Precision Medicine and the future of Cardiovascular Diseases: A Clinically Oriented Comprehensive Review

Cardiac diseases form the lion’s share of the global disease burden, owing to the paradigm shift to non-infectious diseases from infectious ones. The prevalence of CVDs has nearly doubled, increasing from 271 million in 1990 to 523 million in 2019. Additionally, the global trend for the years lived with disability has doubled, increasing from 17.7 million to 34.4 million over the same period. The advent of precision medicine in cardiology has ignited new possibilities for individually personalized, integrative, and patient-centric approaches to disease prevention and treatment, incorporating the standard clinical data with advanced “omics”. These data help with the phenotypically adjudicated individualization of treatment. The major objective of this review was to compile the evolving clinically relevant tools of precision medicine that can help with the evidence-based precise individualized management of cardiac diseases with the highest DALY. The field of cardiology is evolving to provide targeted therapy, which is crafted as per the “omics”, involving genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics, for deep phenotyping. Research for individualizing therapy in heart diseases with the highest DALY has helped identify novel genes, biomarkers, proteins, and technologies to aid early diagnosis and treatment. Precision medicine has helped in targeted management, allowing early diagnosis, timely precise intervention, and exposure to minimal side effects. Despite these great impacts, overcoming the barriers to implementing precision medicine requires addressing the economic, cultural, technical, and socio-political issues. Precision medicine is proposed to be the future of cardiovascular medicine and holds the potential for a more efficient and personalized approach to the management of cardiovascular diseases, contrary to the standardized blanket approach.

The rectitude of medicine has always emphasized treating the patient rather than the disease. Today's medicine is honing itself to be more precise and patient-centric. Precision medicine is an innovative clinical approach that uses individual genomic, environmental, and lifestyle information to guide medical management. It has already revolutionized oncology [7]; CVDs form their current epicenter owing to their heterogeneity and multicausality, which leads to altered responses to treatment for each patient. The long-old treatment principles are succored by technological evolution in the "omics"-genomics, transcriptomics, epigenomics, metabolomics, proteomics, and microbiomics-which, together, help frame the position for future medicine [8]. "Omics" is aided by advanced "big data" analysis, which has helped in the development of in-depth clinical, biological, and molecular phenotyping, promoting better-integrated healthcare with early diagnosis, enhanced risk stratification, and disease management with the least possible side effects [9].
Most CVDs stem from a complex interplay of modifiable and non-modifiable factors which aggravate the set "omic" predisposition. In contemporary cardiology, most diagnostic criteria and therapeutic approaches rely on population-based studies, with less focus on approaches tailored to individualize patient treatment [10]. As such, a comprehensive analysis of phenotypes and the "omics" can help cluster patient groups sheathing disparities, simultaneously reinforcing patient-centric clinical care. Further, it will enhance patient quality of life (QOL) and help reduce complications via novel biomarkers, improved AI-assisted diagnostics, targeted therapeutics, and appropriate long-term risk assessment [11].
With technological advancements in data science and machine learning, the applicability of precision medicine in CVDs seems within reach, especially with the significant evolving literature in the pipeline over the past decade. As such, we aim to: 1. pool evolving clinically relevant information on precision medicine in cardiology, and 2. provide a comprehensive synthesis of the relevant literature to date. Thus, this will help with the evidence-based precise management of cardiac diseases and identification of possible challenges.

