Cited by 841Lipoproteins and Cardiovascular HealthHealth Promotion and Cardiovascular PreventionCardiovascular Health and Risk Factors

The DOI matches its Crossref record, with a small difference in title or year; 27 of the 27 references it cites were found in a registry or an index.

C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men

Open access

Ridker, Paul M, Paynter, Nina P., Rifai, Nader, Gaziano, J. Michael, Cook, Nancy R.·2008·Circulation

DOI 10.1161/circulationaha.108.814251PMC PMC2752381

Open this paper in CoChat — free account Open PDF

Abstract

Background— High-sensitivity C-reactive protein and family history are independently associated with future cardiovascular events and have been incorporated into risk prediction models for women (the Reynolds Risk Score for women); however, no cardiovascular risk prediction algorithm incorporating these variables currently exists for men. Methods and Results— Among 10 724 initially healthy American nondiabetic men who were followed up prospectively over a median period of 10.8 years, we compared the test characteristics of global model fit, discrimination, calibration, and reclassification in 2 prediction models for incident cardiovascular events, one based on age, blood pressure, smoking status, total cholesterol, and high-density lipoprotein cholesterol (traditional model) and the other based on these risk factors plus high-sensitivity C-reactive protein and parental history of myocardial infarction before age 60 years (Reynolds Risk Score for men). A total of 1294 cardiovascular events accrued during study follow-up. Compared with the traditional model, the Reynolds Risk Score had better global fit (likelihood ratio test P <0.001), a superior (lower) Bayes information criterion, and a larger C-index ( P <0.001). For the end point of all cardiovascular events, the Reynolds Risk Score for men reclassified 17.8% (1904/10 724) of the study population (and 20.2% [1392/6884] of those at 5% to 20% 10-year risk) into higher- or lower-risk categories, with markedly improved accuracy among those reclassified. For this model comparison, the net reclassification index was 5.3%, and the clinical net reclassification index was 14.2% (both P <0.001). In models based on the Adult Treatment Panel III preferred end point of coronary heart disease and limited to men not taking lipid-lowering therapy, 16.7% of the study population (and 20.1% of those at 5% to 20% 10-year risk) were reclassified to higher- or lower-risk groups, again with significantly improved global fit, larger C-index ( P <0.001), and markedly improved accuracy among those reclassified. For this model, the net reclassification index was 8.4% and the clinical net reclassification index was 15.8% (both P <0.001). Conclusions— As previously shown in women, a prediction model in men that incorporates high-sensitivity C-reactive protein and parental history significantly improves global cardiovascular risk prediction.

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

Crossref · 10.1161/circulationaha.108.814251

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Magnesium Replacement Improves the Metabolic Profile in Obese and Pre-Diabetic Patients with Mild-to-Moderate Chronic Kidney Disease: A 3-Month, Randomised, Double-Blind, Placebo-Controlled Study
Toprak, Ö., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,772
Summary of the second report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II)
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
Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III)
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
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
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
Implications of Recent Clinical Trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines
Grundy, S. M., Cleeman, J. I., Merz, C. N. B., Brewer, H. B., Clark, L. T., Hunninghake, D. B., Pasternak, R. C., Smith, S. C., & Stone, N. J. (2004) · Circulation · doi:10.1161/01.cir.0000133317.49796.0e · Cited by 6,902
Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
Harrell (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7 · Cited by 6,878
Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond
Pencina, 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
Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction
Cook, N. R. (2007) · Circulation · doi:10.1161/circulationaha.106.672402 · Cited by 2,156
Self-citation
Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women: The Reynolds Risk Score
Ridker, P. M., Buring, J. E., Rifai, N., & Cook, N. R. (2007) · JAMA · doi:10.1001/jama.297.6.611 · Cited by 1,814
Self-citation

