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)

Lars Rydén (ESC Chairperson) (Sweden); Peter J. Grant (EASD Chairperson) (UK); Stefan D. Anker (Germany); Christian Berne (Sweden); Francesco Cosentino (Italy); Nicolas Danchin (France); Christi Deaton (UK); Javier Escaned (Spain); Hans-Peter Hammes (Germany); Heikki Huikuri (Finland); Michel Marre (France); Nikolaus Marx (Germany); Linda Mellbin (Sweden); Jan Ostergren (Sweden); Carlo Patrono (Italy); Petar Seferovic (Serbia); Miguel Sousa Uva (Portugal); Marja-Riita Taskinen (Finland); Michal Tendera (Poland); Jaakko Tuomilehto (Finland); Paul Valensi (France); Jose Luis Zamorano (Spain); Jose Luis Zamorano (Chairperson) (Spain); Stephan Achenbach (Germany); Helmut Baumgartner (Germany); Jeroen J. Bax (Netherlands); Héctor Bueno (Spain); Veronica Dean (France); Christi Deaton (UK); Çetin Erol (Turkey); Robert Fagard (Belgium); Roberto Ferrari (Italy); David Hasdai (Israel); ArnoW. Hoes (Netherlands); Paulus Kirchhof (Germany UK); Juhani Knuuti (Finland); Philippe Kolh (Belgium); Patrizio Lancellotti (Belgium); Ales Linhart (Czech Republic); Petros Nihoyannopoulos (UK); Massimo F. Piepoli (Italy); Piotr Ponikowski (Poland); Per Anton Sirnes (Norway); Juan Luis Tamargo (Spain); Michal Tendera (Poland); Adam Torbicki (Poland); William Wijns (Belgium); Stephan Windecker (Switzerland); Guy De Backer (Review Coordinator) (Belgium); Per Anton Sirnes (CPG Review Coordinator) (Norway); Eduardo Alegria Ezquerra (Spain); Angelo Avogaro (Italy); Lina Badimon (Spain); Elena Baranova (Russia); Helmut Baumgartner (Germany); John Betteridge (UK); Antonio Ceriello (Spain); Robert Fagard (Belgium); Christian Funck-Brentano (France); Dietrich C. Gulba (Germany); David Hasdai (Israel); Arno W. Hoes (Netherlands); John K. Kjekshus (Norway); Juhani Knuuti (Finland); Philippe Kolh (Belgium); Eli Lev (Israel); Christian Mueller (Switzerland); Ludwig Neyses (Luxembourg); Peter M. Nilsson (Sweden); Joep Perk (Sweden); Piotr Ponikowski (Poland); Zeljko Reiner (Croatia); Naveed Sattar (UK); Volker Schächinger (Germany); André Scheen (Belgium);

Disclosures

Eur Heart J. 2013;34(39):3035-3087. 

In This Article

5. Cardiovascular Risk Assessment in Patients With Dysglycaemia

The aim of risk assessment is to categorize the population into those at low, moderate, high and very-high CVD risk, to intensify preventive approaches in the individual. The 2012 Joint European Society guidelines on CVD prevention recommended that patients with DM, and at least one other CV risk factor or target organ damage, should be considered to be at very high risk and all other patients with DM to be at high risk.[89] Developing generally applicable risk scores is difficult, because of confounders associated with ethnicity, cultural differences, metabolic and inflammatory markers—and, importantly, CAD and stroke scores are different. All this underlines the great importance of managing patients with DM according to evidence-based, target-driven approaches, tailored to the individual needs of the patient.

5.1 Risk Scores Developed for People Without Diabetes

Framingham Study risk equations based on age, sex, blood pressure, cholesterol (total and HDL) and smoking, with DM status as a categorical variable,[90] have been validated prospectively in several populations.[91,92] In patients with DM, results are inconsistent, underestimating CVD risk in a UK population and overestimating it in a Spanish population.[93,94] Recent results from the Framingham Heart Study demonstrate that standard risk factors, including DM measured at baseline, are related to the incidence of CVD events after 30 years of follow-up.[95]

The European Systematic Coronary Risk Evaluation (SCORE®) for fatal coronary heart disease and CVD was not developed for application in patients with DM.[89,93]

The DECODE Study Group developed a risk equation for cardiovascular death, incorporating glucose tolerance status and FPG.[96] This risk score was associated with an 11% underestimation of cardiovascular risk.[93]

The Prospective Cardiovascular Münster (PROCAM)[97] scoring scheme had poor calibration, with an observed/predicted events ratio of 2.79 for CVD and 2.05 for CAD.[98]

The Myocardial Infarction Population Registry of Girona (REGICOR)[99] tables, applied to a Mediterranean (Spanish) population, underestimated CVD risk.[94]

5.2 Evaluation of Cardiovascular Risk in People With Pre-diabetes

Data from the DECODE study showed that high 2hPG, but not FPG, predicted all-cause mortality, CVD and CAD, after adjustment for other major cardiovascular risk factors (for further details see Section 3.2).[43,100]

5.3 Risk Engines Developed for People With Diabetes

The United Kingdom Prospective Diabetes Study (UKPDS) risk score for CAD has a good sensitivity (90%) in a UK population,[101,102] overestimated risk in a Spanish population,[94] and had moderate specificity in a Greek population.[103] Moreover, this risk score was developed before the advent of modern strategies for CVD prevention.

