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Nontraditional Risk Factors in Cardiovascular Disease Risk Assessment: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force
Open accessAbstract
Importance: Incorporating nontraditional risk factors may improve the performance of traditional multivariable risk assessment for cardiovascular disease (CVD). Objective: To systematically review evidence for the US Preventive Services Task Force on the benefits and harms of 3 nontraditional risk factors in cardiovascular risk assessment: the ankle-brachial index (ABI), high-sensitivity C-reactive protein (hsCRP) level, and coronary artery calcium (CAC) score. Data Sources: MEDLINE, PubMed, and the Cochrane Central Register of Controlled Trials for studies published through May 22, 2017. Surveillance continued through February 7, 2018. Study Selection: Studies of asymptomatic adults with no known cardiovascular disease. Data Extraction and Synthesis: Independent critical appraisal and data abstraction by 2 reviewers. Main Outcomes and Measures: Cardiovascular events, mortality, risk assessment performance measures (calibration, discrimination, or risk reclassification), and serious adverse events. Results: Forty-three studies (N = 267 244) were included. No adequately powered trials have evaluated the clinical effect of risk assessment with nontraditional risk factors on patient health outcomes. The addition of the ABI (10 studies), hsCRP level (25 studies), or CAC score (19 studies) can improve both discrimination and reclassification; the magnitude and consistency of improvement varies by nontraditional risk factor. For the ABI, improvements in performance were the greatest for women, in whom traditional risk assessment has poor discrimination (C statistic change of 0.112 and net reclassification index [NRI] of 0.096). Results were inconsistent for hsCRP level, with the largest analysis (n = 166 596) showing a minimal effect on risk prediction (C statistic change of 0.0039, NRI of 0.0152). The largest improvements in discrimination (C statistic change ranging from 0.018 to 0.144) and reclassification (NRI ranging from 0.084 to 0.35) were seen for CAC score, although CAC score may inappropriately reclassify individuals not having cardiovascular events into higher-risk categories, as determined by negative nonevent NRI. Evidence for the harms of nontraditional risk factor assessment was limited to computed tomography imaging for CAC scoring (8 studies) and showed that radiation exposure is low but may result in additional testing. Conclusions and Relevance: There are insufficient adequately powered clinical trials evaluating the incremental effect of the ABI, hsCRP level, or CAC score in risk assessment and initiation of preventive therapy. Furthermore, the clinical meaning of improvements in measures of calibration, discrimination, and reclassification risk prediction studies is uncertain.
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“A recent meta-analysis from the US Preventive Services Task Force summarized the available evidence on the value of non-traditional risk factors for risk prediction, and found that, although there are no randomized trials showing that the use of CAC reduces health outcomes, nevertheless it improves both discrimination and reclassification [19].”
From 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk (2019) · cites this paper as [19]
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Cites
(2014) · Medical Entomology and Zoology · Cited by 17,633
Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association
Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., de Ferranti, S. D., Floyd, J., Fornage, M., Gillespie, C., et al. (2017) · Circulation · doi:10.1161/cir.0000000000000485 · Cited by 12,591
Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)
Grundy, S. M., Becker, D. M., Clark, L. T., Cooper, R., Denke, M. A., Howard, J. L., Hunninghake, D. B., Illingworth, D. R., Luepker, R. V., McBride, P. E., et al. (2002) · Civil War Book Review · Cited by 12,198
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
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
Stone, N. J., Robinson, J. G., Lichtenstein, A. H., Bairey Merz, C. N., Blum, C. B., Eckel, R. H., Goldberg, A. C., Gordon, D., Levy, D., Lloyd-Jones, D. M., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437738.63853.7a · Cited by 7,081
Rosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein
Ridker, P. M., Danielson, E., Fonseca, F. A. H., Genest, J., Gotto, A. M., Kastelein, J. J. P., Koenig, W., Libby, P., Lorenzatti, A. J., MacFadyen, J. G., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0807646 · Cited by 6,674
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170
Research works printed but not linked: 1
- US Preventive Services Task Force, 2015, US Preventive Services Task Force Procedure Manual
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Mach, F., Baigent, C., Catapano, A. L., Koskinas, K. C., Casula, M., Badimon, L., Chapman, M. J., De Backer, G. G., Delgado, V., Ference, B. A., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz455 · Cited by 8,961
2021 ESC Guidelines on cardiovascular disease prevention in clinical practice
Visseren, F. L. J., Mach, F., Smulders, Y. M., Carballo, D., Koskinas, K. C., Bäck, M., Benetos, A., Biffi, A., Boavida, J.-M., Capodanno, D., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab484 · Cited by 6,485
Mach, F., Baigent, C., Catapano, A. L., Koskinas, K. C., Casula, M., Badimon, L., Chapman, M. J., De Backer, G. G., Delgado, V., Ference, B. A., et al. (2019) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2019.08.014 · Cited by 4,854
2024 ESC Guidelines for the management of peripheral arterial and aortic diseases
Mazzolai, L., Teixidó‐Turà, G., Lanzi, S., Boc, V., Bossone, E., Brodmann, M., Bura-Rivière, A., Backer, J. F. A. D., Déglise, S., Corte, A. D., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae179 · Cited by 1,313
2025 Focused Update of the 2019 ESC/EAS Guidelines for the management of dyslipidaemias
Mach, F., Koskinas, K. C., Roeters van Lennep, J. E., Tokgözoğlu, L., Badimon, L., Baigent, C., Benn, M., Binder, C. J., Catapano, A. L., De Backer, G. G., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf190 · Cited by 688
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Visseren, F. L. J., Mach, F., Smulders, Y. M., Carballo, D., Koskinas, K. C., Bäck, M., Bénétos, A., Biffi, A., Boavida, J.-M., Capodanno, D., et al. (2021) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwab154 · Cited by 586
Piepoli, M. F., Abreu, A., Albus, C., Ambrosetti, M., Brotons, C., Catapano, A. L., Corrà, U., Cosyns, B., Deaton, C., Graham, I. M., et al. (2019) · European Journal of Preventive Cardiology · doi:10.1177/2047487319893035 · Cited by 226
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US Preventive Services Task Force, Curry, S. J., Krist, A. H., Owens, D. K., Barry, M. J., Caughey, A. B., Davidson, K. W., Doubeni, C. A., Epling, J. W., Kemper, A. R., et al. (2018) · JAMA · doi:10.1001/jama.2018.8359 · Cited by 232
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Eng, D. K.-M., Chute, C., Khandwala, N., Rajpurkar, P., Long, J., Shleifer, S., Khalaf, M. H., Sandhu, A. T., Rodríguez, F., Maron, D. J., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00460-1 · Cited by 189
2025 Focused Update of the 2019 ESC/EAS Guidelines for the management of dyslipidaemias
