Review Article

Clinical and Translational Imaging

, Volume 1, Issue 5, pp 325-339

First online:

Cardiovascular risk stratification in diabetic patients

  • Mario PetrettaAffiliated withDepartment of Translational Medical Sciences, Federico II University
  • , Wanda AcampaAffiliated withNational Research Council, Institute of Biostructure and Bioimaging
  • , Giovanni FiumaraAffiliated withSDN Foundation, Institute of Diagnostic and Nuclear Development
  • , Alberto CuocoloAffiliated withDepartment of Advanced Biomedical Sciences, Federico II University Email author 


Several studies, reporting that diabetes is associated with a marked increase in the risk of coronary artery disease (CAD), introduced the concept that diabetes is CAD equivalent, and thereby provided a rationale for treating cardiovascular risk factors in diabetic patients as aggressively as in non-diabetic subjects with prior myocardial infarction. However, the concept of CAD risk equivalence in diabetes has been challenged by other studies. The classification of individuals with diabetes as having a risk level equivalent to that of individuals without diabetes who survived a cardiovascular event is an approach that fails to account for the fact that cardiovascular risk is not uniformly distributed, but follows a gradient from the lowest to the highest risk. Prediction models, based on numbers of risk factors, have been proposed for patients with diabetes. Most cardiovascular prediction models have been developed in general populations, including diabetes as a predictor, while a smaller number of studies have endeavored to construct CAD risk scores primarily in diabetic patients. The detection of occult CAD is the objective of non-invasive of testing diabetic patients for risk stratification purposes. Among the non-invasive imaging modalities, stress myocardial perfusion single-photon emission computed tomography has been widely used in diabetic patients. This review discusses the importance of using clinical prediction models in the diabetic population and the different challenges of non-invasive imaging for risk stratification, considering that the questions of when, how, and which diabetic patients should be studied are still open ones.


Diabetes mellitus Myocardial perfusion imaging Prognosis