The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
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.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
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
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 401–500 of 1,055
Kim, J.-H., Cho, J. J., & Park, Y. S. (2015) · Journal of Korean Medical Science · doi:10.3346/jkms.2015.30.3.264 · Cited by 126
Stergiou, G. S., Palatini, P., Modesti, P. A., Asayama, K., Asmar, R., Bilo, G., de la Sierra, A., Dolan, E., Head, G., Kario, K., et al. (2020) · Journal of Hypertension · doi:10.1097/hjh.0000000000002341 · Cited by 130
Lilly, C. M., Zuckerman, I. H., Badawi, O., & Riker, R. R. (2011) · CHEST Journal · doi:10.1378/chest.11-0718 · Cited by 131
Burden of Rare Sarcomere Gene Variants in the Framingham and Jackson Heart Study Cohorts
Bick, A. G., Flannick, J. A., Ito, K., Cheng, S. C., Vasan, R. S., Parfenov, M. G., Herman, D. S., DePalma, S. R., Gupta, N., Gabriel, S., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.07.017 · Cited by 130
Self-citationBye, A., Røsjø, H. R., Nauman, J., da Silva, G. J. J., Follestad, T., Omland, T., & Wisløff, U. (2016) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2016.05.009 · Cited by 130
Ferencik, M., Pencina, K. M., Liu, T., Ghemigian, K., Baltrusaitis, K., Massaro, J. M., D’Agostino, R. B., O’Donnell, C. J., & Hoffmann, U. (2017) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.117.006592 · Cited by 130
Self-citationLeft Atrioventricular Coupling Index as a Prognostic Marker of Cardiovascular Events: The MESA Study
Pezel, T., Ambale‐Venkatesh, B., Vasconcellos, H. D. D., Kato, Y., Shabani, M., Xie, E., Heckbert, S. R., Post, W. S., Shea, S., Allen, N. B., et al. (2021) · Hypertension · doi:10.1161/hypertensionaha.121.17339 · Cited by 130
Prevention of depression in older age
Almeida, O. P. (2014) · Maturitas · doi:10.1016/j.maturitas.2014.03.005 · Cited by 132
Expanding the Role of Nurses to Improve Hypertension Care and Control Globally
Himmelfarb, C. D., Commodore-Mensah, Y. Y., & Hill, M. N. (2016) · Annals of Global Health · doi:10.1016/j.aogh.2016.02.003 · Cited by 153
Felix, A. S., Bower, J. K., Pfeiffer, R. M., Raman, S. V., Cohn, D. E., & Sherman, M. E. (2016) · International Journal of Cancer · doi:10.1002/ijc.30470 · Cited by 130
Aging in the Cardiovascular System: Lessons from Hutchinson-Gilford Progeria Syndrome
Hamczyk, M. R., del Campo, L., & Andrés, V. (2017) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021317-121454 · Cited by 134
Anstee, Q. M., Berentzen, T. L., Nitze, L. M., Jara, M., Jensen, A. B., Kjær, M. S., Mangla, K. K., Tarp, J. M., & Khunti, K. (2023) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2023.100780 · Cited by 137
Dowlatshahi, E. A., Kavousi, M., Nijsten, T. E. C., Ikram, M. A., Albert Hofman, D., Franco, O. H., & Wakkee, M. (2013) · Journal of Investigative Dermatology · doi:10.1038/jid.2013.131 · Cited by 127
Ford, E. S. (2013) · Preventing Chronic Disease · doi:10.5888/pcd10.130244 · Cited by 127
Bekelman, J. E., Lu, H. L., Pugh, S. L., Baker, K., Berg, C. D., de González, A. B., Braunstein, L. Z., Bosch, W. R., Chauhan, C., Ellenberg, S. S., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025556 · Cited by 127
Arts, E., Popa, C. D., den Broeder, A. A., Donders, R., Sandoo, A., Toms, T. E., Rollefstad, S., Ikdahl, E., Semb, A. G., Kitas, G. D., et al. (2015) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2014-206879 · Cited by 127
Koppelmans, V., de Groot, M., de Ruiter, M. B., Boogerd, W. J., Seynaeve, C., Vernooij, M. W., Niessen, W. J., Schagen, S. B., & Breteler, M. M. B. (2012) · Human Brain Mapping · doi:10.1002/hbm.22221 · Cited by 130
The prevalence and mechanisms of metabolic syndrome in schizophrenia: a review
Papanastasiou, E. (2012) · Therapeutic Advances in Psychopharmacology · doi:10.1177/2045125312464385 · Cited by 131
Erbel, R. M., & Budoff, M. J. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs076 · Cited by 126
