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General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
Open accessAbstract
Background— Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. Methods and Results— We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions (“general CVD” algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P <0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non–laboratory-based predictors. Conclusions— A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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“Many risk assessment systems are available and have been comprehensively reviewed, including different Framingham models, 41 Systemic Coronary Risk Estimation (SCORE), 42 ASSIGN (CV risk estimation model from the Scottish Intercollegiate Guidelines Network), 43 Q-Risk, 44 Prospective Cardiovascular Munster Study (PROCAM), 45 Reynolds, 46,47 CUORE, 48 the Pooled Cohort equations 49 and Globorisk. 5”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [41]
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“The predicted 10-year CVD risk score was calculated using the BMI-based sex-specific risk equations (supplementary material online, Methods), which were developed from the Framingham Heart Study by D’Agostino et al.15”
From Tirzepatide compared with semaglutide and 10-year cardiovascular disease risk reduction in obesity: post-hoc analysis of the SURMOUNT-5 trial (2025) · cites this paper as [15]
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“The CAC score was measured manually by expert readers using the method by Agatston and Janowitz5,6 on dedicated workstations, as reported in the parent FHS17, PROMISE19, and ROMICAT-II20 studies.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [17]
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“According to a recent large-scale cross-sectional study conducted in Japan on visceral fat area and accumulated risk factors, the average number of obesity-related cardiovascular risk factors (dyslipidemia, high blood pressure, and high blood glucose) was more than 1.0 at 100 cm2 for visceral fat area in both men and women330).”
From Japan Atherosclerosis Society (JAS) guidelines for prevention of atherosclerotic cardiovascular diseases 2017 (2018) · cites this paper as [330]
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Citations
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297
Expert Panel on Detection, E. (2001) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.285.19.2486 · Cited by 26,011
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al. (2004) · The Lancet · doi:10.1016/s0140-6736(04)17018-9 · Cited by 12,026
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Self-citationPencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
Self-citationLopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., & Murray, C. J. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68770-9 · Cited by 5,630
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project
Conroy, R. (2003) · European Heart Journal · doi:10.1016/s0195-668x(03)00114-3 · Cited by 5,290
Rosamond, W., Flegal, K., Friday, G., Furie, K., Go, A., Greenlund, K., Haase, N., Ho, M., Howard, V., Kissela, B., et al. (2007) · Circulation · doi:10.1161/circulationaha.106.179918 · Cited by 3,277
Cited by
Sorted by citations
Items 501–600 of 1,055
Kaffashian, S., Dugravot, A., Nabi, H., Batty, G. D., Brunner, E. J., Mika Kivimaki, B., & Singh‐Manoux, A. (2011) · European Heart Journal · doi:10.1093/eurheartj/ehr133 · Cited by 110
Cysique, L. A., Moffat, K., Moore, D. M., Lane, T. A., Davies, N. W. S., Carr, A. D., Brew, B. J., & Rae, C. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061738 · Cited by 105
