Pharmacogenomics of Cardiovascular Diseases: The Path to Precision Therapy

Abstract

Cardiovascular diseases (CVD) represent a substantial global health burden, leading to significant morbidity and mortality rates. However, the efficacy and safety of CVD therapies are markedly influenced by individual variability in drug responses and adverse reactions, often attributable to genetic factors. This chapter discusses how pharmacogenomics impacts the safety and efficacy of cardiovascular therapies through advanced genetic testing methods, like genome-wide association studies, polygenic risk scores, and multi-omics analyses. Additionally, the chapter addresses challenges and future perspectives, with a focus on the role of artificial intelligence and machine learning in integrating pharmacogenomics and genotype-based personalized interventions into the routine CVD care to improve long-term health outcomes.

1. Introduction

Cardiovascular diseases (CVD) remain the leading cause of global disability and mortality, responsible for about 38% of all deaths and straining healthcare systems. These disorders influence the heart and blood circulations, including coronary heart disease, peripheral arterial disease, cerebrovascular disease, and other conditions [1]. However, the response to CVD drugs varies significantly among individuals, leading to suboptimal outcomes and potential adverse reactions. Genetics plays a vital role in drug response variability, with specific CVD therapies showing up to 90% dependence on genetic factors [2]. Given the multifactorial nature of CVD, which involves genetic, environmental, and lifestyle influences, precise and personalized therapeutic strategies become essential for effective management [3].

In recent years, pharmacogenomics has emerged as a promising approach in cardiovascular therapy, offering new opportunities for safe and effective targeted therapies. The field of pharmacogenomics focuses on understanding how genetic variations influence an individual’s response to medications. Genetic variations primarily alter cardiovascular drug’s concentration (pharmacokinetics), mechanism of action (pharmacodynamics), and the underlying mechanism of CVD [2]. By identifying the genetic factors affecting drug metabolism, receptor interactions, and signaling pathways, pharmacogenomics paves the way for tailored cardiovascular treatment regimens aligned with an individual’s genetic profile [4]. This holds particular significance due to the widespread prevalence of CVD, affecting approximately 523 million individuals [1].

The significant advancements in genome-wide association studies (GWAS) and ‘omic’ technologies, such as proteomics, metabolomics, and transcriptomics, have identified numerous genetic loci linked to cardiovascular conditions, deepening our understanding of the genetic basis of CVD [5, 6]. This information has facilitated pharmacogenomics research, bringing us closer to the goal of precision medicine in managing the global health challenges caused by these diseases [7]. This chapter focuses on utilizing genetic information to optimize standard cardiovascular therapy and improve patient outcomes. It provides comprehensive discussions on key cardiovascular pharmacogenomics, including antithrombotic (antiplatelets and anticoagulants), lipid-lowering agents (statins), and antihypertensive treatments (beta-blockers, angiotensin-converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARBs), vasodilators, and diuretics), along with future directions in this field.

2. Cardiovascular pharmacogenetics

The influence of genetic makeup on individual response to cardiovascular medications has gained substantial recognition. By 2023, genetic information had been integrated into the Food and Drug Administration (FDA, https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling) approved labeling for around 15 cardiovascular treatments. The Clinical Pharmacogenetics Implementation Consortium (CPIC, https://cpicpgx.org/genes-drugs/) [8] and Dutch Pharmacogenetics Working Group (DPWG, https://www.knmp.nl/dossiers/farmacogenetica) [9] provide authoritative guidance through the Pharmacogenomics Knowledgebase (PharmGKB, https://www.pharmgkb.org) on integrating genetics into drug prescribing [10]. These organizations offer evidence-based guidelines for specific gene-drug pairs, optimizing pharmacotherapy decisions in the clinical practice. CPIC and PharmGKB categorize evidence from A (strong) to D (limited/conflicting) [8, 10]. CPIC has issued level A guidelines for several cardiovascular drugs, including clopidogrel, warfarin, and simvastatin [8]. Table 1 provides an overview of pharmacogenetic biomarkers and clinical recommendations derived from prevalent guidelines for cardiovascular medications.