Blomberg, B. A., de Jong, P. A., Thomassen, A., Lam, M. G. E. H., Vach, W., Olsen, M. H., Mali, W. P. T. M., Narula, J., Alavi, A. A., & Høilund‐Carlsen, P. F. (2016) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-016-3552-9 · Cited by 128
Lamelas, P. M., Díaz, R., Orlandini, A., Avezum, Á., Oliveira, G. B. F., Mattos, A. J. C., Lanas, F. T., Pamela Serón, S., Oliveros, M. J., López-Jaramillo, P. A., et al. (2019) · Journal of Hypertension · doi:10.1097/hjh.0000000000002108 · Cited by 128
Karmali, K. N., Lloyd‐Jones, D. M., van der Leeuw, J., Goff, D. C., Yusuf, S., Zanchetti, A., Glasziou, P., Jackson, R., Woodward, M., Rodgers, A., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002538 · Cited by 127
Pétursson, H., Sigurdsson, J. A., Bengtsson, C., Nilsen, T. I. L., & Getz, L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026621 · Cited by 128
Kvelde, T., Lord, S. R., Close, J., Reppermund, S., Kochan, N. A., Sachdev, P. S., Brodaty, H., & Delbaere, K. (2014) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2014.09.003 · Cited by 120
Zavodni, A. E. H., Wasserman, B. A., McClelland, R. L., Gomes, A. S., Folsom, A. R., Polak, J. F., Lima, J. A. C., & Bluemke, D. A. (2014) · Radiology · doi:10.1148/radiol.14131020 · Cited by 125
Eggers, K. M., Venge, P., Lindahl, B., & Lind, L. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.12.048 · Cited by 125
Li, L., Scott, C. A., & Rothwell, P. M. (2022) · JAMA · doi:10.1001/jama.2022.12759 · Cited by 124
Aging and the evolution of comorbidities among HIV-positive individuals in a European cohort
Pelchen–Matthews, A., Ryom, L., Borges, Á. H., Edwards, S., Duvivier, C., Stephan, C., Sambatakou, H., Maciejewska, K., Portu, J. J., Weber, J. N., et al. (2018) · AIDS · doi:10.1097/qad.0000000000001967 · Cited by 125
Ferrell, M., Wang, Z., Anderson, J. T., Li, X. S., Witkowski, M., DiDonato, J. A., Hilser, J. R., Hartiala, J., Haghikia, A., Čajka, T., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02793-8 · Cited by 129
Carotid Intima-Media Thickness in Children with CKD
Brady, T. M., Schneider, M. F., Flynn, J. T., Cox, C., Samuels, J., Saland, J. M., White, C. T., Furth, S. L., Warady, B. A., & Mitsnefes, M. M. (2012) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03130312 · Cited by 122
Aerobic exercise training reduces arterial stiffness in metabolic syndrome
Donley, D., Fournier, S., Reger, B., DeVallance, E., Bonner, D., Olfert, I. M., Frisbee, J. C., & Chantler, P. D. (2014) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00151.2014 · Cited by 122
Chen, Y., Chow, E. J., Oeffinger, K. C., Border, W. L., Leisenring, W. M., Meacham, L. R., Mulrooney, D. A., Sklar, C. A., Stovall, M., Robison, L. L., et al. (2019) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djz108 · Cited by 122
Unique cardiovascular risk factors in women
Young, L. D., & Cho, L. (2019) · Heart · doi:10.1136/heartjnl-2018-314268 · Cited by 126
How much does HDL cholesterol add to risk estimation? A report from the SCORE Investigators
Cooney, M. T., Dudina, A., De Bacquer, D., Fitzgerald, A., Conroy, R., Sans, S., Menotti, A., De Backer, G., Jousilahti, P., Keil, U., et al. (2009) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e3283213140 · Cited by 121
Subclinical Atherosclerosis Measures for Cardiovascular Prediction in CKD
Matsushita, K., Sang, Y., Ballew, S. H., Shlipak, M. G., Katz, R., Rosas, S. E., Peralta, C. A., Woodward, M., Kramer, H., Jacobs, D. R., et al. (2014) · Journal of the American Society of Nephrology · doi:10.1681/asn.2014020173 · Cited by 122
Brown, D. K., Barton, J., Pretty, J., & Gladwell, V. (2014) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.3421 · Cited by 122
Genetic Markers Enhance Coronary Risk Prediction in Men: The MORGAM Prospective Cohorts
Hughes, M., Saarela, O., Stritzke, J., Kee, F., Silander, K., Klopp, N., Kontto, J. P., Karvanen, J., Willenborg, C., Salomaa, V. V., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040922 · Cited by 120
Erectile dysfunction in fit and healthy young men: psychological or pathological?