Mach, F., Koskinas, K. C., van Lennep, J. E. R., Tokgözoğlu, L. S., Badimón, L., Baigent, C., Benn, M., Binder, C. J., Catapano, A. L., Backer, G. D., et al. (2025) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2025.120479 · Cited by 190
Role of aspirin in primary prevention of cardiovascular disease
Patrono, C., & Baigent, C. (2019) · Nature Reviews Cardiology · doi:10.1038/s41569-019-0225-y · Cited by 163
Prognostic value of cardiovascular biomarkers in the population
Neumann, J. T., Twerenbold, R., Weimann, J., Ballantyne, C. M., Benjamin, E. J., Costanzo, S., de Lemos, J. A., deFilippi, C. R., Di Castelnuovo, A., Donfrancesco, C., et al. (2024) · JAMA · doi:10.1001/jama.2024.5596 · Cited by 125
Determinants of Progression and Regression of Subclinical Atherosclerosis Over 6 Years
Mendieta, G., Pocock, S., Mass, V., Moreno, A. D., Owen, R., García‐Lunar, I., López‐Melgar, B., Fuster, J. J., Andrés, V., Pérez-Herreras, C., et al. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.09.814 · Cited by 125
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Mechanick, J. I., Farkouh, M. E., Newman, J. D., & Garvey, W. T. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.11.046 · Cited by 113
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
Risk assessment tools for osteoporosis and fractures in 2022
CAREY, J. J., Wu, P. C.-H., & Bergin, D. (2022) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2022.101775 · Cited by 106
JCS 2018 Guideline on Diagnosis of Chronic Coronary Heart Diseases
Yamagishi, M., Tamaki, N., Akasaka, T., Ikeda, T., Ueshima, K., Uemura, S., Otsuji, Y., Kihara, Y., Kimura, K., Kimura, T., et al. (2021) · Circulation Journal · doi:10.1253/circj.cj-19-1131 · Cited by 100
Bell, K., White, S., Hassan, O., Zhu, L., Scott, A. M., Clark, J. M., & Glasziou, P. (2022) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.1262 · Cited by 94
From organic and inorganic phosphates to valvular and vascular calcifications
Bäck, M., & Michel, J. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab038 · Cited by 83
Guía ESC 2021 sobre la prevención de la enfermedad cardiovascular en la práctica clínica
Visseren, F. L. J., Mach, F., Smulders, Y. M., Carballo, D., Koskinas, K. C., Bäck, M., Bénétos, A., Biffi, A., Boavida, J. M., Capodanno, D., et al. (2022) · Revista Española de Cardiología · doi:10.1016/j.recesp.2021.10.016 · Cited by 89
Perone, F., Bernardi, M., Redheuil, A. B., Mafrica, D., Conte, E., Spadafora, L., Ecarnot, F., Tokgözoğlu, L. S., Santos‐Gallego, C. G., Kaiser, S., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12175563 · Cited by 78
Patel, S. B., Wyne, K. L., Afreen, S., Belalcazar, L. M., Bird, M. D., Coles, S., Marrs, J. C., Peng, C. C., Pulipati, V. P., Sultan, S., et al. (2025) · Endocrine Practice · doi:10.1016/j.eprac.2024.09.016 · Cited by 80
Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases
Tabassum, R., & Ripatti, S. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03715-4 · Cited by 76
O’Malley, P. G., Arnold, M. J., Kelley, C., Spacek, L., Buelt, A., Natarajan, S., Donahue, M., Vagichev, E., Ballard‐Hernandez, J., Logan, A., et al. (2020) · Annals of Internal Medicine · doi:10.7326/m20-4648 · Cited by 70
Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals
Sánchez‐Cabo, F., Rosselló, X., Fuster, V., Benito, F., Manzano, J. P., Silla-Castro, J. C., Fernández-Alvira, J. M., Oliva, B., Fernández‐Friera, L., López‐Melgar, B., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.08.017 · Cited by 67
Romero-Cabrera, J. L., Ankeny, J., Fernández-Montero, A., Kales, S. N., & Smith, D. L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113540 · Cited by 63
Sex Differences in Modifiable Risk Factors and Severity of Coronary Artery Disease