Medications	Biomarker	SNP ID	Genotype	Effect on response/safety	Clinical recommendations	Guideline
Antiplatelets
Acetylsalicylic acid (Aspirin) & abciximab	PTGS1	rs3842787	T	Reduce response	Monitor for thrombosis in patients with resistance.	PharmGKB
	PTGS2	rs20417	C
	GPIIIa PlA	rs2317676	AG	Reduce response & increase CVE	Consider higher doses of Aspirin & abciximab.
	ITGA2	rs1126643	CT/TT	Reduce response	Limited evidence & require further validation.	—
		rs1062535	AA/AG
	CYP2C9	—	*1	Increase response	Consider lower doses of Aspirin & abciximab.	CPIC
		rs4244285	*2
		rs4986893	*3
	P2Y1R	rs1065776	TT/CT		Limited evidence & require further validation.	PharmGKB
Clopidogrel	CYP2C19	rs4244285	*2	Reduce response & increase CVE	Consider alternative antiplatelet agents, such as prasugrel or ticagrelor.	CPIC, DPWG, & FDA “Black Box”
		rs4986893	*3
		rs3758581	*17	Increase response & AE (bleeding risk)	Limited evidence & require further validation.
		rs28399504	*4	Reduce response & increase CVE
		rs56337013	*5
		rs72552267	*6
		rs72558186	*7
		rs41291556	*8
		rs17884712	*9
		rs140278421	*10
		rs6413438	*22
		rs118203757	*24
		rs12769205	*35
	ABCB1	rs1045642	AA/AG		Inconsistent & require further validation especially on adverse CVE.	PharmGKB
	PON1	rs662	TT			CPIC
	CYP3A5	rs776746	*3	Inconsistent effect	Require further validation.	CPIC
	CYP2C9	rs1799853	*2	Reduce response	Limited evidence & require further validation.	CPIC
		rs1057910	*3	Reduce response & increase CVE		CPIC
Prasugrel & ticagrelor	CYP2C19	rs3758581	*17	Increase response & AE (bleeding risk)		DPWG & FDA
	CYP3A4	rs35599367	*22	Inconsistent effect		—
	CYP4F2	rs3093135	TT	Inconsistent reduce response & increase CVE (MI & CAS)
	SLCO1B1	rs113681054	C	Inconsistent increase response
Anticoagulants
Warfarin	CYP2C9	rs1799853	*2	Increase response & AE	Consider lower warfarine dose & more frequently INR monitoring.	CPIC, DPWG, & FDA
		rs1057910	*3
		rs12777823	*8		Consider 15–30% lower warfarine dose & more frequently INR monitoring in African.
		rs56165452	*4
		rs28371686	*5
		rs9332131	*6
		rs28371685	*11
	CYP2C19	rs11188082	T			—
	CYP4F2	rs2108622	*3	Reduce response	Consider 5–10% higher warfarine dose in European & Asian.	CPIC
	VKORC1	rs9923231	TT	Increase response & AE (bleeding risk)	Consider lower warfarine dose & more frequently INR monitoring.	CPIC, DPWG, & FDA
		rs749671	G
		rs7294	CT/TT
		rs9934438	AG/GG
Acenocoumarol	CYP2C9	rs1799853	*2	Increase response & AE	Consider lower acenocoumarol dose & more frequently INR monitoring, especially with NSAID.
	CYP4F2	rs2108622	*3	Reduce response	Consider higher acenocoumarol dose.	DPWG
	VKORC1	rs9923231	AG/GG	Increase response & AE (bleeding risk)	Consider 50% lower acenocoumarol dose & more frequent INR monitoring, especially with NSAID.
Heparin	ITGB3, FCGR2A, TDAG8, PTPRJ, IL10, KIR2DS5, HLA-DRA, HLA-DR*3	—	—	Increase risk of heparin-induced thrombocytopenia	Limited evidence & require further validation.	PharmGKB
Direct oral anticoagulants (apixaban, dabigatran, edoxaban, & rivaroxaban)	ABCB1	rs4148738	CT/TT	Reduce response & increase CVE (thromboembolism)
		rs1045642	AA
	CES1	rs2244613	GT/TT	Increase response AE (bleeding risk)
	AGT	rs699	TT	Reduce response
	PRKCQ	rs500766	CT/TT	Reduced AE
	ETV6	rs2724635	AA/AG
	CYP3A4, CES1, SLCO1B1, ABCG2, & SULT	—	—	Increase response & AE	Avoid combining this medication with potent CYP3A4/5 inhibitors, require further validation.	FDA
Lipid-lowering agents
Pravastatin	KIF6	rs20455	AA/GA	Increase response & reduce CVE	Limited evidence & require further validation.	PharmGKB
	SLCO1B1	rs4149015	*17	Increase response & AE (statin-induced myopathy)	Consider patient genotyping prior to dosing with lowering the initial dose.	CPIC, DPWG, & FDA
		rs2306283
		rs4149056
Atorvastatin, simvastatin, rosuvastatin, pravastatin, & lovastatin, pitavastatin		rs4149056	*5
		rs2306283	*14
		rs4363657
		rs4149056
		rs2306283	*3
		rs11045819
Fluvastatin		rs4149056
	CYP2C9	rs1057910	*3	Increase response	CPIC guidelines now cover fluvastatin dosing.	CPIC
Atorvastatin, simvastatin, rosuvastatin, pravastatin, & lovastatin, pitavastatin	CYP3A5	rs776746	*3	Inconsistent effect	Limited evidence & require further validation.	PharmGKB
	CYP3A4	rs35599367	*22
		rs55951658	*4
		rs4986910	*3	Reduce response
		rs2740574	*1B
		rs2242480	*1G
	CYP3A7	rs45446698	G
	ABCB1	rs1128503	AA	Increase response
		rs2032582
		rs1045642
	ABCG2	rs2231142	CC		Consider lower statin dose, alternatives, or combination therapy (e.g., statin + ezetimibe).	CPIC
	UGT1A	rs887829	*2	Reduce response	Limited evidence & require further validation.	PharmGKB
	HMGCR	rs17244841	AT	Reduce response
	CLMN	rs8014194	A	Variable (increase) response
	APOE	rs429358	E4	Increase response & reduce CVE
		rs7412	E2
	CETP	rs708272	AA	Reduce response & increase CVE
Beta-blockers
Atenolol, bisoprolol, carvedilol, metoprolol, nebivolol, & propranolol	ADRB1	rs1801252	AG/GG	Reduce response & increase CVE	Limited evidence & require further validation.	PharmGKB
		rs1801253
	ADRB2	rs1042713	GG
		rs1042714	CG/GG
		rs1800888	AA
	ADD1	rs4961	GT/TT	Increase response & reduce CVE
	NPPA	rs5065	GG			—
		rs4149601	AA			PharmGKB
		rs292449	CG/GG
	NEDD4L	rs75982813	AG/GG
	GRK5	rs2230345	TT/AT	Increase response & reduce CVE
	CYP2D6	rs1135840	GG	Increase response & AE (bradycardia)		FDA
	CYP2D6	rs1065852	AG/GG	Reduce response
Direct-acting vasodilators
Hydralazine, minoxidil, & sodium nitroprusside	NAT2	—	*4	Reduce response	Limited evidence & require further validation.	PharmGKB
		rs1801280	*5D	Increase response & AE (lupus-like symptoms)
		rs1799930	*6B
		rs1799931	*7A
		rs1801279	*14B
Renin-angiotensin system inhibitors
ACEI (saptopril, enalapril, & lisinopril)	ACE	rs4646994	DD	Reduce response & increase CVE (mortality)	Limited evidence & require further validation.	PharmGKB
	AGT	rs699	AA	Reduce response & increase CVE (blood pressure)
	PRKCQ	rs500766	CC	Increase AE (angioedema)
	ETV6	rs2724635	GG
ARBs (candesartan, losartan, & olmesartan)	AGTR1	rs5186	AA	Increase response
Diuretics
Aldosterone antagonist (Spironolactone)	ADD1	rs4961	GG/GT	Increase response & reduce CVE (MI)	Limited evidence & require further validation.	PharmGKB
Loop diuretics (Furosemide)
Thiazide diuretics (Hydrochlorothiazide, Chlorthalidone & indapamide)
	NPPA	rs5065	GG
		rs4149601	CC/CG	Increase response
		rs292449
	NEDD4L	rs75982813	AG/GG
	PRKCA	rs16960228	A
	YEATS4	rs7297610	CC
Antiarrhythmics
Amiodarone, flecainide, sotalol	KCNE1	rs1805128	T	Increase AD (DITdP risk)	Limited evidence & require further validation.	PharmGKB
	SCN5A	rs7626962	GT/TT	Reduce response & increase CVE (arrhythmias)
	NOS1AP	rs10919035	T	Increase AD (ventricular arrhythmia & QT prolongation risk)
Procainamide	NAT2	—	—	Increase response & AE		FDA
Amiodarone, Sotalol flecainide, quinidine, & propafenone	PITX2	rs10033464	GT/TT	Reduced response & CVE (atrial fibrillation recurrence)		PharmGKB
	ADRB1	rs1801253	CC	Increase response
Quinidine & propafenone	CYP2D6 & CYP3A4	—	—	Increase risk (pro-arrhythmia)	Consider patient genotyping prior to dosing with lowering the initial dose.	DPWG & FDA

Table 1.

Pharmacogenomics and clinical recommendations of cardiovascular medications based on common guidelines.

The sources of drug-gene interactions are from common pharmacogenomic guidelines, including PharmGKB (https://www.pharmgkb.org), CPIC (Clinical Pharmacogenetics Implementation Consortium, https://cpicpgx.org/genes-drugs/), DPWG (Dutch Pharmacogenetics Working Group, https://www.knmp.nl/dossiers/farmacogenetica), & FDA (U.S. Food & Drug Administration, https://www.fda.gov/drugs/science-&-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling). Abbreviations: CVE, cardiovascular events; AE, adverse effect; DITdP, drug-induced Torsade de Pointes; MI, myocardial infarction; SNP, single nucleotide polymorphism.

