Cited by 176Diabetes, Cardiovascular Risks, and LipoproteinsHealth Promotion and Cardiovascular PreventionDiabetes Treatment and Management

The DOI matches its Crossref record; 65 of the 66 references it cites were found in a registry or an index.

Cardiovascular risk assessment scores for people with diabetes: a systematic review

Open access

Chamnan, P., Simmons, R. K., Sharp, S. J., Griffin, S. J., Wareham, N. J.·2009·Diabetologia

DOI 10.1007/s00125-009-1454-0PMC PMC2744770

Open this paper in CoChat — free account Open PDF

Abstract

People with type 2 diabetes have an increased risk of cardiovascular disease (CVD). Multivariate cardiovascular risk scores have been used in many countries to identify individuals who are at high risk of CVD. These risk scores include those originally developed in individuals with diabetes and those developed in a general population. This article reviews the published evidence for the performance of CVD risk scores in diabetic patients by: (1) examining the overall rationale for using risk scores; (2) systematically reviewing the literature on available scores; and (3) exploring methodological issues surrounding the development, validation and comparison of risk scores. The predictive performance of cardiovascular risk scores varies substantially between different populations. There is little evidence to suggest that risk scores developed in individuals with diabetes estimate cardiovascular risk more accurately than those developed in the general population. The inconsistency in the methods used in evaluation studies makes it difficult to compare and summarise the predictive ability of risk scores. Overall, CVD risk scores rank individuals reasonably accurately and are therefore useful in the management of diabetes with regard to targeting therapy to patients at highest risk. However, due to the uncertainty in estimation of true risk, care is needed when using scores to communicate absolute CVD risk to individuals.

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 · 10.1007/s00125-009-1454-0

Cites

65 of 66 references verified1 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.

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
Mortality from Coronary Heart Disease in Subjects with Type 2 Diabetes and in Nondiabetic Subjects with and without Prior Myocardial Infarction
Haffner, S. M., Lehto, S., Rönnemaa, T., Pyörälä, K., & Laakso, M. (1998) · New England Journal of Medicine · doi:10.1056/nejm199807233390404 · Cited by 7,000
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
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
Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes
Gæde, P., Vedel, P., Larsen, N., Jensen, G. V. H., Parving, H.-H., & Pedersen, O. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa021778 · Cited by 4,378
Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial
Colhoun, H. M., Betteridge, D. J., Durrington, P. N., Hitman, G. A., W Neil, H. A., Livingstone, S. J., Thomason, M. J., Mackness, M. I., Charlton-Menys, V., & Fuller, J. H. (2004) · The Lancet · doi:10.1016/s0140-6736(04)16895-5 · Cited by 3,755
Effect of a multifactorial intervention on mortality in type 2 diabetes
Gæde, P., Lund-Andersen, H., Parving, H.-H., & Pedersen, O. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0706245 · Cited by 3,465 · cited 1×
“Type 2 diabetes is a strong risk factor for cardiovascular disease (CVD), with some studies suggesting that it confers an equivalent risk to having had a myocardial infarction [1, 2]. Multifactorial interventions, such as the Steno-2 study, have been effective in reducing the risk of non-fatal [3] and fatal CVD [ 4 ]…”
Background
MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial
(2003) · The Lancet · doi:10.1016/s0140-6736(03)13636-7 · Cited by 2,876
Cardiovascular disease risk profiles
Anderson, K. M., Odell, P. M., Wilson, P. W. F., & Kannel, W. B. (1991) · American Heart Journal · doi:10.1016/0002-8703(91)90861-b · Cited by 2,163
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

