.2012.01285.x · Cited by 22
Huang, J., Stein, T. D., Wang, Y., Ang, T. F. A., Tao, Q., Lunetta, K. L., Massaro, J., Akhter‐Khan, S. C., Mez, J., Au, R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12851 · Cited by 14
Naghavi, M., Azimi, A., Atlas, K., Reeves, A. P., Zhang, C., Mirjalili, S. R., Mozafarybazargany, M., Jolfayi, A. G., Hashemi, A., Atlas, T., et al. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101698 · Cited by 2
Najarian, R. M. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.1.106
Metadata
- Authors
- KANNEL, W. B., FEINLEIB, M., McNAMARA, P. M., GARRISON, R. J., & CASTELLI, W. P.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 1979-09
- Volume
- 110
- Issue
- 3
- Pages
- 281-290
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2211287272
- Cited by
- 1,645
- References
- 0
- FWCI
- 2.48
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
