Genetic Risk Factors Related to Coronary Artery Disease and Role of Transforming Growth Factor Beta 1 Polymorphisms

Coronary artery disease (CAD) is one of the leading causes of mortality globally and has long been known to be heritable; however, the specific genetic factors involved have yet to be identified. Recent advances have started to unravel the genetic architecture of this disease and set high expectations about the future use of novel susceptibility variants for its prevention, diagnosis, and treatment. In the past decade, there has been major progress in this area. New tools, like common variant association studies, genome-wide association studies, meta-analyses, and genetic risk scores, allow a better understanding of the genetic risk factors driving CAD. In recent years, researchers have conducted further studies that confirmed the role of numerous genetic factors in the development of CAD. These include genes that affect lipid and carbohydrate metabolism, regulate the function of the endothelium and vascular smooth muscles, influence the coagulation system, or affect the immune system. Many CAD-associated single-nucleotide polymorphisms have been identified, although many of their functions are largely unknown. The inflammatory process that occurs in the coronary vessels is very important in the development of CAD. One important mediator of inflammation is TGFβ1. TGFβ1 plays an important role in the processes leading to CAD, such as by stimulating macrophage and fibroblast chemotaxis, as well as increasing extracellular matrix synthesis. This review discusses the genetic risk factors related to the development of CAD, with a particular focus on polymorphisms of the transforming growth factor β (TGFβ) gene and its receptor.


Introduction
Atherosclerosis is associated with several risk factors that are mostly preventable and manageable, such as hypertension, hypercholesterolemia, smoking, and diabetes. Several unavoidable risk factors, such as advanced age, gender, and family history of early myocardial infarction, also have a significant impact. The complex process of atherosclerosis begins early in life. It is thought to initiate with dysfunction of the endothelial cells that line the coronary arteries; these cells are no longer able to appropriately regulate vascular tone, leading to a narrowing of the vessel and obstruction of the blood flow [1,2]. Studies in recent years have shown that atherosclerosis is a chronic inflammatory process that takes place in the vessels, causing endothelial damage and dysfunction [1].
The ongoing inflammatory process causes accumulation of atherogenic lipoproteins in the vessel wall, leading to the development of atherosclerotic plaque, the instability of which increases the risk of its rupture, which can lead to acute coronary syndromes [2].
Numerous cells of the immune system, such as leukocytes, macrophages, and lymphocytes, are involved in this process, and produce mediators that increase and maintain

CAD Genetic Background
An individual's risk of developing CAD is modulated by a set of genetic and lifestyle factors, as with many other complex diseases. Clinical observations from the mid-20th century have supported the belief that CAD risk is heritable [4]. In the Framingham Heart Study, a family history of cardiovascular disease in a parent or sibling was a strong predictor of CAD [5]. These studies laid the groundwork for applying new tools of human genetics to understand the genetic basis of CAD and translating these findings into clinical practice. The familial risk of CAD was first described in studies involving twins and prospective cohorts [6,7]. The Swedish Twins Registry involved nearly 21,000 individuals who were observed for more than 35 years; the inheritance of fatal CAD events among this group was calculated to be 0.57 and 0.38 for males and females, respectively. It is worth noting that hereditary effects are most pronounced in younger individuals [8].
Case-control studies examining the likelihood of CAD compared to healthy controls have revealed many risk factors in recent years. However, this scientific approach is not always suitable for detecting new genetic risk factors. The chromosome 9p21 locus is the most widely replicated genetic CAD risk locus identified to date, with an estimated 15% to 35% increased risk in carriers of the variant allele in prospective population-based studies and case-control studies [9]. The exact mechanism underlying the 9p21 association remains unknown. Preliminary evidence suggests that 9p21 variants alter the expression of antisense non-coding RNA at the INK4 (inhibitors of cyclin-dependent kinase 4) locus (ANRIL), thereby altering the activity of two nearby cyclin-dependent kinase inhibitors (CDKN2A and CDKN2B) involved in cell cycle regulation and cellular proliferation [10]. ANRIL is a well-defined genetic risk locus in Chr9p21 that is associated with endothelial dysfunction and triggering atherosclerotic processes [11]. A recent cohort study showed that variation on chromosome 9p21 has no significant association with CAD risk [12].