Show all 66 references

Sorted by citations

Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
Davies, M. J., Aroda, V. R., Collins, B. S., Gabbay, R. A., Green, J., Maruthur, N. M., Rosas, S. E., Del Prato, S., Mathieu, C., Mingrone, G., et al. (2022) · Diabetes Care · doi:10.2337/dci22-0034 · Cited by 2,104
ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD
Authors Task Force Members, Rydén, L., Grant, P. J., Anker, S. D., Berne, C., Cosentino, F., Danchin, N., Deaton, C., Escaned, J., Hammes, H., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht108 · Cited by 1,994
Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
Davies, M. J., Aroda, V. R., Collins, B. S., Gabbay, R. A., Green, J., Maruthur, N. M., Rosas, S. E., Del Prato, S., Mathieu, C., Mingrone, G., et al. (2022) · Diabetologia · doi:10.1007/s00125-022-05787-2 · Cited by 1,048
ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. The Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD)
Rydén, L., Grant, P. J., Anker, S. D., Berne, C., Cosentino, F., Danchin, N., Deaton, C., Escaned, J., Hammes, H., Huikuri, H., et al. (2014) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2013.10.013 · Cited by 609
Risk assessment tools for identifying individuals at risk of developing type 2 diabetes
Buijsse, B., Simmons, R. K., Griffin, S. J., & Schulze, M. B. (2011) · Epidemiologic Reviews · doi:10.1093/epirev/mxq019 · Cited by 297
Self-citation
Plasma Lipidomic Profiles Improve on Traditional Risk Factors for the Prediction of Cardiovascular Events in Type 2 Diabetes Mellitus
Alshehry, Z. H., Mundra, P. A., Barlow, C. K., Mellett, N. A., Wong, G., McConville, M. J., Simes, J. R., Tonkin, A., SULLIVAN, D. R., Barnes, E. H., et al. (2016) · Circulation · doi:10.1161/circulationaha.116.023233 · Cited by 270
Validation of the IMS CORE Diabetes Model
McEwan, P. C., FOOS, V., Palmer, J. L., Lamotte, M., Lloyd, A., & Grant, D. J. W. (2014) · Value in Health · doi:10.1016/j.jval.2014.07.007 · Cited by 237
Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review
van Dieren, S., Beulens, J. W. J., Kengne, A. P., Peelen, L. M., Rutten, G. E. H. M., Woodward, M., van der Schouw, Y. T., & Moons, K. G. M. (2011) · Heart · doi:10.1136/heartjnl-2011-300734 · Cited by 219
Contemporary model for cardiovascular risk prediction in people with type 2 diabetes
Kengne, A. P., Patel, A. A., Marre, M., Travert, F., Lièvre, M. M., Zoungas, S., Chalmers, J. P., Colagiuri, S., Grobbee, D. E., Hamet, P., et al. (2011) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1177/1741826710394270 · Cited by 183
Psychological interventions for diabetes-related distress in adults with type 2 diabetes mellitus
Chew, B. H., Vos, R. C., Metzendorf, M.-I., Scholten, R. J., & Rutten, G. E. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011469.pub2 · Cited by 138
The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes: the Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) Study
for the ADVANCE Collaborative Group, Kengne, A. P., Patel, A. A., Colagiuri, S., Heller, S. R., Hamet, P., Marre, M., Pan, C., Zoungas, S., Grobbee, D. E., et al. (2010) · Diabetologia · doi:10.1007/s00125-010-1681-4 · Cited by 132
Screening for type 2 diabetes: a short report for the National Screening Committee
Waugh, N., Shyangdan, D., Taylor‐Phillips, S., Suri, G., & Hall, B. J. (2013) · Health Technology Assessment · doi:10.3310/hta17350 · Cited by 128
Predicting Macro- and Microvascular Complications in Type 2 Diabetes
Tanaka, S., Tanaka, S., Iimuro, S., Yamashita, H., Katayama, S., Akanuma, Y., Yamada, N., Araki, A., Ito, H., Sone, H., et al. (2013) · Diabetes Care · doi:10.2337/dc12-0958 · Cited by 130
Melatonin improves insulin sensitivity independently of weight loss in old obese rats
Zanuto, R., de Siqueira-Filho, M. A., Caperuto, L. C., Bacurau, R. F. P., Hirata, E., Garcia, R. A. P., do Amaral, F. G., Marçal, A. C., Ribeiro, L. M., Camporez, J. P., et al. (2013) · Journal of Pineal Research · doi:10.1111/jpi.12056 · Cited by 98
Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings
Dziopa, K., Asselbergs, F. W., Gratton, J., Chaturvedi, N., & Schmidt, A. F. (2022) · Diabetologia · doi:10.1007/s00125-021-05640-y · Cited by 106
External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes
van Dieren, S., Peelen, L. M., Nöthlings, U., van der Schouw, Y. T., Rutten, G. E. H. M., Spijkerman, A. M. W., van der A, D. L., Sluik, D., Boeing, H., Moons, K. G. M., et al. (2010) · Diabetologia · doi:10.1007/s00125-010-1960-0 · Cited by 94
Use of a Metabolic Syndrome Severity Z Score to Track Risk During Treatment of Prediabetes: An Analysis of the Diabetes Prevention Program
DeBoer, M. D., Filipp, S. L., & Gurka, M. J. (2018) · Diabetes Care · doi:10.2337/dc18-1079 · Cited by 92