Manfrini, O., Yoon, J., van der Schaar, M., Kedev, S. A., Vavlukis, M., Stankovic, G. R., Scarpone, M., Miličić, D., Vasiljević, Z., Badimón, L., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.017235 · Cited by 63
Advancements in risk stratification and management strategies in primary cardiovascular prevention
Barkas, F., Şener, Y. Z., Golforoush, P. A., Kheirkhah, A., Rodríguez‐Sánchez, E., Novák, J., Apellániz-Ruiz, M., Akyea, R. K., Bianconi, V., Ceasovschih, A., et al. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.117579 · Cited by 61
Cardiovascular risk assessment tools in Asia
Zhang, Y., Miao, H., Chia, Y. C., Buranakitjaroen, P., Siddique, S., Shin, J., Turana, Y., Park, S., Tsoi, K., Chen, C., et al. (2022) · Journal of Clinical Hypertension · doi:10.1111/jch.14336 · Cited by 57
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Paixao, G. M., Santos, B. C., de Araujo, R. M., Ribeiro, M. H., de Moraes, J. L., & Ribeiro, A. L. P. (2022) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20200596 · Cited by 65
Ahmad, A., Lim, L., Morieri, M. L., Tam, C. H. T., Cheng, F., Chikowore, T., Dudenhöffer‐Pfeifer, M., Fitipaldi, H., Huang, C., Kanbour, S., et al. (2024) · Communications Medicine · doi:10.1038/s43856-023-00429-z · Cited by 70
Zuraini, N. Z. A., Sekar, M., Wu, Y. S., Gan, S. H., Bonam, S. R., Rani, N. N. I. M., Begum, M. Y., Lum, P. T., Subramaniyan, V., Fuloria, N. K., et al. (2021) · Vascular Health and Risk Management · doi:10.2147/vhrm.s328096 · Cited by 53
C-reactive protein and cardiovascular risk in the general population
Kurt, B., Reugels, M., Schneider, K. M., Spießhoefer, J., Milzi, A., Gombert, A., Fordyce, C. B., Wenzl, F. A., Pagidipati, N. J., Rocha, V. Z., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf937 · Cited by 53
Valensi, P. E., Henry, P., Boccara, F., Cosson, E., Prévost, G., Emmerich, J., Ernande, L., Marcadet, D. M., Mousseaux, É., Rouzet, F., et al. (2020) · Diabetes & Metabolism · doi:10.1016/j.diabet.2020.08.002 · Cited by 54
Guo, X., & Ma, L. (2022) · Coronary Artery Disease · doi:10.1097/mca.0000000000001198 · Cited by 54
Greenland, P., & Lloyd‐Jones, D. M. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2021.3948 · Cited by 44
Cardiovascular risk assessment tools in Asia
Zhang, Y., Miao, H., Chia, Y. C., Buranakitjaroen, P., Siddique, S., Shin, J., Turana, Y., Park, S., Tsoi, K. K. F., Chen, C. H., et al. (2022) · Sunway Institutional Repository (Sunway University) · Cited by 44
Do Polygenic Risk Scores Improve Patient Selection for Prevention of Coronary Artery Disease?
Khan, S. S., Cooper, R., & Greenland, P. (2020) · JAMA · doi:10.1001/jama.2019.21667 · Cited by 42
Regassa, L. D., Tola, A., & Ayele, Y. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2020.532719 · Cited by 41
Preventive care recommendations to promote health equity
Persaud, N., Sabir, A., Woods, H., Sayani, A., Agarwal, A., Chowdhury, M., de Leon-Demare, K., Ibezi, S., Jan, S. H., Katz, A., et al. (2023) · Canadian Medical Association Journal · doi:10.1503/cmaj.230237 · Cited by 46
Chinese expert consensus on blood lipid management in patients with diabetes (2024 edition)
Xiao, Y., Yu, B., Chen, C., Wang, S., Hu, D., Wu, C., Luo, Y., Xie, L., Li, C., Peng, D., et al. (2024) · Journal of Translational Internal Medicine · doi:10.2478/jtim-2024-0014 · Cited by 37
Han, E., Fritzer‐Szekeres, M., Szekeres, T., Gehrig, T., Gyöngyösi, M., & Bergler-Klein, J. K. (2022) · The Journal of Applied Laboratory Medicine · doi:10.1093/jalm/jfac069 · Cited by 37
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Kolber, M. R., Klarenbach, S. W., Cauchon, M., Cotterill, M., Regier, L. D., Marceau, R., Duggan, N., Whitley, R., Halme, A. S., Poshtar, T., et al. (2023) · Canadian Family Physician · doi:10.46747/cfp.6910675 · Cited by 43
Levels of ankle–brachial index and the risk of diabetes mellitus complications