Flagged first, then by citations

Show all 27 references

Research works printed but not linked: 2

10.1161/circ.86.5.1330360 · DOI
10.1161/hyp.39.1.105 · DOI

Sorted by citations

Items 301–400 of 840

Efficacy, effectiveness and real life goal attainment of statins in managing cardiovascular risk
Glueck, C. J., & Goldenberg, N. (2009) · Vascular Health and Risk Management · doi:10.2147/vhrm.s3241 · Cited by 33
Secretory vimentin is associated with coronary artery disease in patients and induces atherogenesis in ApoE−/− mice
Gong, D., Dai, Y., Chen, S., Wang, X. Q., Yan, X. X., Shen, Y. H., Liu, J., YANG, Z. K., Hu, J., Yu, L. J., et al. (2019) · International Journal of Cardiology · doi:10.1016/j.ijcard.2019.02.032 · Cited by 33
MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome—A Systematic Review
Dragoș, H.-M., Stan, A. D., Pintican, R. M., Feier, D. S., Lebovici, A., Panaitescu, P.-Ș., Dina, C., Strilciuc, Ș., & Muresanu, D. F. (2023) · Diagnostics · doi:10.3390/diagnostics13050857 · Cited by 35
Gender differences in cardiovascular mortality by C-reactive protein level in the United States: Evidence from the National Health and Nutrition Examination Survey III
Doran, B., Zhu, W., & Muennig, P. A. (2013) · American Heart Journal · doi:10.1016/j.ahj.2013.03.017 · Cited by 33
Risk Factors for Near-Term Myocardial Infarction in Apparently Healthy Men and Women
Nordestgaard, B. G., Adourian, A. S., Freiberg, J., Guo, Y. F., Muntendam, P., & Falk, E. (2010) · Clinical Chemistry · doi:10.1373/clinchem.2009.139964 · Cited by 32
Cardiovascular Imaging for the Primary Prevention of Atherosclerotic Cardiovascular Disease Events
Weber, L., Cheezum, M. K., Reese, J. M., Lane, A. B., Haley, R. D., Lutz, M. W., & Villines, T. C. (2015) · Current Cardiovascular Imaging Reports · doi:10.1007/s12410-015-9351-z · Cited by 32
Deep Neural Survival Networks for Cardiovascular Risk Prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)
Hathaway, Q. A., Yanamala, N. V. K., Budoff, M. J., Sengupta, P. P., & Zeb, I. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104983 · Cited by 32
Review on cardiovascular risk prediction
Ruwanpathirana, T., Owen, A., & Reid, C. M. (2015) · Cardiovascular Therapeutics · doi:10.1111/1755-5922.12110 · Cited by 31
Cardiac Autonomic Testing and Treating Heart Disease. “A Clinical Perspective”
DePace, N. L., Mears, J. P., Yayac, M. F., & Colombo, J. (2014) · Heart International · doi:10.5301/heartint.5000216 · Cited by 31
Dislipidemias: un reto pendiente en prevención cardiovascular. Documento de consenso CEIPC/SEA
Royo‐Bordonada, M. Á., Bejarano, J. M. L., Núñez-Cortés, J. M., Álvarez, F. V., Cuixart, C. B., Camafort, M., Guijarro, C., Zarzosa, C. D. P., Pedro‐Botet, J., & Nocito, A. D. S. (2011) · Medicina Clínica · doi:10.1016/j.medcli.2011.02.008 · Cited by 30
Cardiovascular risk reduction following metabolic and bariatric surgery
English, W. J., Spann, M. D., Aher, C. V., & Williams, D. B. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.01.88 · Cited by 30
Monitoring and Managing Cardiovascular Risk in Immune Mediated Inflammatory Diseases
Anyfanti, P., Dara, A., Angeloudi, E., Bekiari, E., Dimitroulas, T., & Kitas, G. D. (2021) · Journal of Inflammation Research · doi:10.2147/jir.s276986 · Cited by 33
Implementing Cardiovascular Risk Reduction in Patients with Cardiovascular Disease and Diabetes Mellitus
Nandish, S., Wyatt, J., Bailon, O., Smith, M., OLIVEROS, R. A., & CHILTON, R. J. (2011) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2011.03.015 · Cited by 29
Cardiovascular Epidemiology in a Changing World--Challenges to Investigators and the National Heart, Lung, and Blood Institute
Sorlie, P. D., Bild, D. E., & Lauer, M. S. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws138 · Cited by 29
Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications
Bushnell, G., Stürmer‎, T., White, A., Pate, V., Swanson, S. A., Azrael, D. R., & Miller, M. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.02.012 · Cited by 29
A PROgramme of Lifestyle Intervention in Families for Cardiovascular risk reduction (PROLIFIC Study): design and rationale of a family based randomized controlled trial in individuals with family history of premature coronary heart disease
Jeemon, P., Harikrishnan, S., Sanjay, G., Sivasubramonian, S., Lekha, T. R., Padmanabhan, S., Tandon, N., & Prabhakaran, D. (2017) · BMC Public Health · doi:10.1186/s12889-016-3928-6 · Cited by 30
Family history of premature cardiovascular disease: blood pressure control and long-term mortality outcomes in hypertensive patients