Role of Single-Nucleotide Polymorphisms in CAD
Cardiovascular monogenic diseases such as vascular disorders, inherited cardiomyopathies, and rhythm disorders are mostly associated with large, rare DNA variants. In contrast, other complex diseases may be influenced by common DNA variants at numerous, scattered loci distributed throughout the entire genome. Single-nucleotide variations typically have small effects and do not drive classic Mendelian inheritance patterns (familial inheritance). They also require large-scale, population-based, association studies for discovery.
A single-nucleotide polymorphism (SNP) is a common genetic variation at a single position in a DNA sequence among individuals (occurs at a specific position in the genome), being present in more than 1% of the population [13]. A SNP may be a marker of disease susceptibility.
The sequencing of the human genome has played a key role in understanding the genes associated with CAD. Advances in understanding the genetics of CAD have been stimulated by the development of DNA microarray technology using chips containing up to one million DNA markers consisting of SNPs. These have provided the basis for association studies, known as genome-wide association studies (GWAS) [14]. Genetic association studies examine the correlation between disease and chromosomal abnormalities and gene mutations, such as the aforementioned SNPs, but also variable number tandem repeats (VNTRs) and copy number variations (CNVs) to identify risk or protective alleles that play a role in the development of a specific disease. An abnormal frequency of a risk allele or genotype in affected individuals with a specific disease suggests that the analysed variant increases the risk of a specific disease [15]. GWAS have been used to create genetic risk scores to improve CAD risk prediction [2]. These studies use high-throughput genotyping technologies to map thousands of SNPs in the human genome and correlate them with clinical conditions or measurable traits. GWAS are very useful in discovering genetic variants related to different diseases (like CAD); however, they have some limitations. These studies are based on genes with a known or suspected role in defining the phenotype in question and do not provide new information on biological pathways linked to the disease. Candidate gene associations generally cannot be replicated for several reasons, one of these may be a too low statistical power related to inadequate study group size [14]. The first association of common variants (CVAS) for ischemic heart disease was published by three independent teams in 2007, and they described common variants at the 9p21 locus associated with a~30% increase in CAD risk [16][17][18]. Subsequent studies have both replicated this finding and extended the link to other vascular phenotypes, including carotid atherosclerosis [19], peripheral artery disease [20], and stroke [21]. SNP genotyping will always be associated with false positives and false negatives, differences in the selected study populations, and simple genotyping errors [22]. Metaanalyses partially address some of these limitations. They combine the results of single-gene association studies, providing an excellent opportunity to observe the role of a single genetic variation in a more heterogeneous and larger population. Over the past 10-15 years, metaanalyses have looked at a wide range of conditions associated with heart disease, including CAD itself [23][24][25][26]. Unfortunately, meta-analyses, like GWAS, can be biased by population stratification, variability in control group selection, different study approaches, or different scales of biochemical values. Most SNPs identified by GWAS still have very insignificant effects on disease outcomes, at least when considered individually.
A wider view on this application leads to combining candidate risk SNPs with other conservative and well-known clinical risk factors, creating a new study approach-the genetic risk score (GRS) (Figure 1). genetic risk scores to improve CAD risk prediction [2]. These studies use high-throughput genotyping technologies to map thousands of SNPs in the human genome and correlate them with clinical conditions or measurable traits. GWAS are very useful in discovering genetic variants related to different diseases (like CAD); however, they have some limitations.