Coronary artery disease risk assessment from unstructured electronic health records using text mining
Jonnagaddala, J., Liaw, S., RAY, P. K., Kumar, M., Chang, N., & Dai, H.-J. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.08.003 · Cited by 89
Serum Resistin, Cardiovascular Disease and All-Cause Mortality in Patients with Type 2 Diabetes
Menzaghi, C., Bacci, S., Salvemini, L., Mendonça, C., Palladino, G., Fontana, A., Bonis, C. D., Marucci, A., Goheen, E., Prudente, S., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064729 · Cited by 87
Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication
Zarkogianni, K., Αθανασίου, Μ., Thanopoulou, A. C., & Nikita, K. S. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2765639 · Cited by 92
Severity of Diabetic Retinopathy and the Risk of Future Cerebrovascular Disease, Cardiovascular Disease, and All-Cause Mortality
Modjtahedi, B. S., Wu, J., Luong, T. Q., Gandhi, N. K., Fong, D. S., & Chen, W. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.12.019 · Cited by 82
A new model for 5-year risk of cardiovascular disease in type 2 diabetes, from the Swedish National Diabetes Register (NDR)
Zethelius, B., Eliasson, B., Eeg‐Olofsson, K., Svensson, A., Guðbjörnsdóttir, S., & Cederholm, J. (2011) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2011.05.037 · Cited by 82
Basing information on comprehensive, critically appraised, and up-to-date syntheses of the scientific evidence: a quality dimension of the International Patient Decision Aid Standards
Montori, V. M., LeBlanc, A., Buchholz, A., Stilwell, D., & Τσάπας, Α. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-s2-s5 · Cited by 84
Common carotid intima-media thickness does not add to Framingham risk score in individuals with diabetes mellitus: the USE-IMT initiative
den Ruijter, H. M., Peters, S. A. E., Groenewegen, K. A., Anderson, T. J., Britton, A. R., Dekker, J. M., Engström, G., Eijkemans, M. J. C., Evans, G. W., Graaf, J. D., et al. (2013) · Diabetologia · doi:10.1007/s00125-013-2898-9 · Cited by 75
Coronary Artery Calcium Score Compared with Cardio-Ankle Vascular Index in the Prediction of Cardiovascular Events in Asymptomatic Patients with Type 2 Diabetes
Chung, S., Yang, C.-C., Chen, C.-C., Hsu, Y.-C., & Lei, M. (2015) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.29926 · Cited by 71
The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus
van der Leeuw, J., van Dieren, S., Beulens, J. W. J., Boeing, H., Spijkerman, A. M. W., van der Graaf, Y., van der A, D. L., Nöthlings, U., Visseren, F. L. J., Rutten, G. E. H. M., et al. (2014) · Heart · doi:10.1136/heartjnl-2014-306068 · Cited by 68
Development of a new diabetes risk prediction tool for incident coronary heart disease events: The Multi-Ethnic Study of Atherosclerosis and the Heinz Nixdorf Recall Study
Yeboah, J., Erbel, R. M., Delaney, J. A., Nance, R. M., Guo, M., Bertoni, A. G., Budoff, M. J., Moebus, S., Jöckel, K., Burke, G. L., et al. (2014) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2014.07.035 · Cited by 70
Performance of Cardiovascular Disease Risk Scores in People Diagnosed With Type 2 Diabetes: External Validation Using Data From the National Scottish Diabetes Register
Read, S. H., van Diepen, M., Colhoun, H. M., Halbesma, N., Lindsay, R. S., McKnight, J. A., McAllister, D., Pearson, E. R., Petrie, J. R., Philip, S., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0578 · Cited by 66
Age ≥60years was an independent risk factor for diabetes-related complications despite good control of cardiovascular risk factors in patients with type 2 diabetes mellitus
Chew, B., Ghazali, S. S., Ismail, M., Haniff, J., & Bujang, M. A. (2013) · Experimental Gerontology · doi:10.1016/j.exger.2013.02.017 · Cited by 64
Associations of Age at Diagnosis and Duration of Diabetes With Morbidity and Mortality Among Older Adults
Cigolle, C. T., Blaum, C. S., Lyu, C., Ha, J., Kabeto, M. U., & Zhong, J. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.32766 · Cited by 68
The Framingham Study, Diabetes Mellitus and Cardiovascular Disease: Turning Back the Clock
Kengne, A. P., Turnbull, F., & MacMahon, S. W. (2010) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2010.02.010 · Cited by 65
Biomarkers of cardiovascular disease: contributions to risk prediction in individuals with diabetes
Bachmann, K. N., & Wang, T. J. (2017) · Diabetologia · doi:10.1007/s00125-017-4442-9 · Cited by 56
External Validation of the UKPDS Risk Engine in Incident Type 2 Diabetes: A Need for New Type 2 Diabetes–Specific Risk Equations
Bannister, C. A., Poole, C., Jenkins‐Jones, S., Morgan, C. L., Elwyn, G. J., Spasić, ‪., & Currie, C. J. (2013) · Diabetes Care · doi:10.2337/dc13-1159 · Cited by 51