Alves‐Cabratosa, L., Comas‐Cufí, M., Ponjoan, A., García-Gil, M., Martí‐Lluch, R., Blanch, J., Elosua-Bayés, M., Parramon, D., Camós, L., Guzmán, L., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000977 · Cited by 36
Cardiovascular consequences of financial stress: A systematic review and meta-analysis
Swarup, S. S., Asha, K., Padhi, B. K., Satapathy, P., Shabil, M., Bushi, G., Gandhi, A. P., Khatib, M. N., Gaidhane, S., Zahiruddin, Q. S., et al. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.102153 · Cited by 35
Hou, Y., Lu, C., Zheng, C., Chen, R., Lin, Y., Liu, W.-C., Yen, T., Chen, R., & Lu, K. (2019) · Nutrients · doi:10.3390/nu11010152 · Cited by 34
Hou, Y., Lu, C., Zheng, C., Liu, W.-C., Yen, T., Chen, R., Lin, Y., Chao, C., & Lu, K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072466 · Cited by 34
Air temperature variability and high-sensitivity C reactive protein in a general population of China
Kang, Y., Tang, H., Jiang, L., Wang, S., Wang, X., Chen, Z., Zhang, L., Zheng, C., Wang, Z., Huang, G., et al. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141588 · Cited by 34
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Lehker, A., & Mukherjee, D. (2020) · Current Vascular Pharmacology · doi:10.2174/1570161118666200403143518 · Cited by 33
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Huff, J. L., Plante, I., Blattnig, S. R., Norman, R. B., Little, M. P., Khera, A., Simonsen, L. C., & Patel, Z. S. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.873597 · Cited by 34
Alhabeeb, H., Sohouli, M. H., Lari, A., Fatahi, S., Shidfar, F., Alomar, O., Salem, H., Al-Badawi, I. A., & Abu‐Zaid, A. (2020) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2020.1865263 · Cited by 37
Immunological Effects of a Single Hemodialysis Treatment
Angeletti, A., Zappulo, F., Donadei, C., Cappuccilli, M. L., Certo, G. D., Conte, D., Comai, G., Donati, G., & Manna, G. L. (2020) · Medicina · doi:10.3390/medicina56020071 · Cited by 31
PDAY risk score predicts cardiovascular events in young adults: the CARDIA study
Gidding, S. S., Colangelo, L. A., Nwabuo, C. C., Lewis, C. E., Jacobs, D. R., Schreiner, P. J., Lima, J. A. C., & Allen, N. B. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac009 · Cited by 31
Wang, Y., Chen, Y., Chen, R., Xu, Y., Zheng, H., Xu, J., Xia, J., Cai, Y., Xu, H., & Wang, X. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-04310-5 · Cited by 31
Inflammation during acute coronary syndromes — Risk of cardiovascular events and bleeding
Nanchen, D., Klingenberg, R., Gencer, B., Räber, L., Carballo, D., von Eckardstein, A., Windecker, S., Rodondi, N., Lüscher, T. F., Mach, F., et al. (2019) · International Journal of Cardiology · doi:10.1016/j.ijcard.2019.03.049 · Cited by 29
Non-Traditional Risk Factors as Contributors to Cardiovascular Disease
Wang, L., Lei, J., Wang, R., & Li, K. (2023) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2405134 · Cited by 29
Qin, M., Zhu, Q., Lai, W., Ma, Q., Liu, C., Chen, X., Zhang, Y., Wang, Z., Chen, H., Yan, H., et al. (2020) · Clinical and Translational Medicine · doi:10.1002/ctm2.189 · Cited by 28
Wang, F., Zhu, H., Lu, R., Zheng, Y., & Li, H. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3026631 · Cited by 27
Collado, A., Domingo, E., Piqueras, L., & Sanz, M. (2021) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2021.106066 · Cited by 32
Pan, Y., Liu, Z., Tu, R., Feng, X., Yu, F., Wei, M., Feng, J., Xie, W., Bi, D., Xia, J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-97538-7 · Cited by 25
Viswanathan, V., Jamthikar, A. D., Gupta, D., Puvvula, A., Khanna, N. N., Saba, L., Višković, K., Mavrogeni, S. I., Laird, J. R., Pareek, G., et al. (2020) · Angiology · doi:10.1177/0003319720941730 · Cited by 25
