Coronary artery disease (CAD) is a complex, multifactorial disease driven by the cumulative and interactive modular effects of gene–gene, gene–environment and epigenetic interactions [1]. Notwithstanding intense investigation in the postgenomic era, the fundamental biological pathways underlying the multidecade process of atherosclerotic formation and chronic inflammation in CAD have not yet been addressed [2]. The need for unraveling the molecular and genetic underpinnings of CAD at a deeper level is stressed nowadays, due to exceptionally high mortality rates of CAD, despite the expanded arsenal of precision medicine [3]. Therefore, defining CAD will enable the treatment of patients on the basis of a better understanding of their clinical presentations.

The potential for genotype-guided precision medicine is pointed out by recently emerging evidence from large scale studies investigating various gene expressions in patients with CAD. Hitherto, several Genome-Wide Association Studies (GWAS) have mapped more than 150 single-nucleotide polymorphisms (SNPs) potently implicated in CAD pathogenesis [1, 4‐7]. These candidate variants are not yet established though and as Next Generation Sequencing (NGS) becomes the heart of high-throughput genotyping technologies, several plausible genetic variants linked with multifactorial traits of CAD might be discovered, shedding light on the road of personalized medicine [8]. Meanwhile, significant therapeutic implications emerge from the integration of genetic data into predictive risk scores. Specifically, several studies have been envisaged, in order to correlate distinct genetic variants with modulation of the risk for CAD occurrence or progression [9‐11]. In those studies the severity of CAD has been assessed via clinical, laboratory or imaging parameters, but not with the Synergy Between Percutaneous Coronary Intervention With Taxus and Coronary Artery Bypass Graft Surgery (SYNTAX) score yet [12‐15].

The SYNTAX score is the best-known scoring algorithm to evaluate CAD complexity as a comprehensive angiographic grading tool taking into consideration anatomic risk factors [16]. According to the extent of CAD, this score facilitates the objective guidance of decision-making between coronary artery bypass grafting (CABG) surgery and percutaneous coronary intervention (PCI). Despite, the SYNTAX score relies on invasive coronary angiography findings and the discovery of risk stratification algorithms that facilitate non-invasive estimation of CAD complexity could alter the prognostic plan in patients with CAD.

The rationale behind this prospective study is to associate, for the first time, the severity of CAD, as assessed by the SYNTAX score, with patients’ genomic profile in a real-world setting of patients undergoing coronary angiography [16]. The desirable goal is to corroborate genomic and pharmacogenetic research on CAD exploring the potential clinical association of 228 selected SNPs with CAD and individualized response to clopidogrel and statin therapy, which could disentangle gene expression alterations in blood of patients with CAD. Ultimately, the GESS trial aspires to develop a genetic SYNTAX score that could non-invasively enable the identification of patients with complex and severe CAD after a blood-based gene expression analysis. This study is designed to contribute to recent calls for implementing genotype-guided precision medicine decisions, by aiding the clinicians to achieve improved prediction and therapy outcomes for CAD patients [3].

GESS is a prospective ongoing study designed to determine the impact of the presence of several genetic variants on CAD severity. The aim of this study is to further understand the pathogenesis of CAD by utilizing 3 fundamental pillars: (1) invasive coronary angiography and standardized SYNTAX score calculation; (2) revolutionary NGS technologies; and (3) systems biology-based bioinformatics. To our knowledge, hitherto, this is the first study designed to establish a prognostic blood assay for the association of the presence of a large number of SNPs with CAD severity, as evaluated via the SYNTAX score.