Performance of the recommended ESC/EASD cardiovascular risk stratification model in comparison to SCORE and NT-proBNP as a single biomarker for risk prediction in type 2 diabetes mellitus
Prausmüller, S., Resl, M., Arfsten, H., Spinka, G., Wurm, R., Neuhold, S., Bartko, P. E., Goliasch, G., Strunk, G., Pávó, N., et al. (2021) · Cardiovascular Diabetology · doi:10.1186/s12933-021-01221-w · Cited by 49
Carotid Artery Plaque and LDL-to-HDL Cholesterol Ratio Predict Atherosclerotic Status in Coronary Arteries in Asymptomatic Patients with Type 2 Diabetes Mellitus
Fujihara, K., Suzuki, H., Sato, A., Kodama, S., Heianza, Y., Saito, K., IWASAKI, H. O., Kobayashi, K., Yatoh, S., Takahashi, A., et al. (2013) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.14977 · Cited by 47
Retinopathy Signs Improved Prediction and Reclassification of Cardiovascular Disease Risk in Diabetes: A prospective cohort study
Ho, H., Cheung, C. Y.-L., Sabanayagam, C., Yip, W., Ikram, M. K., Ong, P. G., Mitchell, P. L. R., Chow, K. Y., Cheng, C., Tai, E. S., et al. (2017) · Scientific Reports · doi:10.1038/srep41492 · Cited by 41
Early Vascular Ageing – A Concept in Development
Nilsson, P. M. (2015) · touchREVIEWS in Endocrinology · doi:10.17925/ee.2015.11.01.26 · Cited by 44
Heart sparing radiotherapy in breast cancer: the importance of baseline cardiac risks
Gaasch, A., Schönecker, S., Simonetto, C., Eidemüller, M., Pazos, M., Reitz, D., Rottler, M., Freislederer, P., Braun, M., Würstlein, R., et al. (2020) · Radiation Oncology · doi:10.1186/s13014-020-01520-8 · Cited by 41
Association between lipids profile and thyroid parameters in euthyroid diabetic subjects: a cross-sectional study
Zhang, Y., Lü, P., Zhang, L., & Xiao, X. (2015) · BMC Endocrine Disorders · doi:10.1186/s12902-015-0008-3 · Cited by 39
Review on cardiovascular risk prediction
Ruwanpathirana, T., Owen, A., & Reid, C. M. (2015) · Cardiovascular Therapeutics · doi:10.1111/1755-5922.12110 · Cited by 31
Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project
Nabrdalik, K., Kwiendacz, H., Drożdż, K., Irlik, K., Hendel, M., Wijata, A. M., Nalepa, J., Correa, E. S., Hajzler, W., Janota, O., et al. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.101694 · Cited by 34
The ADVANCE cardiovascular risk model and current strategies for cardiovascular disease risk evaluation in people with diabetes
Kengne, A. P. (2013) · Cardiovascular journal of South Africa/Cardiovascular journal of Southern Africa · doi:10.5830/cvja-2013-078 · Cited by 32
Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics
Chowdhury, M., Yeasmin, F., Rabi, D. M., Ronksley, P. E., & Turin, T. C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025579 · Cited by 31
Comparison of the Framingham Risk Score, UK Prospective Diabetes Study (UKPDS) Risk Engine, Japanese Atherosclerosis Longitudinal Study-Existing Cohorts Combine (JALS-ECC) and Maximum Carotid Intima-Media Thickness for Predicting Coronary Artery Stenosis in Patients with Asymptomatic Type 2 Diabetes
Fujihara, K., Suzuki, H., Sato, A., Ishizu, T., Kodama, S., Heianza, Y., Saito, K., IWASAKI, H. O., Kobayashi, K., Yatoh, S., et al. (2014) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.20487 · Cited by 30
Refitting of the UKPDS 68 Risk Equations to Contemporary Routine Clinical Practice Data in the UK
McEwan, P. C., Bennett, H., Ward, T. T., & Bergenheim, K. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0225-z · Cited by 30
Diabetes and cardiovascular disease: from evidence to clinical practice – position statement 2014 of Brazilian Diabetes Society
Bertoluci, M. C., Netto, A. P., Pires, A. C., Pesaro, A. E. P., Schaan, B. D., Caramelli, B., Polanczyk, C. A., Junior, C. V. S., Gualandro, D. M., Malerbi, D. A. C., et al. (2014) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-6-58 · Cited by 30
Comparative performance of diabetes-specific and general population-based cardiovascular risk assessment models in people with diabetes mellitus
Echouffo-Tcheugui, J. B., & Kengne, A. P. (2013) · Diabetes & Metabolism · doi:10.1016/j.diabet.2013.07.002 · Cited by 28
Cardiovascular risk assessment tools: A scoping review
Sacramento-Pacheco, J., Clíments, G. D., Gómez-Salgado, J. C., Romero‐Martín, M., & Gómez, M. B. S. (2019) · Australian Critical Care · doi:10.1016/j.aucc.2018.09.008 · Cited by 27
Elevated NT-proBNP and coronary calcium score in relation to coronary artery disease in asymptomatic type 2 diabetic patients with elevated urinary albumin excretion rate
Reinhard, H., Hansen, P. R., Persson, F., Tarnow, L., Wiinberg, N., Kjær, A., Petersen, C., Winther, K., Parving, H. ‐., Rossing, P. R., et al. (2011) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfr009 · Cited by 27
Effects of blood pressure lowering on cardiovascular outcomes in different cardiovascular risk groups among participants with type 2 diabetes