Machine learning improves risk stratification of coronary heart disease and stroke
Chen, B., Ruan, L., Yang, L., Zhang, Y., Lu, Y., Sang, Y., Jin, X., Bai, Y., Zhang, C., & Li, T. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-1916 · Cited by 25
Elevated C-reactive protein and cardiovascular risk
Chia, J. E., & Ang, S. P. (2025) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000001215 · Cited by 23
Armario, P., Brotons, C., Elosúa, R., de Leciñana, M. A., Castro, A., Clarà, A. B. I., Cortés, O., Rodríguez, Á. D., Herranz, M., Justo, S., et al. (2021) · Clínica e Investigación en Arteriosclerosis (English Edition) · doi:10.1016/j.artere.2021.02.001 · Cited by 20
Liu, Z., Tang, J., Cheng, M.-D., Jiang, L.-Z., Guo, Q., Zhang, J., Zhang, Z.-L., Song, F.-H., Wang, K., Fan, L., et al. (2021) · Coronary Artery Disease · doi:10.1097/mca.0000000000001021 · Cited by 20
Bernardi, M., Fedullo, A. L., Giacinto, B. D., Squeo, M. R., Aiello, P., Dante, D., Romano, S., Magaudda, L., Peluso, I., Palmery, M., et al. (2019) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2019/6798140 · Cited by 20
Recomendaciones preventivas cardiovasculares. Actualización PAPPS 2020
Orozco‐Beltrán, D., Cuixart, C. B., Sánchez, J. J. A., Banegas, J. R., Cuenca, A. M. C., Gil-Guillén, V. F., Rioboó, E. M., & Pérez, J. N. (2020) · Atención Primaria · doi:10.1016/j.aprim.2020.08.002 · Cited by 24
Wang, Q.-H., Guo, Q., Zhou, L., Li, W., Yuan, Y. L., Lei, W., Liu, K., Xu, M., Diao, T., Gao, H. Y., et al. (2021) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.62970 · Cited by 20
Cardiovascular Health Trajectories and Elevated C‐Reactive Protein: The CARDIA Study
Ruiz‐Ramie, J. J., Barber, J. L., Lloyd‐Jones, D. M., Gross, M. D., Rana, J. S., Sidney, S., Jacobs, D. R., Lane‐Cordova, A. D., & Sarzynski, M. A. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.019725 · Cited by 20
Effect of No-Charge Coronary Artery Calcium Scoring on Cardiovascular Prevention
Al-Kindi, S. G., Tashtish, N., Rashid, I., Gupta, A. B., Ansari‐Gilani, K., Gilkeson, R., Cainzos‐Achirica, M., Nasir, K., Pronovost, P. J., Simon, D. I., et al. (2022) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2022.03.019 · Cited by 19
Niu, J., Deng, C., Zheng, R., Xu, M., Lu, J., Wang, T., Zhao, Z., Chen, Y., Wang, S., Dai, M., et al. (2020) · Global Heart · doi:10.5334/gh.790 · Cited by 18
Naami, R., Tashtish, N., Neeland, I. J., Katz, J., Sinh, P., Nasir, K., Chittajallu, V., Mansoor, E., Rajagopalan, S., & Al-Kindi, S. G. (2023) · American Heart Journal · doi:10.1016/j.ahj.2023.08.011 · Cited by 18
Martins, A. M. A., Paiva, M. U. B., Paiva, D. V. N., de Oliveira, R. M., Machado, H. L., Alves, L. J. S. R., Picossi, C. R. C., Faccio, A. T., Tavares, M. F. M., Barbas, C., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.788062 · Cited by 18
Wang, Z., Chen, S., Zhang, F., Akhmedov, S., Weng, J., & Xu, S. (2025) · Research · doi:10.34133/research.0618 · Cited by 16
Non-Traditional Cardiovascular Risk Factors: Tailored Assessment and Clinical Implications
Perone, F., Bernardi, M., Spadafora, L., Betti, M., Cacciatore, S., Saia, F., Fogacci, F., Jaiswal, V., Asher, E., Paneni, F., et al. (2025) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd12050171 · Cited by 19
Combined clinical, metabolomic, and polygenic scores for cardiovascular risk prediction
Ritchie, S. C., Jiang, X., Pennells, L. A. M., Xu, Y., Coffey, C., Liu, Y., Gibson, J. T., Surendran, P., Karthikeyan, S., Lambert, S. A., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf947 · Cited by 19
Mehrabani-Zeinabad, K., Feizi, A., Sadeghi, M., Roohafza, H., Talaei, M., & Sarrafzadegan, N. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02169-5 · Cited by 20
Vascular Age Concept: Which Approach Is Preferable in Clinical Practice?