2.1 Antiplatelets

Anti-platelet therapy stands as a cornerstone in managing CVDs, effectively preventing thrombotic incidents of acute coronary syndromes (ACS) and post-percutaneous coronary intervention (PCI) [11]. Gene polymorphisms potently influence various individual responses to this treatment, encompassing acetylsalicylic acid (aspirin), thienopyridine derivatives (clopidogrel, ticlopidine, ticagrelor, and prasugrel), and GP IIb/IIIa receptor inhibitors (tirofiban, lamifiban, epifibatide, and abciximab) [12].

Acetylsalicylic acid modifies cyclooxygenase (COX1) activities to lower thromboxane A2 levels and prevent platelet aggregation. Despite acetylsalicylic acid use, resistance leads to thrombosis in 5–57% of patients [13]. Notably, two specific variants in the genes encoding COX1 and COX2, rs3842787 in prostaglandin-Endoperoxide Synthase (PTGS1) and rs20417 in PTGS2 affect thromboxane B2 levels [14, 15, 16]. The ITGB3 gene encoding glycoprotein (GP)IIIa harbors different variants (Pro33Leu, rs2317676GG/AG) that necessitate higher aspirin and abciximab doses and augment major ischemic incidents [14]. Genetic variants in ITGA2 encoding the glycoprotein Ia (GPIa) (rs1126643 and rs1062535) heighten platelet reactivity [16], while the purinergic (P2RY1) receptor variant (rs1065776TT/CT, C893T) reduces platelet aggregation [14]. These genetic insights underscore aspirin’s complexity in patients’ outcomes.

The prodrug clopidogrel, a widely used medication, is metabolized and activated by hepatic cytochrome P450 (CYP) enzymes, majorly CYP2C19, CYP3A5, and CYP3A4 (Figure 1) [12]. A widely observed loss-of-function variant of CYP2C19*2, rs4244285, has been linked to diminished treatment responses and attenuated anti-platelet effects through genome-wide association studies. Individuals with homozygous loss-of-function alleles, such as CY2C19*2/*2, display a complete lack of enzyme functionality and poor metabolism compared to those with reference CY2C19*1/*1 or heterozygous CY2C19*1/*2 alleles, which show extensive or intermediate metabolism [17]. This polymorphism is most prevalent among Asian, African, and European populations, constituting approximately 33%, 18%, and 17% of their respective allele frequencies (Table 2) [18].

Figure 1.

Pharmacogenetic associations supported by clinical evidence with cardiovascular medications. The pharmacokinetic and pharmacodynamic of cardiovascular medications, including antiplatelets, anticoagulants, DOACs, statins, beta-blockers, direct-acting vasodilators (vasodilators), and antiarrhythmics, are influenced by genetic variations within essential genes. These genes include members of cytochrome P450 family (CYP2C9, CYP2C19, CYP3A5, CYP4F2, and CYP2D6), as well as PON1, VKORC1, SLCO1B1, ABCG2, and NAT2. The illustration is generated with BioRender.com.