Rastrelli, G., & Maggi, M. (2017) · Translational Andrology and Urology · doi:10.21037/tau.2016.09.06 · Cited by 125
Chang, J., Ko, A., Park, S. M., Choi, S., Kim, K., Kim, S. M., Yun, J. M., Канг, У., Shin, I. H., Shin, J. Y., et al. (2020) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2020.03.027 · Cited by 119
Cognitive impairment in persons with rheumatoid arthritis
Shin, S. Y., Katz, P., Wallhagen, M. I., & Julian, L. J. (2012) · Arthritis Care & Research · doi:10.1002/acr.21683 · Cited by 120
Han, M., Qie, R., Shi, X., Yang, Y., Lu, J., Hu, F., Zhang, M., Zhang, Z., Hu, D., & Zhao, Y. (2022) · British Journal of Sports Medicine · doi:10.1136/bjsports-2021-104876 · Cited by 119
Beddhu, S., Rocco, M. V., Toto, R. D., Craven, T. E., Greene, T., Bhatt, U. Y., Cheung, A. K., Cohen, D. L., Freedman, B. I., Hawfield, A. T., et al. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2966 · Cited by 120
Menni, C., Gudelj, I., Macdonald-Dunlop, E., Mangino, M., Zierer, J., Bešić, E., Joshi, P. K., Trbojević‐Akmačić, I., Chowienczyk, P., Spector, T. D., et al. (2018) · Circulation Research · doi:10.1161/circresaha.117.312174 · Cited by 119
Laurent, S., Châtellier, G., Azizi, M., Calvet, D., Choukroun, G. J., Danchin, N., Delsart, P., Girerd, X. J., Gosse, P., Khettab, H., et al. (2021) · Hypertension · doi:10.1161/hypertensionaha.121.17579 · Cited by 122
Deep Lipidomics in Human Plasma: Cardiometabolic Disease Risk and Effect of Dietary Fat Modulation
Eichelmann, F., Sellem, L., Wittenbecher, C., Jäger, S., Kuxhaus, O., Prada, M., Cuadrat, R. R. C., Jackson, K. G., Lovegrove, J. A., & Schulze, M. B. (2022) · Circulation · doi:10.1161/circulationaha.121.056805 · Cited by 120
The influence of hip circumference on the relationship between abdominal obesity and mortality
Cameron, A. J., Magliano, D. J., Shaw, J. E., Zimmet, P., Carstensen, B., Alberti, K. G. M. M., Tuomilehto, J. O., Barr, E., Pauvaday, V., Kowlessur, S., et al. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr198 · Cited by 124
Weng, S. F., Vaz, L., Qureshi, N., & Kai, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214365 · Cited by 118
Schwartz, S. M., Wildenhaus, K. J., Bucher, A., & Byrd, B. (2020) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.00031 · Cited by 118
Accelerated biological aging elevates the risk of cardiometabolic multimorbidity and mortality
Jiang, M., Tian, S., Liu, S., Wang, Y., Guo, X., Huang, T., Lin, X., Belsky, D. W., Baccarelli, A. A., & Gao, X. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00438-8 · Cited by 117
Chia, Y. C., Gray, S. Y. W., Ching, S. M., Lim, H. M., & Chinna, K. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007324 · Cited by 122
C-Reactive Protein: Clinical and Epidemiological Perspectives
Salazar, J., Martínez, M. S., Chávez, M., Toledo, A., Añez, R., Torres, Y., Apruzzese, V., Silva, C., Rojas, J., & Bermúdez, V. (2014) · Cardiology Research and Practice · doi:10.1155/2014/605810 · Cited by 117
Divala, O., Amberbir, A., Ismail, Z., Beyene, T., Garone, D., Pfaff, C., Singano, V., Akello, H., Joshua, M., Nyirenda, M., et al. (2016) · BMC Public Health · doi:10.1186/s12889-016-3916-x · Cited by 117
Rapp, D., Scharhag, J., Wagenpfeil, S., & Scholl, J. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-018697 · Cited by 120
Exercise training reverses endothelial dysfunction in nonalcoholic fatty liver disease
