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

Flagged first, then by citations

Show all 66 references

Sorted by citations

Items 101–176 of 176

Assessment of Cardiovascular Risk in Adults With Type 2 Diabetes and Metabolic Syndrome
Kim, C., Kang, H. S., Schlenk, E. A., & Chae, S. (2015) · The Science of Diabetes Self-Management and Care · doi:10.1177/0145721715572154 · Cited by 7
Cardiovascular Risk Assessment in Developing World
Chamnan, P., & Aekplakorn, W. (2017) · InTech eBooks · doi:10.5772/intechopen.69041 · Cited by 7
Self-citation
Incremental Prognostic Value of a Coronary Heart Disease Polygenic Risk Score in Type 2 Diabetes
Mordi, I. R., Li, I., George, G., McCrimmon, R. J., Palmer, C. N., Pearson, E. R., Lang, C. C., & Doney, A. S. F. (2024) · Diabetes Care · doi:10.2337/dc24-1489 · Cited by 7
Significance of the results of genome-wide association studies for primary prevention of type 2 diabetes mellitus and its complications. Personalised approach
Dedov, I. I., Smirnova, O. M., & Кононенко, И. В. (2014) · Diabetes Mellitus · doi:10.14341/dm2014210-19 · Cited by 6
Effect of Physical Activity on Cardiovascular Event Risk in a Population-Based Cohort of Patients with Type 2 Diabetes
Enguita‐Germán, M., Tamayo, I., Galbete, A., Librero, J., Cambra, K., & Ibáñez, B. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182312370 · Cited by 6
Estimation of 10-year risk of coronary heart disease in nepalese patients with type 2 diabetes: Framingham versus United Kingdom prospective diabetes study
Pokharel, D. R., Sigdel, M., Yadav, N. K., Sapkota, L. B., Sapkota, R. M., Khadka, D., Kafle, R., Nepal, S., & Choudhary, N. K. (2015) · North American Journal of Medical Sciences · doi:10.4103/1947-2714.163642 · Cited by 6
Identifying individuals at risk of type 2 diabetes using risk assessment tools: an overview
Anil, D., Doddaiah, S. K., & Murthy, M. R. N. (2022) · International Journal of Community Medicine and Public Health · doi:10.18203/2394-6040.ijcmph20223241 · Cited by 6
Prognostic models for short-term annual risk of severe complications and mortality in patients living with type 2 diabetes using a national medical claim database
Vimont, A., Béliard, S., Valéro, R., Leleu, H., & Durand‐Zaleski, I. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01105-x · Cited by 5
Diabetic retinopathy related homeostatic dysregulation and its association with mortality among diabetes patients: A cohort study from NHANES
Li, W., Lai, Z., Tang, N., Tang, F., Huang, G., Lu, P., Jiang, L., Lei, D., & Xu, F. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.111081 · Cited by 5
Degree of Agreement between Cardiovascular Risk Stratification Tools
Garcia, G. T., Stamm, A. M. N. D. F., Rosa, A. C., Marasciulo, A. C. E., Marasciulo, R. C., Battistella, C., & Remor, A. A. D. C. (2017) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20170057 · Cited by 5
Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records
Xu, Z., Arnold, M., Sun, L., Stevens, D., Chung, R., Ip, S., Barrett, J., Kaptoge, S. K., Pennells, L. A. M., Angelantonio, E. D., et al. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac140 · Cited by 5
The effects of 10-week different exercise interventions on Framingham risk score and metabolic syndrome severity scores in overweight women with type 2 diabetes
Banitalebi, E., Ghahfarrokhi, M. M., Faramarzi, M., & Nasiri, S. (2018) · Journal of Shahrekord University of Medical Sciences · doi:10.34172/jsums.2019.01 · Cited by 5
Risk Models and Scores of Cardiovascular Disease in Patients with Diabetes Mellitus
Kostopoulos, G., Antza, C., Doundoulakis, I., & Toulis, K. A. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666201210112743 · Cited by 4
