Genetic and Clinical Determinants of MACE and Haemorrhage in Antiplatelet Therapy: Insights from Pharmacogenomic Analysis

doi:10.21203/rs.3.rs-4917079/v1

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Genetic and Clinical Determinants of MACE and Haemorrhage in Antiplatelet Therapy: Insights from Pharmacogenomic Analysis

https://doi.org/10.21203/rs.3.rs-4917079/v1

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Background: Variability in responses to clopidogrel and aspirin therapy for coronary artery disease has driven interest in pharmacogenomics. This study investigates the role of genetic variants in CYP2C19, ABCB1, and PON1 in predicting adverse cardiovascular events and guiding personalised antiplatelet therapy.

Methods: A retrospective cohort study designed to compare the effectiveness and safety of the risk levels from CYP2C19 (*2, *3, *17), ABCB1 C3435T, and PON1 Q192R polymorphisms. The primary outcome was the incidence of haemorrhage and major adverse cardiovascular events (MACE). Kaplan Merir curves and Cox regression with IPTW adjustments were used for analysis.

Results: Group A (treatment consistent with multigene testing) showed significantly lower MACE incidence than Group B. Multigene testing more accurately predicted clopidogrel effectiveness than single-gene testing and reduced adverse events without increasing haemorrhagic risk.

Conclusion: Multigene-guided antiplatelet therapy is more effective in reducing adverse cardiovascular events. Further prospective studies are needed to validate these findings, incorporating genetic, environmental, and lifestyle factors for a comprehensive personalised medicine approach.

Biological sciences/Genetics/Gene regulation

Health sciences/Diseases/Cardiovascular diseases

Antiplatelet Therapy

clopidogrel

Pharmacogenomics

Multigene testing

Personalized Medicine

Cardiovascular Events.

As medical research develops in depth, the significance of antiplatelet drugs in the treatment of coronary artery disease (CAD) has increasingly come into focus. Among these, clopidogrel, in combination with aspirin, constitutes the cornerstone of CAD therapy, widely and routinely used in clinical practice and recommended by guidelines (1). Although clopidogrel shows remarkable clinical efficacy, there is significant variability in patient responses to the same dosage, and this interindividual heterogeneity has attracted widespread attention in the medical community. Studies have found that approximately 4–30% of patients exhibit poor antiplatelet effects after receiving standard doses of clopidogrel, leading to the so-called "clopidogrel resistance" phenomenon (2).

The variability in clopidogrel's therapeutic effects results from a combination of genetic and non-genetic factors (3). Genome-wide association studies have revealed that over 83% of the individual variability in clopidogrel response may be regulated by genetic effects related to the enzymes involved in its absorption and metabolism pathways (4). As a prodrug, clopidogrel's absorption, metabolism, and action involve the expression of numerous crucial genes. The ABCB1 gene encodes P-glycoprotein, which is responsible for the drug's transport and absorption (5), while the CYP2C19 and PON1 genes directly affect the metabolic activation of clopidogrel (6). The polymorphism of the CYP2C19 gene is particularly noteworthy since the enzyme it encodes plays an indispensable role in the metabolism of clopidogrel (7), and its allelic variations such as CYP2C19*2, *3, *17 directly affect the efficacy and metabolic rate of clopidogrel (8, 9).

In studies on the relationship between major adverse cardiovascular events (MACE) and clopidogrel treatment, the role of CYP2C19 gene polymorphism cannot be underestimated (10, 11). The U.S. Food and Drug Administration (FDA) pointed out as early as 2010 that the loss-of-function alleles of CYP2C19 are associated with poor response to clopidogrel therapy and required a warning in the drug labelling. Drug labels in China similarly indicate potential issues for slow metabolizers of CYP2C19 when using clopidogrel (12). Recent research further supports the importance of genotype-guided therapy in improving the efficacy and safety of clopidogrel treatment, especially in patients with ST-segment elevation myocardial infarction (STEMI), where genotype guidance can significantly reduce the risk of recurrent myocardial infarction, cardiovascular death, and major bleeding (13). Moreover, the latest meta-analyses also show that genotype-guided antiplatelet therapy can significantly improve treatment outcomes compared to standard therapy, particularly in the Chinese population or patients with acute coronary syndrome (ACS) (14, 15).

