
Contents
19.4 Cardiovascular risk assessment in diabetes and pre-diabetes
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Published:July 2018
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Abstract
Patients with diabetes exhibit an increased propensity to develop cardiovascular disease with an increased mortality. Early risk assessment, especially for coronary artery disease, is important to initiate therapeutic strategies to reduce cardiovascular risk. This chapter reviews the current literature on risk scores in patients with type 1 and type 2 diabetes and summarizes the role of risk assessment based on biomarkers and different imaging strategies. Current guidelines recommend that patients with diabetes are characterized as high-risk or very high-risk patients. In the presence of target organ damage or other risk factors such as smoking, marked hypercholesterolaemia, or hypertension, patients with diabetes are classified as very high-risk patients while most other people with diabetes are categorized as high-risk patients.
This chapter provides the background information and detailed discussion of the data for the following current ESC Guidelines on: diabetes, pre-diabetes, and cardiovascular disease - https://doi.org/10.1093/eurheartj/ehz486
Summary
Patients with diabetes exhibit an increased propensity to develop cardiovascular disease with an increased mortality. Early risk assessment, especially for coronary artery disease, is important to initiate therapeutic strategies to reduce cardiovascular risk. This chapter reviews the current literature on risk scores in patients with type 1 and type 2 diabetes and summarizes the role of risk assessment based on biomarkers and different imaging strategies. Current guidelines recommend that patients with diabetes are characterized as high-risk or very high-risk patients. In the presence of target organ damage or other risk factors such as smoking, marked hypercholesterolaemia, or hypertension, patients with diabetes are classified as very high-risk patients while most other people with diabetes are categorized as high-risk patients.
Introduction
Current European and American guidelines for the prevention of cardiovascular disease recommend the assessment of cardiovascular risk, since therapeutic approaches to prevent cardiovascular disease should be individualized based on the individual risk profile. It is recommended that cardiovascular risk should be assessed in a systematic way in individuals at increased cardiovascular risk, for example, those with a history of premature cardiovascular disease, familial hyperlipidaemia, major risk factors (smoking, high blood pressure, diabetes, or dyslipidaemia) or co-morbidities associated with an increased severe risk.
General population risk scores
European guidelines for cardiovascular prevention published in 2016 suggest that total risk estimation should use risk estimation systems such as Systematic COronary Risk Evaluation: High & Low cardiovascular Risk Charts (SCORE risk charts) to define patients being at risk or very high risk for cardiovascular disease.1 Various risk scores have previously been developed such as the Framingham Score, Prospective Cardiovascular Münster Study (PROCAM), and others. However, none of these risk calculators has been established in a dysglycaemic population, thus making it difficult to apply these risk scores in patients with pre-diabetes or diabetes. In addition, data from applying these scores in subjects with diabetes suggest that risk is usually underestimated.
These limitations have led to the development of risk scores that are more specific for subjects with diabetes.
Risk scores in subjects with type 2 diabetes
The UK Prospective Diabetes Study (UKPDS) risk score is based on the UKPDS, but the good sensitivity achieved in patients from the UK is different to that achieved in subjects from other areas.2,3 Depending on the region examined, the UKPDS risk score overestimated the risk of coronary artery disease with suboptimal discrimination (see also Chapter 31.17).4 Most importantly, the risk score was developed more than two decades ago, not considering recent therapeutic advances such as statin therapy, angiotensin-converting enzyme inhibitors, and others. The Swedish National Diabetes Register (NDR) was applied in a homogeneous Swedish population and reported a good calibration with observed and predicted cardiovascular disease rate of 0.96–0.99.5 The Hong Kong Diabetes Registry for Coronary Artery Disease predicts cardiovascular risk in a Chinese population with a sensitivity of 64% and a specificity of 68%, respectively.6
Risk prediction in patients with type 1 diabetes
Various risk engines have been developed to determine cardiovascular risk in patients with type 1 diabetes. The Swedish NDR study developed a model based on 3661 type 1 diabetes patients and included diabetes duration, systolic blood pressure, lipids, glycated haemoglobin, macroalbuminuria, as well as smoking as contributors to the model. In this model, more than 90% of patients had no history of cardiovascular disease and it predicted the incidents of non-fatal myocardial infarction, unstable angina, percutaneous coronary intervention, and/or coronary bypass grafting whereas stroke was defined as fatal or non-fatal.7
Recently, the Steno Risk Engine has been published, including parameters such as age, sex, diabetes duration, glycated haemoglobin, systolic blood pressure, low-density lipoprotein cholesterol, albuminuria, estimated glomerular filtration rate, smoking, and exercise. This risk score—developed as a primary prevention model for a first fatal or non-fatal cardiovascular event—included ischaemic heart disease, ischaemic stroke heart failure, and peripheral arterial disease from 4306 patients with type 1 diabetes.8 This risk score showed a higher prediction of cardiovascular events than the Swedish NDR study. However, the Steno Risk Engine has several limitations including the fact that very few type 1 diabetes patients with a diagnosis in early childhood were included.