Development and validation of a risk score model for prediction of lower extremity arterial disease in Chinese with type 2 diabetes aged over 50 years
Zhang, X., Xu, Z., Ran, X., & Ji, L. (2021) · Endocrine Connections · doi:10.1530/ec-21-0152 · Cited by 4
The Concept of Early Vascular Ageing – An Update in 2015
Nilsson, P. M. (2015) · EMJ Diabetes · doi:10.33590/emjdiabet/10312465 · Cited by 5
Assessing Framingham cardiovascular risk scores in subjects with diabetes and their correlation with diabetic retinopathy
Roy, P. (2013) · Indian Journal of Ophthalmology · doi:10.4103/0301-4738.105057 · Cited by 4
Psychological interventions for diabetes-related distress in adults with type 2 diabetes mellitus
Chew, B., Vos, R. C., Heijmans, M., Metzendorf, M., Scholten, R. J. P. M., & Rutten, G. E. H. M. (2015) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011469 · Cited by 4
A Review of Emerging Biomarkers Connecting Diabetes and Ischemic Stroke: Implications for Early Detection and Risk Stratification
Hussain, N., Ramadan, A., Haddad, A. H. I. A., & Alfahl, Z. (2026) · Journal of Diabetes Research · doi:10.1155/jdr/2719491 · Cited by 4
An exploratory analysis of dynamic change of metabolic syndrome in relation to the risk of developing cardiovascular disease in a chinese cohort.
Zhou, H., Guo, Z., Hu, X., Yu, L., Xu, B., Wu, M., Zhou, Z., & Yang, C. (2012) · Europe PMC (PubMed Central) · Cited by 3
Role of LDL and Triglycerides in Hyperlipidemia in Diabetes Mellitus
Bethelli, S., & Oroszi, T. L. (2023) · Journal of Family Medicine and Disease Prevention · doi:10.23937/2469-5793/1510155 · Cited by 3
Complicaciones macrovasculares de la diabetes. Evaluación del riesgo cardiovascular y objetivos terapéuticos. Estrategias de prevención y tratamiento
Orna, J. A. G. (2016) · Medicine - Programa de Formación Médica Continuada Acreditado · doi:10.1016/j.med.2016.09.002 · Cited by 4
Management of type 2 diabetes mellitus and prediction of cardiovascular complications
van Dieren, S., Health, C., Health, J. O. C., Economics, E. &. H., van der Schouw, Y. T., Beulens, J. W. J., & Peelen, L. M. (2013) · Utrecht University Repository (Utrecht University) · Cited by 3
Models of Diabetes Care Across Different Resource Settings
Levitt, N. S., Surka, S., Farmer, A. A., & Mash, R. J. (2016) · Wiley eBooks · doi:10.1002/9781118924853.ch65 · Cited by 3
Machine learning for predicting cardiovascular events in older adults with type 2 diabetes using Medicare claims and electronic health records
Htoo, P. T., Paik, J. M., Everett, B. M., Glynn, R. J., Bykov, K., Hahn, G., Liu, J., Wexler, D. J., & Patorno, E. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.112001 · Cited by 2
Achieving sensible targets for a diabetes care cascade in LMICs
Chamnan, P. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00403-4 · Cited by 2
Self-citation
Associations of High Density Lipoprotein Cholesterol and Framingham Cardiovascular Risk with Diabetic Retinopathy in African Type 2 Diabetics
Longo‐Mbenza, B., Muaka, M. M., Gombet, T. R., Phemba, I. L., Cook, S., & Mengome, E. M. (2014) · World Journal of Cardiovascular Diseases · doi:10.4236/wjcd.2014.44026 · Cited by 2
Stratifizierung des kardiovaskulären Risikos bei Patienten mit Typ-2-Diabetes
Grossmann, V., Schmitt, V. H., Jünger, C., Münzel, T. F., & Wild, P. S. (2014) · Der Diabetologe · doi:10.1007/s11428-014-1213-9 · Cited by 2
Impact of UKPDS risk estimation added to a first subjective risk estimation on management of coronary disease risk in type 2 diabetes – An observational study
Wind, A. E., Gorter, K. J., van den Donk, M., & Rutten, G. E. H. M. (2015) · Primary care diabetes · doi:10.1016/j.pcd.2015.04.006 · Cited by 2
Which risk engines are best to assess CVD risk in diabetes?