However, the polymorphisms of the CYP2C19 gene cannot fully account for the interindividual variability, suggesting that the individual differences in the therapeutic efficacy of clopidogrel are the complex result of multigenic interactions (4). Some studies have indicated that in addition to CYP2C19, the PON1 and ABCB1 genes also play a crucial role in the therapeutic effects of clopidogrel (16, 17). Mutations at the C3435T locus of the ABCB1 gene can enhance the efflux action of P-glycoprotein, reducing the effective plasma concentration of clopidogrel (18), and research has also found that this mutation is associated with the in vivo concentration of both clopidogrel and its active metabolites (19).

Integrating the results from single-gene and multi-gene studies, we recognize that there is an upward trend in the risk of cardiovascular events when the ABCB1 C3435T and CYP2C19*2 alleles coexist (20). These findings underscore the potential value of multigene testing to predict a patient's response to clopidogrel. These findings underscore the potential value of multigene testing to predict a patient's response to clopidogrel. Thereby, we hypothesize that specific genetic variants, alongside clinical characteristics, significantly influence the risk of MACE and hemorrhage in patients undergoing antiplatelet therapy. Given this context, this study aims to investigate the genetic and clinical determinants that influence the risk of MACE and haemorrhage in patients on antiplatelet therapy, with a focus on genes involved in clopidogrel metabolisms, including CYP2C19, PON1, and ABCB1.

Study Design and Subjects

This cohort study included patients undergoing antiplatelet therapy with coronary artery disease (CAD) who were hospitalized at the First Affiliated Hospital of Xinjiang Medical University from January 2016 to December 2020, with detailed data collection on genetic profiles and clinical characteristics. The study protocol was approved by the institutional ethical review board (Approval No. K202106-24).

Inclusion and Exclusion Criteria

Inclusion criteria were patients aged 18 and above receiving clopidogrel therapy, including conformed to the diagnostic criteria for coronary artery disease (1); underwent genetic testing for CYP2C19, ABCB1, and PON1; were scheduled to receive antiplatelet therapy with clopidogrel or ticagrelor in combination with aspirin. Exclusion criteria included patients with incomplete genetic data or those on alternative antiplatelet regimens: those lacking basic information, genetic testing, or other relevant test data; those with a history of severe liver or kidney dysfunction or malignant tumours; those also using other anticoagulant medications such as warfarin or rivaroxaban; those whose antiplatelet therapy lasted less than six months. 5) Those unable to complete follow-up.

Data Collection

Data from the participating patients were collected through the medical record management system, including but not limited to gender, age, body mass index (BMI), hypertension, diabetes, smoking history, disease classification, surgical history, and diagnosis and treatment information. In addition, related biochemical indicators were collected, such as routine blood tests, liver and kidney function, lipid levels, and imaging data, such as left ventricular ejection fraction. Genomic DNA was extracted from blood samples, including the *2, *3, *17 alleles of CYP2C19, ABCB1 C3435T, and PON1 Q192R. In this study, due to the complexity of real-world data and the lack of standardization, we first cleansed the dataset to ensure the accuracy and validity of the analysis.

Outcome Measures and Follow-Up

The primary outcome measure of this study was the incidence rate of major cardiovascular adverse events (MACE) and bleeding events following treatment. Information on the patients' detailed use of antiplatelet drugs and any occurrences and timing of adverse cardiovascular events was collected through rehospitalization records and telephone follow-ups. In this study, MACE include cardiac death, non-fatal myocardial infarction, heart failure, revascularization, ischemic stroke, and cardiac rehospitalization, among others. Bleeding events are presented in Table 1 (21).

Table 1

Definition of bleeding classification
Definition	Manifestation (Meets one of the following)	Haemoglobin drop Value	Transfusion of whole blood or packed red cells
Life-Threatening Bleeding	Fatal bleeding, intracranial haemorrhage, or cardiac tamponade due to haemorrhage, bleeding leading to shock or hypotension requiring vasopressor therapy or surgery	> 50 g/L	≥ 4 U
Other Serious Bleeding	Disabling (e.g., permanent loss of vision)	30–50 g/L	2–3 U
Moderate Bleeding	Requires medical intervention for haemostasis (e.g., epistaxis requiring a device for haemostasis)
Minor Bleeding	Other (gum bleeding, bruising, bleeding at the injection site) not requiring medical intervention

