Biomarkers Predicting Acute Myocardial Infraction in Patients Without Standard Modifiable Risk Factors

Coronary artery disease (CAD) 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, it has been shown that atherogenesis, as well as the subsequent inflammatory processes that can lead to AMI, are not only closely related to dyslipidemia, hypertension, DM and smoking, but also to pro-inflammatory cytokines and proteins (such as Lipoprotein a [LPa], interleukin 6 [IL-6]) and other indicators of inflammation, such as soluble urokinase plasminogen activator receptor (suPAR). This could render these biomarkers potential risk factors for the occurence of AMI. Furthermore, clinical entities, such as various autoimmune and collagen diseases (e.g. rheumatoid arthritis), as well as psychiatric syndromes (such as depression) have been associated with an increased risk of atherosclerotic disease. In addition, newer techniques and measurements in trans-thoracic echocardiography, such as GLS (Global Longitudinal peak systolic strain) and Myocardial Work have been linked to "silent" myocardial ischemia and -there is emerging evidence indicating that they- might predict future cardiovascular events. Therefore, the two groups of patients (with SMuRFs vs SMuRF-less) will be compared regarding their clinical, basic and advanced laboratory, and imaging fingerprints for the assessment of predictive factors leading to SMuRF-less AMIs. 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 clinical, laboratory and imaging profile of patients and SMuRF-less AMI. This could lead to the development of predictive risk stratification algorithms for patients without SMuRFs and coronary artery disease.

Patients will be enrolled in two academic hospitals and a general military hospital in Thessaloniki, Greece. A complete and comprehensive medical interview will be conducted in each eligible patient after revascularization. From this interview, the following demographic and clinical information will be obtained: age, sex, contact information, exact symptoms and disease history, detailed medical history, diagnostic and therapeutic interventions performed in the past and medication received. In addition, the patient's laboratory data will be recorded on admission and during hospitalization. These will include: complete blood count, biochemical control, coagulation mechanism control, hormonal control, HbA1c, NTproBNP, HsTnT on admission, higher-peak value of HsTnT, and NTproBNP, and LPa, IL-6, suPAR levels on admission. Furthermore, each patient will undergo a full echocardiographic examination with a specific protocol that includes specialized and modern measurements such as Global Longitudinal Strain (GLS) of the left ventricle and the rest heart cavities, as well as non-invasive calculation of myocardial work of the left ventricle (Global Constructive Work, Global Wasted Work, Global Work Index, Global Work Efficiency; Myocardial Work). Univariate and multivariate analysis with linear and logistic regression models will be used to investigate independent prognostic factors contributing to the occurrence of SMuRF-less AMIs. The potential application in daily clinical practice of a derived clinical predictive model-algorithm, possibly including a clinical, laboratory and echocardiographic biomarkers, will contribute to the early prognosis and personalized prevention of such a particular category of AMIs.