Gene	SNP ID	Consequences	Allele frequency in different populations						HGVSc	Functional role
			Global	African	European	American	South Asian	Middle Eastern
ABCB1	rs4148738	Intron	0.611	0.770	0.540	0.603	0.403	0.570	ENST00000622132.5:c.2482-2236G>T	Transports molecules and drugs across cellular membranes.
	rs1045642	Missense	0.572	0.797	0.469	0.568	0.411	0.519	ENST00000622132.5:c.3435T>G
ABCG2	rs2231142	Missense	0.000	0.000	0.000	0.000	0.000	0.000	ENST00000237612.8:c.421C>G
ADD1	rs4961	Missense	0.169	0.079	0.194	0.182	0.189	0.127	ENST00000683351.1:c.1378G>A	Part of the cytoskeleton and regulates ion transport, mainly in renal cells.
ADRB1	rs1801253	Missense	0.696	0.592	0.732	0.776	0.738	0.624	ENST00000369295.4:c.1165G>A	Mediates the physiological effects of the epinephrine and norepinephrine, mainly in heart.
	rs1801252	Missense	0.167	0.227	0.130	0.216	0.125	0.067	ENST00000369295.4:c.145A>G
ADRB2	rs1800888	Missense	0.009	0.002	0.014	0.013	0.004	0.025	ENST00000305988.6:c.491C T	Regulates specific and rapid signaling by the G protein-coupled receptor and gene transcription.
	rs1042714	Stop gained	0.682	0.821	0.632	0.757	0.794	0.760	ENST00000305988.6:c.79G>T
	rs1042713	Missense	0.426	0.494	0.372	0.435	0.442	0.402	ENST00000305988.6:c.46G>A
AGTR1	rs5186	3 prime UTR	0.206	0.063	0.296	0.258	0.081	0.206	ENST00000349243.8:c.*86A>C	Regulates the cardiovascular effects of angiotensin II.
APOE	rs7412	Missense	0.078	0.105	0.079	0.042	0.037	0.045	ENST00000252486.9:c.526C>T	Critical for the catabolism of atherogenic lipoprotein constituents.
	rs429358	Missense	0.157	0.215	0.138	0.112	0.105	0.067	ENST00000252486.9:c.388T>C
CES1	rs2244613	Intron	0.769	0.755	0.816	0.760	0.592	0.740	ENST00000360526.8:c.1171-33C>T	Regulates drugs hydrolysis or transesterification.
CETP	rs708272	Intron	0.385	0.264	0.435	0.419	0.461	0.402	ENST00000200676.8:c.118 + 279G>A	Transfer cholesteryl ester from high density lipoprotein (HDL) to other lipoproteins.
CTBP2P2	rs12777823	Downstream gene	0.190	0.250	0.151	0.141	0.334	0.095	—	A transcriptional repressor, regulates epithelial to mesenchymal transition.
CYP2C19	rs72558186	Splice donor	—	—	—	—	—	—	ENST00000371321.9:c.819 + 2T>A	Catalyze drug metabolism and synthesis reactions.
	rs72552267	Missense	0.000	0.000	0.000	0.001	0.000	0.000	ENST00000371321.9:c.395G>A
	rs6413438	Missense	0.001	0.003	0.000	0.001	0.000	0.000	ENST00000371321.9:c.680C>T
	rs56337013	Missense	0.000	0.000	0.000	0.000	0.000	0.000	ENST00000371321.9:c.1297C>T
	rs4986893	Stop gained	0.003	0.000	0.000	0.001	0.005	0.000	ENST00000371321.9:c.636G>A
	rs4244285	Synonymous	0.168	0.177	0.148	0.130	0.331	0.087	ENST00000371321.9:c.681G>A
	rs41291556	Missense	0.002	0.000	0.003	0.001	0.001	0.000	ENST00000371321.9:c.358T>C
	rs3758581	Missense	0.952	0.987	0.937	0.953	0.886	0.927	ENST00000371321.9:c.991A>C
	rs28399504	Start lost	0.002	0.001	0.003	0.006	0.001	0.000	ENST00000371321.9:c.1A>G
	rs17884712	Missense	0.004	0.012	0.000	0.001	0.000	0.000	ENST00000371321.9:c.431G>A
	rs140278421	Missense	0.000	0.000	0.000	0.000	0.000	0.000	ENST00000371321.9:c.557G>A
	rs12769205	Intron	0.173	0.197	0.148	0.133	0.330	0.092	ENST00000371321.9:c.332-23A>G
	rs118203757	Missense	0.000	0.001	0.000	0.000	0.000	0.000	ENST00000371321.9:c.1004G>A
	rs11188082	Intron	0.910	0.840	0.930	0.940	0.890	0.916	ENST00000371321.9:c.820-12218A>C
CYP2C9	rs1799853	Missense	0.088	0.024	0.127	0.098	0.039	0.136	ENST00000260682.8:c.430C>T
	rs1057910	Missense	0.049	0.013	0.066	0.049	0.114	0.073	ENST00000260682.8:c.1075A>C
CYP2D6	rs1135840	Missense	0.577	0.643	0.559	0.490	0.552	0.621	ENST00000645361.2:c.1457G>C
	rs1065852	Missense	0.191	0.125	0.223	0.153	0.168	0.114	ENST00000645361.2:c.100C>T
CYP3A4	rs4986910	Missense	0.005	0.001	0.007	0.002	0.000	0.000	ENST00000651514.1:c.1334T>C
	rs35599367	Intron	0.032	0.009	0.049	0.025	0.009	0.016	ENST00000651514.1:c.522-191C>T
	rs2242480	Intron	0.310	0.732	0.094	0.358	0.346	0.228	ENST00000651514.1:c.1026 + 12G>A
CYP3A5	rs776746	Intron	0.729	0.305	0.931	0.780	0.699	0.880	ENST00000222982.8:c.219-237A>G
CYP3A7	rs45446698	Upstream gene	0.026	0.010	0.036	0.028	0.014	0.022	—
CYP4F2	rs3093135	Intron	0.151	0.136	0.166	0.154	0.156	0.174	ENST00000221700.11:c.344-979T>A
	rs2108622	Missense	0.229	0.099	0.286	0.258	0.402	0.395	ENST00000221700.11:c.1297G>C
ETV6	rs2724635	Intron	0.556	0.396	0.621	0.661	0.493	0.669	ENST00000396373.9:c.34-5411G>A	Maintenance hematopoiesis and vascular network development.
GRK5	rs2230345	Missense	0.086	0.261	0.013	0.045	0.068	0.047	ENST00000392870.3:c.122A>T	Phosphorylates the active G protein-coupled receptors, with unclear mechanisms in cardiovascular diseases.
ITGB3	rs2317676	3 prime UTR	0.096	0.133	0.073	0.087	0.118	0.165	ENST00000559488.7:c.*713A>G	Regulates platelet function
KCNE1	rs1805128	Missense	—	—	—	—	—	—	ENST00000399286.3:c.253G>A	Modulates potassium ions flow through ear and cardiac channels.
KIF6	rs20455	Missense	0.489	0.798	0.361	0.386	0.445	0.443	ENST00000287152.12:c.2155T>C	Transport cellular intracellular transport of protein complexes.
PITX2	rs10033464	Downstream gene	0.859	0.819	0.904	0.817	0.782	0.908	—	Guiding the development of asymmetric cardiac morphology.
NAT2	rs1801280	Missense	0.382	0.305	0.447	0.352	0.338	0.452	ENST00000286479.4:c.341T>C	Catalyzes the acetylation, and thus, activation of drugs.
	rs1801279	Missense	0.024	0.083	0.000	0.011	0.000	0.003	ENST00000286479.4:c.191G>A
	rs1799931	Missense	0.041	0.033	0.026	0.096	0.074	0.048	ENST00000286479.4:c.857G>A
	rs1799930	Missense	0.273	0.259	0.292	0.207	0.366	0.287	ENST00000286479.4:c.590G>A
NEDD4L	rs75982813	Upstream gene	0.063	0.091	0.061	0.041	0.037	0.032	—	Regulates the surface expression of renal epithelial sodium channels by ubiquitin-mediated endocytosis and lysosomal targeting.
	rs4149601	Intron	0.321	0.344	0.335	0.263	0.189	0.326	ENST00000400345.8:c.49-16229G>A
	rs292449	Intron	0.421	0.514	0.343	0.462	0.444	0.440	ENST00000400345.8:c.123-17578G>C
NOS1AP	rs10919035	Intron	0.198	0.214	0.147	0.214	0.265	0.204	ENST00000361897.10:c.178-13122C>G	Modulates intracellular calcium, and up-regulated in dystrophic cardiomyopathy.
P2RY1	rs1065776	Synonymous	0.309	0.675	0.149	0.240	0.216	0.209	—	Regulate platelet aggregation.
PON1	rs662	Missense	0.420	0.670	0.280	0.423	0.390	0.338	ENST00000222381.8:c.575A>T	Degrades oxidized lipids in LDL, potentially delaying atherosclerosis.
PRKCA	rs16960228	Intron	0.128	0.309	0.046	0.109	0.039	0.060	ENST00000413366.8:c.1854 + 3730G>A	Regulator heart contractility
PRKCQ	rs500766	Intron	0.298	0.377	0.273	0.248	0.276	0.348	ENST00000263125.10:c.319-1132G>A	Phosphorylate proteins that regulate heart functions and rates.
PTGS1	rs3842787	Missense	0.082	0.132	0.066	0.033	0.017	0.041	ENST00000362012.7:c.50C>T	Modulates angiogenesis in endothelial cells.
PTGS2	rs20417	Upstream gene	0.201	0.334	0.158	0.180	0.164	0.220	—	Regulates prostagland synthesis, and functions as a dioxygenase and peroxidase.
SCN5A	rs7626962	Missense	0.000	0.000	0.000	0.000	0.000	0.000	ENST00000423572.7:c.3305C>T	Initiates sodium upstroke of the action potential in the heart.
SLCO1B1	rs4363657	Intron	0.182	0.148	0.171	0.177	0.088	0.193	ENST00000256958.3:c.1498-1331T>A	Regulates the sodium-independent uptake of different molecules and drugs.
	rs4149056	Missense	0.121	0.032	0.159	0.130	0.049	0.206	ENST00000256958.3:c.521T>C
	rs2306283	Missense	0.532	0.768	0.402	0.560	0.516	0.477	ENST00000256958.3:c.388A>C
	rs113681054	Intergenic	0.183	0.153	0.171	0.181	0.093	0.199	—
UGT1A4	rs887829	Intron	0.361	0.449	0.321	0.324	0.410	0.339	ENST00000373409.8:c.868-7110C>T	Catalyze drug metabolism and synthesis reactions.
VKORC1	rs9934438	Intron	0.319	0.100	0.378	0.391	0.179	0.513	ENST00000394975.3:c.174-136C>T	Reduces of inactive vitamin K 2,3-epoxide to active vitamin K in the endoplasmic reticulum membrane.
	rs9923231	Upstream gene	0.319	0.100	0.370	0.390	0.178	0.519	—
	rs749671	Upstream gene	0.316	0.099	0.374	0.388	0.178	0.472	—
	rs7294	3 prime UTR	0.403	0.458	0.383	0.400	0.712	0.250	ENST00000394975.3:c.*134G>A
YEATS4	rs7297610	Intergenic	0.124	0.303	0.063	0.072	0.022	0.079	—	Involves in transcriptional activation.

Table 2.

Functions of cardiovascular genetic factors and their allele frequencies among diverse populations.