The Swedish National Diabetes Register (NDR) was applied in a homogeneous Swedish population and reported a good calibration.[104]

The Framingham Study. Stroke has only undergone validation in a Spanish group of 178 patients and overestimated the risk.[105,106]

The UKPDS for stroke underestimated the risk of fatal stroke in a US population.[107]

The Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) is a contemporary model for cardiovascular risk prediction, developed from the international ADVANCE cohort.[108] This model, which incorporates age at diagnosis, known duration of DM, sex, pulse pressure, treated hypertension, atrial fibrillation, retinopathy, HbA1c, urinary albumin/creatinine ratio and non-HDL cholesterol at baseline, displayed an acceptable discrimination and good calibration during internal validation. The external applicability of the model was tested on an independent cohort of individuals with T2DM, where similar discrimination was demonstrated.

A recent meta-analysis reviewed 17 risk scores, 15 from predominantly white populations (USA and Europe) and two from Chinese populations (Hong Kong). There was little evidence to suggest that using risk scores specific to DM provides a more accurate estimate of CVD risk.[109] Risk scores for the evaluation of DM have good results in the populations in which they were developed, but validation is needed in other populations.

5.4 Risk Assessment Based on Biomarkers and Imaging

The Atherosclerosis Risk In Communities (ARIC) study prospectively evaluated whether adding C-reactive protein or 18 other novel risk factors individually to a basic risk model would improve prediction of incident CAD in middle-aged men and women. None of these novel markers added to the risk score.[110] A Dutch study involving 972 DM patients evaluated baseline UKPDS risk score and the accumulation of advanced glycation end-products (AGEs) in skin[111] using auto-fluorescence. The addition of skin AGEs to the UKPDS risk engine resulted in re-classification of 27% of the patients from the low- to the high-risk group. The 10-year cardiovascular event rate was higher in patients with a UKPDS score >10% when skin AGEs were above the median (56 vs. 39%).[112] This technique may become a useful tool in risk stratification in DM but further information is needed for this to be verified.

In patients with T2DM, albuminuria is a risk factor for future CV events, CHF and all-cause, even after adjusting for other risk factors.[113] Elevated circulating NT-proBNP is also a strong predictor of excess overall and cardiovascular mortality, independent of albuminuria and conventional risk factors.[114]

Subclinical atherosclerosis, measured by coronary artery calcium (CAC) imaging, has been found superior to established risk factors for predicting silent myocardial ischaemia and short-term outcome. CAC and myocardial perfusion scintigraphy findings were synergistic for the prediction of short-term cardiovascular events.[115]

Ankle-brachial index (ABI),[116] carotid intima-media thickness and detection of carotid plaques,[117] arterial stiffness by pulse wave velocity,[118] and cardiac autonomic neuropathy (CAN) by standard reflex tests[119] may be considered as useful cardiovascular markers, adding predictive value to the usual risk estimate.

Coronary artery disease (CAD) is often silent in DM patients and up to 60% of myocardial infarctions (MI) may be asymptomatic, diagnosed only by systematic electrocardiogram (ECG) screening.[120] Silent myocardial ischaemia (SMI) may be detected by ECG stress test, myocardial scintigraphy or stress echocardiography. Silent myocardial ischaemia affects 20–35% of DM patients who have additional risk factors, and 35–70% of patients with SMI have significant coronary stenoses on angiography whereas, in the others, SMI may result from alterations of coronary endothelium function or coronary microcirculation. SMI is a major cardiac risk factor, especially when associated with coronary stenoses on angiography, and the predictive value of SMI and silent coronary stenoses added to routine risk estimate.[121] However, in asymptomatic patients, routine screening for CAD is controversial. It is not recommended by the ADA, since it does not improve outcomes as long as CV risk factors are treated.[122] This position is, however, under debate and the characteristics of the patients who should be screened for CAD need to be better defined.[123] Further evidence is needed to support screening for SMI in all high-risk patients with DM. Screening may be performed in patients at a particularly high risk, such as those with evidence of peripheral artery disease (PAD) or high CAC score or with proteinuria, and in people who wish to start a vigorous exercise programme.[124]

Cardiovascular target organ damage, including low ABI, increased carotid intima-media thickness, artery stiffness or CAC score, CAN and SMI may account for a part of the cardiovascular residual risk that remains, even after control of conventional risk factors. The detection of these disorders contributes to a more accurate risk estimate and should lead to a more intensive control of modifiable risk factors, particularly including a stringent target for LDL-cholesterol (LDL-C) of <1.8 mmol/L (~70 mg/dL).[125] In patients with SMI, medical treatment or coronary revascularization may be proposed on an individual basis. However the cost-effectiveness of this strategy needs to be evaluated.

5.5 Gaps in Knowledge

  • There is a need to learn how to prevent or delay T1DM.

  • There is a need for biomarkers and diagnostic strategies useful for the early detection of CAD in asymptomatic patients.

  • Prediction of CV risk in people with pre-diabetes is poorly understood.

5.6 Recommendations for Cardiovascular Risk Assessment in Diabetes

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