These studies are based on genes with a known or suspected role in defining the phenotype in question and do not provide new information on biological pathways linked to the disease. Candidate gene associations generally cannot be replicated for several reasons, one of these may be a too low statistical power related to inadequate study group size [14]. The first association of common variants (CVAS) for ischemic heart disease was published by three independent teams in 2007, and they described common variants at the 9p21 locus associated with a ~30% increase in CAD risk [16][17][18]. Subsequent studies have both replicated this finding and extended the link to other vascular phenotypes, including carotid atherosclerosis [19], peripheral artery disease [20], and stroke [21]. SNP genotyping will always be associated with false positives and false negatives, differences in the selected study populations, and simple genotyping errors [22]. Meta-analyses partially address some of these limitations. They combine the results of single-gene association studies, providing an excellent opportunity to observe the role of a single genetic variation in a more heterogeneous and larger population. Over the past 10-15 years, meta-analyses have looked at a wide range of conditions associated with heart disease, including CAD itself [23][24][25][26]. Unfortunately, meta-analyses, like GWAS, can be biased by population stratification, variability in control group selection, different study approaches, or different scales of biochemical values. Most SNPs identified by GWAS still have very insignificant effects on disease outcomes, at least when considered individually.
A wider view on this application leads to combining candidate risk SNPs with other conservative and well-known clinical risk factors, creating a new study approach-the genetic risk score (GRS) (Figure 1).  Multiple individual genetic markers with small effect sizes may be used in combination to generate a greater effect size. The simultaneous use of the most common and strongest risk markers (like SNPs) and other clinical risk factors (like high LDL cholesterol, low HDL cholesterol, high blood pressure, family history, diabetes, smoking, etc.) may have the desired discriminatory accuracy to distinguish between diseased and healthy subjects.
A common genetic background in some diseases (i.e., autoimmune) provides us with an interesting study on generating a multilocal GRS based on common variants that have already been associated with CAD. An analysis that quantified heritability using updated genome-wide approaches estimated the heritability of CAD to be between 40 and 50% [27].
Many CAD-associated SNPs have been identified, but many of their functions are largely unknown. Although most loci identified seem to act through induction of atheroscle-rosis, little is known about the underlying mechanisms. The main part of the CADassociated SNPs is in non-coding regions of the human genome. Studies indicate that the loci primarily exert their effect through regulation of nearby genes, but a large proportion of these genes have not previously been linked to CAD or risk factors for CAD [28,29].
Most CAD GWAS studies have been conducted in populations of European-Caucasian origin. Fewer but important studies have been conducted in East and South Asian and African populations. Most GWAS loci associated with CAD discovered in Europeans have only been confirmed in East Asian populations, with strong and significant correlations with other cardiovascular diseases. In general, these two populations share common risk factor loci [30,31].
Elevated LDL levels are causally associated with increased CAD risk, according to studies of familial hypercholesterolemia involving mutations in the LDLR gene. Genetic variants in the PCSK9, NPC1L1, and HMGCR genes are associated with serum LDL levels and are considered predictors of CAD risk [37,38]. For many years, plasma triglyceride concentration was considered an independent risk factor for CAD. The primary metabolism of plasma triglycerides depends on lipoprotein lipase (LPL). Genetic variants associated with reduced LPL function are known to be associated with increased cardiovascular risk [39]. LPL activity is increased by apolipoprotein A5 (ApoA5) and inhibited by ApoC3 and angiopoietin-like proteins 3 and 4. Variants within the APOC3 gene are associated with lower plasma triglyceride levels and CAD risk, while functional mutations in the APOA5 gene have the opposite effects [40,41].
Genetic conditions, as well as inflammation, contribute to the development of CAD. Inflammation is a major factor in the development of CAD. Inflammatory cells, such as Th1 cells, CD4 + cells, macrophages, and dendritic cells, secrete cytokines actively involved in the disease process, which affect the arterial wall to promote lipoprotein retention and atherosclerotic plaque formation [42]. Several biomarkers of inflammation have been shown to be useful in predicting cardiovascular disease risk. Christiansen et al. suggested that a common variant at the TOMM40/APOE rs2075650 locus was significantly associated with lower levels of C-reactive protein (CRP) in patients with stable CAD; however, there was no association with other inflammatory genes (APOA1, IL6R, MRAS and PLG) [43].