van Dieren, S., Kengne, A. P., Chalmers, J. P., Beulens, J. W. J., Cooper, M. E., Grobbee, D. E., Harrap, S. B., Mancia, G., Neal, B. C., Patel, A. A., et al. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2012.05.002 · Cited by 25
Associations of a metabolic syndrome severity score with coronary heart disease and diabetes in fasting vs. non-fasting individuals
DeBoer, M. D., Filipp, S. L., & Gurka, M. J. (2019) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2019.08.010 · Cited by 24
Incidence and predictors for cardiovascular disease in Chinese patients with type 2 diabetes mellitus – a population-based retrospective cohort study
Wan, E. Y. F., Fong, D., Fung, C. S. C., & Lam, C. L. K. (2015) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2015.12.010 · Cited by 22
On the importance of global cardiovascular risk assessment in people with type 2 diabetes
Echouffo-Tcheugui, J. B., & Kengne, A. P. (2013) · Primary care diabetes · doi:10.1016/j.pcd.2013.03.002 · Cited by 21
Clinical Development Approaches and Statistical Methodologies to Prospectively Assess the Cardiovascular Risk of New Antidiabetic Therapies for Type 2 Diabetes
Geiger, M. J., Mehta, C. R., Turner, J. R., Arbet‐Engels, C., Hantel, S., Hirshberg, B., Koglin, J., Mendzelevski, B., Sager, P. T., Shapiro, D., et al. (2014) · Therapeutic Innovation & Regulatory Science · doi:10.1177/2168479014549860 · Cited by 24
Retinal Information is Independently Associated with Cardiovascular Disease in Patients with Type 2 diabetes
Guo, V. Y., Chan, J. C. N., Chung, H. H. Y., Ozaki, R., So, W., Luk, A. O. Y., Lam, A. T., Lee, J., & Zee, B. C.-Y. (2016) · Scientific Reports · doi:10.1038/srep19053 · Cited by 23
Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes
Kavarić, N., Klisic, A. N., & Ninić, A. (2018) · Open Medicine · doi:10.1515/med-2018-0086 · Cited by 23
Volumes of coronary plaque disease in relation to body mass index, waist circumference, truncal fat mass and epicardial adipose tissue in patients with type 2 diabetes mellitus and controls
Gullaksen, S., Funck, K. L., Laugesen, E., Hansen, T. K., Dey, D., & Poulsen, P. L. (2019) · Diabetes and Vascular Disease Research · doi:10.1177/1479164119825761 · Cited by 19
Does early intensive multifactorial therapy reduce modelled cardiovascular risk in individuals with screen-detected diabetes? Results from the ADDITION-Europe cluster randomized trial
Black, J. A., Sharp, S., Wareham, N., Sandbaek, A., Rutten, G., Lauritzen, T., Khunti, K., Davies, M. J., Borch-Johnsen, K., Griffin, S., et al. (2014) · Diabetic Medicine · doi:10.1111/dme.12410 · Cited by 23
Self-citation
Estimation of the Long-term Cardiovascular Events Using UKPDS Risk Engine in Metabolic Syndrome Patients.
Shivakumar, V., Kandhare, A. D., Rajmane, A. R., Adil, M., Ghosh, P., Lb, B., Saraf, M. N., & Bodhankar, S. L. (2014) · Europe PMC (PubMed Central) · Cited by 20
Cardiovascular disease risk prediction among Iranian patients with diabetes mellitus in Isfahan Province, Iran, in 2014, by using Framingham risk score, atherosclerotic cardiovascular disease risk score, and high-sensitive C-reactive protein.
Faradonbeh, N. A., Nikaeen, F., Akbari, M., Almasi, N., & Vakhshoori, M. (2018) · Europe PMC (PubMed Central) · doi:10.22122/arya.v14i4.1685 · Cited by 19
A Multidisciplinary Intervention Utilizing Virtual Communication Tools to Reduce Health Disparities: A Pilot Randomized Controlled Trial
Emerson, J., Welch, M., Rossman, W., Carek, S. M., Ludden, T., Templin, M. A., Moore, C. G., Tapp, H., Dulin, M., & McWilliams, A. D. (2015) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph13010031 · Cited by 19
Predictive Factors Associated with the Period of Time before Initiation of Hemodialysis in CKD Stages 4 and 5
Furukawa, M., Io, H., Tanimoto, M., Hagiwara, S., Horikoshi, S., & Tomino, Y. (2010) · Nephron Clinical Practice · doi:10.1159/000321512 · Cited by 18
Does Insulin Resistance Drive the Association between Hyperglycemia and Cardiovascular Risk?
Færch, K., Bergman, B. C., & Perreault, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039260 · Cited by 18
Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population
Bansal, D., Nayakallu, R., Gudala, K., Vyamasuni, R., & Bhansali, A. K. (2015) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2015.39.4.321 · Cited by 18
A randomized, placebo-controlled, double-blind, prospective trial to evaluate the effect of vildagliptin in new-onset diabetes mellitus after kidney transplantation
Haidinger, M., Werzowa, J. M., Voigt, H.-C., Pleiner, J., Stemer, G., Hecking, M., Döller, D., Hörl, W. H., Weichhart, T., & Säemann, M. D. (2010) · Trials · doi:10.1186/1745-6215-11-91 · Cited by 18
Repeat Cardiovascular Risk Assessment after Four Years: Is There Improvement in Risk Prediction?