Lassale, C. M., Gunter, M. J., Romaguera, D., Peelen, L. M., van der Schouw, Y. T., Beulens, J. W. J., Freisling, H., Muller, D. C., Ferrari, P., Huybrechts, I., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159025 · Cited by 112
Gaziano, T. A., Abrahams‐Gessel, S., Gómez-Olivé, F. X., Wade, A. N., Crowther, N. J., Alam, S., Manne‐Goehler, J., Kabudula, C. W., Wagner, R. G., Rohr, J. K., et al. (2017) · BMC Public Health · doi:10.1186/s12889-017-4117-y · Cited by 108
Multimodality Strategy for Cardiovascular Risk Assessment
de Lemos, J. A., Ayers, C. R., Levine, B. D., deFilippi, C. R., Wang, T. J., Hundley, W. G., Berry, J. D., Seliger, S. L., McGuire, D. K., Ouyang, P., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.027272 · Cited by 106
Risk assessment of coronary heart disease based on cloud-random forest
Wang, J., Rao, C., Goh, M., & Xiao, X. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10170-z · Cited by 107
The role of polyamine metabolism in cellular function and physiology
Schibalski, R. S., Shulha, A. S., Tsao, B. P., Palygin, O., & Ilatovskaya, D. V. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00074.2024 · Cited by 118
Lipoprotein(a): more interesting than ever after 50 years
Dubé, J. B., Boffa, M. B., Hegele, R. A., & Koschinsky, M. L. (2012) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e32835111d8 · Cited by 105
Implementation and utilization of genetic testing in personalized medicine
Scott, S., Abul-Husn, N., Owusu Obeng, A., Sanderson, S., & Gottesman, O. (2014) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s48887 · Cited by 106
The global anti-phospholipid syndrome score in primary APS
Sciascia, S., Sanna, G., Murru, V., Roccatello, D., Khamashta, M. A., & Bertolaccini, M. L. (2015) · Rheumatology · doi:10.1093/rheumatology/keu307 · Cited by 107
Borisenko, O., Adam, D., Funch-Jensen, P. M., Ahmed, A. R., Zhang, R., Colpan, Z., & Hedenbro, J. (2015) · Obesity Surgery · doi:10.1007/s11695-014-1567-5 · Cited by 111
Fujiwara, T., Yano, Y., Hoshide, S., Kanegae, H., & Kario, K. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.1233 · Cited by 106
Associations Between Vascular Risk Across Adulthood and Brain Pathology in Late Life
Lane, C., Barnes, J., Nicholas, J. M., Sudre, C. H., Cash, D. M., Malone, I. B., Parker, T. D., Keshavan, A., Buchanan, S. M., Keuss, S. E., et al. (2019) · JAMA Neurology · doi:10.1001/jamaneurol.2019.3774 · Cited by 106
Islam, S. M. S., Talukder, A., Awal, M. A., Siddiqui, M. M. U., Ahamad, M. M., Ahammed, B., Rawal, L. B., Alizadehsani, R., Abawajy, J., Laranjo, L., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.839379 · Cited by 123
Three-Dimensional Carotid Ultrasound Plaque Texture Predicts Vascular Events
van Engelen, A., Wannarong, T., Párraga, G., Niessen, W. J., Fenster, A., Spence, J. D., & de Bruijne, M. (2014) · Stroke · doi:10.1161/strokeaha.114.005752 · Cited by 106
Vistisen, D., Witte, D. R., Tabák, Á. G., Herder, C., Brunner, E. J., Mika Kivimaki, B., & Færch, K. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001602 · Cited by 106
Improving Global Vascular Risk Prediction With Behavioral and Anthropometric Factors
Sacco, R. L., Khatri, M., Rundek, T., Xu, Q., Gardener, H. E., Boden-Albala, B. M., Tullio, M. R. D., Homma, S., Elkind, M. S. V., & Paik, M. C. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.07.047 · Cited by 106
Otsuka, T., Kawada, T., Ibuki, C., & Seino, Y. (2010) · American Heart Journal · doi:10.1016/j.ahj.2010.02.036 · Cited by 105
Clinical utility of metabolic syndrome severity scores: considerations for practitioners
DeBoer, M. D., & Gurka, M. J. (2017) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s101624 · Cited by 113