Endothelial dysfunction, oxidative stress and inflammation, which are the products of a multifactorial interplay between inherited and environmental risk factors, are established determinants of the atherosclerotic burden and CAD prognosis [4, 28]. Large GWAS have been conducted in order to locate CAD-associated variants (SNPs) and decipher the underlying genetic fundament of the disease [6, 29‐35]. To date, a great number of susceptible multi-SNP loci have been identified with some of them reaching the stringent level of significance [6, 32, 34, 36, 37]. More specifically, more than 150 SNPs, in over 100 candidate genes have been annotated as CAD-relevant with specific loci, such as 9p21.3, 6q25.1, 2q36.3, showing the strongest association with disease phenotypic variance [5, 8, 38, 39]. The CARDIoGRAMplusC4D Consortium has carried out a meta-analysis in a total sample size of over 190.000 patients and demonstrated a highly significant correlation of 36 SNPs with CAD [6]. Furthermore, Liu et al. reported that the most studied multi-loci genes are those of angiotensin I converting enzyme, lipid and lipoprotein metabolism [1]. Hence, individual GWAS and meta-analyses have confirmed the speculated deterministic role of genetic predisposition in occurrence, progression of atherosclerosis and coronary plaque calcification, with multiple converging pathways, including cardiac muscle contraction, glycerolipid metabolism, and glycosaminoglycan biosynthesis [5, 32, 37, 40].

Nevertheless, GWAS have only provided population-attributable risk data and could not be transferred to an individual with CAD. During the last decade, the advent of NGS has enabled researchers to perform parallel analyses of hundreds of genes in an unbiased approach [8]. This is attracting widespread attention enhancing CAD translational study and aiding to close the gap between genotype and phenotype. In 2013 the CARDIoGRAMplusC4D Consortium reported that targeted sequencing with NGS can discover rare variants with high sensitivity, rendering NGS an essential genetics approach in the post-GWA study era [38].

Apart from genetic mapping, GWAS and NGS studies have also explored the clinical utility of genetic biomarkers for the creation of genetic risk scores [10, 11, 36, 41]. These algorithms would ideally predict the severity of CAD and the subsequent adverse outcomes aiming to identify patients with potential benefit from preventive care. For their development, researchers have examined the prognostic value of blood-based genetic panels, in comparison with imaging (myocardial perfusion imaging or coronary computed tomography angiography), angiographic (visual or quantitative assessment of coronary artery stenosis or Gensini) or clinical (GRACE) predictive scores [12, 15, 42‐44]. COMPASS and PREDICT trials created 2 gene-expression scores outperforming clinical factors and non-invasive imaging in discriminating patients with > 50% stenosis [45, 46]. Despite, Labos et al. reported that the addition of their developed polygenic risk score to the GRACE risk score could not significantly improve risk classification in acute coronary syndrome admissions [42]. Moreover, weighted multi-locus risk scores have been created to predict recurrent vascular events or statin efficacy and atherosclerotic burden alterations in CAD populations [10, 47‐49]. Nevertheless, limited data exist about the utility of genetic risk scores for the prediction of MACCE [11‐13, 41].

To the best of our knowledge, GESS is the first study yet to investigate the association of such a large number of candidate SNPs (228) with SYNTAX-score-based CAD complexity. To this end, GESS emerges as a part of a research project aspiring to complement traditional risk factor assessment with panels of significant metabolomic and genomic biomarkers [50, 51]. The co-evaluation of novel risk factors and the complexity of CAD could significantly expand the concept of cardiovascular precision medicine.

Admittedly, the GESS trial is subject to some limitations that merit discussion. First, the single-center character of the study and the enrollment of patients from a Greek-based population may limit the generalizability of our findings, even if our sample will represent a broad spectrum of patients with CAD. Furthermore, patients of different age groups will comprise the study population, which might affect the rate of genetic influence in CAD severity, since the genetic component of variability is conceivably more common among younger individuals. Future studies should explore the combination of proposed genetic risk scores from multi-ethnic populations with panels of metabolomics, transcriptomics or proteomics, to achieve the desirable transition from “omics” to “panomics” [44]. Therefore, we could define CAD at the deepest level and clinical cardiologists would be guided in decision-making via an absolutely personalized approach.

In conclusion, genotyping of patients presenting with CAD symptoms could potentially disentangle genetic risk variants implicated in CAD progression. The development of a panel with genetic markers combined with clinical and angiographic characteristics might contribute to implementing accurate risk stratification algorithms in CAD populations, with the potential to predict the emergence of CAD as well as the hazard for subsequent adverse events and modify therapeutic strategies. Besides that, the design of the study creates an interdisciplinary infrastructure that allows the clinical translation of molecular knowledge to guide decisions for individual and/or CAD patient groups. Importantly, such direction contributes to the establishment and application of processes that successfully implement genomics knowledge in the clinical setting within the concept of pharmacogenomics and precision medicine.