Williamson, C., Jeemon, P., Hastie, C. E., McCallum, L., Muir, S. W., Dawson, J., Walters, J., Sloan, W. T., Morrison, D. S., Dominiczak, A. F., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht539 · Cited by 29
Virtual Educational Outreach Intervention in Primary Care Based on the Principles of Academic Detailing
Baldwin, L., Fischer, M. A., Powell, J., Holden, E., Tuzzio, L., Fagnan, L. J., Hummel, J., & Parchman, M. L. (2018) · Journal of Continuing Education in the Health Professions · doi:10.1097/ceh.0000000000000224 · Cited by 30
Long‐Term Intake of Pork Meat Proteins Altered the Composition of Gut Microbiota and Host‐Derived Proteins in the Gut Contents of Mice
Xie, Y., Wang, C., Zhao, D., Zhou, C., & Li, C. (2020) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202000291 · Cited by 29
Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank
Widén, E., Raben, T. G., Lello, L., & Hsu, S. (2021) · Genes · doi:10.3390/genes12070991 · Cited by 33
Cardiac Autonomic Testing and Diagnosing Heart Disease. “A Clinical Perspective”
DePace, N. L., Mears, J. P., Yayac, M. F., & Colombo, J. (2014) · Heart International · doi:10.5301/heartint.5000218 · Cited by 29
Should C-Reactive Protein Be a Target of Therapy?
Oh, J., Teoh, H., & Leiter, L. A. (2011) · Diabetes Care · doi:10.2337/dc11-s211 · Cited by 30
Biomarkers and Cardiovascular Disease
Shah, S. H., & de Lemos, J. A. (2009) · JAMA · doi:10.1001/jama.2009.949 · Cited by 29
Prevention of heart failure in the elderly: when, where and how to begin?
Jugdutt, B. I. (2012) · Heart Failure Reviews · doi:10.1007/s10741-012-9299-x · Cited by 26
Heart failure and inflammation-related biomarkers as predictors of new-onset diabetes in the general population
Suthahar, N., Meijers, W. C., Brouwers, F. P. J., Heerspink, H. J. L., Gansevoort, R. T., van der Harst, P., Bakker, S. J. L., & de Boer, R. A. (2017) · International Journal of Cardiology · doi:10.1016/j.ijcard.2017.10.035 · Cited by 29
Sex Differences in Circulating Soluble Urokinase‐Type Plasminogen Activator Receptor (suPAR) Levels and Adverse Outcomes in Coronary Artery Disease
Mehta, A., Desai, S., Ko, Y., Liu, C., Dhindsa, D. S., Nayak, A., Hooda, A., Martini, M. A., Ejaz, K., Sperling, L. S., et al. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.015457 · Cited by 28
Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease
Viswanathan, V., Jamthikar, A. D., Gupta, D., Puvvula, A., Khanna, N. N., Saba, L., Višković, K., Mavrogeni, S. I., Turk, M., Laird, J. R., et al. (2020) · International Angiology · doi:10.23736/s0392-9590.20.04338-2 · Cited by 28
Assessing racial bias in type 2 diabetes risk prediction algorithms
Cronjé, H. T., Katsiferis, A., Elsenburg, L. K., Andersen, T. O., Rod, N. H., Nguyen, T., & Varga, T. V. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001556 · Cited by 34
The Jupiter study, CRP screening, and aggressive statin therapy-implications for the primary prevention of cardiovascular disease
KONES, R. J. (2009) · Therapeutic Advances in Cardiovascular Disease · doi:10.1177/1753944709337056 · Cited by 29
High prevalence of peripheral arterial disease in hypertensive patients
Farkas, K., Járai, Z., Kolossváry, E., Ludányi, A., Clement, D. L., & Kiss, I. (2012) · Journal of Hypertension · doi:10.1097/hjh.0b013e3283559a6a · Cited by 28
Should Your Family History of Coronary Heart Disease Scare You?
Prabhakaran, D., & Jeemon, P. (2012) · Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine · doi:10.1002/msj.21348 · Cited by 28
Measuring hsCRP—An Important Part of a Comprehensive Risk Profile or a Clinically Redundant Practice?
McCormack, J. P., & Allan, G. M. (2010) · PLoS Medicine · doi:10.1371/journal.pmed.1000196 · Cited by 28
The Effects of Statins on Cardiovascular and Inflammatory Biomarkers in Primary Prevention: A Systematic Review and Meta-Analysis
He, W. B., Ko, H. T. K., Curtis, A. J., Zoungas, S., Woods, R. L., Tonkin, A., Neumann, J. T., Turner, S. L., & Hopper, I. (2023) · Heart, Lung and Circulation · doi:10.1016/j.hlc.2023.04.300 · Cited by 27
Rationale and design of a trial to personalize risk assessment in familial coronary artery disease
Marwick, T. H., Whitmore, K., Nicholls, S. J., Stanton, T., Mitchell, G. K., Tonkin, A. M., Blizzard, C. L., Neil, A. L., Jones, C., & Watts, G. F. (2017) · American Heart Journal · doi:10.1016/j.ahj.2017.09.011 · Cited by 27
The Heart of New Ulm Project: Using Community-Based Cardiometabolic Risk Factor Screenings in a Rural Population Health Improvement Initiative