In this article, we focus only on a small number of SNPs that are associated with the inflammation process and its outcome. There is good evidence that several of these genetic risk variants predispose to CAD through inflammatory pathways [28,44].

Transforming Growth Factor-β1
TGFβ1 is a protein encoded by the TGFβ1 gene located on chromosome 19 (19q13.1). TGFβ1 has received special attention due to its multiple roles in important pathological processes, such as stimulating chemotaxis of macrophages and fibroblasts, stimulating extracellular matrix synthesis, and causing abnormalities in vascular cell proliferation [45,46]. TGFβ1 can be secreted by several cell types, such as peripheral blood mononuclear cells, endothelial cells, macrophages, platelets, vascular smooth muscle cells (VSMCs), myofibroblasts, and renal cells [47]. TGFβ1 has a direct regulatory function on endothelial cells in the vessel wall, vascular smooth muscle cells, and the extracellular matrix.
Intensive research into the mechanisms of TGFβ1 signalling over the past two decades has led to the development of a well-known signalling cascade. To initiate the TGFβ signalling pathway, TGFβ ligands must first bind to TGFβR2 on the cell surface. This binding results in the activation of TGFβR1, which leads to the phosphorylation of mothers against decapentaplegic homolog 2 (SMAD2) and SMAD3 proteins, which enable binding to SMAD4 and translocation to the cell nucleus. Nucleus SMAD proteins interact with other transcription factors to regulate transcription of target genes to control the cell response [46][47][48][49] (Figure 2). However, the exact mechanisms controlling the TGFβ1 signalling within the arterial vasculature are still incompletely understood.

matrix.
Intensive research into the mechanisms of TGFβ1 signalling over the past two decades has led to the development of a well-known signalling cascade. To initiate the TGFβ signalling pathway, TGFβ ligands must first bind to TGFβR2 on the cell surface. This binding results in the activation of TGFβR1, which leads to the phosphorylation of mothers against decapentaplegic homolog 2 (SMAD2) and SMAD3 proteins, which enable binding to SMAD4 and translocation to the cell nucleus. Nucleus SMAD proteins interact with other transcription factors to regulate transcription of target genes to control the cell response [46][47][48][49] (Figure 2). However, the exact mechanisms controlling the TGFβ1 signalling within the arterial vasculature are still incompletely understood. TGFβ proteins play a key role in the development of myocardial fibrosis [50]. After myocardial infarction (MI), TGFβ signalling is altered and plays a key role in infarct healing and cardiac remodelling [51]. TGFβ1 and TGFβ2 are induced early after MI, while TGFβ3 shows delayed and prolonged regulation [52]. TGFβ has been described to act as a tumour suppressor in early carcinogenesis by inducing apoptosis or inhibiting cell growth, while promoting tumorigenesis and metastasis by suppressing local immune surveillance and promoting tumour progression in advanced stages of cancer [53,54]. TGFβ proteins play a key role in the development of myocardial fibrosis [50]. After myocardial infarction (MI), TGFβ signalling is altered and plays a key role in infarct healing and cardiac remodelling [51]. TGFβ1 and TGFβ2 are induced early after MI, while TGFβ3 shows delayed and prolonged regulation [52]. TGFβ has been described to act as a tumour suppressor in early carcinogenesis by inducing apoptosis or inhibiting cell growth, while promoting tumorigenesis and metastasis by suppressing local immune surveillance and promoting tumour progression in advanced stages of cancer [53,54]. TGFβ1 protein levels in the blood may not reflect normal TGFβ1 levels in the vascular interstitium, which are directly involved in the pathogenesis of CAD. Arterial wall fibroblasts send paracrine signals to VSMCs, causing macrophages to migrate toward the damaged area of the stented arterial wall in the presence of locally released TGFβ [55]. Platelets, which are involved in thrombus formation in the arterial wall, release large amounts of TGFβ1. Lower levels of serum TGFβ1 protein are generally considered useful biomarkers for the diagnosis and risk stratification of CAD [56,57]. Serum cytokine levels most often vary among populations, and their role in determining CAD is limited [58,59]. The fact that TGFβ1 levels in peripheral blood are genetically determined and depend on other inheritance mechanisms causes confusion about its independent role in CAD. Also, inconsistent results regarding the role of TGFβ1 levels in the development of CAD have been reported in studies of CAD in animal models [52]. Gichkun et al. hypothesized that the TGFβ1 gene and its multiple variants are involved in creating a genetic predisposition to myocardial fibrosis in heart transplant recipients [60].