Chamnan, P., Simmons, R. K., Sharp, S. J., Khaw, K., Wareham, N. J., & Griffin, S. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147417 · Cited by 17
Self-citation
Choosing Targets for Glycaemia, Blood Pressure and Low-Density Lipoprotein Cholesterol in Elderly Individuals with Diabetes Mellitus
Kirsh, S. R., & Aron, D. C. (2011) · Drugs & Aging · doi:10.2165/11594750-000000000-00000 · Cited by 17
Guía de práctica clínica de la ESC sobre diabetes, prediabetes y enfermedad cardiovascular, en colaboración con la European Association for the Study of Diabetes
Rydén, L., estable, G. D. T. D. L. S. E. D. C. S. D. Y. T. D. L. C. I., Grant, P. J., Anker, S. D., Berne, C., Cosentino, F., Danchin, N., Deaton, C., Escaned, J., Hammes, H. P., et al. (2014) · Revista Española de Cardiología · doi:10.1016/j.recesp.2013.10.015 · Cited by 20
A comparison of the effects of low- and high-dose atorvastatin on lipoprotein metabolism and inflammatory cytokines in type 2 diabetes: Results from the Protection Against Nephropathy in Diabetes with Atorvastatin (PANDA) randomized trial
Soran, H., Liu, Y., Adam, S., Siahmansur, T. J., Ho, J. H., Schofield, J. D., Kwok, S., Gittins, M., France, M. W., Younis, N., et al. (2017) · Journal of clinical lipidology · doi:10.1016/j.jacl.2017.10.011 · Cited by 17
Cardiovascular Risk, Risk Knowledge, and Related Factors in Patients With Type 2 Diabetes
Zehirlioğlu, L., Mert, H., Sezgin, D., & Özpelit, E. (2019) · Clinical Nursing Research · doi:10.1177/1054773819844070 · Cited by 16
Risk prediction of major complications in individuals with diabetes: the A therosclerosis R isk in C ommunities S tudy
Parrinello, C. M., Matsushita, K., Woodward, M., Wagenknecht, L. E., Coresh, J., & Selvin, E. (2016) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12686 · Cited by 16
Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
Goliasch, G., Silbernagel, G., Kleber, M. E., Grammer, T. B., Pilz, S., Tomaschitz, A., Bartko, P. E., Maurer, G., Köenig, W., Niessner, A., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-04935-8 · Cited by 16
Comparison of four cardiovascular risk prediction functions among C hinese patients with diabetes mellitus in the primary care setting
Jiao, F. F., Lam, C. L. K., Fung, C. S. C., & McGhee, S. M. (2014) · Journal of Diabetes Investigation · doi:10.1111/jdi.12188 · Cited by 14
Maximum BMI and microvascular complications in a cohort of Japanese patients with type 2 diabetes: the Japan Diabetes Complications Study
Tanaka, S., Tanaka, S., Iimuro, S., Ishibashi, S., Yamashita, H., Moriya, T., Katayama, S., Akanuma, Y., Ohashi, Y., Yamada, N., et al. (2016) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2016.02.020 · Cited by 15
Prevalence of Cardiovascular Risk Factors and 10-Years Risk for Coronary Heart Disease in the United Arab Emirates
Shehab, A., Bakir, S., Sabbour, H. M., Elnour, A. A., Mahmeed, W. A., Salam, A. M., & Elkholy, D. (2022) · Current Diabetes Reviews · doi:10.2174/1573399818666220421113607 · Cited by 14
Change in cardiovascular risk factors following early diagnosis of type 2 diabetes: a cohort analysis of a cluster-randomised trial
Black, J. A., Sharp, S., Wareham, N., Sandbæk, A., Rutten, G., Lauritzen, T., Khunti, K., Davies, M., Borch-Johnsen, K., Griffin, S., et al. (2014) · The British journal of general practice : the journal of the Royal College of General Practitioners · doi:10.3399/bjgp14x677833 · Cited by 14
Self-citation
Risk-related standards of competence are a nonsense
Pickering, N. J., Newton‐Howes, G., & Walker, S. (2022) · Journal of Medical Ethics · doi:10.1136/medethics-2021-108107 · Cited by 13
A simple risk score using routine data for predicting cardiovascular disease in primary care
Chamnan, P., Simmons, R. K., Sharp, S. J., Wareham, N. J., Griffin, S. J., Hori, H., & Khaw, K. (2010) · British Journal of General Practice · doi:10.3399/bjgp10x515098 · Cited by 13
Self-citation
Clinical use of polygenic scores in type 2 diabetes: challenges and possibilities
Prasad, R. B., Hakaste, L., & Tuomi, T. (2025) · Diabetologia · doi:10.1007/s00125-025-06419-1 · Cited by 16
Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project
Kwiendacz, H., Wijata, A. M., Nalepa, J., Piaśnik, J., Kulpa, J., Herba, M., Boczek, S., Kegler, K., Hendel, M., Irlik, K., et al. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01938-w · Cited by 14
KNOWLEDGE OF MODIFIABLE RISK FACTORS OF HEART DISEASE AMONG PATIENTS WITH CARDIOVASCULAR RISK
Gandhi, A., Ayyappan, P., Sahana, C. L., Poovizhi, S., & Sivasakthi, K. (2016) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2017.v10i1.14180 · Cited by 12
Cardiovascular disease risk assessment in Nigerian adults with type 2 diabetes and metabolic syndrome using the Framingham's risk score