VanWormer, J. J., Johnson, P. J., Pereira, R. F., Boucher, J. L., Britt, H. R., Stephens, C. W., Thygeson, N. M., & Graham, K. J. (2012) · Population Health Management · doi:10.1089/pop.2011.0027 · Cited by 28
New directions in cardiovascular risk assessment: the role of secondary risk stratification markers
Wierzbicki, A. S. (2012) · International Journal of Clinical Practice · doi:10.1111/j.1742-1241.2012.02956.x · Cited by 26
Comparison of Quantity of Coronary Atherosclerotic Plaques Detected by Computed Tomography Versus Angiography
Kolossváry, M., Szilveszter, B., Edes, I. F., Nardai, S., Vörös, V., Hartyánszky, I. L., Merkely, B. P., Vörös, S., & Maurovich‐Horvat, P. (2016) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2016.03.031 · Cited by 26
LDL-cholesterol control in the primary prevention of cardiovascular diseases: An expert opinion for clinicians and health professionals
Poli, A., Catapano, A. L., Corsini, A., Manzato, E., Werba, J. P., Catena, G., Cetin, I., Cicero, A. F. G., Cignarella, A., Colivicchi, F., et al. (2022) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2022.10.001 · Cited by 27
Vegetable and Fruit Intake Variety and Cardiovascular Health and Mortality: A Systematic Review and Meta-Analysis of Observational Studies
Nishi, S. K., Khoury, N., Valle‐Hita, C., Zurbau, A., Salas‐Salvadó, J., & Babió, N. (2023) · Nutrients · doi:10.3390/nu15234913 · Cited by 31
ST-T Wave Abnormality in Lead aVR and Reclassification of Cardiovascular Risk (from the National Health and Nutrition Examination Survey-III)
Badheka, A. O., Patel, N., Grover, P. M., Shah, N. N., Singh, V., Deshmukh, A. J., Mehta, K. D., Chothani, A., Hoosien, M., Rathod, A. D., et al. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.04.058 · Cited by 26
Risk Estimation Versus Screening Performance: A Comparison of Six Risk Algorithms for Cardiovascular Disease
Simmonds, M. C., & Wald, N. (2012) · Journal of Medical Screening · doi:10.1258/jms.2012.012076 · Cited by 26
Cardiovascular risk algorithms in primary care: Results from the DETECT study
Grammer, T. B., Dressel, A., Gergei, I., Kleber, M. E., Laufs, U., Scharnagl, H., Nixdorff, U., Klotsche, J., Pieper, L., Pittrow, D. B., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37092-7 · Cited by 30
Cardiovascular Risk Assessment in the 21st Century
Gaziano, J. M., & Wilson, P. W. F. (2012) · JAMA · doi:10.1001/2012.jama.10456 · Cited by 26
Self-citation
Challenges to Improve Coronary Heart Disease Risk Assessment
Wilson, P. W. F. (2009) · JAMA · doi:10.1001/jama.2009.1765 · Cited by 26
On Cross-Sectional Associations of Leukocyte Telomere Length with Cardiac Systolic, Diastolic and Vascular Function: The Asklepios Study
Denil, S. L. I. J., Rietzschel, E. R., Buyzere, M. D., daele, C. M. V., Segers, P., Bacquer, D. D., Criekinge, W. V., Bekaert, S., Gillebert, T. C., Meyer, T. D., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115071 · Cited by 25
Communities) Study In Prediction of Coronary Heart Disease Risk: The ARIC (Atherosclerosis Risk Carotid Intima-Media Thickness and Presence or Absence of Plaque Improves
Volcik, K. A., Boerwinkle, E., Ballantyne, C. M., Nambi, V., Chambless, L. E., Folsom, A. R., He, M. M., & Hu, Y. (2011) · Cited by 25
Synergy of circulating miR-212 with markers for cardiovascular risks to enhance estimation of atherosclerosis presence
Jeong, H. S., Kim, J.-Y., Lee, S. H., Hwang, J., Shin, J. W., Song, K. S., Lee, S., & Kim, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177809 · Cited by 25
Risk assessment and aspirin use in Asian and Western populations
Phillips (2010) · Vascular Health and Risk Management · doi:10.2147/vhrm.s9400 · Cited by 25
Emerging inflammatory markers for assessing coronary heart disease risk
Corson, M. A. (2009) · Current Cardiology Reports · doi:10.1007/s11886-009-0065-1 · Cited by 25
Carotid Intima-Media Thickness as a Cardiovascular Risk Factor and Imaging Pathway of Atherosclerosis
Centurión, O. A. (2016) · Critical Pathways in Cardiology A Journal of Evidence-Based Medicine · doi:10.1097/hpc.0000000000000087 · Cited by 26
What is the role of calcium scoring in the age of coronary computed tomographic angiography?
Joshi, P. H., Blaha, M. J., Blumenthal, R. S., Blankstein, R., & Nasir, K. (2012) · Journal of Nuclear Cardiology · doi:10.1007/s12350-012-9626-6 · Cited by 24
Prognostic Value of C-Reactive Protein in Resistant Hypertension
Cortez, A. F., Muxfeldt, E. S., Cardoso, C. R. L., & Salles, G. F. (2016) · American Journal of Hypertension · doi:10.1093/ajh/hpw011 · Cited by 25
The prediction of therapy-benefit for individual cardiovascular disease prevention: rationale, implications, and implementation