Pugh, C. J. A., Sprung, V. S., Kemp, G. J., Richardson, P., Shojaee-Moradie, F., Umpleby, A. M., Green, D. J., Cable, N. T., Jones, H., & Cuthbertson, D. J. (2014) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00306.2014 · Cited by 116
Shojaee-Moradie, F., Cuthbertson, D. J., Barrett, M., Jackson, N. C., Herring, R., Thomas, E. L., Bell, J., Kemp, G. J., Wright, J., & Umpleby, A. M. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2353 · Cited by 116
Cobo, I., Tanaka, T. N., Mangalhara, K. C., Lana, A. J., Yeang, C., Han, C. Z., Schlachetzki, J. C. M., Challcombe, J., Fixsen, B. R., Sakai, M., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.06.022 · Cited by 117
Flavan-3-ols and Cardiometabolic Health: First Ever Dietary Bioactive Guideline
Crowe-White, K. M., Evans, L. W., Kuhnle, G. G. C., Milenković, D., Stote, K. S., Wallace, T. C., Handu, D., & Senkus, K. E. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac105 · Cited by 121
Bozorgmanesh, M., Hadaegh, F., & Azizi, F. (2010) · Lipids in Health and Disease · doi:10.1186/1476-511x-9-45 · Cited by 113
Vock, D. M., Wolfson, J. A., Bandyopadhyay, S., Adomavičius, G., Johnson, P. E., Vazquez‐Benitez, G., & O’Connor, P. J. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.009 · Cited by 126
Aminian, A., Brethauer, S. A., Andalib, A., Punchai, S., Mackey, J. E., Rodriguez, J., Rogula, T. G., Kroh, M., & Schauer, P. R. (2016) · Annals of Surgery · doi:10.1097/sla.0000000000001857 · Cited by 118
Prediction of Early Recurrence After Acute Ischemic Stroke
Arsava, E. M., Kim, G., Filho, J. O., Güngör, L., Noh, H. J., Lordelo, M. J., Avery, R., Maier, I., & Ay, H. (2016) · JAMA Neurology · doi:10.1001/jamaneurol.2015.4949 · Cited by 114
Arena, R. A., Guazzi, M., Lianov, L. S., Whitsel, L. P., Berra, K. A., Lavie, C. J., Kaminsky, L. A., Williams, M. A., Hivert, M., Franklin, N. C., et al. (2015) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2015.05.001 · Cited by 118
Kokubo, Y., Watanabe, M., Higashiyama, A., Nakao, Y. M., Nakamura, F., & Miyamoto, Y. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.007720 · Cited by 114
Wald, N., Simmonds, M. C., & Morris, J. K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018742 · Cited by 113
Zeller, T., Hughes, M., Palosaari, T., Schillert, A., Conrads‐Frank, A., den Ruijter, H. M., Schnabel, R. B., Kee, F., Salomaa, V. V., Siebert, U., et al. (2014) · European Journal of Epidemiology · doi:10.1007/s10654-014-9952-x · Cited by 114
Kluzek, S., Sanchez-Santos, M. T., Leyland, K. M., Judge, A., Spector, T. D., Hart, D., Cooper, C. C., Newton, J. L., & Arden, N. (2015) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2015-208056 · Cited by 117
Prediction versus aetiology: common pitfalls and how to avoid them
van Diepen, M., Ramspek, C. L., Jager, K. J., Zoccali, C., & Dekker, F. W. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfw459 · Cited by 116
Association of Coronary Artery Calcium Score vs Age With Cardiovascular Risk in Older Adults
Yano, Y., O’Donnell, C. J., Kuller, L. H., Kavousi, M., Erbel, R., Ning, H., D’Agostino, R. B., Newman, A. B., Nasir, K., Albert Hofman, D., et al. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2017.2498 · Cited by 113
Self-citationSmith, G. N., Louis, J. M., & Saade, G. R. (2019) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000003363 · Cited by 113
Impact of Sex on Cardiovascular Outcome in Patients at High Cardiovascular Risk