Machine learning to predict cardiovascular risk
Quesada, J. A., López‐Pineda, A., Gil-Guillén, V. F., Durazo-Arvizú, R., Orozco‐Beltrán, D., López-Domenech, A., & Carratalá‐Munuera, C. (2019) · International Journal of Clinical Practice · doi:10.1111/ijcp.13389 · Cited by 103
Saba, L., Jamthikar, A. D., Gupta, D., Khanna, N. N., Višković, K., Suri, H. S., Gupta, A. K., Mavrogeni, S. I., Turk, M., Laird, J. R., et al. (2020) · International Angiology · doi:10.23736/s0392-9590.19.04267-6 · Cited by 106
Predicting myocardial infarction through retinal scans and minimal personal information
Diaz‐Pinto, A., Ravikumar, N., Attar, R., Suinesiaputra, A., Zhao, Y., Levelt, E., Dall’Armellina, E., Lorenzi, M., Chen, Q., Keenan, T. D. L., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00427-7 · Cited by 107
Fosnot, J., Fischer, J. P., Smartt, J. M., Low, D. W., Kovach, S. J., Wu, L. C., & Serletti, J. M. (2010) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0b013e3181fed560 · Cited by 105
Geldsetzer, P., Manne‐Goehler, J., Theilmann, M., Davies, J. I., Awasthi, A., Danaei, G., Gaziano, T. A., Vollmer, S., Jaacks, L. M., Bärnighausen, T., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002581 · Cited by 109
Casey, D. E., Thomas, R. J., Bhalla, V., Commodore-Mensah, Y. Y., Heidenreich, P. A., Kolte, D. S., Muntner, P., Smith, S. C., Spertus, J. A., Windle, J. R., et al. (2019) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/hcq.0000000000000057 · Cited by 102
Nurmohamed, N. S., van Rosendael, A. R., Danad, I., Ngo‐Metzger, Q., Taub, P. R., Ray, K. K., Figtree, G. A., Bonaca, M. P., Hsia, J. A., Rodríguez, F., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae190 · Cited by 114
Seyedsadjadi, N., & Grant, R. (2020) · Antioxidants · doi:10.3390/antiox10010015 · Cited by 108
Færch, K., Witte, D. R., Tabák, Á. G., Perreault, L., Herder, C., Brunner, E. J., Mika Kivimaki, B., & Vistisen, D. (2013) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(13)70008-1 · Cited by 112
Abdel-Qadir, H. M., Thavendiranathan, P., Austin, P. C., Lee, D. S., Amir, E., Tu, J. V., Fung, K., & Anderson, G. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz460 · Cited by 102
Ammous, F., Zhao, W., Ratliff, S. M., Mosley, T. H., Bielak, L. F., Zhou, X., Peyser, P. A., Kardia, S. L. R., & Smith, J. A. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01035-3 · Cited by 106
Clinical utility and implementation of polygenic risk scores for predicting cardiovascular disease
Schunkert, H., Angelantonio, E. D., Inouye, M., Patel, R. S., Ripatti, S., Widén, E., Sanderson, S. C., Kaski, J. P., McEvoy, J. W., Vardas, P. E., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae649 · Cited by 107
Dietary flavonoid intake and cardiovascular risk: a population-based cohort study
Ponzo, V., Goitre, I., Fadda, M., Gambino, R., Francesco, A. D., Soldati, L., Gentile, L., Magistroni, P., Cassader, M., & Bo, S. (2015) · Journal of Translational Medicine · doi:10.1186/s12967-015-0573-2 · Cited by 116
Barriers to cardiovascular disease risk scoring and primary prevention in Europe
Hobbs, R. F., Jukema, J. W., Silva, P. M. D., McCormack, T., & Catapano, A. L. (2010) · QJM · doi:10.1093/qjmed/hcq122 · Cited by 104
C-reactive protein: Risk factor, biomarker and/or therapeutic target?
Genest, J. J. (2010) · Canadian Journal of Cardiology · doi:10.1016/s0828-282x(10)71061-8 · Cited by 102
Cardiovascular Health and Cognitive Function: The Maine-Syracuse Longitudinal Study
Crichton, G. E., Elias, M. F., Davey, A., & Alkerwi, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089317 · Cited by 108