Jaspers, N. E. M., Ridker, P. M., Dorresteijn, J. A. N., & Visseren, F. L. J. (2018) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000554 · Cited by 25
Self-citation
Should patients with rheumatoid arthritis receive statin therapy?
Ridker, P. M., & Solomon, D. H. (2009) · Arthritis & Rheumatism · doi:10.1002/art.24521 · Cited by 24
Self-citation
Cardiovascular Disease Risk Prediction in Women: Is There a Role for Novel Biomarkers?
Paynter, N. P., Everett, B. M., & Cook, N. R. (2013) · Clinical Chemistry · doi:10.1373/clinchem.2013.202796 · Cited by 23
Self-citation
Should We Measure C‐reactive Protein on Earth or Just on JUPITER?
Bajpai, A., Goyal, A., & Sperling, L. S. (2010) · Clinical Cardiology · doi:10.1002/clc.20681 · Cited by 25
The Association of HLA-class I Genes and the Extent of Atherosclerotic Plaques in Patients with Psoriatic Disease
Eder, L., Abji, F., Rosen, C. F., Chandran, V., Cook, R. J., & Gladman, D. D. (2016) · The Journal of Rheumatology · doi:10.3899/jrheum.151469 · Cited by 23
Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study
Ikezaki, H., Fisher, V. A., Lim, E., Ai, M., Liu, C., Cupples, L. A., Nakajima, K., Asztalos, B. F., Furusyo, N., & Schaefer, E. J. (2019) · Clinical Chemistry · doi:10.1373/clinchem.2019.304600 · Cited by 23
Effect of Important Food Sources of Fructose-Containing Sugars on Inflammatory Biomarkers: A Systematic Review and Meta-Analysis of Controlled Feeding Trials
Qi, X., Chiavaroli, L., Lee, D., Ayoub‐Charette, S., Khan, T. A., Au‐Yeung, F., Ahmed, A., Cheung, A., Liu, Q., Mejía, S. B., et al. (2022) · Nutrients · doi:10.3390/nu14193986 · Cited by 25
High sensitivity C-reactive protein predicts the development of new carotid artery plaques in older persons
Molino-Lova, R., Macchi, C., Gori, A. M., Marcucci, R., Polcaro, P., Cecchi, F., Lauretani, F., Bandinelli, S., Abbate, R., Beghi, E., et al. (2010) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2010.02.003 · Cited by 23
Can we really predict risk of cancer?
Thrift, A. P., & Whiteman, D. C. (2013) · Cancer Epidemiology · doi:10.1016/j.canep.2013.04.002 · Cited by 22
Differences in absolute risk of cardiovascular events using risk-refinement tests: A systematic analysis of four cardiovascular risk equations
deGoma, E. M., Dunbar, R. L., Jacoby, D. S., & French, B. C. (2012) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.12.025 · Cited by 23
C-reactive protein and serum creatinine, but not haemoglobin A1c, are independent predictors of coronary heart disease risk in non-diabetic Chinese
Salim, A., Tai, E. S., Tan, V. Y., Welsh, A. H., Liew, R., Naidoo, N., Wu, Y., Yuan, J., Koh, W., & van Dam, R. M. (2016) · European Journal of Preventive Cardiology · doi:10.1177/2047487315626547 · Cited by 23
Cardiovascular risk scores in the prediction of subclinical atherosclerosis in young adults: evidence from the cardiovascular risk in a young Finns study
Raiko, J. R. H., Magnussen, C. G., Mika Kivimaki, B., Taittonen, L. M., Laitinen, T. P., Kähönen, M. A. P., Hutri‐Kähönen, N., Jula, A., Loo, B.-M., Thomson, R. J., et al. (2010) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e3283386419 · Cited by 23
Changes in cardiovascular risk factors after 5 years of implementation of a population-based program to reduce cardiovascular disease: The Heart of New Ulm Project
Sidebottom, A. C., Sillah, A., Miedema, M. D., Vock, D. M., Pereira, R. F., Benson, G. A., Boucher, J. L., Knickelbine, T., Lindberg, R., & VanWormer, J. J. (2016) · American Heart Journal · doi:10.1016/j.ahj.2016.02.006 · Cited by 24
Usefulness of Coronary and Carotid Imaging Rather than Traditional Atherosclerotic Risk Factors to Identify Firefighters at Increased Risk for Cardiovascular Disease
Ratchford, E. V., Carson, K. A., Jones, S. R., & Ashen, M. D. (2014) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2014.02.003 · Cited by 22
Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review
Munjral, S., Ahluwalia, P., Jamthikar, A. D., Puvvula, A., Saba, L., Faa, G., Singh, I. M., Chadha, P. S., Turk, M., Johri, A. M., et al. (2021) · Frontiers in Bioscience-Landmark · doi:10.52586/5026 · Cited by 22
Risk prediction of incident coronary heart disease in the Netherlands: re-estimation and improvement of the SCORE risk function
Merry, A. H. H., Boer, J. M. A., Schouten, L. J., Ambergen, T., Steyerberg, E. W., Feskens, E. J. M., Verschuren, W. M. M., Gorgels, A. P. M., & van den Brandt, P. A. (2011) · European Journal of Preventive Cardiology · doi:10.1177/1741826711410256 · Cited by 22
Atherosclerotic Cardiovascular Risk in Japan