Table 1 < Here>

Group strategy

Genotyping of the CYP2C19 gene includes wild type *1/*1, heterozygous mutants *1/*2, *1/*3, *1/*17, *2/*17, *3/*17, and homozygous mutants *2/*2, *3/*3, *2/*3, *17/*17; ABCB1 C3435T genotypes are categorized as wild type (CC), heterozygous (CT), and homozygous mutant (TT). PON1 Q192R genotypes are classified as wild type (GG), heterozygous (GA), and homozygous mutant (AA). Based on the rate of drug metabolism, CYP2C19 genotypes are classified into four metabolizer phenotypes: ultra-rapid metabolizer (UM) *17/*17, *1/*17; extensive metabolizer (EM) *1/*1; intermediate metabolizer (IM) *1/*2, *1/*3, *2/*17, *3/*17; and poor metabolizer (PM) *2/*2, *2/*3, *3/*3 (9). The combined effects of CYP2C19*2, *3, *17, ABCB1 C3435T, and PON1 Q192R genes were categorized as 6 levels in this study: Level − 1 (bleeding risk), Level 0 (normal), Level 1 (low risk of clopidogrel resistance), Level 2 (moderate risk of clopidogrel resistance), Level 3 (high risk of clopidogrel resistance), and Level 4 (very high risk of clopidogrel resistance), as detailed in Table 2.

Table 2

The risk level of the gene combination
Level	CYP2C19	PON1 Q192R	ABCB1 C3435T
Level − 1 (bleeding risk)	UM (1/17 or 17/17)	GG	CC/CT/TT
Level 0 (normal)	EM (1/1)	GG	CC/CT
Level 0 (normal)	UM (1/17 or 17/17)	GA/AA	CC/CT/TT
Level 1 (low risk of clopidogrel resistance)	EM (1/1)	GG	TT
	EM (1/1)	GA	CC/CT
	IM (1/2 or 1/3 or 2/17 or 3/17)	GG	CC/CT/TT
Level 2 (moderate risk of clopidogrel resistance)	EM (1/1)	GA	TT
Level 2 (moderate risk of clopidogrel resistance)	IM (1/2 or 1/3 or 2/17 or 3/17)	GA	CC/CT/TT
Level 3 (high risk of clopidogrel resistance)	EM (1/1)	AA	CC/CT/TT
	IM (1/2 or 1/3 or 2/17 or 3/17)	AA	CC/CT/TT
	PM (2/3)	GG/GA	CC/CT/TT
	PM(3/3)	GG/GA/AA	CC/CT/TT
Level 4 (very high risk of clopidogrel resistance)	PM(2/3 or 2/2)	AA	CC/CT/TT
Note: Ultra-rapid metabolizer (UM) 17/17, 1/17; Extensive metabolizer (EM) 1/1; Intermediate metabolizer (IM) 1/2, 1/3, 2/17, 3/17; and Poor metabolizer (PM)

Table 2 < Here>

According to the guidelines for coronary heart disease and the latest expert consensus on dual antiplatelet therapy (1, 22), referring to the clinical guidelines published by PharmGKB and CPIC (9), and taking into account the actual conditions of our institution, has developed the following antiplatelet therapy plans. For patients at Level − 1, a combined treatment plan of clopidogrel (75 mg/day) and aspirin (100 mg/day) is recommended. If the patient experiences bleeding events or is at risk of bleeding, it is advised to reduce the clopidogrel dose to 50 mg/day. At Level 0 and Level 1, patients maintain a combination of clopidogrel (75 mg/day) with aspirin (100 mg/day). For patients at Level 2, ticagrelor (90 mg, twice a day) is recommended, or increasing the clopidogrel dose to 150 mg/day, combined with aspirin (100 mg/day). For patients at Level 3 and Level 4, it is suggested to use ticagrelor (90 mg, twice a day) in place of clopidogrel, in combination with aspirin (100 mg/day).

Based on whether the patient's antiplatelet treatment plan is consistent with the recommendations of the genetic testing results, the patients are divided into two groups: Group A: Treatment plan consistent with genetic testing recommendations; Group B: Treatment plan not consistent with genetic testing recommendations. This study aims to compare the incidence of major adverse cardiovascular events (MACE) in patients with coronary heart disease between the two treatment plans and to assess the clinical value of antiplatelet therapy guided by the results of multigene testing.