The allele frequencies of genes associated with the pharmacogenomic of cardiovascular medications are identified using the Genome Aggregation Database (gnomAD, https://gnomad.broadinstitute.org, accessed on September 5th, 2023), and annotated using the Ensembl Effect Predictor release 108 (VEP, https://grch37.ensembl.org/index.html, accessed on September 4th, 2023). Abbreviations: HGVSc, Human Genome Variation Society sequence nomenclature descriptions from Ensembl; SNP, single nucleotide polymorphism, AF; allele frequency.

Other rare loss-of-function alleles, namely CYP2C19 (*3, rs4986893), (*4, rs28399504), (*5, rs56337013), (*6, rs72552267), (*7, rs72558186), (*8, rs41291556), (*9, rs17884712), (*10, rs6413438), (*22, rs140278421), (*24, rs118203757), and (*35, rs12769205), along with CYP2C9 (*3, rs1057910) and (*2, rs1799853) have also been identified with uncertain impact, necessitating further validation [19]. Conversely, a gain-of-function variant in the CYP2C9 (*17, rs3758581) has significantly correlated with elevated enzyme functionality, rapid metabolism, and bleeding risk [20]. This variant exhibit minor allele frequencies of 0.3, 0.93, and 0.98 in Caucasian, European, and African populations, respectively (Table 2) [18]. However, the uncertain clinical implications for carriers of *17 necessitate additional research due to inconsistent findings.

Genetic polymorphisms in the CYP2C19 gene markedly impact enzymatic activity and link to adverse cardiovascular events, including mortality, myocardial infarction (MI), and stroke [17]. Consequently, the Food and Drug Administration (FDA) issued a “Black Box” warning for clopidogrel, suggesting CYP2C19 genotyping and the consideration of alternative antiplatelet agents before prescribing antiplatelet therapies for PCI or ACS patients with a high risk of poor responses, as also recommended by CPIC and DPWG [9].

CYP3A4, CYP2C19, and CYP2B6 primarily activate Prasugrel, while CYP3A4 activates ticagrelor’s active metabolite [12]. Clinical trials indicate better cardiovascular event risk reduction with ticagrelor and prasugrel than clopidogrel in ACS [21]. Polymorphisms in the ATP-binding cassette B1 (ABCB1, rs1045642) and paraoxonase 1 (PON1, rs662) genes lower clopidogrel bioavailability, with inconsistent impact on adverse cardiovascular outcomes. Clinical guidelines exclude ABCB1 or CYP2C19 variants for prasugrel or ticagrelor [22]. However, those agents have higher bleeding rates, costs, and discontinuation [12]. Genetic variants, including CYP3A4 (*22, rs35599367), CYP3A5 (*3, rs776746), CYP4F2 (rs3093135), CYP2C19 (*17, rs3758581), and solute carrier organic anion transporter 1B1 (SLCO1B1, rs113681054) [23, 24], may impact prasugrel and ticagrelor antiplatelet outcomes, necessitating further research.

2.2 Anticoagulants

Oral anticoagulants, particularly Vitamin K antagonists and direct-acting oral anticoagulants (DOACs), are critical in preventing and treating thromboembolic disorders [12]. These medications have achieved significant success in cardiovascular therapeutics through candidate genes and GWAS investigations [25, 26].

2.2.1 Vitamin K antagonists

The narrow therapeutic index for Vitamin K antagonists, such as warfarin and acenocoumarol, necessitates close monitoring of the international normalized ratio (INR). This monitoring ensures the achievement of optimal anticoagulation within the INR range of 2–3 and prevents hemorrhage [27]. The metabolism (inactivation) of warfarin occurs predominantly in the liver through CYP2C9, with minimal involvement of CYP4F2. These therapeutic agents function as inhibitors of the vitamin K epoxide reductase complex-1 (VKORC1), subsequently impeding the biological activation of vitamin K1. VKORC1 activates coagulation factors by enhancing Vitamin K reduction, thus disrupting the biological activation of vitamin K1 (Table 2) [12].

Common genotypes, particularly CYP2C9 (*3, rs1057910), (*2, rs1799853), and rs9332238, have been detected as key contributors to variability in dose requirements of warfarin, accounting for around 50% [28]. Individuals carrying loss-of-function alleles of CYP2C9 are at elevated risk of hemorrhagic consequences and demand reduced warfarin dosages [26]. GWAS have additionally linked the *3 allele (rs2108622, V433M) variant in CYP4F2 as a distinct factor influencing variability in warfarin dosing. CYP4F2 plays a role in metabolizing vitamin K1, acting alongside VKORC1 to prevent excessive vitamin K accumulation [29]. In European and Asian populations, the *3 allele requires 5–10% higher warfarin doses than the *1 allele. However, this association is absent within African populations where CYP2C9*8 (rs12777823) and the CYP2C19 intron variant (rs11188082) are predominant, primarily due to disparities in allele frequencies [25].

Genetic mutations in the promoter region of VKORC1, commonly rs9923231 (1639G>A), rs9934438 (1173T>C), rs749671, and rs7294 (3730G>A), impact the hepatic abundance of VKORC1 mRNA transcripts and the sensitivity of warfarin [26]. Individuals with reduced VKORC1 expression levels require less warfarin doses, whereas those with gain-of-function mutations require higher doses to maintain a steady anticoagulant effect. Numerous uncommon variations in VKORC1 with non-synonymous alterations (defined as alterations to the protein’s amino acid sequence) have been associated with warfarin resistance and heightened dosage requirements [30].

Pharmacogenomics of warfarin underscores the significant role of genetic heritage on drug responses. Asian individuals often demand reduced amounts due to the prevalence of VKORC1 loss-of-function variants, while increased doses are needed for African individuals because gain-of-function VKORC1 variants are more prevalent. Among individuals of African ancestry, there are additional CYP2C9 variants (*5, rs28371686; *6, rs28371686; *8, rs9332131; and *11, rs28371685 alleles) that reduce warfarin dose requirements by 15–30% and are more common than *2 and *3 alleles compared to Caucasians (Table 2) [31]. The Clarification of Optimal Anticoagulation through Genetics (COAG) trial underscored that ignoring these genetic variations resulted in warfarin overdosing in African Americans [32].

Acknowledging the significance of these genetic patterns, the FDA has updated drug packaging to include dosage instructions that consider pharmacogenetic testing of CYP2C9 and VKORC1 genotypes. The CPIC and DPWG guidelines recommend this therapeutic approach before initiating warfarin treatment in pediatrics and adults. Studies showed that genotype-guided early warfarin treatment shortened stabilization time, improved cost-effect anticoagulation, and led to better clinical outcomes [33].

Acenocoumarol, a coumarin derivative, is primarily utilized to prevent instances of thromboembolism and deep vein thrombosis, with its effectiveness influenced by genetic polymorphisms in the CYP2C9*2 rs1799853, CYP4F2*3 rs2108622, and VKORC1 (rs9923231, 1639G>A) genes. Several algorithms predict dosage requirements of acenocoumarol based on genetic factors, ethnicity, and demographic data, such as Verde et al.’s “acenocoumarol-dose genotype score” [34, 35, 36]. DPWG guidelines emphasize close INR monitoring during non-steroidal anti-inflammatory drug (NSAID) changes for those with specific genetic profiles on acenocoumarol. The VKORC1 genotype significantly impacts dosing variability, although precise recommendations remain limited due to rigorous monitoring requirements [37].