Udenze, I. C., & Amadi, C. E. (2018) · International Journal of Noncommunicable Diseases · doi:10.4103/jncd.jncd_33_17 · Cited by 12
Assessing 10-Year Cardiovascular Disease Risk in Malaysians With Type 2 Diabetes Mellitus: Framingham Cardiovascular Versus United Kingdom Prospective Diabetes Study Equations
Yew, S. Q., Chia, Y. C., & Theodorakis, M. J. (2019) · Asia Pacific Journal of Public Health · doi:10.1177/1010539519873487 · Cited by 12
Applicability of the Existing CVD Risk Assessment Tools to Type II Diabetics in Oman: A Review
Al-Rawahi, A., & Lee, P. J. (2015) · Oman Medical Journal · doi:10.5001/omj.2015.65 · Cited by 11
Estimation of Absolute Cardiovascular Risk in Individuals with Diabetes Mellitus: Rationale and Approaches
Echouffo-Tcheugui, J. B., Ogunniyi, M. O., & Kengne, A. P. (2011) · ISRN Cardiology · doi:10.5402/2011/242656 · Cited by 11
Tools for Assessing Cardiovascular Disease Risk Factors in Underserved Young Adult Populations: A Systematic Review
Opoku-Acheampong, A. A., Rosenkranz, R. R., Adhikari, K., Muturi, N., Logan, C., & Kidd, T. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413305 · Cited by 14
Assessment of risk and prevention of type 2 diabetes in primary health care
Saaristo, T. (2011) · Cited by 10
Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus
Gokhale, K. M., Chandan, J. S., Sainsbury, C. A. R., Peter Tiňo, B., Tahrani, A. A., Toulis, K. A., & Nirantharakumar, K. (2023) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.10.008 · Cited by 10
A Review of Risk Factors and Cardiovascular Disease in Diabetes Care - 2011
Nilsson, P. M., & Cederholm, J. (2011) · European Journal of Cardiovascular Medicine · doi:10.5083/ejcm.20424884.43 · Cited by 10
Free Triiodothyronine and Cholesterol Levels in Euthyroid Elderly T2DM Patients
Strollo, F., Carucci, I., Morè, M., Marico, G., Strollo, G., Masini, M. A., & Gentile, S. (2012) · International Journal of Endocrinology · doi:10.1155/2012/420370 · Cited by 10
Recomendaciones de buena práctica en el tratamiento de la dislipemia en la diabetes
Pozo, A. H., Mancera‐Romero, J., Pérez-Unanua, M. P., Alonso-Fernández, M., López‐Simarro, F., & Bravo, J. J. M. (2014) · Medicina de Familia SEMERGEN · doi:10.1016/j.semerg.2014.11.007 · Cited by 10
Cardiovascular risk stratification in diabetic patients
Petretta, M., Acampa, W., Fiumara, G., & Cuocolo, A. (2013) · Clinical and Translational Imaging · doi:10.1007/s40336-013-0032-0 · Cited by 11
2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD
Cosentino, F., Grant, P. J., Aboyans, V., Bailey, C. J., Ceriello, A., Delgado, V., Federici, M., Filippatos, G., Grobbee, D. E., Hansen, T. B., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz486 · Cited by 4,763
Association between Hyperhomocysteinemia and Thyroid Hormones in Euthyroid Diabetic Subjects
Zhang, Y., Wang, Q., Li, Q., & Lü, P. (2015) · BioMed Research International · doi:10.1155/2015/196379 · Cited by 9
RSSDI consensus recommendations for dyslipidemia management in diabetes mellitus
Saboo, B., Agarwal, S., Makkar, B. M., Chawla, R., Ghosh, S. K., Viswanathan, V., Gupta, S., Kumar, C. V., Maheshwari, A., Sreenivasamurthy, L., et al. (2022) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-022-01063-6 · Cited by 9
Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol
Wan, E. Y. F., Yu, E. Y. T., Chin, W. Y., Fung, C. S. C., Kwok, R. L. P., Chao, D. V. K., Chan, K. H., Hui, E. M., Tsui, W. W. S., Tan, K. C. B., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-023070 · Cited by 8
Framingham Risk Score Assessment in Subjects with Pre-diabetes and Diabetes: A Cross-Sectional Study in Korea
Kwon, H., Song, K. H., Yu, J. M., Kim, D. S., Shon, H. S., Ahn, K. J., Choi, S. H., Ko, S., Kim, W. B., Lee, K. H., et al. (2021) · Journal of Obesity & Metabolic Syndrome · doi:10.7570/jomes20137 · Cited by 10
Practical cardiovascular risk calculator for asymptomatic patients with type 2 diabetes mellitus: PRECISE‐DM risk score
Choi, Y., Yang, Y., Hwang, B., Lee, E. Y., Yoon, K., Chang, K., Jaffer, F. A., & Cho, J. (2020) · Clinical Cardiology · doi:10.1002/clc.23405 · Cited by 8
Social Determinants of Health and Cardiovascular Risk among Adults with Diabetes: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study
Zhang, L., Reshetnyak, E., Ringel, J. B., Pinheiro, L. C., Carson, A. P., Cummings, D. M., Durant, R. W., Malla, G., & Safford, M. M. (2024) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2023.0380 · Cited by 7
Trends in predicted 10-year risk for cardiovascular diseases among patients with type 2 diabetes in Thailand, from 2014 to 2018
Lertsakulbunlue, S., Mungthin, M., Rangsin, R., Kantiwong, A., & Sakboonyarat, B. (2023) · BMC Cardiovascular Disorders · doi:10.1186/s12872-023-03217-8 · Cited by 7