Statistical Methods

Descriptive statistics will be reported for baseline data. For continuous data, normality tests will be performed first; if each group satisfies normality and the variances between groups are equal, the t-test will be used for intergroup comparison; otherwise, the non-parametric Wilcoxon rank-sum test will be considered. The chi-square test was used for unordered outcomes for categorical data, and the non-parametric Wilcoxon rank-sum test was used for ordered data.

Multifactorial Cox regression models were utilized to assess the impact of genetic and clinical factors on MACE and haemorrhage outcomes, both before and after IPTW adjustment. Confounding factors were controlled to ensure robust analysis. This study used the propensity score to estimate the inverse probability of treatment weight (IPTW), achieved through the following steps: Selecting covariates based on multivariate Cox regression and clinical expert opinion to select related confounding factors. Based on past literature studies and expert opinions, 15 variables are included as confounding factors: gender, age, ethnicity, BMI, smoking status, whether diabetes was diagnosed and disease type, whether surgical procedure was given, monocyte percentage, platelet count, mean platelet volume, uric acid, low-density lipoprotein, left ventricular ejection fraction, and bleeding risk. The standard mead difference of each selected covariate was reported. All statistical analyses will be performed using R software.

The cohort consisted of 601 patients, and the distribution of genetic polymorphisms and clinical characteristics is detailed in Table 3. Of 601 patients, 85.71% of Level − 1 patients, 68.87% of Level 0 patients, 79.43% of Level 1 patients, 24.22% of Level 2 patients, 31.69% of Level 3 patients, and 22.22% of Level 4 patients were classified into Group A (53.74%); 14.29% of Level − 1 patients, 31.13% of Level 0 patients, 20.57% of Level 1 patients, 75.78% of Level 2 patients, 68.31% of Level 3 patients, and 77.78% of Level 4 patients were classified into Group B (46.26%). See Table 4 for details. The standardized mean difference before and after IPTW were less than 0.1, indicating a well-balance of selected variables (Fig. 1).

Table 3

The Details of Variable Before/After Adjustment by IPTW [example(%)]
Variable	A group (n = 323)	B group (n = 278)	Before IPTW			After IPTW
Variable	A group (n = 323)	B group (n = 278)	P(MACE)	P(BE)	SMD	P(MACE)	P(BE)	SMD
Gender			0.391	0.229	0.007	0.158	0.052	0.010
Female	66(20.43)	56(20.14)
Male	257(79.57)	222(79.86)
Age (years)			0.004	0.411	0.083	< 0.001	0.249	0.011
< 55	98(30.34)	91(32.73)
≥ 55 and < 65	111(34.37)	98(35.25)
≥ 65 and < 75	78(24.15)	58(20.86)
≥ 75	36(11.15)	31(11.15)
Nation			0.003	0.006	0.188	< 0.001	< 0.001	0.015
Han	217(67.18)	168(60.43)
Uygur	70(21.67)	83(29.86)
Other	36(11.15)	27(9.71)
BMI (kg/m2)			0.184	0.447	0.014	0.149	0.134	0.003
< 24	88(27.24)	74(26.62)
≥ 24	235(72.76)	204(73.38)
Smoking history			0.124	0.150	0.065	0.020	0.114	0.007
No	151(46.75)	139(50.00)
Yes	172(53.25)	139(50.00)
Diabetes			0.521	0.059	0.002	0.594	0.003	0.014
No	196(60.68)	169(60.79)
Yes	127(39.32)	109(39.21)
Disease type			0.049	0.492	0.031	0.003	0.233	< 0.001
Chronic coronary syndrome	114(35.29)	94(33.81)
Acute coronary syndrome	209(64.71)	184(66.19)
Surgical intervention			0.194	0.067	0.148	0.018	0.001	0.009
No	71(21.98)	76(27.34)
PCI	247(76.47)	195(70.14)
CABG	5(1.55)	7(2.52)
Monocyte percentage (%)			0.308	0.424	0.061	0.111	0.101	0.005
< 10.00	283(87.62)	249(89.57)
≥ 10.00	40(12.38)	29(10.43)
Platelet count(10^9/L)			0.277	0.866	0.319	0.023	1	0.021
<300.00	298(92.26)	227(81.65)
≥ 300.00	25(7.74)	51(18.35)
Mean platelet volume(fL)			0.431	0.515	0.226	0.032	0.638	0.041
<12.50	305(94.43)	274(98.56)
≥ 12.50	18(5.57)	4(1.44)
Uric acid (µmol/L)			0.247	0.368	0.033	0.078	0.291	0.001
< 428.00	286(88.54)	249(89.57)
≥ 428.00	37(11.46)	29(10.43)
Low density lipoprotein (mmol/L)			0.313	0.575	0.061	0.358	0.324	0.004
< 1.80	121(37.46)	96(34.53)
≥ 1.80	202(62.54)	182(65.47)
Left ventricular ejection fraction ( %)			0.518	0.474	0.173	0.117	0.265	0.006
Normal (≥ 50.00)	291(90.09)	256(92.09)
Low (< 50.00)	32(9.91)	22(7.91)
Elevated risk of haemorrhage^#			0.739	0.894	0.070	0.292	1	0.019
No	300(92.88)	244(87.77)
Yes	23(7.12)	34(12.23)
Note: BE: Bleeding events;
#Elevated risk of bleeding is a composite variable defined as one or more: a history of peptic ulcer or reflux oesophagus or gastritis; history of previous haemorrhage; high blood creatinine (≥ 115 µmol/L).

