Atherosclerosis is a pathological process strongly correlated with metabolic disorders of lipid oxidation stress [1]. Its consequent inflammatory alterations on the endothelium could lead to coronary artery disease (CAD), including stable and acute coronary syndrome. Risk factors associated with the initiation and the propagation of the atherosclerotic process have been identified as a result of the intense investigation over the last decades in this scientific field. Apart from traditional risk factors, such as age, sex, race, smoking, LDL and HDL cholesterol, systolic blood pressure, family history of premature myocardial infarction, diabetes mellitus and triglycerides, there are multiple studies integrating novel biomarkers, such as a genetic ones, cardiac troponin-T, high sensitivity C-reactive protein, apolipoprotein B/ apolipoprotein A-1 ratio, homocysteine or coronary artery calcium in CAD risk stratification [2, 3].

However, despite the remarkable progress in diagnosing and treating CAD during the last decade, it remains the leading cause of morbidity and mortality worldwide [4], while prognostic prediction remains challenging in this cohort of patients. This could be attributed to a lack of effective diagnostic methods for CAD in its early stages, and a poor understanding of its pathophysiology. Thus, further studies are still warranted to increase our understanding of the complex pathophysiological mechanisms underlying CAD and to shed light on novel CAD risk parameters.

Recently emerging scientific evidence supports that cardiac metabolism disturbances indicate high probability of cardiovascular disease [5]. Hence, metabolomics, one of the newest and most rapidly evolving omics techniques, could be employed as a targeted tool for determining cardiac metabolism perturbation [6, 7]. According to the American Heart Association, cardiovascular metabolomics is a promising field able to provide detailed metabolic profiling of biospecimens and investigate both the intrinsic and extrinsic factors that contribute to CAD risk [8]. More specifically, multiple studies revealed that various metabolites such as BCAAs (branched-chain amino acids), specific unsaturated lipid species [8], trimethylamine-N-oxide [9], choline [10, 11], cysteine, N-acetylalanine and theophylline [12] are linked with an advanced cardiometabolic risk profile derived through metabolomics-based analysis using Liquid Chromatography-Mass Spectrometry or Nuclear Magnetic Resonance Spectroscopy. Furthermore, there are also data suggesting that other already established biochemical markers, such as cholesterol and triglycerides, have been strongly associated with CAD pathogenesis [6]. Therefore, their clinical importance in prediction and risk stratification of cardiovascular events should be thoroughly evaluated.

The rationale of this real-world observational study is by measuring metabolic profiles to expand our knowledge on the biochemical process of atherogenesis and to identify clinically significant metabolic biomarkers associated with the pathogenesis and the prognosis of CAD. Previous studies have already been conducted trying to establish potential correlations between metabolic profiling and CAD [11‐14]. However, to the best of our knowledge, this is the first study aiming to associate the severity and the complexity of CAD, as assessed by the SYNTAX score [15] with patients’ metabolic profile. This study's ultimate goal is to suggest a clinical tool of personalized medicine based on the patients' metabolic fingerprint to distinguish the patients at high risk for adverse cardiovascular events.

CorLipid is an ongoing trial aspiring to create a unique tool to complement traditional risk factor assessment by applying novel metabolomics-based techniques. These techniques enable combined profiling of different metabolites that could serve as disease biomarkers with the potential to inform clinical decision-making for patients with CAD.

Currently it is an established concept that alterations in the serum metabolome may be detectable in at-risk individuals and other relative studies have already demonstrated strong correlation between metabolites’ levels and CAD risk [8]. For instance, a multi-center trial in China determined the plasma metabolomic profiles of 2,324 patients undergoing coronary angiography by liquid chromatography-quadrupole time-of-flight mass spectrometry and made 12 cross-comparisons to and within CAD identifying 89 differential metabolites closely related to the occurrence and development of CAD [14]. Distinct plasma ceramide ratios have been found to be significant predictors of cardiovascular death both in patients with stable CAD and acute coronary syndrome, over and above currently used lipid markers [18, 19].

Another study conducted by Jiang et al. [13] analyzed the serum metabolomics pertubation on atherosclerotic plaque stability of 252 patients sustaining acute coronary syndrome and also undergoing emergency coronary angiography and revealed significantly altered betaine, acetylcarnitine, 1-heptadecanoyl-sn-glycero-3-phosphocholine, and isoundecylic acid levels in patients with vulnerable plagues. Furthermore, they proposed a combined model of serum betaine and ejection fraction as a potential diagnostic biomarker to distinguish stable from vulnerable plagues patients. Eventually, an insight from the STABILITY trial [20] featured a Ceramide‐Phospholipid based risk (CERT2) score as a risk indicator in patients with stable coronary heart disease.

The present study seeks to contribute to the literature as mentioned above by examining the association between the complexity and the severity of CAD with novel metabolomics biomarkers in a large patient cohort with the potential to implement a sensitive metabolomics-based risk score for the assessment of such patients. State-of-the-art advanced metabolomics-based techniques, which enable targeted and untargeted profiling of a wide range of metabolites (ceramides, acyl-carnitines, fatty acids, small molecules and polar compounds) and protein markers serving as precursors and products in different metabolic pathways, will be employed aiming to reveal potential metabolites differences amongst the participants. Metabolomic profiling will not only provide useful insights into the understanding of the underlying mechanisms of CAD by uncovering novel disease-related pathways, but it will also enable us to accurately identify patients at risk of CAD and predict patients’ response to specific treatments. To this end, the metabolomics-based algorithm will be combined with patients’ clinical and angiographic characteristics in order to enhance and support clinical diagnosis and disease prognosis.

In this trial, there are also some limitations that should be acknowledged. The main limitation of our study is the single-center character of its design and thus covering specific geographical location. Our study population mainly consists of Greek patients. In future studies, the scope could be widened to include other ethnicities within Europe and other races such as Africans in order to account for inherent variability of different patient populations. Additionally we should consider that potential differences in the nutritional and medical history among the participants could make difficult the interpretation of our results, since they could affect their metabolism. In order to identify such confounders, detailed information about dietary habits and medical treatment will be recorded upon recruitment.

Future studies should include the integration of metabolomics platform with other "omics" techniques directing to the application of multi- “omics”. In that way the cardiovascular scientific community will gain deep insight into pathophysiological interactions of genes, proteins, metabolites and disease states, while promoting individualized medicine.

In conclusion, deciphering the details of serum metabolome has the potential to identify novel cardiovascular disease biomarkers, which can accurately predict the risk for coronary artery disease and subsequent cardiovascular events, and to significantly alter the risk stratification strategies of coronary artery disease.