Nojiri, S., & Daida, H. (2017) · Japanese Clinical Medicine · doi:10.1177/1179066017712713 · Cited by 23
Distribution and Performance of Cardiovascular Risk Scores in a Mixed Population of HIV-Infected and Community-Based HIV-Uninfected Individuals in Uganda
Muiru, A. N., Bibangambah, P., Hemphill, L., Sentongo, R. N., Kim, J., Triant, V. A., Bangsberg, D. R., Tsai, A. C., Martin, J. N., Haberer, J. E., et al. (2018) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000001696 · Cited by 22
The use of blood biomarkers in precision medicine for the primary prevention of atherosclerotic cardiovascular disease: a review
Sweeney, T. E., Quispe, R., Das, T. M., Juraschek, S. P., Martin, S. S., & Michos, E. D. (2021) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2021.1930531 · Cited by 24
Fatalism Moderates the Relationship Between Family History of Cardiovascular Disease and Engagement in Health‐Promoting Behaviors Among At‐Risk Rural Kentuckians
Mudd‐Martin, G., Rayens, M. K., Lennie, T. A., Chung, M. L., Gokun, Y., Wiggins, A. T., Biddle, M., Bailey, A. L., Novak, M. J., Casey, B. R., et al. (2014) · The Journal of Rural Health · doi:10.1111/jrh.12094 · Cited by 21
The Challenges and Benefits of Cardiovascular Risk Assessment in Clinical Practice
Grover, S. A., & Lowensteyn, I. (2011) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2011.04.008 · Cited by 21
Association between C-reactive Protein and Type of Coronary Arterial Plaque in Asymptomatic Patients: Assessment with Coronary CT Angiography
Yang, D. H., Kang, J., Kim, H., Choe, J., Baek, S., Kim, S. H., Park, G., Kim, Y., Kim, T., Tseng, W. I., et al. (2014) · Radiology · doi:10.1148/radiol.14130772 · Cited by 21
Cardiovascular risk assessment in rheumatoid arthritis – controversies and the new approach
Bonek, K., & Głuszko, P. (2016) · Reumatologia/Rheumatology · doi:10.5114/reum.2016.61214 · Cited by 24
Consensus clinical recommendations for the management of plasma lipid disorders in the Middle East
Sayed, N., Waili, K. A., Alawadi, F., Al-Ghamdi, S. M. G., Almahmeed, W. A., Alnouri, F., Rukhaimi, M. A., Al‐Rasadi, K., Awan, Z. A., Farghaly, M., et al. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.09.081 · Cited by 22
Development and validation of alternative cardiovascular risk prediction equations for population health planning: a routine health data linkage study of 1.7 million New Zealanders
Mehta, S., Jackson, R., Pylypchuk, R. D., Poppe, K., Wells, S., & Kerr, A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy137 · Cited by 21
Inflammatory Markers Related to Innate and Adaptive Immunity in Atherosclerosis: Implications for Disease Prediction and Prospective Therapeutics
Hong, L., Xue, Q., & Shao, H. J. (2021) · Journal of Inflammation Research · doi:10.2147/jir.s294809 · Cited by 19
Invited Commentary: Circulating Inflammation Markers and Cancer Risk--Implications for Epidemiologic Studies
Chaturvedi, A. K., Moore, S. C., & Hildesheim, A. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws357 · Cited by 21
Clinical Utility of Genetic Variants for Cardiovascular Risk Prediction
Angelantonio, E. D., & Butterworth, A. S. (2012) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.112.964148 · Cited by 21
2022 Saudi Guidelines for the Management of Dyslipidemia
Alrahimi, J. S., AlSaif, S. M., Alasnag, M. A., Awan, Z. A., Almutairi, F., Mudaiheem, H. Y. A., Gencer, B., Catapano, A. L., Mach, F., & Tash, A. A. (2023) · Heart Views · doi:10.4103/heartviews.heartviews_102_22 · Cited by 21
Insulin secretion based on the late oral glucose tolerance test period and incident diabetes: the San Antonio Heart Study
Lorenzo, C., Williams, K. M., & Haffner, S. M. (2012) · Diabetic Medicine · doi:10.1111/j.1464-5491.2012.03660.x · Cited by 20
Prediction models for risk classification in cardiovascular disease
Petretta, M., & Cuocolo, A. (2012) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-012-2254-1 · Cited by 21
Coronary atherosclerosis in outlier subjects at the opposite extremes of traditional risk factors: Rationale and preliminary results of the Coronary Atherosclerosis in outlier subjects: Protective and novel Individual Risk factors Evaluation (CAPIRE) study
Magnoni, M., Andreini, D., Gorini, M., Moccetti, T., Modena, M. G., Canestrari, M., Berti, S. L., Casolo, G., Gabrielli, D., Marraccini, P., et al. (2015) · American Heart Journal · doi:10.1016/j.ahj.2015.11.017 · Cited by 20
Assessing Cardiovascular Risk and Testing in Type 2 Diabetes
Saeed, A., & Ballantyne, C. M. (2017) · Current Cardiology Reports · doi:10.1007/s11886-017-0831-4 · Cited by 20