Precision Medicine and Cardiology
The advent of precision medicine has the potential to revolutionize the future of cardiovascular disease (CVD) healthcare via its application through "omics" in cardiology ( Figure 1). It empowers a physician to treat cardiac diseases on an individual basis-based on the patient's unique profile. Recent times have seen a growing body of literature underlining the application of precision medicine in cardiology. Table 1 presents a compilation underlining the clinical significance of all "reviews" published over the past decade regarding the same, while Tables 2-4 present an omic-stratified and disease-specific compilation of the literature for myocardial infarction, hypertension, and heart failure, respectively. As such, precision medicine in cardiology promises to improve health and revolutionize the management previously manifested in oncology. The evolution of precision medicine in cardiology has been remarkable ( Figure 2). Its applicability can have the best impact if enacted on the diseases with the highest impact (associated with the highest DALY), these include-myocardial infarction, hypertension, and heart failure.   The research focuses on how AI may bridge the gap between data-rich technologies and their deployment.
AI has the potential to use data-rich technologies in patient care, cardiovascular research, and health policy research. [12] 2 Michael Simeon et al. 2021 It will be possible to use human pluripotent stem cells (hPSCs) in cardiovascular clinical care by developing isogenic hPSC cell lines as a control for hPSCs with disease-specific mutations and a large number of hPSC lines with gene mutations, for the in vitro modeling of human diseases with complex genotypes and phenotypes.
hPSCs with disease-specific mutations have application for use in cardiovascular clinical care. [13] 3 Soni Savai Pullamsetti et al. 2021 Novel biomarkers can distinguish between left and right ventricular hypertrophy/failure.
Novel biomarkers can distinguish left ventricular hypertrophy/failure from right ventricular hypertrophy/failure, assess right ventricular disease severity, and potentially identify maladaptive changes in RV size, function, and architecture. [14] 4 Farwah Iqbal et al. 2021 In both healthy and pathological cardiovascular tissues, scRNA-seq has enabled the characterization of heterogeneous cell subpopulations with distinct genetic profiles.
These can shed light on the pathological mechanisms underlying atherosclerosis and suggest new potential treatments for calcific aortic valve disease. [15] 5 Concetta Schiano et al. 2021 Numerous ncRNAs, including miR-93, miR-340, miR-433, miR-765, CHROME, and large epigenetic changes in DNA methylation have been linked to atherogenesis in endothelial, smooth muscle, and macrophage cells.
In pro-inflammatory macrophages of the human carotid plaque, elevated HDAC9 was related to matrix metalloproteinase 1 (MMP1) and MMP2 production, while decreased HDAC9 was seen to promote resolution of inflammation and reverse cholesterol transfer, which may halt or reverse the disease process. [16] 6 Christian Schulte et al. 2020 Emerging biomarkers-cardiac myosin binding protein C, SNPs, and non-coding RNAs.
Newer biomarkers, adding more specificity to the diagnosis. [17] 7 Damien Gruson et al. 2020 In cardiovascular medicine, AI has shown promise as a tool to improve patient care and increase the efficiency of cardiologists. Genomics-customized diagnostic and therapeutic strategies. [20]  Sex/gender have not yet been completely studied in precision medicine; nonetheless, the prospect of using molecular data to better properly manage men and women with cardiovascular disorders has been recognized. [10]

Myocardial Infarction
Myocardial infarction (MI) is the leading cause of death globally-16% of total deaths. Its pathogenesis is peculiar in terms of its heterogeneous causality and largely varied genetic predisposition. MI is a critical medical emergency, true to its scientific adage "Time is equal to Myocardium". An opportune diagnosis with sensitive markers, optimal intervention, and the prevention of complications and recurrence is extremely consequential.
Precision medicine may find its applications in all these areas (Table 2) and may guide research and drug development to add to the pharmacotherapeutic armamentarium for this disease [29,30].   Branch-chain amino acids, medium-chain acylcarnitines, short-chain and long-chain dicarboxylacylcarnitines, and fatty acids were all independently linked to mortality. Fatty acids, on the other hand, were predictors of MI/death, as were short-chain, long-chain, and dicarboxylacylcarnitines. [49]

Metabolomics
The association between an individual's metabolic profile and MI has been explored a lot recently. Svati Shah et al. demonstrated an independent association between peripheral blood metabolites and the presence of CAD (coronary artery disease). They showed that simple metabolites such as factor 4 (branched-chain amino acid metabolites) and factor 9 (urea cycle metabolites) can help diagnose CAD [48,50]. Many new metabolites have recently been associated with CAD, including but not limited to: medium-chain acylcarnitines, short-chain dicarboxylacylcarnitines, long-chain dicarboxylacylcarnitines, long-chain acylcarnitines, short-chain acylcarnitines, medium-chain acylcarnitines, ketone related, cholesterol, lipids, fatty acids, glucose, and branched-chain amino acids [49]. Another recent observational study by Tanzilli G et al. found that low serum albumin levels are associated with adverse events in STEMI patients [46]. Uchino Y et al. and Ijichi C et al. showed that supplementing patients with BCAAs (branch-chain amino acids) showed increased serum albumin levels [51,52]. The combination of one or more newer metabolic markers can aid the diagnosis for a certain subset of patients, personalizing their care and increasing the sensitivity of MI detection.
Metabolomics can improve treatment options in addition to its predictive and diagnostic capabilities. Currently, the mainstay of acute coronary syndrome treatment is revascularization via emergency percutaneous coronary intervention (PCI). Tanzilli G et al. proposed ways to improve PCI by using early and prolonged glutathione infusions to blunt the inflammatory response via a chain of processes: 1. reduced NOX2 activation; 2. hsCRP generation; 3. TNF-levels; 4. cTpT release; 5. reduced neutrophil generation to protect myocardial cells; and 6. prevention of aberrant cardiac remodeling, allowing better left ventricular size and function post-PCI [46].