2.2.2 Heparin

Heparin-induced thrombocytopenia (HIT) is a dangerous immune reaction to heparin anticoagulants. Predicting HIT is challenging due to limited confirmed cases and genetic complexity. Studies have inconsistently identified risk genes associated with HIT-related clotting, such as integrin beta 3 (ITGB3, encodes GPIIIa), Fc gamma receptor IIa (FCGR2A), T-cell death-associated gene-8 (TDAG8), protein tyrosine phosphatase receptor type J (PTPRJ, also known as CD148), interleukin 10 (IL10), and human leukocyte antigen DR (HLA-DRA) [38, 39, 40, 41, 42].

A GWAS identified a substantial association of missense genetic variant near the TDAG8 with elevated platelet factor 4/heparin antibodies, even in non-heparin-treated patients [43]. Candidate genomic study determined a strong link between the HLA-DRB3*01:01 allele and a higher risk of HIT. They also noted an interaction between the killer cell immunoglobulin like receptor 2 domains, short cytoplasmic tail 5 (KIR2DS5) and the HLA-C1 KIR binding group. This suggests that HLA variations and CD4+ T cells might play a role in HIT development, pending confirmation through functional assays [38]. Furthermore, comprehensive multiethnic studies with confirmed HIT cases are needed to identify genomic biomarkers for effective preventive strategies.

2.2.3 Direct oral anticoagulants

DOACs, like apixaban, dabigatran, edoxaban, and rivaroxaban, have gained popularity due to their advantageous pharmacokinetics and pharmacodynamics, eliminating the need for regular INR monitoring. However, instances of varied plasma DOAC levels and increased hemorrhagic risks have enhanced pharmacogenomic research [27]. Plasma level variation mainly arises from drug interactions and genetics, particularly depending on the absorption and activation functions of intestinal P-glycoprotein, coded by the adenosine triphosphate (ATP)-binding cassette B1 transporter/multidrug resistance-1 (ABCB1/MDR1) gene, and hepatic carboxylesterase 1 (CES1) [12].

The ABCB1 polymorphisms (such as rs1045642 and rs4148738) and CES1 polymorphism (rs2244613) inconsistently affect dabigatran levels [44]. Plasma levels of rivaroxaban and apixaban were associated significantly with genetic variations in CYP3A4, ABCB1, ATP Binding Cassette G2 (ABCG2), and sulfotransferase (SULT), while edoxaban linked to CYP3A4, CES1, SLCO1B1, and ABCB1 [45]. The FDA recently advises against combining this medication with potent CYP3A4/5 inhibitors, like carbamazepine, phenobarbital, phenytoin, or rifampin, as well as inducers, such as boceprevir, itraconazole, and clarithromycin [46]. Exploring these genetic factors, particularly in elderly patients with multiple conditions and concurrent medications, is crucial for understanding drug-gene interactions.

2.3 Lipid-lowering agents

The high occurrence of CVD, obesity, and metabolic syndrome necessitates the utilization of statins, a class of drugs known as 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibitors, including atorvastatin, rosuvastatin, simvastatin, pravastatin, fluvastatin, lovastatin and pitavastatin [47]. Statins effectively decrease total and low-density lipoprotein (LDL) cholesterol levels by up to 55%, leading to a 20–30% reduction in cardiovascular events. The clinical response to statin treatment varies significantly among individuals. Approximately one-third of individuals achieve insufficient desired LDL reductions, with rare yet severe adverse consequences to the medication, including myopathy and rhabdomyolysis. Genetic variations linked to cholesterol synthesis, absorption, and transport can impact the effectiveness of statin therapy alongside environmental influences and patient adherence. Genomic studies identified genes associated with lipid metabolisms, such as apolipoprotein E (APOE), apolipoprotein B (APOB), cholesteryl ester transfer protein (CETP), LDL receptor (LDLR), cholesterol transport, such as ABCG5/8, ABCG2, ABCB1/MDR1, kinesin-like protein 6 (KIF6), solute carrier organic anion transporter 1B1 (SLCO1B1), calmin (CLMN), and CYP450 metabolizing enzymes family [47].

The utilization of simvastatin, a prominent HMGCR inhibitor, has faced a decline attributed to its link to myopathy risk in about 28% of patients [48]. This risk exhibits a dose-dependent pattern, prompting regulatory measures by the FDA to restrict the maximum allowable dose [49]. GWAS suggest a link between SLCO1B1 genetic variations, including rs4149056 (c388A-c521C), rs2306283 (388A>G), and noncoding rs4363657 (c.1498-1331T>C), and statin-induced myopathy, including simvastatin and atorvastatin. These polymorphisms reduce the function of the organic anion transporting polypeptides B1 (OATP1B1) found on hepatocytes’ sinusoidal membrane, leading to increased circulating levels of simvastatin active form. GWAS revealed a threefold elevated risk associated with each C genotype, particularly in homozygous individuals. Carriers of SLCO1B1*5 allele have a 4- to 5-fold higher risk of severe creatine kinase-positive statin-induced myopathy and a 2- to 3-fold higher risk of creatine kinase-negative myopathy [50].

Variations in the CYP3A4 gene have been connected to different responses to statin treatment, including simvastatin, atorvastatin, and lovastatin. Individuals carrying the homozygous/heterozygous CYP3A4*22 (rs35599367) or homozygous CYP3A5*3 (rs776746) polymorphisms, along with CYP3A4*4 (rs55951658) haplotype on statin therapy exhibit an escalated response that decreased total and LDL cholesterol levels [51, 52]. Conversely, those with the missense CYP3A4*3 (rs4986910, M445T), CYP3A4 promoter polymorphism (rs2740574, A290G), or CYP3A4*1G haplotype (rs2242480) might not achieve the anticipated lipid-lowering effects from statins [53, 54, 55]. One GWAS on the plasma level of atorvastatin identified associations with polymorphisms near CYP3A7 (rs45446698) and UDP Glucuronosyltransferase-1A (UGT1A, rs887829) [56]. Studies suggest that genetic variability in CYP2C9*3 (rs1057910) is connected to elevated levels of fluvastatin [57].

The ABCB1 genetic polymorphisms influence the efficacy of simvastatin, with less common variants (rs1128503, 1236T; rs2032582, 2677 non-G; and rs1045642, 3435T) occurring in patients who experience muscle-related side effects [58]. The pharmacokinetics of atorvastatin and rosuvastatin are affected through variants in the ABCG2 gene, which encodes breast cancer resistance protein (BCRP) transporter. Individuals with the CC genotype at rs2231142 experience more significant reductions in LDL cholesterol levels compared to those with AA genotypes [59].

In some studies, kinesin-like protein 6 (KIF6), involved in intracellular transport, has been linked to coronary artery disease. These studies suggest potential statin benefits for carriers of the rs20455 (Trp719Arg) missense polymorphism in the KIF6 gene [60]. However, a meta-analysis of 19 studies discovered an inconsistent link between this polymorphism and nonfatal coronary artery disease [61]. The CLMN polymorphism, rs8014194, explains only 1% of statin response variability, with carriers experiencing notably greater reductions in total cholesterol. Initial GWAS suggest that the APOE E2 (rs7412) (526C>T) and E4 (rs429358, 388T>C) alleles might significantly reduce the LDL levels and lower instances of nonfatal myocardial infarction and mortality when treated with lipid-lowering therapy [62]. However, support for these associations varies across all studies [63].