Rotar, O. P., Alieva, A. S., Анатольевна, Б. М., Tolkunova, K., & Конради, А. О. (2019) · Kardiologiia · doi:10.18087/cardio.2019.2.10229 · Cited by 16
Future directions in cardiovascular disease risk prediction
Doust, J. A., Bonner, C., & Bell, K. (2020) · Australian Journal of General Practice · doi:10.31128/ajgp-02-20-5231 · Cited by 16
Slater, L. V., Gilbertson, N. M., & Hyngstrom, A. S. (2020) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2020.12.003 · Cited by 16
Koufakis, T., Vlahakos, D., Vlachopoulos, C., Kallistratos, E., Kotsa, K., Liberopoulos, E. N., Stefanidis, I., & Hatziagelaki, E. E. (2025) · American Journal of Cardiovascular Drugs · doi:10.1007/s40256-025-00761-w · Cited by 15
Sun, Y., Xing, Z., Liang, S., Miao, Z., Zhuo, L., Jiang, W., Zhao, H., Gao, H., Xie, Y., Zhou, Y., et al. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100840 · Cited by 13
Classification of the Occurrence of Dyslipidemia Based on Gut Bacteria Related to Barley Intake
Maruyama, S., Matsuoka, T., Hosomi, K., Park, J., Nishimura, M., Murakami, H., Konishi, K., Miyachi, M., Kawashima, H., Mizuguchi, K., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.812469 · Cited by 13
Eurlings, C., Bektas, S., Wijk, S. S., Tsirkin, A., Vasilchenko, V., Meex, S. J. R., Failer, M., Oehri, C., Ruff, P., Zellweger, M. J., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-055170 · Cited by 14
Özbiçer, S., Yüksel, G., & Urgun, Ö. D. (2022) · Diabetes and Vascular Disease Research · doi:10.1177/14791641221136203 · Cited by 14
Pan, D., Wang, J.-B., Guo, J., Su, Z., Wang, J., Guo, J., Shi, X., & Gu, Y. (2023) · Journal of Clinical Hypertension · doi:10.1111/jch.14659 · Cited by 13
Akimov, A. M. (2023) · Сибирский научный медицинский журнал · doi:10.18699/ssmj20230313 · Cited by 13
Armario, P., Brotons, C., Elosúa, R., de Leciñana, M. A., Castro, A., Clarà, A. B. I., Cortés, O., Rodríguez, Á. D., Herranz, M., Justo, S., et al. (2020) · Hipertensión y Riesgo Vascular · doi:10.1016/j.hipert.2020.07.004 · Cited by 13
Filho, M. M. P., Brant, L. C. C., dos Reis, R. C. P., Giatti, L., Duncan, B. B., Lotufo, P. A., da Fonseca, M. D. J. M., Mill, J. G., de Almeida, M. D. C. C., Macfarlane, P. W., et al. (2020) · Heart · doi:10.1136/heartjnl-2020-318097 · Cited by 13
Hadaegh, F., Asgari, S., Moosaie, F., Orangi, M., Sarvghadi, F., Khalili, D., & Azizi, F. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-020-02686-1 · Cited by 13
Jennings, G., Audehm, R. G., Bishop, W., Chow, C. K., Liaw, S., Liew, D., & Linton, S. M. (2021) · The Medical Journal of Australia · doi:10.5694/mja2.51039 · Cited by 13
Vlad, C.-E., Foia, L. G., Florea, L., Costache, I. I., Covic, A. S., Popescu, R. M., Reurean‐Pintilei, D., & Covic, A. C. (2021) · Lipids in Health and Disease · doi:10.1186/s12944-020-01428-y · Cited by 12