Genomics
Advances in genomics are helping the knowledge gained to be incorporated into the treatment and diagnosis of MI. An RCT by Scott R et al. discovered six genes (CNR2, DPP4, GLP1R, SLC5A1, HTR2C, MCHR1) that could be potentially used to develop drugs to treat type 2 diabetes or obesity without incremental CVD risk [45]. At present, post-PCI, dual antiplatelet therapy (DAPT) is initiated to reduce the risk of thrombosis or MI. DAPT usually consists of aspirin and clopidogrel. However, patients with loss-of-function CYP2C19 mutation have elevated chances of ischemic events if treated with standard DAPT therapy [44]. Pereira N et al. showed that the genotype CYP2C19-guided selection of P2Y12 inhibitor was superior to standard treatment concerning thrombotic events and resulted in a lower incidence of bleeding. They also proposed that a genotype-guided P2Y12 inhibitor such as ticagrelor should be selected for such patients [43].

Biomarkers
The development of biomarkers in precision medicine has been astounding and is still evolving. Mangion K et al. suggested the use of computational modeling to provide precise diagnostic care to patients and improve individual risk prediction. Computed biomechanical parameters such as contractility, stiffness, myofilament kinetics, strain, and stress provide information about LV function, thus determining the extent of cardiac damage and future prognosis [37]. Crea F et al. established biomarkers such as myeloperoxidase and hyaluronidase-2 to identify plaque erosion among ACS patients [35]. The CD44v6 splicing variant of the hyaluronan receptor was significantly higher in patients with plaque erosion than in plaque fissures, which can help detect silent myocardial infarctions. Adding to the evidence of the benefit of therapy individualization, Pasea L et al. concluded that the decision to prolong DAPT therapy should be assessed individually for each patient [38]. For assessment, prognostic models should look at demographics and behavior, cardiovascular history, non-cardiovascular history, biomarkers, and drugs. Tong G et al. explored the use of basic fibroblast growth factor (bFGF) in myocardial ischemia injury [33]. Oxidative stress plays a major role in myocardial injury, and bFGF can reduce oxidative stress by promoting the activation of NrF2 via the Akt/GSK3b/Fyn pathway, which reduces cardiomyocytes apoptosis and thus the infarct size to a larger extent, ultimately alleviating the heart injury. More studies are being conducted to study the cardioprotective effect of bFGF, which is certainly a novel preventive tool to treat MI. Further, Oni-Orisan A et al. found that epoxyeicosatrienoic acid (EET) elicits potent anti-inflammatory, vasodilatory, anti-apoptotic, pro-angiogenic, fibrinolytic, and smooth muscle cell anti-migratory effects with-in the cardiovascular system [41], and thus can be used in the treatment of acute MI. Wen Z et al. investigated the application of telmisartan-doped co-assembly nanofibers (TDCNfs), dual-ligand supramolecular nanofibers that synergistically counter-regulate RAS through targeted delivery, and presented it as an option for combined therapy against cardiac deterioration post-MI [32]. It reduces apoptosis, alleviates inflammatory response, and inhibits fibrosis to potentially mitigate post-MI outcomes [32]. Aside from MI, the treatment for myocardial infarction with no obstructive coronary arteries (MINOCA) also requires an investigation of cause to determine the patient-specific treatment [36]. Collectively, precision medicine helps to individualize the therapy by blending various diagnostic modalities such as ECG, cardiac enzymes, other biomarkers, echocardiography, coronary angiography, coronary vasomotion, and intravascular imaging techniques.

Hypertension
Hypertension (HTN) is a major modifiable risk factor for cardiovascular morbidity and mortality. Globally, it forms a major share of NCD (Non-communicable diseases); about 1.28 billion adults aged 30-79 years have HTN [53]. Due to its insidious onset, the majority of HTN cases remain unidentified and run a silent but catastrophic course. Early detection and optimal control can considerably reduce the cardiovascular burden associated with it. The pathophysiology of hypertension includes an interplay between genetic, physiological, biochemical, and environmental factors, which vary amongst individuals [54]. Precision medicine in hypertension can specifically identify patient subgroups with distinct disease causation mechanisms and their differential responses to diverse antihypertensive treatments. A compilation of recent evidence for the application of PM in hypertension is listed in Table 3. More adverse events (ADE) were seen in women compared to most antihypertensive drugs, however, aldosterone antagonists showed an increased incidence of ADE in men. [56]