It has been proven that some polymorphisms within the TGFβ1 gene can affect its expression and protein synthesis. Dysregulation of the TGFβ1 signalling pathway caused by a mutation can be associated with an increased risk of cardiovascular diseases. Tseng et al. analysed 54 TGFβ SNPs with a potential functional role in CAD (TGFβ2: 47 SNPs; TGFβ3: 7 SNPs); none of them remained statistically significant with an adjusted p < 0.05 [61].
The mechanism for the effect of the rs1800469 polymorphism on the development of end-stage heart failure remains unclear. A study by Barsova et al. indicated a positive association between TGFβ1 rs1800469 and a genetic predisposition to early MI in patients aged ≤ 50 years [58]. It is possible that the rs1800469 polymorphism alters promoter affinity for the transcription factors and inhibits TGFβ expression, thus activating proinflammatory cytokines (tumour necrosis factor α and interleukin 1) that contribute to the progression of CAD [58]. No association between the 509C/T polymorphism and CAD was detected in a group of German patients [60]. The inconclusive data from several studies on the rs1800469 promoter SNP still do not support its role in CAD.
One of the most studied genetic variants of TGFβ1 is the rs1800470 polymorphism-a proline to leucine swap at codon 10 (Pro10Leu) of the protein [60][61][62][63]. The A allele of this SNP is known to be associated with elevated levels of TGFβ1 in peripheral blood [60]. The rs1800470 genotype has previously been associated with the risk of developing cardiovascular disease, including cerebral infarction [64], silent myocardial ischemia in patients with DM [65], and complications of CAD [66]. Interesting conclusions have come from observations of the Han population in China. Yang et al. suggested that gender may influence the role of TGFβR in CAD and the TGFβ1 rs1800470 polymorphism was associated with CAD severity [67]. The frequency of the T allele was significantly and positively correlated with the number of stenosed coronary arteries (three or more vessels) [67]. In one of our previous studies, we examined the association between polymorphisms in the TGFβ1 gene (rs1800469, rs1800470) and the risk of unstable angina and selected clinical parameters affecting CAD risk [66]; however, we did not find any significant risk factors for unstable angina in the Polish population. In a Mexican population, the rs1800470 polymorphism was associated with the risk of developing restenosis after coronary stent implantation [68]. Gichkun et al. analysed the genetic predisposition to myocardial fibrosis in heart transplant recipients and found a significant association with the rs1800470 polymorphism [60].
Studies have shown that the minor G allele of the TGFβ1 rs1800471 polymorphism is associated with higher levels of gene expression and increased serum TGFβ1 protein levels [60,69,70]. Cambien et al. found that rs1800471 was associated with complications of CAD [71]. Morris et al., using a large meta-analysis, confirmed significant positive associations between the TGFβ1 SNPs rs1800469, rs1800470, and rs1800471 and CAD complications [24]. These associations between SNPs and CAD were evaluated using additive, dominant, and recessive family inheritance models. Significant positive associations were observed between CAD complications and the minor alleles of rs1800469 (p = 0.031), rs1800470 (p = 0.021), and rs1800471 (p = 0.021). The odds ratios from this meta-analysis differed from the results of previous studies [24].