Table 3 < Here>

Figure 1 < Here>

Figures 2a and 2b demonstrate the MACE (Major Adverse Cardiovascular Event) event probabilities over a 60-month follow-up period before and after IPTW (Inverse Probability of Treatment Weighting), respectively. In Fig. 2a, before applying IPTW, there is no significant difference between Group A and Group B (Log-rank p = 0.051), with Group A showing slightly lower event probabilities than Group B throughout the follow-up period. The number at risk decreases steadily for both groups over time, with Group A starting with 323 individuals and Group B starting with 278 individuals. In contrast, Fig. 2b illustrates the MACE event probabilities after IPTW adjustment, showing a significant difference between the two groups (Log-rank p = 0.006). Group A again exhibits lower event probabilities than Group B, but the divergence between the curves is more pronounced post-IPTW.

Figures 2c and 2d illustrate the haemorrhagic event probabilities over a 60-month follow-up period before and after IPTW (Inverse Probability of Treatment Weighting), respectively. In Fig. 2c, prior to IPTW adjustment, there is no significant difference between Group A and Group B regarding haemorrhagic event probabilities, with a Log-rank p-value of 0.608. Both groups display similar trends in event probabilities, and the number at risk decreases steadily over time, with Group A starting with 323 individuals and Group B with 278 individuals. Following the IPTW adjustment in Fig. 2d, the comparison remains non-significant, although the Log-rank p-value improves slightly to 0.281.

Figure 2 < Here>

The results of the multifactorial Cox regression analysis presented in Fig. 3 reveal several significant predictors of MACE both before and after adjustment using IPTW. Patients in Group A demonstrated a significantly lower risk of MACE compared to Group B, with a hazard ratio (HR) of 0.691 (95% CI: 0.542–0.879, P = 0.002) before IPTW adjustment, which remained significant after IPTW adjustment (HR 0.671, 95% CI: 0.526–0.855, P = 0.001). Age was a notable predictor, with patients aged 65–75 years having a significantly higher risk of MACE compared to those under 55 years, both before (HR 1.860, 95% CI: 1.338–2.585, P < 0.001) and after IPTW adjustment (HR 1.940, 95% CI: 1.356–2.775, P < 0.001). Ethnicity also played a role, with Uygur and other non-Han ethnicities showing an increased risk of MACE. Uygur patients had an HR of 1.364 (95% CI: 1.027–1.812, P = 0.031) before IPTW and 1.388 (95% CI: 1.036–1.858, P = 0.027) after IPTW. Smoking was another significant factor, associated with a higher risk of MACE before (HR 1.504, 95% CI: 1.117–2.026, P = 0.007) and after adjustment (HR 1.546, 95% CI: 1.127–2.121, P = 0.006). Additionally, elevated uric acid levels (≥ 428.00µmol/L) were consistently associated with an increased risk of MACE both before (HR 1.614, 95% CI: 1.121–2.324, P = 0.009) and after IPTW adjustment (HR 1.646, 95% CI: 1.148–2.360, P = 0.006). On the contrary, undergoing coronary artery bypass grafting (CABG) was associated with a lower risk of MACE, with a significant reduction in hazard before (HR 0.309, 95% CI: 0.096–0.992, P = 0.048) and after adjustment (HR 0.266, 95% CI: 0.087–0.814, P = 0.020). (Details of the results are shown in Supplementary file Table 1)