Hypertension Pharmacogenomics
Genetic polymorphisms and response to diuretics: Current evidence identifies maximum target polymorphisms in response to thiazide diuretics. As opposed to the findings of the GenHAT study, the homozygous carriers of ACE I/D polymorphisms, ACE II, showed a small reduction in blood pressure response to hydrochlorothiazide, compared to homozygous carriers of ACE DD alleles [72,75]. Another study reported the response to thiazide diuretic in African Americans with SNP rs7297610 CC located on chromosome 12q15 [76]. Other genetic association studies showed that PRKCAA allele carriers had a better blood pressure response than GG homozygote patients. Another study targeting the UMOD gene polymorphism showed differential BP response to loop diuretics in hypertensives. They divided patients into two groups: 1. UMOD group (AA genotype), which showed a good response to loop diuretics, and 2. "Low" UMOD group, which exhibited a lower BP response to loop diuretics [70].
Genetic polymorphisms and response to other antihypertensives: Blood pressure regulation in humans involves at least 70 genes and presents with a complex set of individual differences. The human beta (1)-adrenergic receptor (ADRB1) has two common functional polymorphisms (Ser49Gly and Gly389Arg), which are associated with varied responses to metoprolol in essential hypertension. Furthermore, 49Ser389Arg/49Ser389Arg and 49Ser389Arg/49Gly389Arg polymorphisms have been seen as good responders, whilst 49Ser389Gly/49Gly389Arg and 49Ser389Gly/49Ser389Gly polymorphisms have been seen to be non-responders [77]. ADRB1 polymorphisms further revealed that C allele homozygotes showed a better response to metoprolol than G allele carriers [78]. Another study reported a possible link between nephrin (NPNS1) gene variants and a good response to the angiotensin receptor antagonist, losartan, in hypertensive patients [79]. Other less commonly studied genetic polymorphisms include GRK4 polymorphisms, namely R65L, A142V, and A486V. Studies have revealed that homozygote double variants of 65 L and 142 V require more aggressive antihypertensive therapy than the homozygous single variants or heterozygous carriers to achieve a target mean arterial blood pressure [71].
Another study aimed at finding rare and common variants associated with hypertension identified 31 novel genetic regions-rare missense variants in RBM47, COL21A1, and RRAS [67]. These allelic variations can lay the foundation for newer drug targets. Furthermore, by using polygenic risk score (based on the total number of genetic loci required to be assessed to estimate the risk of developing a disease), five potential loci (PKD2L1, SLC12A2, CACNA1C, CACNB4, and CA7) have been reported as novel therapeutic targets for hypertensive therapy. More than 1000 hypertension-associated loci have now been identified, with drug-target genes expected to expand in the future [65]. Loganathan et al. identified other hypertension-associated loci and SNPs (Single Nucleotide Polymorphisms) such as RAAS signaling and Cytochrome P (CYP) genes, which govern individual and population differences in drug tolerance [61]. In 2018, the GWAS catalog found seven candidate genes with an established pathophysiological role in hypertension, namely ACE1, ACE2, ADRB1, ADRB2, MME, CACNA2D2, and UMOD [63].
Recently, newer drugs such as Rostafuroxin have disrupted the binding of mutant alpha-adducin and the ouabain-activated Na-K pump with the Src-SH2 domain, in rats as well as in human cell cultures, posing as potential antihypertensives [80]. Evidence also suggests that riboflavin, a co-factor for MTHFR, also has an antihypertensive effect via the MTHFR 677TT genotype-specific mechanism [81]. Another potential choice is aldosterone, which targets the epigenetically modified sodium channel epithelial 1α subunit (SCNN1A). It hypomethylates the histone protein (H3) at lysine 79 (H3K79) at subregions of the promoter in a subgroup of hypertensives [68].

Hypertension Metabolomics
The effect of metabolomic factors in hypertension is another limb to study for the precision medicine domain. The most commonly identified metabolomic factors include sex, gender, race, and plasma renin activity; their response to antihypertensives was studied. A growing body of literature found that hypertensive women have lower plasma renin activity as opposed to men, and thus were more responsive to diuretics and Calcium Channel Blockers (CCB) as compared to angiotensin-converting enzyme inhibitors (ACEI) and beta blockers [82]. Another study comparing renin profiling-guided (plasma renin activity) treatment to clinical judgment in uncontrolled hypertensive patients showed equal or better hypertension control while using the renin profile-guided treatment approach [83]. Adverse drug events following antihypertensive medications were more common in females, though a notable exception was aldosterone antagonists [56]. It has been shown that African Americans respond to diuretics and CCBs better than ACEIs, probably due to their RAAS genes and increased plasma level in conjunction with suppressed plasma renin activity. These factors are hypothesized to cause variations in drug responses, Additionally, the African race is predisposed to severe hypertension, courtesy of their enhanced vascular contractility and salt-retaining capacity [59]. Furthermore, a raised sympathetic tone amongst obese patients responds better to beta blockers [57]. Ongoing clinical trials are investigating biochemical pathways, pharmaco-metabolomics, and pharmacogenomics in antihypertensive drug responses.

Resistant Hypertension
A recent experimental study by Bazzell et al., on hypertension transcriptomics, measured mRNA transcripts in the human urine supernatant to detect mineralocorticoid receptor activation and predict its response to mineralocorticoid receptor antagonists in hypertensive patients. The results of the RNA sequencing of urine extracellular vesicles match those of the human kidney. Alterations in mRNA in urine supernatant were associated with changes in human endocrine signaling (MR activation). These findings can aid in individualizing pharmaceutical therapy in patients with mineralocorticoid signaling abnormalities, such as resistant hypertension. These findings could be utilized to noninvasively discover possible indicators of abnormal renal and cardiorenal physiology [74]. In light of the current evidence available, it can be concluded that what we know about the pharmacogenomics of hypertension is only the tip of the iceberg, and finding more precise targets and therapies is imperative.