Authors: Chamnan, P., Simmons, R. K., Sharp, S. J., Griffin, S. J., & Wareham, N. J.
Affiliations: Addenbrooke's Hospital (GB)
Corresponding author: Wareham, N. J.
ORCID: Chamnan, P.
Simmons, R. K.
Sharp, S. J.
Griffin, S. J.
Wareham, N. J.
Venue: Diabetologia
Publisher: Springer Science and Business Media LLC
Published: 2009-07-24
Volume: 52
Issue: 10
Pages: 2001-2014
ISSN: 0012-186X (ISSN-L)
1432-0428
DOI: 10.1007/s00125-009-1454-0
PMC: PMC2744770
OpenAlex: W1969752830
Cited by: 176
References: 66
Citations by year: 2026: 7 · 2025: 4 · 2024: 3 · 2023: 11 · 2022: 11 · 2021: 10 · 2020: 4 · 2019: 9 · 2018: 11 · 2017: 13 · 2016: 13 · 2015: 19 · 2014: 20 · 2013: 17 · 2012: 4
FWCI: 1.05
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: Diabetologia (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: University of Cambridge
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords: cardiovascular risk score, type 2 diabetes, cardiovascular risk, risk prediction, risk score validation, risk score comparison, predictive performance, risk communication, diabetes management
MeSH terms: Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Risk Factors, 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

Cardiovascular risk assessment scores for people with diabetes: a systematic review

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