Pharmacogenetic research has focused on the CETP gene, particularly examining the TaqIB variant, rs708272. Individuals with a B1B1 genotype on statin treatment experience slower coronary artery disease progression than B2B2 carriers. While some studies, including Boekholdt et al.’s meta-analysis, find no connection between the TaqIB polymorphism and pravastatin treatment [64], the Regression Growth Evaluation Statin Study (REGRESS) revealed potential pharmacogenetic interactions. This study identified higher 10-year mortality in male statin-treated patients with the B2 allele, compared to those with the B1B1 genotype (Table 1) [65]. In the future, there is an expectation of a more comprehensive understanding of statins’ function and the individual variations in response.

Consequently, clinical guidelines suggest adjusting simvastatin and atorvastatin dosages according to the SLCO1B1 genotype, although the significance has diminished due to the availability of safer statins, such as rosuvastatin and or pravastatin [66]. The FDA has eliminated the requirement for further confirmatory testing of SLCO1B1-simvastatin genotyping [67]. CPIC guidelines now cover rosuvastatin dosing based on ABCG2 and SLCO1B1 genotypes and fluvastatin dosing based on CYP2C9 genotype [66]. This expansion to high-intensity statins, including atorvastatin and rosuvastatin, is expected to enhance the use of pharmacogenetic-guided approaches for selecting and dosing statins in clinical practice.

2.4 Beta-blockers

Beta-blockers function through competitive antagonism of endogenous catecholamines at the beta-1 and beta-2 adrenergic receptors, encoded by ADBR1 and ADBR2, in the heart and blood vessels. These medications are commonly prescribed to reduce heart rate, lower blood pressure, and enhance survival and left ventricular ejection fraction following an MI, including atenolol, bisoprolol, carvedilol, metoprolol, nebivolol, propranolol. However, the response to beta-blockers can vary significantly among individuals due to genetic factors influencing the pharmacokinetics (CYP2D6) and the pharmacodynamics (ADBR1, ADBR2, and GRK5).

Genetic variants of ADBR1, commonly rs1801252 (Ser49Gly) and rs1801253 (Arg389Gly), have been linked to impaired down-regulation and altered signal transduction in vitro [68, 69]. Associations of Gly49 are linked to more significant reductions in the end diastolic diameter than the Ser49 genotype. Moreover, Ser49 carriers have higher heart rates and increased mortality than Gly49 carriers on beta-blocker treatment [70]. Clinical research indicates that individuals with homozygous Arg389 genotype tend to respond more positively to beta-blockers, enhancements in the left ventricular ejection fraction (LVEF), and overall risk reduction of hospitalizations and mortality compared to Gly389 [71, 72]. However, this correlation and reports of improved beta-blocker response on blood pressure and heart rate demonstrated inconsistency [73, 74].

Prevalent polymorphisms in the ADBR2, mainly rs1042713 (Arg16Gly), rs1042714 (Gln27Gly), and rs1800888 (Thr164Ile), have shown increased adenylyl cyclase activity, leading to enhanced agonist-induced downregulation of ADBR2 [75]. However, associations of these variants with cardiovascular outcomes are inconsistent. Most studies observed no link between these genetic variants and cardiovascular improvement [76], but smaller studies suggested the Glu27 allele might be associated with higher LVEF than the Gln27 allele when using beta-blockers [77]. A prevalent four amino-acid deletion (Del322-325) reduces alpha(2C)-AR (ADRA2C) activity in cells and associates with adverse heart failure outcomes [78], though it did not affect the beta-blocker Evaluation of Survival Trial (BEST) with bucindolol [72].

The CYP2D6 enzyme predominantly metabolizes carvedilol, metoprolol, nebivolol, propranolol, and timolol. CYP2D6 is unnecessary for metabolizing other beta-blockers, like atenolol, bisoprolol, and nadolol. Common genetic variants of CYP2D6 lead to a range of phenotypes, varying from increased enzyme function due to gene duplication to complete loss of function. Such as homozygous rs1135840 and rs1065852, caused by gene deletion or splicing defects [79]. Approximately, 5–10% of the population carries two or more CYP2D6 alleles, such as the *4 allele, with reduced activity. This raises the circulating levels of beta-blockers, leading to substantial blood pressure and heart rate reduction and a greater risk of adverse drug reactions [80]. Although CYP2D6 variations seem to affect heart rate response to beta-blockers, their significant influence on blood pressure response and cardiovascular risk reduction remains uncertain [81].

The G protein-coupled receptor kinase 5, encoded by GRK5, is an intracellular component that attenuates signaling from beta-adrenergic receptors. The rs2230345 (Gln41Leu) variant heightens GRK5 activity, mimicking the impact of a beta-blocker [82]. Those with the 41Leu variant show improved heart failure and hypertension outcomes, potentially reducing beta-blockers effectiveness [83]. This could favor alternative medications depending on the medical condition. Nevertheless, further research is needed to establish the clinical implications of these variants for guiding beta-blocker therapy.

Considering the established pharmacogenomic interactions of beta-blockers and ADBR1 polymorphisms, utilizing multiple risk alleles could enhance the strategic management of this therapy [84]. Genetic variations in CYP2D6 might impact beta-blocker pharmacokinetics, but application in prescribing is limited. The FDA-approved metoprolol label downplays the impact of CYP2D6-dependent metabolism on safety but advises caution with potent CYP2D6 inhibitors, such as quinidine, fluoxetine, paroxetine, and propafenone [85]. The DPWG guidance suggests tailored dosing, including gradual titration for intermediate and poor metabolizers and considering alternatives, like bisoprolol, for ultra-rapid metabolizers as needed [9].

2.5 Direct-acting vasodilators

Direct-acting vasodilators decrease blood pressure by relaxing vascular smooth muscle, including hydralazine, minoxidil, and sodium nitroprusside. Hydralazine is recommended for secondary hypertension treatment and primarily undergoes hepatic metabolism by N-acetyltransferase type 2 (NAT2). Genetic variants in NAT2 determine acetylation rates, with NAT2*4 indicating rapid acetylation, and the common *5 rs1801280 (341T>C), *6 rs1799930 (590G>A), *7 rs1799931 (857G>A), and the rare *14 rs1801279 (191G>A) suggesting slower rates [86]. Rapid acetylators have lower hydralazine exposure, affecting blood pressure response. Slow acetylators might experience more potent antihypertensive effects due to increased drug exposure [87], potentially elevating the risk of rare adverse reactions, like lupus-like symptoms [88]. Nonetheless, the evidence supporting NAT2 genotyping for predicting hydralazine’s safety and efficacy remains inconclusive.