Kappert, K., Böhm, M., Schmieder, R. E., Schumacher, H., Teo, K. K., Yusuf, S., Sleight, P., & Unger, T. (2012) · Circulation · doi:10.1161/circulationaha.111.086660 · Cited by 114
Systematic Examination of the Updated Framingham Heart Study General Cardiovascular Risk Profile
Perak, A. M., & Lloyd‐Jones, D. M. (2009) · Circulation · doi:10.1161/circulationaha.108.835470 · Cited by 113
Identifying Adults at High Risk for Diabetes and Cardiovascular Disease Using Hemoglobin A1c
Ackermann, R. T., Cheng, Y. J., Williamson, D. F., & Gregg, E. W. (2011) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.09.022 · Cited by 111
Chamnan, P., Simmons, R. K., Khaw, K., Wareham, N. J., & Griffin, S. J. (2010) · BMJ · doi:10.1136/bmj.c1693 · Cited by 113
Methylation‐based estimated biological age and cardiovascular disease
Lind, L., Ingelsson, E., Sundström, J., Siegbahn, A., & Lampa, E. (2017) · European Journal of Clinical Investigation · doi:10.1111/eci.12872 · Cited by 116
Fernández‐Álvarez, V., Linares-Sánchez, M., López, F., Nieto, C. S., Mäkitie, A. A., Olsen, K. D., & Ferlito, A. (2022) · Cardiology and Therapy · doi:10.1007/s40119-022-00261-x · Cited by 112
Peripheral vascular function in spinal cord injury: a systematic review
West, C. R., AlYahya, A., Laher, I., & Krassioukov, A. V. (2012) · Spinal Cord · doi:10.1038/sc.2012.136 · Cited by 112
Tan, H. E. I., Lan, N. S. R., Knuiman, M. W., Divitini, M., Swanepoel, D. W., Hunter, M. L., Brennan‐Jones, C. G., Hung, J., Eikelboom, R. H., & Maria, P. L. S. (2017) · Clinical Otolaryngology · doi:10.1111/coa.12936 · Cited by 111
Berti, A., Matteson, E. L., Crowson, C. S., Specks, U., & Cornec, D. (2018) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2018.02.010 · Cited by 112
González, H. M., Tarraf, W., Schneiderman, N., Fornage, M., Vásquez, P. M., Zeng, D., Youngblood, M., Gallo, L. C., Daviglus, M. L., Lipton, R. B., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.08.202 · Cited by 111
Cardiovascular Risk Prediction Models and Scores in the Era of Personalized Medicine
Sofogianni, A., Stalikas, N., Antza, C., & Τζιόμαλος, Κ. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12071180 · Cited by 120
Residual cardiovascular risk in treated hypertension and hyperlipidaemia: the PRIME Study
Blacher, J., Evans, A., Arveiler, D., Amouyel, P., Ferrières, J., Bingham, A., Yarnell, J., Haas, B., Montaye, M., Ruidavets, J., et al. (2009) · Journal of Human Hypertension · doi:10.1038/jhh.2009.34 · Cited by 111
Smith, G. N., Pudwell, J., WALKER, M. C., & Wen, S. W. (2012) · Journal of Obstetrics and Gynaecology Canada · doi:10.1016/s1701-2163(16)35381-6 · Cited by 113
ACCF/AHA 2009 Performance Measures for Primary Prevention of Cardiovascular Disease in Adults
Redberg, R. F., Benjamin, E. J., Bittner, V., Braun, L. T., Goff, D. C., Havas, S., Labarthe, D. R., Limacher, M. C., Lloyd‐Jones, D. M., Mora, S., et al. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.08.005 · Cited by 110
Learning the natural history of human disease with generative transformers
Shmatko, A., Jung, A. W., Gaurav, K., Brunak, S., Mortensen, L. H., Birney, E., Fitzgerald, T., & Gerstung, M. (2025) · Nature · doi:10.1038/s41586-025-09529-3 · Cited by 110
Bath, M., Gokani, V. J., Sidloff, D. A., Jones, L. R., Choke, E. T. C., Sayers, R. D., & Bown, M. J. (2015) · British journal of surgery · doi:10.1002/bjs.9837 · Cited by 110
Arrieta, A., Hong, J. C., Khera, R., Virani, S. S., Krumholz, H. M., & Nasir, K. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2017.3655 · Cited by 110