Given the paucity of data on score-based risk assessment in asymptomatic subjects with diabetes, the European guidelines for cardiovascular prevention categorize patients with diabetes as very high-risk or high-risk patients. Patients with diabetes are considered very high-risk patients in the presence of target organ damage such as proteinuria or with a major risk factor such as smoking or marked hypercholesterolaemia or marked hypertension. Most other people with diabetes (with the exception of young people with type 1 diabetes and without major risk factors that may be at low or moderate risk) are categorized as high-risk patients. In addition, the guidelines state that individuals automatically at high to very high cardiovascular risk do not need the use of a risk score and require immediate attention to risk factors.9
Risk assessment based on biomarkers
Currently, there is no clear evidence that in patients with diabetes the use of biomarkers such as high-sensitive C-reactive protein provides additional benefit with respect to risk stratification. Still, it is indicated to estimate the urinary albumin excretion rate but mainly to detect changes in kidney function and to guide blood pressure lowering towards target values of less than 130/80 mmHg.9
Risk assessment based on exercise stress testing
In asymptomatic patients with diabetes, exercise stress testing helps to identify patients with silent ischaemia as shown in the ‘Does coronary Atherosclerosis Deserve to be Diagnosed and treated early in Diabetics?’ (DADDY-D) trial but fails to shown a significant benefit with respect to cardiac events reduction.10 Thus, the role of exercise stress testing in risk assessment is very limited.
Risk assessment based on imaging
Various non-invasive imaging modalities have been tested for risk assessment in asymptomatic patients with diabetes. Among them, carotid intima–media thickness and detection of carotid plaques were not prognostically useful in screening studies. Similarly, coronary computed tomography angiography (see Chapter 12.3) does not provide prognostic benefit and no prognostic data on screening of asymptomatic subjects with diabetes is available for echocardiography, as reviewed by Budoff and colleagues.11
Different studies suggest that the presence of coronary artery calcium (CAC; see Chapter 12.2) is associated with myocardial ischaemia and the short-term outcome.12,13,14,15,16,17,18,19,20 The absence of CAC predicted a low short-term risk of death in subjects with diabetes with about 1% mortality in 5 years and an increase in each CAC score category increased the mortality risk.21 Myocardial perfusion imaging is able to detect silent ischaemia in 6–50% of all asymptomatic patients with diabetes depending on the pre-test risk, but has not been shown to improve the prognosis in various studies.11 Still, CAC and myocardial perfusion scintigraphy findings were synergistic for the prediction of short-term cardiovascular events leading to a proposed algorithm in the 2010 American College of Cardiology Foundation/American Heart Association guideline for assessment of cardiovascular risk in asymptomatic adults suggesting that asymptomatic subjects with diabetes over 40 years of age should undergo stress imaging if the CAC score is greater than 400.22 However, there is no evidence that this approach can improve prognostic outcome.
Overall, there are many gaps in knowledge on the prognostic value of the different risk assessment tools. Based on the available information, the European Society of Cardiology Guidelines9 give the recommendations shown in Table 19.4.1
Recommendation . | . |
---|---|
It should be considered to classify patients with DM as at very high or high risk for cardiovascular disease depending on the presence of concomitant risk factor and target organ damage. | Class IIa; level C |
It is not recommended to assess the risk for cardiovascular disease in patients with DM based on risk scores developed for the general population. | Class III; level C |
It is indicated to estimate the urinary albumin excretion rate when performing risk stratification in patients with diabetes | Class I; level B |
Screening for silent myocardial ischaemia may be considered in selected high risk patients with DM | Class IIb; level C |
Recommendation . | . |
---|---|
It should be considered to classify patients with DM as at very high or high risk for cardiovascular disease depending on the presence of concomitant risk factor and target organ damage. | Class IIa; level C |
It is not recommended to assess the risk for cardiovascular disease in patients with DM based on risk scores developed for the general population. | Class III; level C |
It is indicated to estimate the urinary albumin excretion rate when performing risk stratification in patients with diabetes | Class I; level B |
Screening for silent myocardial ischaemia may be considered in selected high risk patients with DM | Class IIb; level C |
References
1. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, Cooney MT, Corrà U, Cosyns B, Deaton C, Graham I, Hall MS, Hobbs FD, Løchen ML, Löllgen H, Marques-Vidal P, Perk J, Prescott E, Redon J, Richter DJ, Sattar N, Smulders Y, Tiberi M, van der Worp HB, van Dis I, Verschuren WM, Authors/Task Force Members.
2. Guzder RN, Gatling W, Mullee MA, Mehta RL, Byrne CD.
3. Stevens RJ, Kothari V, Adler AI, Stratton IM, United Kingdom Prospective Diabetes Study Group.
4. Protopsaltis ID, Konstantinopoulos PA, Kamaratos AV, Melidonis AI.
5. Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Nilsson PM, Gudbjörnsdottir S, Swedish National Diabetes Register.