Heart Failure
Heart failure (HF) is one of the most challenging cardiovascular disorders to manage. Despite recent advances in symptom management and the possibility of halting disease progression, the structural and functional impairment associated with HF is irreversible. It is a disorder with heterogenous causality and a strong genetic predisposition. Thus, risk assessment, prevention, and early screening are key in its management. Precision medicine poses to fill the existing gap in preventive medical management and risk stratification (Table 4).  Copeptin can be used to predict a 5-year all-cause mortality in patients with heart failure. [92] 11 Glick D et al. 2013 Observational Study cTnT and NT-proBNP.
Systolic dysfunction, incident HF, and CV death can all be predicted from the long-term trajectory of cardiac troponin T(cTnT) and N-terminal pro-brain natriuretic peptide(NT-proBNP) in older adults without HF.
[50] Patients with HFpEF had higher levels of mediumand long-chain acylcarnitines and ketone bodies than those with HFrEF.
[98] Increased mortality risk over the long run was associated with elevated TMAO levels. [102]

Genomics
The relevance of common genetic variation in the susceptibility to and heritability of HF has recently been investigated through large-scale genome-wide techniques. F Dominguez found that DCM (dilated cardiomyopathy) caused by mutations in BAG3 has high penetrance in carriers >40 years of age and increases the risk of progressive heart failure [103]. Shah S et al. found that loci KLHL3 and SYNPOL2-AGAP5 are implicated in HF, and also BAG3 and CDKN1A are associated with LV systolic dysfunction [104]. Maurer, MS et al., documented that tafamidis reduced death and hospitalizations associated with cardiovascular events in patients with transthyretin-associated cardiomyopathy [85]. Dumeny et al. found that NR3C2, which codes the target protein of spironolactone, or CYP11B2, which is involved in aldosterone synthesis, was associated with better spironolactone response in diastolic HF patients [84].

Proteomics
Glick D found that among older adults without HF with initially low cardiac troponin T(cTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP), the long-term trajectory of both biomarkers predicts systolic dysfunction, incident HF, and CV death [93]. Pozsonyi et al. defined that copeptin predicted 5-year all-cause mortality in heart failure patients. Drum et al., found that plasma TB4 is elevated in women with HFpEF, which predicts mortality independent of clinical risk factors and NT-proBNP in women with HF [92]. Feng SD et al. found that β-endorphin (β-EP) and brain natriuretic peptide BNP have both high specificity and sensitivity to detecting early acute left heart failure and atrial fibrillation in patients [89]. Pellicori et al., investigated the effects of spironolactone on the serum markers of collagen metabolism and cardiovascular structure and function in people at risk of developing HF, as well as the potential interactions with a marker of fibrogenic activity, galectin-3 [88]. G Michael Felker found that Hs-cTnT was elevated in the majority of acute heart failure (AHF) patients. Baseline, peak, and peak change hs-cTnT were associated with worse outcomes, mainly 180-day cardiovascular mortality [91]. Shah et al. found that MR-proANP seems accurate in diagnosing acute decompensated heart failure (ADHF), whilst both mid-regional pro-atrial natriuretic peptide (MR-proANP) and mid-regional pro-adrenomedullin (MR-proADM) acclimatize prognosis [50]. Sjoukje I Lok found that growth differentiation factor 15 (GDF) levels are increased in patients with HF and correlate with the extent of myocardial fibrosis, hence they are used as a biomarker for cardiac remodeling [95]. Natalia Lopez-Andrès et al. found that increased galectin-3 (Gal-3) and N-terminal propeptide III procollagen (PIIINP), and low metallic metalloproteinase-1 (MMP-1) are associated with adverse long-term heart failure outcomes [96]. Julio Núñez et al. suggested that CA125 is a surrogate of fluid overload, hence it is potentially valuable for guiding decongestion therapy, and a CA125-guided diuretic strategy improved eGFR in patients with acute heart failure with renal dysfunction [87]. Hanna K Gaggin et al. found that the soluble suppression of tumorigenesis (sST2) measurement identifies patients with chronic heart failure in whom higher beta-blocker doses may be beneficial [94].