Versteylen, M. O., Joosen, I. A., Shaw, L. J., Narula, J., & Hofstra, L. (2011) · Journal of Nuclear Cardiology · doi:10.1007/s12350-011-9425-5 · Cited by 102
Brisimi, T. S., Xu, T., Wang, T., Dai, W., Adams, W. G., & Paschalidis, I. C. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2789319 · Cited by 105
Du, Z., Yang, Y., Zheng, J., Li, Q., Lin, D., Li, Y., Fan, J., Cheng, W., Chen, X.-H., & Cai, Y. (2020) · JMIR Medical Informatics · doi:10.2196/17257 · Cited by 107
Fritsche, A., Wagner, R. J., Heni, M., Kantartzis, K., Machann, J., Schick, F., Lehmann, R., Peter, A., Dannecker, C., Fritsche, L., et al. (2021) · Diabetes · doi:10.2337/db21-0526 · Cited by 104
Kao, A. H., Lertratanakul, A., Elliott, J. R., Sattar, A., Santelices, L., Shaw, P., Birru, M. S., Avram, Z., Thompson, T., Sutton-Tyrrell, K., et al. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.05.040 · Cited by 100
Krantz, M. J., Coronel, S. M., Whitley, E. M., Dale, R., Yost, J., & Estacio, R. O. (2013) · American Journal of Public Health · doi:10.2105/ajph.2012.301068 · Cited by 99
Your work may be killing you! Workaholism, sleep problems and cardiovascular risk
Salanova, M., López‐González, Á. A., Llorens, S., Líbano, M. D., Vicente-Herrero, M. T., & Salvá, M. T. (2016) · Work & Stress · doi:10.1080/02678373.2016.1203373 · Cited by 101
Non-alcoholic fatty liver disease (NAFLD) and 10-year risk of cardiovascular diseases
Motamed, N., Rabiee, B., Poustchi, H., Dehestani, B., Hemasi, G. R., Khonsari, M. R., Maadi, M., Saeedian, F. S., & Zamani, F. (2016) · Clinics and Research in Hepatology and Gastroenterology · doi:10.1016/j.clinre.2016.07.005 · Cited by 100
Adams, S. C., DeLorey, D. S., Davenport, M. H., Stickland, M. K., Fairey, A., North, S. A., Szczotka, A., & Courneya, K. S. (2017) · Cancer · doi:10.1002/cncr.30859 · Cited by 100
Akhlaghi, M. (2019) · Nutrition Research Reviews · doi:10.1017/s0954422419000155 · Cited by 105
Vasankari, V., Husu, P., Vähä‐Ypyä, H., Suni, J. H., Tokola, K., Halonen, J., Hartikainen, J. E. K., Sievänen, H. T., & Vasankari, T. J. (2017) · European Journal of Preventive Cardiology · doi:10.1177/2047487317711048 · Cited by 99
Does Estimated Pulse Wave Velocity Add Prognostic Information?
Vishram‐Nielsen, J. K. K., Laurent, S., Nilsson, P. M., Linneberg, A. R., Sehested, T. S. G., Greve, S. V., Pareek, M., Palmieri, L., Giampaoli, S., Donfrancesco, C., et al. (2020) · Hypertension · doi:10.1161/hypertensionaha.119.14088 · Cited by 99
Severe mental illness: cardiovascular risk assessment and management
Polcwiartek, C., O’Gallagher, K., Friedman, D. J., Correll, C. U., Solmi, M., Jensen, S. E., & Nielsen, R. E. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae054 · Cited by 108
Racial and Socioeconomic Determinants of Cardiovascular Health: A Comprehensive Review
Borkowski, P., Borkowska, N., Mangeshkar, S., ADAL, B. H., & Singh, N. (2024) · Cureus · doi:10.7759/cureus.59497 · Cited by 105
Piperni, E., Nguyen, L. H., Manghi, P., Kim, H., Pasolli, E., Andreu‐Sánchez, S., Arrè, A., Bermingham, K. M., Blanco‐Míguez, A., Manara, S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.06.018 · Cited by 108
Banks, E., Crouch, S. R., Korda, R., Stavreski, B., Page, K., Thurber, K. A., & Grenfell, R. D. (2016) · The Medical Journal of Australia · doi:10.5694/mja15.01004 · Cited by 99
Hu, C., Hou, J., Zhou, Y., Sun, H., Yin, W., Zhang, Y., Wang, X., Wang, G., Chen, W., & Yuan, J. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.10.042 · Cited by 98
Timpka, S., Fraser, A., Schyman, T., Stuart, J. J., Åsvold, B. O., Mogren, I., Franks, P. W., & Rich-Edwards, J. W. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0429-1 · Cited by 99