Cardiovascular Prevention: Current Gaps and Future Directions
Dores, H., Santos, J. F., Gil, V. M., & Gonçalves, P. D. A. (2025) · Diagnostics · doi:10.3390/diagnostics16010016 · Cited by 16
USPSTF Recommendations for Assessment of Cardiovascular Risk With Nontraditional Risk Factors
Wilkins, J. T., & Lloyd‐Jones, D. M. (2018) · JAMA · doi:10.1001/jama.2018.9346 · Cited by 11
Vlad, C.-E., Foia, L. G., Tanasa, M. P., Toma, V., Florea, L., Voroneanu, L. E., Apetrii, M., Dodi, G., & Covic, A. C. (2021) · International Urology and Nephrology · doi:10.1007/s11255-021-02919-2 · Cited by 11
You, A., Li, Y., Shen, C., Fan, H., He, J., Liu, Z., Xue, Q., Zhang, Y., & Zheng, L. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01047-4 · Cited by 11
Chen, C.-C., Hsu, W.-C., Han-ming, W., Wang, J., Yang, P., & Lin, I. (2021) · Medicina · doi:10.3390/medicina57080807 · Cited by 11
Rhee, A. J., Pandit, K., Berger, J. S., Iturrate, E., Coresh, J., Khan, S. S., Shin, J., Hochman, J. S., Reynolds, H. R., & Grams, M. E. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.038991 · Cited by 9
Ning, G. (2021) · Cardiology Discovery · doi:10.1097/cd9.0000000000000024 · Cited by 9
Eat Even More Vegetables and Fruits to Protect Your Heart
Estruch, R., Casas, R. M., & Ros, E. (2020) · Annals of Internal Medicine · doi:10.7326/m20-2504 · Cited by 9
Metadata
- Authors
- Lin, J. S., Evans, C. V., Johnson, E., Redmond, N., Coppola, E. L., & Smith, N.
- Affiliations
- Kaiser Permanente (US)
- Corresponding author
- Lin, Jennifer S.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2018-07-17
- Volume
- 320
- Issue
- 3
- Pages
- 281
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC13564044
- OpenAlex
- W2852998865
- Cited by
- 216
- References
- 100
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 24 · 2023: 30 · 2022: 27 · 2021: 38 · 2020: 30 · 2019: 14 · 2018: 6
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- JAMA (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, public-domain)
- Funders
- U.S. Department of Health and Human Services
- Agency for Healthcare Research and Quality (HHSA 290-2015-00007-I)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Keywords
- cardiovascular disease risk assessment, non-traditional risk factors, ankle-brachial index, high-sensitivity C-reactive protein, coronary artery calcium score, risk reclassification, risk discrimination, cardiovascular events, cardiovascular mortality, radiation exposure, women, asymptomatic adults, USPSTF recommendations, cardiovascular risk prediction models, computed tomography, net reclassification improvement
- MeSH terms
- Adult, C-Reactive Protein, Cardiovascular Diseases, Clinical Trials as Topic, Coronary Artery Disease, Humans, Risk Factors, Biomarkers, Risk Assessment, Ankle Brachial Index, Vascular Calcification
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