Metabolomics
Metabolomics is the study of tiny, organic compounds within metabolic pathways. With the improvement of technology, nuclear magnetic resonance, gas chromatography, and mass spectrometry have enabled the discovery and analysis of enormous databases of metabolites implicated in heart failure. The molecular pathways implicated in cardiac failure show that a metabolic transition occurs in the failing myocardium. Metabolic profiles of patients with systolic heart failure have been developed by examining patient serum and breath. These profiles can be used clinically for diagnosis and prognosis in this population [105]. Du Z et al. suggested that 3-hydroxybutyrate, acetone, and succinate were elevated in patients with HFrEF and can predict outcomes in patients with HF [99]. Additionally, Hunter WG et al. found that levels of metabolites in medium-and long-chain acylcarnitines and ketone bodies are higher in patients with HFpEF compared to patients with HfrEF [98]. Wang Li et al. suggested that patients with HFrEF with ischemic causes had higher levels of lactate, alanine, creatinine, proline, isoleucine, and leucine in plasma than healthy subjects [100]. Desmoulin F et al. found that an increased ratio of plasma lactate to total cholesterol is a significant predictor of 30-day mortality in patients with acute decompensated HF (ADHF) [101]. Ahmad T et al. found that increased circulating long-chain acylcarnitine metabolite levels in patients with chronic HF were associated with adverse clinical outcomes [106]. Treating patients with end-stage HF with long-term mechanical circulatory support resulted in significantly decreased circulating long-chain acylcarnitine levels, suggesting that levels of long-chain acylcarnitine can be used to prognosticate HF outcomes. Olivotto

Precision Medicine and Aortic Diseases
The genetic basis of aortic diseases has long been known. Twenty percent of patients with thoracic aortic aneurysms and aortic dissection either have a family history or are associated with a syndrome such as Marfan syndrome, vascular Ehlers-Danlos syndrome, and Loeys-Dietz syndrome. Mutations of ACTA2, MYLK, and MYH11 have been found to be associated with aortic disease [107]. Furthermore, genetic variants of genes FBN1, SMAD3, and ACTA2 have also been shown to cause either syndromic or non-syndromic thoracic aortic aneurysm and dissection [108,109]. The recent updates have added evidence to support the role of pathogenic variants in COL3A1, FBN1, MYH11, SMAD3, TGFB2, TGFBR1, TGFBR2, MYLK, LOX, and PRKG1, predisposing to hereditary thoracic aortic disease. The above insight into the genes associated with aortic diseases can be added to the clinical database. Patients with clinical suspicion can be tested for genetic predisposition, and the results can be saved on their EHR (electronic health records) to help personalize their treatment [109,110].

Precision Cardiology and Artificial Intelligence
The evolution in tools of artificial intelligence (AI) and machine learning models has made it possible to incorporate multimodal and multidimensional omics, which promise enhanced diagnosis and treatment modalities for tomorrow. AI has the potential to usher in the next medical revolution and enhance precision medicine to stratify patients according to their phenotypic characteristics. The incorporation of AI into laboratory medicine and diagnostics can aid in better performing screening and confirmatory tests. AI can be used to generate insights by integrating powerful computing and analysis, thus allowing the system to think, learn and empower clinical decision-making with augmented intelligence [111]. The advances in artificial intelligence and data science have allowed for the automation of various critical thinking processes in medicine, including diagnosis, risk classification, and management, easing the workload of doctors and decreasing the possibility of making errors. It has many different uses in the workplace and the care of patients, from making doctors' life easier to facilitating research. As a field that relies heavily on abstract reasoning and interpretation, cardiology is a natural fit for the introduction of AI. Clinical evaluation, imaging interpretation, diagnosis, prognosis, risk stratification, precision medicine, and therapy for various cardiac diseases have all benefited from the use of artificial intelligence. Clinical diagnostic accuracy, especially for pediatric cardiac diseases, has been bolstered by the application of neural networks and machine learning. AI has helped increase the diagnostic utility of imaging modalities such as cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiograms. In pediatric cardiac surgeries, the introduction of AI-based prediction algorithms greatly improves post-operative outcomes and prognosis. Important clinical results can be used with suitable computer algorithms for risk classification and predicting treatment outcomes [112]. Although artificial intelligence has made medicine more precise and accurate, it still has a long way to go and has some serious limitations. The acceptability is hampered by difficulties such as a lack of adequate algorithms and their infancy, a lack of physician training, a concern of over-mechanization, and dread of missing the "human touch". The generalizability of algorithms developed in standardized research environments employing high-quality data to heterogeneous real-world populations must be rigorously evaluated. Biases in training data, model overfitting, insufficient statistical correction for multiple testing, and limited accountability around the processes by which deep learning algorithms reach their outcome ("black box" systems) are just a few of the pitfalls of AI that can have serious consequences for the patients, and they necessitate careful consideration by researchers, clinicians, and regulatory bodies. Despite the challenges, we believe that AI will be the perfect assistant to clinicians in directing adult and pediatric cardiology in the future [112,113]. Table 5 underlines the clinical applicability of AI in Precision cardiology. Table 5. Clinical applicability of artificial intelligence in precision cardiology [11,111].