Figure 3 < Here>

The multifactorial Cox regression analysis results for haemorrhage, as presented in Fig. 4, indicate that the adjusted hazard ratios (HRs) after IPTW did not substantially differ from the unadjusted analysis. Notably, patients in Group A showed no significant difference in haemorrhage risk compared to Group B, with an HR of 0.851 (95% CI: 0.615–1.177, P = 0.330) before IPTW adjustment and 0.831 (95% CI: 0.598–1.155, P = 0.271) after adjustment. Uygur ethnicity was associated with a significantly lower risk of haemorrhage both before (HR 0.604, 95% CI: 0.386–0.945, P = 0.027) and after IPTW adjustment (HR 0.592, 95% CI: 0.369–0.951, P = 0.030). On the other hand, the type of surgical intervention also showed significance, where undergoing percutaneous coronary intervention (PCI) was associated with a higher risk of haemorrhage before (HR 1.633, 95% CI: 1.087–2.452, P = 0.018) and after IPTW adjustment (HR 1.723, 95% CI: 1.147–2.588, P = 0.008). Other variables, such as age, gender, BMI, smoking status, and diabetes, did not show significant associations with haemorrhage risk after adjustment, although some trends were noted. (Details of the results of Cox regression before and after IPTW are shown in Supplementary file Table 2)

Figure 4 < Here>

The results of this study were integrated with existing literature to delve deeper into the predictive value of combined testing of CYP2C19, ABCB1, and PON1 genes in determining differential responses to clopidogrel treatment. Our findings revealed that compared to the baseline (patients at level 0), the risk of adverse cardiovascular events significantly increased for patients at levels 2, 3, and 4. This reinforces the potential application of a multigene model in predicting clopidogrel efficacy. Specifically, we observed that antiplatelet treatment plans consistent with multigene testing results corresponded with a lower risk of adverse cardiovascular events. Building on our previous research on single-gene testing, the current results further highlight the significant value of comprehensive multigene testing in guiding personalised antiplatelet therapy, particularly in reducing adverse cardiovascular events.

Our findings align with several existing studies. A meta-analysis involving 11,740 patients (14) demonstrated that genotype-guided therapy significantly reduces the risk of all efficacy outcomes compared to standard treatment, without increasing bleeding event risks. Similarly, research by Shi et al. (23) found that individualized treatment based on the CYP2C19 genotype reduced major adverse cardiac and cerebrovascular events in Chinese ACS patients. Another multicenter prospective study (24) that randomly assigned patients to standard treatment or pharmacogenetic testing groups revealed that patients receiving genotype-guided treatment had a significantly lower incidence of ischemic events, with no significant difference in bleeding events.

However, there is ongoing controversy regarding the effectiveness of antiplatelet therapy guided by genetic genotyping, particularly in patients undergoing percutaneous coronary intervention (PCI). Some studies suggest that selecting antiplatelet drugs based solely on the CYP2C19 genotype does not offer additional clinical benefits. A multicenter prospective randomised clinical trial involving 5,302 PCI patients reported that gene-guided antiplatelet therapy did not significantly reduce the incidence of composite cardiovascular events compared to conventional therapy (25). A meta-analysis of 6,845 patients also failed to show the superiority of genotype- or phenotype-guided treatment over conventional methods (26). Furthermore, an observational study in Chinese patients with coronary artery disease indicated that CYP2C19 genotype-based antiplatelet therapy did not improve clinical outcomes (27).

The novelty of this study lies in its comprehensive consideration of multiple genes related to the efficacy of clopidogrel. Besides CYP2C19, the ABCB1 and PON1 genes play crucial roles in the drug's absorption and metabolism. By integrating multiple genetic variants such as CYP2C19 (*2, *3, *17), ABCB1 C3435T, and PON1 Q192R, we explored the effectiveness of antiplatelet therapy guided by multigene testing results in clinical practice. Our retrospective analysis and propensity score matching to control for confounding factors revealed that treatment plans consistent with multigene testing results significantly reduced the incidence of adverse cardiovascular events. This finding demonstrated the potential value of multigene testing in personalized antiplatelet therapy.