Other polymorphisms of the TGFβ1 gene that affect its expression include the rs1982073 polymorphism. The results of a meta-analysis show an association between rs1800469 and rs1982073 in the TGFβ1 gene and the risk of CAD in Caucasian populations, thus confirming that TGFβ1 signalling may be involved in the pathogenesis of CAD. Carriers of rare alleles of two genetic variants (rs1800469 and rs1982073) in TGFβ1 have a 15% increased risk of CAD [26]. Koch et al. studied the rs1800469, rs1982073, rs1800471, and rs1800472 polymorphisms of the TGFβ1 gene in patients with MI. A specific sequence of haplotypes was associated with MI in men, independent of age, hypertension, hypercholesterolemia, smoking, and diabetes. Interestingly, none of the genotypes or haplotypes were associated with MI in women, confirming that gender is an independent risk factor for CAD [72]. Table 1 presents the SNPs in TGFβ and TGFβR genes that are commonly analysed.

Transforming Growth Factor-β1 Receptor
TGFβ receptor (TGFβR) is a serine/threonine protein kinase present mainly on the cell surface. The receptor includes three isoforms: TGFβR1, TGFβR2, and TGFβR3 (the last one lacks kinase activity). TGFβ1-related signalling pathways are activated by the binding of TGFβ1 to its receptor [45,46].
The TGFβR2 gene is located on chromosome 3 (3p22). It contains seven exons and encodes the TGFß type II receptor, a 565-amino-acid transmembrane tyrosine kinase with a molecular weight of about 60 kDa [76]. Genetic variations in TGFβR2 will produce different effects depending on the investigated tissue. TGFβR2 is thought to be a tumour suppressor gene and to play a role in striated cell differentiation and coronary artery remodelling. Mutations in TGFβR2 are associated with many cardiovascular diseases, such as Loeys-Dietz syndrome, familial thoracic aortic aneurysms, and sudden cardiac arrest in CAD patients [61,77,78]. In the cardiovascular system, it may lead to abnormal splicing of TGFβR2, resulting in loss of function of TGFß signalling activity in extracellular matrix formation and causing Marfan syndrome [77]. TGFβR2 in the gastrointestinal tract acts as a tumour suppressor gene; approximately 30% of colorectal cancers carry various TGFβR2 mutations [79].
TGFβ signalling is crucial for smooth muscle cell differentiation and contractile protein function. Mutations in TGFβR2 disrupt TGFβ signalling, creating genetic conditions that predispose to thoracic aortic aneurysm and dissection. Signalling through the TGFβR2 receptor in endothelial cells plays an important role in cardiac development and promotes fibrosis and myocardial remodelling in CAD [80].
Tseng et al. analysed 258 candidate SNPs with a potential functional role in CAD (TGFβR1: 24 SNPs; TGFβR2: 125 SNPs; TGFβR3: 109 SNPs), but only a single SNP in TGFβR2 (rs9838682) remained statistically significant [61]. The rs9838682 polymorphism lies in a region of high LD in a block of haplotypes spanning more than 43,600 base pairs (bp). In our previous study, we examined the association between polymorphisms in the TGFβR2 receptor gene (rs6785358, rs9838682) and CAD. We did not find any significant risk factors for unstable angina, but we were able to link the TGFβR2 rs9838682 polymorphism with effects on lipid parameters in patients with CAD [66]. We observed increased values of total plasma cholesterol and LDL cholesterol, as well as triglycerides in patients with the TGFβR2 rs9838682 AA genotype. The TGFβR2 rs9838682 polymorphism was also associated with sudden cardiac arrest (SCA) [61]. The minor allele of rs6785358 in the Han population was associated with congenital heart defects [48], but not with CAD [64].