Cardiovascular Pharmacology and Precision Medicine
The clinical trials help us judge and predict drug outcomes with the best representative samples that evolve with phases of clinical trials, but the genetic variations, environmental factors, and idiosyncrasies are still strong enough to cause a fair number of adverse events and treatment failures.
The evolving approach of precision medicine advocates the individualization of therapy, directed by local regulations and guidelines based on novel markers and gene targets, which can help us define reasons for failure, thus evolving a better tailored patient-centric approach to curing diseases. Numerous examples of genetic diversity and DNA variants determining the response to a drug are already in common parlance and are continually used to modify treatment. For instance, warfarin, the most commonly prescribed anticoagulant medicine, has a narrow therapeutic window and has shown wide interindividual variations [114,115]. Studies document around 10% to 50% variability in warfarin dose requirements per the patient genotype, notably SNPs in CYP2C9 (CYP2C9*2, CYP2C9*3) and VKORC1 (rs9923231) [115][116][117]. Additionally, genetic variants have been identified that show differences in the response to β-blockers (ADRB1, ADRB2, GRK5, GRK4); angiotensin-converting enzyme inhibitors (ACE, AGTR1); diuretics (ADD1, NPPA, NEDD4L); and Calcium Channel Blockers (CACNB2, CACNA1C) [26]. Further, clopidogrel, an antiplatelet medicine, is a P2Y12 inhibitor, and it shows great inter-individual variability-the clopidogrel non-responders [106,118,119]. The genetics purported behind this involve loss-of-function alleles in CYP2C19 (CYP2C19*2 and CYP2C19*3), which is thought to be associated with poor drug responsiveness, whilst the gain-of-function allele CYP2C19*17 is associated with increased bleeding risk [119,120]. Now, the COAG (Clarification of Optimal Anticoagulation through Genetics) trial and the EU-PACT (European Pharmacogenetics and Anticoagulant Therapy-Warfarin) have produced RCTs advocating genotype-guided drug dosing for warfarin, which prescribe dosing based on CYP2C9 and VKORC1 genotyping [121,122]. Precision medicine aided by pharmacogenomics and pharmacogenetic profiling poses to refine this area, bringing in next-generation care using enhanced phenotyping for disease stratification [123].

Precision Cardiology and the Omics
Cardiovascular research is increasingly a part of the vast, digital, data-driven world made possible by the plethora of molecular, physiological, and environmental data generated by a variety of "omics" technologies. Clinical research and practice will advance from focusing on the "typical patient" to gaining a more sophisticated understanding of specific individuals and populations [124]. With this review, we underline the key areas where the domains of precision medicine (Figure 3) can be implemented in cardiology diagnostics, stratification, therapeutics, and prognostics, and compare its novelty to the existing norm. Precision medicine empowers a physician to treat cardiac diseases individualistically, based on the patient's unique genetic, metabolic, proteomic, or symptomatic profile. The strength of precision medicine lies in the synthesis and analysis of "data" that is rapidly changing from standard clinical, imaging, and laboratory testing to next-generation sequencing, metabolomics, and proteomic studies [8]. Modern-day cardiology is evolving to adopt new genetic, molecular, metabolic, and proteomic tools. In the case of myocardial infarction, newer biomarkers such as bFGF, hsCRP, hs Troponins, and miRNAs have emerged, which have great potential for detecting disease processes with more accuracy and at an earlier stage. Additionally, recent advances have shown that metabolites (such as acylcarnitines, fatty acids, BCAAs) are strong predictors of cardiovascular diseases and can be paired with standard metabolomics such as troponin and lipid levels to promptly predict the occurrence of MI/death in patients with heart disease. Similarly for heart failure, various markers such as 3-hydroxybutyrate, acetone, succinate 2-oxoglutarate, pseudouridine alanine, creatinine, proline, isoleucine, and leucine in plasma have shown usability for the prediction of outcome; various genes have also been identified that can serve the purpose of early risk stratification in the near future [125]. Despite its applicability challenges, genomics has contributed greatly to our understanding of the variability of disease processes, risk propensity, and response to treatment. Future advancements in genetic data generation and tools of application will enable its implementation in the routine management of common diseases. Next-generation sequencing and genome-wide association studies using a variety of computational biology technologies offer hope for improving the diagnosis and treatment of cardiovascular diseases. The mass spectrophotometric characterization of human cardiac proteins may expand the applicability of proteomics methods to CVD. Furthermore, transcriptomics approaches reveal novel information about gene expression, and metabolomics represents the tail end of multi-omics efforts to tackle CVDs early on. The "omics" can thus play a core role in the individualization of therapy in cardiac diseases [125].