Chamnan, P., Simmons, R. K., & Griffin, S. J. (2010) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2009.100-c1 · Cited by 2
Self-citation
Comparison of cardiovascular risk calculators in patients with diabetes
Ribeiro, L. K., Amorim, W. W., Cardoso, I. T. A., Vieira, W. S., Kochergin, C. N., de Medeiros, D. S., Soares, D. A., Louzado, J. A., Silva, K. O., Cortes, M. L., et al. (2021) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.67.02.20200514 · Cited by 1
Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis
Nur, A. A., Tjandra, S., Yumnanisha, D. A., Keane, A., & Bachtiar, A. (2025) · Narra J · doi:10.52225/narra.v5i1.2116 · Cited by 1
Study protocol for the investigator-initiated Danish pragmatic randomised STENO INTEN-CT trial: does screening and intervention for subclinical coronary artery disease in type 2 diabetes reduce cardiovascular events?
Funck, K. L., Borregaard, B., Egstrup, K., Fredslund, E. K., Hansen, T. W., Kallestrup, P., Olsen, M. H., Reventlow, S. D., Rossing, P. R., Sandbæk, A., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-106018 · Cited by 3
Pressure Injury Risk Assessment in Nursing Practice: A Head-to-Head Comparison of the Braden Scale and Machine Learning Models
Barriga-Gallegos, F., Ríos-Vásquez, G., Figueroa-Torrez, P., de la Fuente‐Mella, H., Garrido, C. A., & Febré, N. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15124683 · Cited by 2
Cardiovascular Risk Stratification among Type 2 Diabetes Mellitus
Asegaonkar, S. B. (2016) · Journal of Diabetes Metabolic Disorders & Control · doi:10.15406/jdmdc.2016.03.00075 · Cited by 1
Pentraxin 3 and atherosclerosis among type 2 diabetic patients
Nabrdalik, K., Chodkowski, A., Bartman, W., Tomasik, A. R., Kwiendacz, H., Sawczyn, T., Kukla, M., Grzeszczak, W., & Gumprecht, J. (2017) · Open Life Sciences · doi:10.1515/biol-2017-0010 · Cited by 1
A post hoc analysis of saxagliptin efficacy and safety in patients with type 2 diabetes stratified by UKPDS 10-year cardiovascular risk score
Bonora, E., Bryzinski, B. S., Hirshberg, B., & Cook, W. (2015) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2015.11.004 · Cited by 2
Serum lipid management in patients with type 1 and type 2 diabetes: a hospital-based cohort study
Hasegawa, Y., Nakagami, T., Oya, J., Isago, C., Kurita, M., Tanaka, Y., Ito, A., Tsuzura, R., Hirota, N., Miura, J., et al. (2018) · Diabetology International · doi:10.1007/s13340-018-0365-z · Cited by 1
Screening Strategies for Type 2 Diabetes and Risk Stratification in Minorities
Kengne, A. P. (2018) · Updates in hypertension and cardiovascular protection · doi:10.1007/978-3-319-93148-7_18 · Cited by 1
Models of Diabetes Care across Different Resource Settings
Levitt, N. S., Mash, R. J., Unwin, N. C., Mbanya, J. C., Cho, J., & Yoon, K. (2010) · Wiley eBooks · doi:10.1002/9781444324808.ch58 · Cited by 1
Cardiovascular Disease Risk Scores and Novel Risk Factors in Relation to Race and Gender
Wilson, J. (2016) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1
The Effects of Two Exercise Modalities on Novel Cardiovascular Risk Factors in Overweight Women with Type 2 Diabetes: a Randomized Controlled TrialPreprint
Banitalebi, E., Faramarzi, M., Ghahfarrokhi, M. M., & Nasiri, S. (2019) · doi:10.21203/rs.2.11408/v1 · Cited by 1
Cardiovascular Risk Prediction Tools in Patients With Diabetes—Are Not There Enough? What Is Still Missing?