These findings indicate that clopidogrel efficacy is influenced by a complex mechanism regulated by multiple genes. Compared to single-gene testing, multigene testing can more comprehensively cover various aspects of clopidogrel's mechanism of action. It may provide a more refined molecular basis for developing individualized treatment strategies. Nevertheless, further prospective validation studies on a larger scale and in diverse populations are necessary to confirm whether multigene testing-guided antiplatelet treatment can offer significant statistical and clinical benefits. Additionally, other genetic and non-genetic factors might affect clopidogrel efficacy. Given that cardiovascular disease results from multifactorial and polygenic interactions, future research should further encompass genetic, phenotypic, environmental, and lifestyle factors to optimise individualized antiplatelet treatment strategies.

Despite its robust findings, this study has certain limitations: Firstly, while propensity score matching enhances the credibility of results from retrospective clinical trials, this study's single-center nature and limited sample size may introduce a degree of bias and limit generalizability. Secondly, while propensity score matching balances known confounding factors, it cannot eliminate unknown confounders, nor can it avoid potential confounds undetected in the study. Finally, the inability to record and compare new diseases, blood lipid levels, and liver and kidney function during follow-up periods means it is uncertain whether clinical data during follow-up influenced outcomes. More definitive conclusions await further confirmation by larger sample, multicenter real-world clinical studies.

This study indicates that antiplatelet treatment strategies based on multigene testing results (including variations in CYP2C19, ABCB1, and PON1 genes) are more effective than those guided solely by the CYP2C19 genetic profile. By employing propensity score matching, treatment plans conforming to multigene testing significantly reduced the incidence of adverse cardiovascular events, particularly in post-PCI patients. These findings underscore the importance of considering multiple genetic interactions when assessing clopidogrel responsiveness, providing a precise molecular basis for personalized treatment.

Further prospective research is required to validate the effectiveness and generalizability of multigene testing across diverse populations. Future studies should incorporate genetic, environmental, and lifestyle factors to develop a more comprehensive model for individualized medicine. Such an approach could enhance antiplatelet therapy efficacy and drive advancements in cardiovascular disease management, thereby improving patient quality of life and treatment outcomes.

In summary, the application of multigene testing in individualised cardiovascular disease management holds significant clinical relevance and is expected to become an integral part of future medical practices. Adopting this personalised approach can optimise antiplatelet therapy, mitigate risks, and ensure better patient care and outcomes.

Funding

This study was sponsored by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No.: 2021D01C316), and Tianshan Elites Cultivation Programme for High-Level Talents in Medicine and Health Care: Leading Talent Project of Xinjiang Uygur Autonomous Region (Grant No.: TSYC202301A051)

Acknowledgement

We would like to express our profound gratitude to Prof. Xiang Xie from the Centre of Hypertensive Disease at the First Affiliated Hospital of Xinjiang Medical University for his invaluable guidance and support throughout this study. His deep expertise and insightful feedback were instrumental in successfully completing this research. We sincerely appreciate his continuous encouragement and the academic mentorship he provided, which significantly enhanced the quality of this manuscript. Prof. Xie's contributions cannot be overstated, and we are sincerely thankful for his commitment and dedication to this work.

Competing interests

The authors (Y. WANG, S. YUAN, M. LI4, W. FENG, J. LI, A. BAYERTI, Y. KOU, Y. YUAN, J. ZHAO) have no competing interests to declare that are relevant to the content of this article.

Availability of data and material

All data supporting the findings of this study are available in the paper and its Supplementary Information section.

Code availability

Not applicable.

CRediT author statement

Yubo WANG: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - Original Draft, Visualization;

Shuangli YUAN: Conceptualization, Methodology, Formal analysis, Investigation, Writing - Original Draft, Visualization;

Muyun LI: Formal analysis, Visualization, Writing—review and editing;

Wenlin FENG: Data Curation, Writing—review and editing;

Jing LI: Data Curation, Writing—review and editing;

Aliye BAYERTI: Data Curation, Writing—review and editing;

Yulina KOU: Data Curation, Writing—review and editing;

Yuan YUAN: Conceptualization, Methodology, Validation, Data Curation, Writing—review and editing, Visualization, Supervision, Project administration.