Coronary Artery Calcium Scoring: Current Status and Future Directions
Gupta, A. B., Bera, K., Kikano, E. G., Pierce, J. D., Gan, J. M., Rajdev, M. A., Ciancibello, L. M., Gupta, A., Rajagopalan, S., & Gilkeson, R. (2022) · Radiographics · doi:10.1148/rg.210122 · Cited by 110
Association of Serum Lipids and Coronary Heart Disease in Contemporary Observational Studies
Colantonio, L. D., Bittner, V., Reynolds, K., Levitan, E. B., Rosenson, R. S., Banach, M., Kent, S. T., Derose, S. F., Zhou, H., Safford, M. M., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.011646 · Cited by 105
Lifestyle‐Based Prediction Model for the Prevention of CVD: The Healthy Heart Score
Chiuve, S. E., Cook, N. R., Shay, C. M., Rexrode, K. M., Albert, C. M., Manson, J. E., Willett, W. C., & Rimm, E. B. (2014) · Journal of the American Heart Association · doi:10.1161/jaha.114.000954 · Cited by 116
Heringa, S. M., van den Berg, E., Reijmer, Y. D., Nijpels, G., Stehouwer, C. D. A., Schalkwijk, C., Teerlink, T., Scheffer, P. G., van den Hurk, K., Kappelle, L. J., et al. (2013) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2013.11.011 · Cited by 113
Greenwood, J. P., Herzog, B. A., Brown, J. M., Everett, C., Nixon, J., Bijsterveld, P., Maredia, N., Motwani, M., Dickinson, C. J., Ball, S. G., et al. (2016) · Annals of Internal Medicine · doi:10.7326/m15-1801 · Cited by 109
Fred Saad, C., Efstathiou, E., Attard, G., Flaig, T. W., Franke, F. A., Goodman, O. B., Oudard, S. M., Steuber, T. P., Suzuki, H., Wu, D., et al. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(21)00402-2 · Cited by 110
Physical activity is associated with slower epigenetic ageing—Findings from the Rhineland study
Fox, F. A. U., Liu, D., Breteler, M. M. B., & Aziz, N. A. (2023) · Aging Cell · doi:10.1111/acel.13828 · Cited by 114
Acute coronary syndromes: mechanisms, challenges, and new opportunities
Kraler, S. C., Mueller, C. E., Libby, P., & Bhatt, D. L. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf289 · Cited by 128
Prospective surveillance and management of cardiac toxicity and health in breast cancer survivors
Schmitz, K. H., Prosnitz, R. G., Schwartz, A. L., & Carver, J. R. (2012) · Cancer · doi:10.1002/cncr.27462 · Cited by 111
Prevalence and Prediction of Coronary Artery Disease in Patients With Liver Cirrhosis
An, J., Shim, J. H., Kim, S., Lee, D., Kim, K. M., Lim, Y., Lee, H. C., Chung, Y., & Lee, Y. S. (2014) · Circulation · doi:10.1161/circulationaha.114.009278 · Cited by 110
Cardiovascular disease in patients with rheumatoid arthritis
Liao, K. P. (2016) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2016.07.006 · Cited by 108
Han, E., Kim, G., Hong, N., Lee, Y., Kim, D. W., Shin, H. J., Lee, B., Kang, E. S., Lee, I. K., & Soo, B. (2016) · Cardiovascular Diabetology · doi:10.1186/s12933-016-0436-z · Cited by 110
Vascular Risk and β ‐Amyloid Are Synergistically Associated with Cortical Tau
Rabin, J. S., Yang, H., Schultz, A. P., Hanseeuw, B. J., Hedden, T., Viswanathan, A., Gatchel, J. R., Marshall, G. A., Kilpatrick, E. P., Klein, H., et al. (2018) · Annals of Neurology · doi:10.1002/ana.25399 · Cited by 110
Lee, Y. C., Cha, J., Shim, I., Park, W., Kang, S. W., Lim, D. H., & Won, H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00748-4 · Cited by 116
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
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
