Hunting for a coronary artery disease diagnosis in asymptomatic patients with diabetes mellitus: if, how and when.
Authors
Affiliations (4)
Affiliations (4)
- Department of Medicine and Aging, and Center for Advanced Studies and Technology (CAST), G. d'Annunzio University of Chieti-Pescara, Luigi Polacchi, 66013, Chieti, Italy. [email protected].
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Blalock 524D1, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
- Department of Neuroscience, Imaging and Clinical Sciences, G.d'Annunzio University of Chieti-Pescara, Chieti, Italy.
- Department of Medicine and Aging, and Center for Advanced Studies and Technology (CAST), G. d'Annunzio University of Chieti-Pescara, Luigi Polacchi, 66013, Chieti, Italy.
Abstract
Cardiovascular disease is a leading cause of morbidity and mortality in individuals with type 2 diabetes. This population faces an increased risk of coronary artery disease due to accelerated atherosclerosis, endothelial dysfunction and chronic inflammation. A substantial proportion of cases remain clinically silent, with coronary artery disease often undetected until the occurrence of acute events. This silent progression poses a significant challenge for timely diagnosis and prevention. This review explores the prevalence, mechanisms, and prognosis of asymptomatic coronary artery disease in type 2 diabetes, focusing on current strategies for cardiovascular risk assessment and screening. While traditional risk factors such as hypertension, dyslipidemia, and hyperglycemia remain central to clinical evaluation, they often fail to fully capture the residual cardiovascular risk in diabetic patients. Advances in imaging, particularly coronary calcium scoring and computed tomography angiography, allow for the direct visualization of total plaque burden, providing a more refined assessment of risk. Functional tests and novel biomarkers, including high-sensitivity C-reactive protein and lipoprotein(a), further enhance the precision of risk stratification. Despite these advances, the clinical value of routine screening in asymptomatic diabetic individuals remains controversial. Evidence suggests that a selective, risk-based approach to screening may be more effective than universal testing, identifying high-risk patients who could benefit from more intensive preventive therapies. Emerging technologies, such as artificial intelligence and machine learning, promise to improve risk prediction by integrating clinical, imaging, and biomarker data into personalized care pathways. The review emphasizes the need to move from a reactive approach, based on symptom-driven evaluation, to a proactive strategy aimed at early identification of subclinical disease. By combining advanced imaging techniques, biomarker profiling, and updated risk algorithms, clinicians can better tailor preventive interventions, reduce adverse cardiovascular outcomes, and optimize long-term care. Further prospective studies are required to define the most effective and cost-efficient screening protocols for asymptomatic coronary artery disease in individuals with diabetes.