Evolving Understanding of the Immune Cells and the Future of Precision Cardiology
The rupture of atherosclerotic plaques appears to be the leading primary cause of CVD. Atherosclerosis, the leading cause of CVD, is a chronic inflammatory condition in which immuno-competent cells in lesions produce primarily pro-inflammatory cytokines.
One key target for atherogenic immune responses is heat shock proteins, with other mediators being: pro-inflammatory cytokines, chemokines, and lipid mediators [126].
The evidence has evolved significantly in this domain, highlighting role of immune cells in various cardiac diseases. To cite an example, in the pathophysiology of heart failure, regulatory T cells (Tregs) play a role in immunoregulation and tissue healing. Tregs help the heart by limiting excessive inflammatory response and encouraging stable scar

Evolving Understanding of the Immune Cells and the Future of Precision Cardiology
The rupture of atherosclerotic plaques appears to be the leading primary cause of CVD. Atherosclerosis, the leading cause of CVD, is a chronic inflammatory condition in which immuno-competent cells in lesions produce primarily pro-inflammatory cytokines. One key target for atherogenic immune responses is heat shock proteins, with other mediators being: pro-inflammatory cytokines, chemokines, and lipid mediators [126].
The evidence has evolved significantly in this domain, highlighting role of immune cells in various cardiac diseases. To cite an example, in the pathophysiology of heart failure, regulatory T cells (Tregs) play a role in immunoregulation and tissue healing. Tregs help the heart by limiting excessive inflammatory response and encouraging stable scar formation in the early stages of cardiac damage. However, Treg phenotypes and functions are altered in chronic heart failure by these cells being mutated into antiangiogenic and profibrotic cells. In addition, tumour necrosis factor (TNF)-and tumour necrosis factor receptor (TNFR1) expression rises in HF-activated CD4+ T cells. Immunotherapy for heart failure is now conceivable because of advances in next-generation sequencing and gene editing technologies [127][128][129][130][131].
The majority of pharmaceutical therapies have focused on changing hemodynamics (lowering afterload, regulating blood pressure and volume) or cardiac myocyte function. However, significant contributions of the immune system to normal cardiac function and damage response have lately emerged as attractive research fields. Therapeutic approaches that harness the strength of immune cells have the potential to open up new therapeutic pathways for various cardiac diseases, and these form important targets for providing individualized therapy by exploiting the "omics" and tailoring therapy in line with the immune makeup of the patients [126,131,132].

Challenges to PM in Cardiology
Various experts question the applicability and accessibility of precision medicine, believing that it lacks a global impact on cardiovascular disease management and will merely serve a small group of patients in the developed world, relegating its role to a selected niche only. However, this concern seems implausible due to the limited literature attesting to the validity of this claim [133]. Another challenge is the dearth of acceptability and neophobia to the growing methods both by the providers and the recipients. Precision medicine was historically considered complex, expensive, and inaccessible to underserved populations.
Genomics has undoubtedly accelerated the discovery of mutations underlying cardiac diseases. Exploring genetic sequences, assembly, and the identification of genes is still evolving and seems to have a promising future, although the technology needed to translate this data into clinical interpretation and practice is still challenging. While major research work is focused on the exome (protein-coding DNA), another area of interest in present-day genetic sequencing is the non-protein-coding DNA and its impact on major clinical diseases, which are largely under-discovered. Moreover, the research/development of testing for genetic variants associated with the risk of developing a certain cardiac disease and its role in prevention is encouraging, but affordability and feasibility remain a concern even in developed countries [133,134].
Another challenge to PM in cardiology is the education and training of the stakeholders, including the providers and the general public [135,136]. Education must be aimed at training to use an integrated system approach, allowing healthcare providers and patients to be in congruency to accept and trust the new evolving techniques [136]. An added challenge is the apparent lack of available cohorts with relevant phenotypes to demonstrate statistically meaningful associations. Moreover, the absence of a replication cohort and differences in epigenomic patterns also make research difficult.

Future Perspectives and Conclusions
Precision medicine is the future of medicine and holds promise for the more efficient management of cardiovascular diseases, owing to their gradual onset and heterogeneous, multimorbid, and chronic nature. The pathogenesis of these diseases may begin decades before any ultimate disease manifestation. Therefore, the use of precisely targeted tools for diagnosis and personalized treatment can revolutionize management by allowing the prevention, early diagnosis, and tailored treatment of cardiovascular diseases. Precision medicine is still an evolving field and many of the technologies needed for its implemen-tation are in nascent stages. Moreover, the research and data on precision medicine are limited because of the ethical, social, legal, and economic issues, which may have produced an unavoidable bias in this review as well. This review explored the literature on precision medicine in cardiology and tried to outline and summarize the most clinically relevant sections of the evolving field. As we evolve in our capacity and infrastructure to employ tools exploring the genomics, proteomics, and metabolomics of cardiovascular diseases, we stand to see a future where a more precise therapy tailored to the needs, demands and limitations of an individual patient would no longer be a dream but a responsibility. The future of cardiology is here; we need to assimilate, adapt and make it more accessible by educating the providers about the evolving field and making infrastructure more equitable to the public.