2.6 Renin-angiotensin system inhibitors

Renin-angiotensin system (RAS), including ACEIs (such as captopril, enalapril, and lisinopril) and ARBs (such as candesartan, losartan, and olmesartan) serves as a cornerstone in the management of CVD, encompassing hypertension, ACS, heart failure, and nephropathy. Polymorphisms in renin-angiotensin-aldosterone-related genes, particularly ACE, angiotensinogen (AGT), and angiotensin-II receptors I and II (AGTR1 and AGTR2), can potentially impact the ACEIs and ARBs mechanisms. Individuals with DD homozygosity of the ACE insertion/deletion rs4646994 (287-Alu) variant exhibit elevated ACE activity in both plasma and tissues, which is associated with higher 10-year mortality rates [89]. Around 10% of patients on ACEIs developed a dry cough, likely caused by an accumulation of bradykinin during therapy. Studies have linked this cough to the ACE insertion/deletion variant and a specific polymorphism in the bradykinin B2 (B2R) gene’s promoter region [90].

The AGT rs699 (Met235Thr) allele carriers tend to have elevated angiotensin levels, which may be connected to increased blood pressure [91]. The AGTR1 rs5186 (A1166C) polymorphism has been studied for its beneficial influence on ARB response [92]. A GWAS identified two moderately associated variants in the protein kinase C theta (PRKCQ, rs500766) and ETS transcription 6 (ETV6, rs2724635), genes involved in immune regulation related to ACEI-induced angioedema [93]. Current evidence lacks definitive pharmacogenomic associations between ACE, AGT, or AGT1R polymorphisms and the effectiveness and safety of ACEIs or ARBs. The inconsistent results and study limitations undermine the reliability of these findings, warranting further research to improve precision medicine guidance.

2.7 Diuretics

Thiazide diuretics (such as hydrochlorothiazide, chlorthalidone, and indapamide) play a crucial role in managing hypertension, while loop diuretics (such as bumetanide, ethacrynic acid, and furosemide) and aldosterone antagonist (like spironolactone) are the preferred choice for addressing fluid retention in heart failure.

Research suggests that individuals carrying the rs4961 (Gly460>Trp) variant in the α-adducin gene (ADD1) exhibit more favorable blood pressure responses to thiazide, loop, and spironolactone diuretics, revealing an association with salt sensitivity [94]. Carriers of the Trp460 variant experience a more pronounced protective effect, approximately 38%, against heart attacks (MI) and strokes compared to individuals with the Gly460 genotype [95]. The NPPA rs5065 (T2238C) variants, encode atrial natriuretic peptide, showed that C allele carriers had lower CVD risk and more significant blood pressure reductions with chlorthalidone than amlodipine. However, those with the TT genotype had a higher risk of heart attack, stroke, and all-cause mortality with chlorthalidone [96].

Furthermore, genetic variations in the NEDD4L (rs4149601, rs292449, and rs75982813, encodes neural precursor cell expressed developmentally downregulated 4-like enzyme), PRKCA (rs16960228, encodes Protein kinase C alpha), and YEATS4 (rs7297610, encodes YEATS Domain Containing-4) consistently show a significant link to cardiovascular outcomes with thiazide diuretics (Table 1) [97, 98, 99]. Despite their potential, these gene variations have not yet impacted diuretic prescriptions due to conflicting research. Further studies may lead to personalized diuretic therapies for hypertension and related conditions.

2.8 Antiarrhythmics

Antiarrhythmic drugs block the potassium current channel (IKr) and prolong QT intervals, increasing the drug-induced Torsade de Pointes (DITdP) malignant arrhythmia risk, including amiodarone, flecainide, procainamide, and sotalol. Some common gene variants have been linked to DITdP, constituting around 10% of cases known as congenital long QT syndrome (cLQTS) incomplete penetrance. The rs1805128 (D85N) polymorphism in potassium voltage-gated channel E1 (KCNE1), encoding Ikr subunit, and rs7626962 (S1103Y) sodium voltage-gated Channel Alpha 5 (SCN5A), encoding cardiac sodium channel, exhibit a strong connection to DITdP development [100, 101]. A significant SNP, rs10919035, in nitric oxide synthase 1 adaptor protein (NOS1AP), is notably prevalent in amiodarone related TdP cases and associated with ventricular arrhythmia and QT prolongation [102].

Prevalent polymorphisms at the 4q25 locus near the PITX2 gene, rs10033464, have been linked to an increased risk of atrial fibrillation (AF) and reduced responsiveness to certain antiarrhythmic drugs (Table 1) [103]. Additionally, a genetic variant rs1801253 in ADRB1 (Arg389Gly) is associated with better outcomes with rhythm control strategies for managing AF [104]. CYP2D6 and CYP3A4 metabolize some antiarrhythmic drugs as beta-blockers, such as flecainide, quinidine, and propafenone. Limited evidence suggests that genetic variations in CYP2D6 and CYP3A4 may impact their pharmacokinetics and affect the QTc interval, as indicated by their classification in PharmGKB as level 2A and in CPIC level as B-C [105, 106].

Clinical studies use a QT alert system to monitor high-risk DITdP patients. A polygenic risk score of common variants shows potential for enhancing personalized prevention and guiding AF treatment based on an individual’s genotype [107]. The FDA label for procainamide highlights response differences based on acetylation speed by NAT2 but does not mention any specific genotype for acetylation status determination. The FDA recommends caution with quinidine and propafenone in individuals with CYP2D6 and CYP3A4 inhibitions due to high adverse effects risk, like pro-arrhythmia. Although there are no specific CPIC guidelines for these drugs, the DPWG recommends reducing the standard doses by 50% and 30% in CYP2D6 poor metabolizers (Table 1) [37].

3. Future perspectives in personalized cardiovascular therapy

Advancements in pharmacogenomics offer significant potential for personalized cardiovascular therapy, leveraging an individual’s genetic profile. High-throughput genotyping and next-generation sequencing technologies enhance our understanding of how genetics influence responses to cardiovascular drugs [108]. Integrating pharmacogenomic data into electronic health records enhances clinical decision-making, promising safer and more cost-effective medication regimens. Notably, genetic markers identified through GWAS, and multi-omics analyses enable personalized selection of antiplatelets, statins, and warfarin dosages, reducing the occurrence of adverse cardiovascular events [9, 17, 22, 33, 66].

Following the validation of identified polymorphisms, the transformative genetic approach revolutionizes cardiovascular therapy, including implementing polygenic risk scores. However, cardiovascular pharmacogenomics faces challenges due to patient variability and limited study sizes. These challenges encompass genetic data diversity limitations, difficulties in clinical integration, technical and sociopolitical constraints, and the complexity of genetic interactions. Addressing these issues necessitates collaborative efforts, supported by artificial intelligence and machine learning, to uncover polygenic predictors and fully realize the potential of precision medicine in complex CVDs with multiple drug regimens, like heart failure [109]. While logistical challenges persist in obtaining early genotype results, the prospect of pre-emptive or point-of-care testing offers potential solutions.

4. Conclusion

Substantial evidence underscores genetic associations with efficacy and tolerance of cardiovascular therapies, including antiplatelets, anticoagulants, lipid-lowering agents, and, to moderate extent, beta-blockers. Pharmacogenetic testing has become a standard practice, enhancing cardiovascular treatment outcomes. However, further research is necessary to guide genotype-based drug selection and validate genetic variation’s effect on drug responses and safety among different populations. Ultimately, precision medicine aims to prove its superiority of genotype-based personalized interventions over current standard care and improve overall health outcomes.

Conflict of interest

The authors declare no conflict of interest.