Markers of Inflammation and Cardiovascular Disease
Bertoméu, V., & González‐Juanatey, J. R. (2009) · American Journal of Cardiovascular Drugs · doi:10.2165/1153161-s0-000000000-00000 · Cited by 20
The predictive accuracy of cardiovascular disease risk prediction tools in inflammatory arthritis and psoriasis: an observational validation study using the Clinical Practice Research Datalink
Hughes, D. M., Coronado, J. I. C., Schofield, P., Yiu, Z. Z. N., & Zhao, S. S. (2023) · Lara D. Veeken · doi:10.1093/rheumatology/kead610 · Cited by 19
High Density Lipoprotein Particle Composition, Functionality, Deficiency, and Atherosclerotic Cardiovascular Disease Risk: A Review
Schaefer, E. J., Asztalos, B. F., Vaisar, T., Diffenderfer, M. R., Brewer, H. B., Stock, E. O., & Kane, J. P. (2025) · Current Atherosclerosis Reports · doi:10.1007/s11883-025-01308-9 · Cited by 20
Predictive genetic testing for coronary artery disease
Johansen, C. T., & Hegele, R. A. (2009) · Critical Reviews in Clinical Laboratory Sciences · doi:10.3109/07388550903422075 · Cited by 18
Role of Coronary Artery Calcium Score and Coronary CT Angiography in the Diagnosis and Risk Stratification of Individuals with Suspected Coronary Artery Disease
Wilson, S. R., Lin, F. Y.-H., & Min, J. K. (2011) · Current Cardiology Reports · doi:10.1007/s11886-011-0191-4 · Cited by 19
Most of the patients presenting myocardial infarction would not be eligible for intensive lipid-lowering based on clinical algorithms or plasma C-reactive protein
Sposito, A. C., Alvarenga, B. F., Alexandre, A. S., Araújo, A. L. R., dos Santos, S. N., Andrade, J. M., Ramires, J. A. F., Silva, J. C. Q. E., & Coelho, O. R. (2010) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2010.10.034 · Cited by 19
Women Up, Men Down: The Clinical Impact of Replacing the Framingham Risk Score with the Reynolds Risk Score in the United States Population
Tattersall, M. C., Gangnon, R. E., Karmali, K. N., & Keevil, J. G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044347 · Cited by 22
Relation Between Family History of Premature Coronary Artery Disease and the Risk of Death in Patients With Coronary Artery Disease
Abdi-Ali, A., Shaheen, A. A., Southern, D. A., Zhang, M., Knudtson, M. L., White, J. A., Graham, M. M., James, M. T., & Wilton, S. B. (2015) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2015.11.008 · Cited by 19
Osteoprotegerin promotes intimal hyperplasia and contributes to in-stent restenosis: Role of an αVβ3/FAK dependent YAP pathway
He, Y., Zou, P., Lu, Y., Jia, D., Li, X., Yang, H., Tang, L., Zhu, Z., Tu, T., Tai, S., et al. (2020) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2020.01.006 · Cited by 19
Moving toward new statin guidelines in a post-JUPITER world: Principles to consider
Ridker, P. M. (2009) · Current Atherosclerosis Reports · doi:10.1007/s11883-009-0039-1 · Cited by 19
Self-citation
HeartBeat Connections: A Rural Community of Solution for Cardiovascular Health
Benson, G. A., Sidebottom, A., VanWormer, J. J., Boucher, J. L., Stephens, C., & Krikava, J. (2013) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2013.03.120240 · Cited by 21
Bioactive compounds from Prosthechea karwinskii decrease obesity, insulin resistance, pro-inflammatory status, and cardiovascular risk in Wistar rats with metabolic syndrome
Barragán-Zarate, G. S., Alexander-Aguilera, A., Lagunez-Rivera, L., Solano, R., & Soto-Rodríguez, I. (2021) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2021.114376 · Cited by 19
Managing hypercholesterolemia in adults older than 75 years without a history of atherosclerotic cardiovascular disease: An Expert Clinical Consensus from the National Lipid Association and the American Geriatrics Society
Bittner, V., Linnebur, S. A., Dixon, D. L., Forman, D. E., Green, A. R., Jacobson, T. A., Orkaby, A. R., Saseen, J. J., & Virani, S. S. (2025) · Journal of clinical lipidology · doi:10.1016/j.jacl.2024.09.005 · Cited by 20
Do novel biomarkers add to existing scores of total cardiovascular risk?
Backer, G. D., Graham, I. M., & Cooney, M. T. (2012) · European Journal of Preventive Cardiology · doi:10.1177/2047487312448988 · Cited by 18
The Expanded Risk Score in Rheumatoid Arthritis (ERS-RA): performance of a disease-specific calculator in comparison with the traditional prediction scores in the assessment of the 10-year risk of cardiovascular disease in patients with rheumatoid arthritis
Salaffi, F., Carotti, M., Carlo, M. D., Tardella, M., Lato, V., Becciolini, A., Favalli, E. G., & Giovagnoni, A. (2018) · Swiss Medical Weekly · doi:10.4414/smw.2018.14656 · Cited by 18