Rücker, S. C. M., Tayea, A., Bitilinyu-Bangoh, J., Aza, E. B., Salumu, L., Quiles, I. A., Szumilin, E., Chirwa, Z., Rick, F., & Maman, D. (2017) · AIDS · doi:10.1097/qad.0000000000001700 · Cited by 96
Anand, S., Shivashankar, R., Ali, M. K., Kondal, D., Binukumar, B. K., Montez‐Rath, M. E., Ajay, V. S., Pradeepa, R., Deepa, M., Gupta, R., et al. (2015) · Kidney International · doi:10.1038/ki.2015.58 · Cited by 95
MicroRNAs Within the Continuum of Postgenomics Biomarker Discovery
Mayr, M., Zampetaki, A., Willeit, P., Willeit, J., & Kiechl, S. J. (2013) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.112.300141 · Cited by 97
Cardiovascular risk scores do not account for the effect of treatment: a review
Liew, S. M., Doust, J. A., & Glasziou, P. (2011) · Heart · doi:10.1136/hrt.2010.220442 · Cited by 95
Fahey, M., Crayton, E., Wolfe, C. D. A., & Douiri, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0185402 · Cited by 102
Doeberitz, P. V. K., Cecco, C. N. D., Schoepf, U. J., Duguay, T. M., Albrecht, M. H., van Assen, M., Bauer, M. J., Savage, R. H., Pannell, J. T., Santis, D. D., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5834-z · Cited by 97
Autoimmunity and Inflammation Link to Cardiovascular Disease Risk in Rheumatoid Arthritis
DeMizio, D. J., & Geraldino-Pardilla, L. B. (2019) · Rheumatology and Therapy · doi:10.1007/s40744-019-00189-0 · Cited by 98
Williams, S. A., Ostroff, R., Hinterberg, M. A., Coresh, J., Ballantyne, C. M., Matsushita, K., Mueller, C. E., Walter, J., Jonasson, C., Holman, R. R., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abj9625 · Cited by 97
Association of pericardial fat and coronary high-risk lesions as determined by cardiac CT
Schlett, C. L., Ferencik, M., Kriegel, M. F., Bamberg, F., Ghoshhajra, B. B., Joshi, S. B., Nagurney, J. T., Fox, C. S., Truong, Q. A., & Hoffmann, U. (2012) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.02.029 · Cited by 97
Relationship Between Carotid Disease on Ultrasound and Coronary Disease on CT Angiography
Cohen, G. I., Aboufakher, R., Bess, R. L., Frank, J. J., Othman, M., Doan, D., Mesiha, N., Rosman, H., & Szpunar, S. M. (2013) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2013.06.007 · Cited by 97
Sliwa, K. S., Stewart, S., & Gersh, B. J. (2011) · Circulation · doi:10.1161/circulationaha.110.992362 · Cited by 97
The QT Interval Is Associated With Incident Cardiovascular Events
Beinart, R., Zhang, Y., Lima, J. A. C., Bluemke, D. A., Soliman, E. Z., Heckbert, S. R., Post, W. S., Güallar, E., & Nazarian, S. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2014.08.039 · Cited by 97
Bartlett, J., Predazzi, I. M., Williams, S. M., Bush, W. S., Kim, Y., Havas, S., Toth, P. P., Fazio, S., & Miller, M. (2016) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.115.002436 · Cited by 96
Kunutsor, S. K., Bakker, S. J. L., James, R. W., & Dullaart, R. P. F. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.12.021 · Cited by 97
Modified Framingham Risk Factor Score for Systemic Lupus Erythematosus
Urowitz, M. B., Ibañez, D., Su, J., & Gladman, D. D. (2016) · The Journal of Rheumatology · doi:10.3899/jrheum.150983 · Cited by 96
Sardana, M., Lessard, D., Tsao, C. W., Parikh, N. I., BARTON, B. A., Nah, G., Thomas, R. C., Cheng, S. C., Schiller, N. B., Aragam, J. R., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.008435 · Cited by 97
Self-citationKhanna, N. N., Jamthikar, A. D., Gupta, D., Piga, M., Saba, L., Carcassi, C., Giannopoulos, A. A., Nicolaides, A. N., Laird, J. R., Suri, H. S., et al. (2019) · Current Atherosclerosis Reports · doi:10.1007/s11883-019-0766-x · Cited by 96
Çetın, M., Çakıcı, M., Zencır, C., Taşolar, H., Baysal, E., Ballı, M., & Aktürk, E. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.11.056 · Cited by 96
Marques, M. D., Santos, R. D., Parga, J. R., Rocha-Filho, J. A., Quaglia, L. A., Miname, M. H., & de Ávila, L. F. R. (2009) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2009.10.023 · Cited by 98
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Shah, R. V., & Goldfine, A. B. (2012) · Circulation · doi:10.1161/circulationaha.112.122135 · Cited by 95
Tang, X., Li, S., Wang, Y., Wang, M., Yin, Q., Mu, P., Lin, S., Qian, X., Ye, X., & Chen, Y. (2016) · Clinica Chimica Acta · doi:10.1016/j.cca.2016.08.004 · Cited by 97
Daumit, G. L., Dalcin, A., Dickerson, F., Miller, E. R., Evins, A. E., Cather, C., Jerome, G. J., Young, D. R., Charleston, J. B., Gennusa, J. V., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.7247 · Cited by 96
Quantifying discrimination of Framingham risk functions with different survival C statistics
Pencina, M. J., D’Agostino, R. B., & Song, L. (2012) · Statistics in Medicine · doi:10.1002/sim.4508 · Cited by 95
Self-citationHoshide, S., Yano, Y., Mizuno, H., Kanegae, H., & Kario, K. (2018) · HYPERTENSION · doi:10.1161/hypertensionaha.117.10385 · Cited by 82
Hearts and minds: linking vascular rigidity and aerobic fitness with cognitive aging
Gauthier, C. J., Lefort, M., Mekary, S., Desjardins-Crépeau, L., Skimminge, A., Iversen, P., Madjar, C., Desjardins, M., Lesage, F., Garde, E., et al. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.08.018 · Cited by 98
Mertens, E., Markey, O., Geleijnse, J. M., Lovegrove, J. A., & Givens, D. I. (2017) · European Journal of Nutrition · doi:10.1007/s00394-017-1408-0 · Cited by 97
Personalized medicine—a modern approach for the diagnosis and management of hypertension
Savoia, C., Volpe, M., Grassi, G., Borghi, C., Rosei, E. A., & Touyz, R. M. (2017) · Clinical Science · doi:10.1042/cs20160407 · Cited by 97
Extending the c‐statistic to nominal polytomous outcomes: the Polytomous Discrimination Index
CALSTER, B. V., Belle, V. V., Vergouwe, Y., Timmerman, D., Huffel, S. V., & Steyerberg, E. W. (2012) · Statistics in Medicine · doi:10.1002/sim.5321 · Cited by 94
Ibrahim, N. K., Mahnashi, M., Aldhaheri, A., Al-Zahrani, B., Al-Wadie, E., Aljabri, M. K., Alshanketi, R. M., Alshehri, R. S., Al-Sayes, F. M. O., & Bashawri, J. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-411 · Cited by 93
Nauman, J., Nes, B. M., Lavie, C. J., Jackson, A. S., Sui, X., Coombes, J. S., Blair, S. N., & Wisløff, U. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.10.007 · Cited by 92
Fujiwara, T., Hoshide, S., Kanegae, H., & Kario, K. (2020) · Hypertension · doi:10.1161/hypertensionaha.120.14790 · Cited by 93
Ballestri, S., Mantovani, A., Baldelli, E., Lugari, S., Maurantonio, M., Nascimbeni, F., Marrazzo, A., Romagnoli, D., Targher, G., & Lonardo, A. (2021) · Diagnostics · doi:10.3390/diagnostics11010098 · Cited by 99
Inflammation and aging-related disease: A transdisciplinary inflammaging framework
Andonian, B. J., Hippensteel, J. A., Abuabara, K., Boyle, E. M., Colbert, J. F., Devinney, M. J., Faye, A. S., Kochar, B. D., Lee, J., Litke, R. R., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01364-0 · Cited by 109
Pestana, D., Faria, G. R., Sá, C., Fernandes, V. C., Teixeira, D., Norberto, S., Faria, A., Meireles, M., Marques, C., Correia‐Sá, L., et al. (2014) · Environmental Research · doi:10.1016/j.envres.2014.05.026 · Cited by 93
Zheng, Y., Yu, B., Alexander, D. C., Manolio, T. A., Aguilar, D. A., Coresh, J., HEISS, G. M., Boerwinkle, E., & Nettleton, J. A. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt004 · Cited by 90
Richard, E., Jongstra, S., Soininen, H., Brayne, C., Moll van Charante, E. P., Meiller, Y., van der Groep, B., Beishuizen, C. R. L., Mangialasche, F., Barbera, M., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010806 · Cited by 92