Zghebi, S. S., Kontopantelis, E., & Mamas, M. A. (2023) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.10.055 · Cited by 1
Optimization of sudden cardiac death prevention in type 2 diabetes in France : a public health simulation study on a realistic virtual population
Le, H.-H. (2017) · theses.fr (ABES) · doi:10.70675/b5193892z0a0dz4c44za094z0b60132a3758 · Cited by 1
Can routinely collected primary care data be used to predict future risk of morbidity and mortality in newly-diagnosed type 2 diabetes mellitus?
Ryan, R. P. (2014) · University of Birmingham Institutional Research Archive (University of Birmingham)
Значение результатов полногеномных исследований для первичной профилактики сахарного диабета 2 типа и его осложнений. Персонализированный подход
Дедов, И. И., Смирнова, О. М., & Кононенко, И. В. (2014) · Сахарный диабет · doi:10.14341/dm6495-4476
Cardiovascular disease risk profile of the South-African mixed ancestry population with high incidence of diabetes mellitus: baseline and three year follow-up
Soita, D. J. (2013)
Towards a Health Economic Simulation Model of Type 2 Diabetes in Sweden
Kiadaliri, A. (2014) · Lund University Publications (Lund University)
Neuropatia autonômica no diabetes: a importância de uma complicação pouco investigada
Tannus, L. R. M. (2015) · Revista Hospital Universitário Pedro Ernesto · doi:10.12957/rhupe.2015.20060
This review presents results from recent trials and observational studies on risk factors and cardiovascular disease. Treatment goals are provided that should be preferable for most patients in diabetes care, even if these recommendations should be combined with clinical judgement and individual preferences. A Review of Risk Factors and Cardiovascular Disease in Diabetes Care - 2011
Nilsson, P. M., & Cederholm, J. (2011)
Association between Obesity and Coronary Heart Disease Risk among Saudi Subjects at Madinah Region
Saad, S. Y., & Abdel-Latif, M. (2016) · Journal of Pharmaceutical Research International · doi:10.9734/bjpr/2016/26382
Role of Imaging in Diabetes Mellitus
Cuocolo, A., & Zampella, E. (2015) · Oxford University Press eBooks · doi:10.1093/med/9780199392094.003.0018
Predictive Value of Updating te Score Cardiovascular Risk Assessment Engine with Novel Biomarkers in a Type 2 Diabetes Population
Popa, L. M., Timar, B., Popa, A. R., & Popescu, M. I. (2016) · Romanian Journal of Diabetes Nutrition and Metabolic Diseases · doi:10.1515/rjdnmd-2016-0043
Retrospective Study: Micro and Macrovascular Complications in Type 2 D.M in Tertiary Care Hospital
Santhosh, J. (2017) · Journal of Medical Science And clinical Research · doi:10.18535/jmscr/v5i1.57
The Genetics of Coronary Heart Disease across Ethnicities and in those with Type 2 Diabetes
Beaney, K. (2016) · Doctoral thesis, UCL (University College London).
Kardiologische Diagnostik bei asymptomatischen Patienten mit Diabetes
Willecke, F., & Zirlik, A. (2017) · Der Klinikarzt · doi:10.1055/s-0043-113626
The Epidemiology of Nontraditional Biomarkers of Hyperglycemia and their Prognostic Value
Parrinello, C. M. (2015)
Development of Cardiovascular Risk Assessment Model for People with Type 2 Diabetes in Oman
Alrawahi, A. H. (2017) · Griffith Research Online · doi:10.25904/1912/3256
INFLUENCE OF COMBINED THERAPY ON ARTERIAL HYPERTENSION IN PATIENTS WITH DIABETES MELITTUS TYPE 2
Шелест, Б. О. (2018) · Bulletin of Problems Biology and Medicine · doi:10.29254/2077-4214-2018-1-2-143-242-245
Pericardial fat volume and coronary calcifications for prediction of coronary artery disease extent in patients with type 2 diabetes mellitus
Sorour, S. M. H., Farrag, A. A. M., Salem, M. A., Bakhoum, S., Raslan, H. M., Fares, E., & Morcos, E. (2018) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1016/j.ejrnm.2018.05.007
Managing type 2 diabetes mellitus: Role of family physicians in successful treatment goal achievement
Korzh, O., Lavrova, Y., & Павлова, Е. А. (2019) · Electronic Journal of General Medicine · doi:10.29333/ejgm/110173