Authors: Ridker, P. M., Paynter, N. P., Rifai, N., Gaziano, J. M., & Cook, N. R.
Affiliations: Brigham and Women's Hospital (US)
ORCID: Ridker, Paul M
Paynter, Nina P.
Rifai, Nader
Gaziano, J. Michael
Cook, Nancy R.
Venue: Circulation
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2008-11-25
Volume: 118
Issue: 22
Pages: 2243-2251
ISSN: 0009-7322 (ISSN-L)
1524-4539
DOI: 10.1161/circulationaha.108.814251
PMC: PMC2752381
OpenAlex: W2096807144
Semantic Scholar: 4e13b2445d617740f45915eafd75712fdf825998
Cited by: 841
References: 27
Citations by year: 2026: 11 · 2025: 22 · 2024: 20 · 2023: 25 · 2022: 23 · 2021: 32 · 2020: 31 · 2019: 39 · 2018: 42 · 2017: 37 · 2016: 47 · 2015: 68 · 2014: 63 · 2013: 53 · 2012: 92
FWCI: 80.90
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Circulation (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute (R01CA097193)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
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
Keywords: Reynolds Risk Score, cardiovascular risk prediction, men, family history of myocardial infarction, coronary artery disease, risk reclassification, net reclassification improvement, coronary risk factors, hypertriglyceridemia, hypercholesterolemia, LDL cholesterol, HDL cholesterol, blood pressure, smoking, non-diabetic men
MeSH terms: C-Reactive Protein, Cardiovascular Diseases, Female, Follow-Up Studies, Humans, Inflammation, Life Style, Male, Predictive Value of Tests, Prospective Studies, Sex Characteristics, Biomarkers, Likelihood Functions, 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

C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks