Metabolic Substrate of Patients With Myocardial Infarction With and Without Modifiable Cardiovascular Risk Factors

Coronary heart disease (CHD) is the leading cause of mortality worldwide. Every year, millions of people suffer its most adverse manifestation, an acute myocardial infraction (AMI). The majority of these patients present at least one of the standard modifiable risk factors (SMuRFs). These include smoking, hypertension, dyslipidemia, and diabetes mellitus (DM). However, emerging scientific evidence recognizes a clinically significant proportion of patients presenting with life-threatening AMI without any SMuRF (SMuRF-less patients). This proportion of patients with ACS without SMuRF appears to be increasing during the last two decades and has recently been reported as high as 20% (of total AMIs). To date, there are no scientific data capable of highlighting specific risk factors-biomarkers responsible for the development of AMIs SMuRF-less patients. Concurrently, metabolomics is rapidly evolving as a novel technique of studying small molecule substrates, intermediates and products of cell metabolism. This technique could be utilized to flag patients with higher risk for increased atherosclerotic burden, and subsequent future adverse clinical events. Besides the already established biomarkers, several metabolomic indicators, such as ceramides (C16, C18 και C24), acylcarnitines, apolipoproteins (ApoΒ and ApoA1) and adiponectin, have been separately shown to increase the risk for coronary artery disease development and progression. Therefore, the two groups of patients (with SMuRFs vs SMuRF-less) will be compared regarding their metabolic fingerprints -specifically the aforementioned novel metabolomic biomarkers- and possible predictive factors leading to SMuRF-less AMI will be evaluated. On the basis of the above, the aim is to prospectively analyze a cohort of well-characterized patients with AMI. The rationale of the study is to investigate potential correlations between metabolic profile of patients and SMuRF-less AMI. This could lead to the development of predictiv