Cardiovascular disease (CVD) remains the major cause of death and there is the need to a better stratification of CVD patients. By an unbiased statistical approach we sought to identify clusters of patients to better stratify their risk. 202 patients with chest pain (63% males, age 62?12 yr) undergone to CT coronary angiography (CCTA) were prospectively included and classified using K-means cluster analysis of clinical, imaging and bio-humoral data. The most relevant classification resulted in three phenotypes distinguished according to Framingham score and HMW adiponectin plasma levels. Presence and severity of disease as assessed by CCTA were verified trough these phenotypes. By K-means cluster analysis, we identified CVD phenotypes allowing to stratify patients requiring different diagnostic and therapeutic approach