The study by Choi et al. compared the sequences of eleven SNPs in the TGFβR2 gene between a group with Kawasaki disease and a control group. Three of them-rs1495592, rs6550004, and rs795430-were associated with the development of Kawasaki disease (KD). One SNP-rs1495592-was associated with coronary artery lesions (CALs) in the group with KD [74]. In another study, TGFβR2 rs6550004 was associated with the development of KD, and rs1495592 was associated with coronary artery lesions in children with this disease [74,75]. Neither TGFβR2 rs6785385 nor SNP rs764522 was associated with CAD severity in a Chinese population [73].
The study presented here has some limitations. We only analysed SNPs among TGFβ1 and TGFβR2 genes, not mentioning the numerous signalling pathways of other genes and proteins. In addition, we selected only the most common and intensively analysed SNPs without discussion of rare polymorphisms and insertion/deletion variants.

Conclusions
CAD is caused by reduced permeability of the coronary arteries due to atherosclerotic and thrombotic lesions, as well as ongoing inflammation in the coronary arteries. Previous studies have shown that numerous environmental and genetic factors are involved in the development of this disease. The main environmental factors preventing CAD include proper diet, adequate physical activity, and normal values of lipid and carbohydrate metabolism parameters. The recent years have brought many studies confirming the role of numerous genetic factors in the development of CAD. These include genes affecting lipid and carbohydrate metabolism, regulating endothelial and vascular smooth muscle function, influencing the coagulation system, or affecting the immune system. The inflammatory process occurring in the coronary vessels is very important in the development of CAD. It participates in the initiation of the atherosclerotic process by affecting the formation of foam cells and atherosclerotic plaques, and also affects the stability of the atherosclerotic plaque. The inflammation taking place in the atherosclerotic plaque is the cause of coronary occlusion and the occurrence of acute coronary syndromes. One important mediator of inflammation is TGFβ1. TGFβ1 plays an important role in the processes leading to CAD, such as stimulating chemotaxis of macrophages and fibroblasts, as well as increasing extracellular matrix synthesis. Previous studies have shown that polymorphisms within the TGFβ1 gene are associated with the risk of developing various forms of CAD. Genetic risk factors, including SNPs, have been extensively studied in recent years, and considerable progress has been made to better understand their role in CAD, as well as the function of each new locus.
When evaluating CAD risk factors, it is important to keep in mind the multifactorial basis of this disease, including both environmental and genetic factors. TGFβ1 polymorphisms are among the many genetic polymorphisms associated with the risk of developing CAD. The effect of single polymorphisms on CAD risk is small and should be considered along with other polymorphisms and other disease risk factors. Research on the role of genetic polymorphisms in the development of CAD not only contributes to the search for risk factors for the disease, but also contributes to a better understanding of its pathogenesis. Knowledge of the role of specific pro-inflammatory mediators in the development of CAD may enable the development of new therapeutic strategies for the treatment of CAD in the future, particularly the development of new antiplatelet drugs or the development of strategies to prevent stent occlusion. The complexity of the pathogenesis of CAD prompts us to continue further research to understand the numerous risk factors for this disease, the mechanisms affecting plaque development and regulating endothelial function, as well as the search for new therapeutic strategies.
Search Strategy and Inclusion Criteria: two independent authors (D.M. and A.P.) searched PubMed/MEDLINE/Embase from database inception until 1 February 2023, for GWAS and original studies regarding TGFβ and its receptor polymorphisms in heart impairments. The following search string was used in PubMed (TGFB* OR TGFβ* OR TGFBR* OR TGFβR* OR TGF-β* OR TGF-β* OR "transforming growth factor β"* OR "TGFß receptor") AND (SNP OR "single nucleotide polymorphism" OR polymorphism OR gene) AND ("coronary artery disease" OR CAD OR "coronary heart disease" OR CHD OR "ischemic heart disease" OR IHD OR "myocardial infarction" OR MI). The exclusion criteria were as follows: (i) lack of association with TGFβ, and (ii) SNP citation in more than one publication.