Comparison of IDRS and ADA Diabetes Risk Assessment Tools Reveals Different Rsk Score in Same Population: A Cross-Sectional Analysis in a Tertiary Care Hospital
Akter, N., & Qureshi, N. K. (2021) · Journal of Diabetes and Endocrine Association of Nepal · doi:10.3126/jdean.v5i1.38799
Current literature in diabetes
(2010) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.1017 · Cited by 1
Prognostic models for short-term annual risk of severe acute complications and mortality in patients living with type 2 diabetes using a national medical claim databasePreprint
Vimont, A., Béliard, S., Valéro, R., Leleu, H., & Durand‐Zaleski, I. (2022) · Research Square · doi:10.21203/rs.3.rs-1604014/v1
Prediction models for cardiovascular disease in diabetes mellitus
Adler, A., & Griffin, S. J. (2011) · doi:10.1093/med/9780199235292.003.1544
Self-citation
Prognostic models for short-term annual risk of severe acute complications and mortality in patients living with type 2 diabetes using a national medical claim databasePreprint
Vimont, A., Béliard, S., Valéro, R., Leleu, H., & Durand‐Zaleski, I. (2022) · Research Square · doi:10.21203/rs.3.rs-1604014/v2
Incidence and Cardiovascular Risk Factors Profile in Obese and Non-obese Type 2 Diabetes Mellitus subjects aged between 30 and 80 years in the polyclinic, in an Omani Population: ICD OMAN a Decade-Long Bawshar Study
Unni, S. R., DSouza, M. S., Naguib, H. E., Kindi, N. A., Baluchi, M. A., Pinto, A. D., Kumar, S., Mahrooqi, H., & Landau, S. (2021) · INTERNATIONAL JOURNAL OF DIABETES & METABOLIC SYNDROME · doi:10.52106/2771-1331.1002
Cardiovascular Risk Awareness and Score Among Men Between the Ages of 40 and 65
Tekin, A., Koç, E. M., Aksoy, H., & Sözmen, M. K. (2023) · Eurasian Journal of Family Medicine · doi:10.33880/ejfm.2023120204
External validation of a cardiovascular risk model for Omani patients with type 2 diabetes mellitus: a retrospective cohort study
Oraimi, F. A., Rawahi, A. A., Harrasi, A. A., Albusafi, S., Al-Manji, L. M., Alrawahi, A. H., & Salmani, A. A. A. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-071369
Additional file 1 of 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., Pavo, N., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13698344
Additional file 1 of Prognostic models for short-term annual risk of severe complications and mortality in patients living with type 2 diabetes using a national medical claim database
Vimont, A., Béliard, S., Valéro, R., Leleu, H., & Durand‐Zaleski, I. (2023) · Figshare · doi:10.6084/m9.figshare.23529215
Additional file 1 of 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. (2024) · Figshare · doi:10.6084/m9.figshare.26580617
Indexing metabolic vulnerability stratifies risks of vascular complications of type 2 diabetesPreprint
Xie, Y., Yu, G., Zhu, S., Wu, S., Liu, C., Shen, J., Wang, Y., Ma, H., & Xiang, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8742629/v1
Depression severity and cardiovascular risk in patients with type 2 diabetes mellitus: insights from the framingham risk score
Aliasgharzadeh, S., Yagin, N. L., Namazi, N., Haji, S., Alamdari, N. M., Pourmoradian, S., GHORBANVERDI, Z., & Mobasseri, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-43077-8 · Cited by 1
Evaluation of Body Mass Index in Relation to Vascular Complications in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study in Bangladesh
Islam, M. I., Hoque, S., Islam, M. R., Chowdhury, N. F., & Akhter, S. (2026) · Islami Bank Medical College Journal · doi:10.70945/ibmcj.v021i01.028
An interpretable machine learning model for predicting 1-year major adverse cardiovascular events in patients with type 2 diabetes and hypertension
Lv, J., Wang, X., & Zhang, Z. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871693
Risk prediction models for macrovascular complications in type 2 diabetes mellitus: from traditional risk scores to multimodal intelligent prediction
An, X., Cai, J., Cao, X., & Li, J. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1980631

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