6. Yang X, So WY, Kong AP, Ma RC, Ko GT, Ho CS, Lam CW, Cockram CS, Chan JC, Tong PC.
7. Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Gudbjornsdottir S, Swedish National Diabetes Register.
8. Vistisen D, Andersen GS, Hansen CS, Hulman A, Henriksen JE, Bech-Nielsen H, Jørgensen ME.
9. Rydén L, Grant PJ, Anker SD, Berne C, Cosentino F, Danchin N, Deaton C, Escaned J, Hammes HP, Huikuri H, Marre M, Marx N, Mellbin L, Ostergren J, Patrono C, Seferovic P, Uva MS, Taskinen MR, Tendera M, Tuomilehto J, Valensi P, Zamorano JL.
10. Turrini F, Scarlini S, Mannucci C, Messora R, Giovanardi P, Magnavacchi P, Cappelli C, Evandri V, Zanasi A, Romano S, Cavani R, Ghidoni I, Tondi S, Bondi M.
11. Budoff MJ, Raggi P, Beller GA, Berman DS, Druz RS, Malik S, Rigolin VH, Weigold WG, Soman P, Imaging Council of the American College of Cardiology.
12. Anand DV, Lim E, Hopkins D, Corder R, Shaw LJ, Sharp P, Lipkin D, Lahiri A.
13. Anand DV, Lim E, Lahiri A, Bax JJ.
14. Berman DS, Wong ND, Gransar H, Miranda-Peats R, Dahlbeck J, Hayes SW, Friedman JD, Kang X, Polk D, Hachamovitch R, Shaw L, Rozanski A.
15. Kiramijyan S, Ahmadi N, Isma’eel H, Flores F, Shaw LJ, Raggi P, Budoff MJ.
16. Malik S, Budoff MJ, Katz R, Blumenthal RS, Bertoni AG, Nasir K, Szklo M, Barr RG, Wong ND.
17. Raggi P, Callister TQ, Shaw LJ.
18. Raggi P, Shaw LJ, Berman DS, Callister TQ.
19. Shaw LJ, Blumenthal RS, Raggi P.
20. Wong ND, Rozanski A, Gransar H, Miranda-Peats R, Kang X, Hayes S, Shaw L, Friedman J, Polk D, Berman DS.
21. Yeboah J, Erbel R, Delaney JC, Nance R, Guo M, Bertoni AG, Budoff M, Moebus S, Jöckel KH, Burke GL, Wong ND, Lehmann N, Herrington DM, Möhlenkamp S, Greenland P. Development of a new diabetes risk prediction tool for incident coronary heart disease events: the Multi-Ethnic Study of
22. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, Foster E, Hlatky MA, Hodgson JM, Kushner FG, Lauer MS, Shaw LJ, Smith SC Jr, Taylor AJ, Weintraub WS, Wenger NK, Jacobs AK, American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.
Further reading
Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Gudbjornsdottir S, Swedish National Diabetes R.
Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Nilsson PM, Gudbjörnsdottir S, Swedish National Diabetes Register.
Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, Foster E, Hlatky MA, Hodgson JM, Kushner FG, Lauer MS, Shaw LJ, Smith SC Jr, Taylor AJ, Weintraub WS, Wenger NK, Jacobs AK, American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.
Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, Cooney MT, Corrà U, Cosyns B, Deaton C, Graham I, Hall MS, Hobbs FD, Løchen ML, Löllgen H, Marques-Vidal P, Perk J, Prescott E, Redon J, Richter DJ, Sattar N, Smulders Y, Tiberi M, van der Worp HB, van Dis I, Verschuren WM, Authors/Task Force Members.
Raggi P, Shaw LJ, Berman DS, Callister TQ.
Rydén L, Grant PJ, Anker SD, Berne C, Cosentino F, Danchin N, Deaton C, Escaned J, Hammes HP, Huikuri H, Marre M, Marx N, Mellbin L, Ostergren J, Patrono C, Seferovic P, Uva MS, Taskinen MR, Tendera M, Tuomilehto J, Valensi P, Zamorano JL.
Shaw LJ, Blumenthal RS, Raggi P.
Vistisen D, Andersen GS, Hansen CS, Hulman A, Henriksen JE, Bech-Nielsen H, Jørgensen ME.
Yang X, So WY, Kong AP, Ma RC, Ko GT, Ho CS, Lam CW, Cockram CS, Chan JC, Tong PC.
Yeboah J, Erbel R, Delaney JC, Nance R, Guo M, Bertoni AG, Budoff M, Moebus S, Jöckel KH, Burke GL, Wong ND, Lehmann N, Herrington DM, Möhlenkamp S, Greenland P. Development of a new diabetes risk prediction tool for incident coronary heart disease events: the Multi-Ethnic Study of
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