Validation of the pooled cohort risk score in an Asian population – a retrospective cohort study
Chia, Y. C., Lim, H. M., & Ching, S. M. (2014) · BMC Cardiovascular Disorders · doi:10.1186/1471-2261-14-163 · Cited by 92
Hege, A., Lemke, M. K., Apostolopoulos, Y., & Sönmez, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207322 · Cited by 97
Jamthikar, A. D., Gupta, D., Khanna, N. N., Saba, L., Araki, T., Višković, K., Suri, H. S., Gupta, A. K., Mavrogeni, S. I., Turk, M., et al. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.09.03 · Cited by 93
Feng, L., Nian, S., Tong, Z., Zhu, Y. Y., Li, Y., Zhang, C., Bai, X., Luo, X., Wu, M., & Yan, Z. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-034226 · Cited by 103
Andersson, C., Nayor, M. G., Tsao, C. W., Levy, D. A., & Vasan, R. S. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.01.059 · Cited by 107
Self-citationChen, Z., Ahmed, M., Ha, V., Jefferson, K., Malik, V., Ribeiro, P. A. B., Zuchinali, P., & Drouin‐Chartier, J. (2021) · Advances in Nutrition · doi:10.1093/advances/nmab118 · Cited by 97
Chang, C., Lin, C., Luo, Y., Lee, Y., & Lin, C. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.754909 · Cited by 96
Association of Cardiovascular Risk Trajectory With Cognitive Decline and Incident Dementia
Farnsworth, B., Josefsson, M., Wåhlin, A., Nyberg, L. H., & Karalija, N. (2022) · Neurology · doi:10.1212/wnl.0000000000200255 · Cited by 99
Etiology of White Matter Hyperintensities in Autosomal Dominant and Sporadic Alzheimer Disease
Shirzadi, Z., Schultz, S. A., Yau, W. W., Joseph‐Mathurin, N., Fitzpatrick, C. D., Levin, R., Kantarci, K., Preboske, G. M., Jack, C. R., Farlow, M. R., et al. (2023) · JAMA Neurology · doi:10.1001/jamaneurol.2023.3618 · Cited by 91
Kaambwa, B., Bryan, S., Jowett, S., Mant, J., Bray, E., Hobbs, F. R. R., Holder, R., Jones, M. I., Little, P., Williams, B., et al. (2014) · European Journal of Preventive Cardiology · doi:10.1177/2047487313501886 · Cited by 94
Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk
Fuster-Parra, P., Tauler, P., Bennasar‐Veny, M., Ligęza, A., López‐González, Á. A., & Aguiló, A. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.12.010 · Cited by 92
Metadata
- Authors
- D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B.
- Affiliations
- Boston University (US)
- Venue
- Circulation
- Published
- 2008-02-12
- Volume
- 117
- Issue
- 6
- Pages
- 743-753
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2148092884
- Cited by
- 7,491
- References
- 52
- Citations by year
- 2026: 491 · 2025: 567 · 2024: 552 · 2023: 511 · 2022: 529 · 2021: 592 · 2020: 562 · 2019: 454 · 2018: 475 · 2017: 452 · 2016: 385 · 2015: 389 · 2014: 391 · 2013: 323 · 2012: 294
- FWCI
- 169.19
- Type
- journal article
- Language
- en
- Update notices
- correction (2008-07-22)
- Open access
- bronze
- doi.org
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (n01hc25195-41-0-1)
- National Heart, Lung, and Blood Institute (2K24 HL 04334, N01‐HC25195, K24HL004334)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Keywords
- cardiovascular risk prediction, atherosclerotic cardiovascular disease, Framingham risk score, coronary artery disease, stroke, peripheral arterial disease, heart failure, traditional risk factors, cholesterol, blood pressure, smoking, diabetes mellitus, primary care
- MeSH terms
- Adult, Aged, Algorithms, Cardiovascular Diseases, Female, Humans, Longitudinal Studies, Male, Middle Aged, Primary Health Care, Risk Factors, Sex Factors, Multivariate Analysis, Proportional Hazards Models, Risk Assessment
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