Jun ZHAO: Conceptualization, Methodology, Validation, Data Curation, Writing—review and editing, Visualization, Supervision, Project administration.

Lawton JS, Tamis-Holland JE, Bangalore S, Bates ER, Beckie TM, Bischoff JM, et al. 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022;145(3):e18-e114.
Pereira NL, Rihal CS, So DYF, Rosenberg Y, Lennon RJ, Mathew V, et al. Clopidogrel Pharmacogenetics. Circulation Cardiovascular interventions. 2019;12(4):e007811.
Karaźniewicz-Łada M, Danielak D, Główka F. Genetic and non-genetic factors affecting the response to clopidogrel therapy. Expert opinion on pharmacotherapy. 2012;13(5):663-83.
Shuldiner AR, O'Connell JR, Bliden KP, Gandhi A, Ryan K, Horenstein RB, et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. Jama. 2009;302(8):849-57.
SUN Yue-mei, CUI Ming-xia, LI Wen-bin, ZHANG Juan-hong, CHEN Yu-yan, LU Hui, et al. Research Progress on Clinical Gene Polymorphism of Clopidogrel Resistance. Chinese Pharmaceutical Journal. 2018;53(18):1529-35.
Kazui M, Nishiya Y, Ishizuka T, Hagihara K, Farid NA, Okazaki O, et al. Identification of the human cytochrome P450 enzymes involved in the two oxidative steps in the bioactivation of clopidogrel to its pharmacologically active metabolite. Drug metabolism and disposition: the biological fate of chemicals. 2010;38(1):92-9.
Simon T, Verstuyft C, Mary-Krause M, Quteineh L, Drouet E, Méneveau N, et al. Genetic determinants of response to clopidogrel and cardiovascular events. The New England journal of medicine. 2009;360(4):363-75.
Hassani Idrissi H, Hmimech W, Khorb NE, Akoudad H, Habbal R, Nadifi S. A synergic effect between CYP2C19*2, CYP2C19*3 loss-of-function and CYP2C19*17 gain-of-function alleles is associated with Clopidogrel resistance among Moroccan Acute Coronary Syndromes patients. BMC research notes. 2018;11(1):46.
Scott SA, Sangkuhl K, Stein CM, Hulot JS, Mega JL, Roden DM, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clinical pharmacology and therapeutics. 2013;94(3):317-23.
Dai ZL, Chen H, Wu XY. Relationship between cytochrome P450 2C19*17 genotype distribution, platelet aggregation and bleeding risk in patients with blood stasis syndrome of coronary artery disease treated with clopidogrel. Zhong xi yi jie he xue bao = Journal of Chinese integrative medicine. 2012;10(6):647-54.
Xi Z, Fang F, Wang J, AlHelal J, Zhou Y, Liu W. CYP2C19 genotype and adverse cardiovascular outcomes after stent implantation in clopidogrel-treated Asian populations: A systematic review and meta-analysis. Platelets. 2019;30(2):229-40.
Al-Abcha A, Radwan Y, Blais D, Mazzaferri EL, Jr., Boudoulas KD, Essa EM, et al. Genotype-Guided Use of P2Y12 Inhibitors: A Review of Current State of the Art. Frontiers in cardiovascular medicine. 2022;9:850028.
Al-Rubaish AM, Al-Muhanna FA, Alshehri AM, Al-Mansori MA, Alali RA, Khalil RM, et al. Bedside testing of CYP2C19 vs. conventional clopidogrel treatment to guide antiplatelet therapy in ST-segment elevation myocardial infarction patients. International journal of cardiology. 2021;343:15-20.
Tang B, Wang X, Wang X, Liu L, Ma Z. Genotype-Guided Antiplatelet Therapy Versus Standard Therapy for Patients with Coronary Artery Disease: An Updated Systematic Review and Meta-Analysis. Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques. 2022;25:9-23.
Zhang Y, Shi XJ, Peng WX, Han JL, Lin BD, Zhang R, et al. Impact of Implementing CYP2C19 Genotype-Guided Antiplatelet Therapy on P2Y(12) Inhibitor Selection and Clinical Outcomes in Acute Coronary Syndrome Patients After Percutaneous Coronary Intervention: A Real-World Study in China. Frontiers in pharmacology. 2020;11:582929.
Calderón-Cruz B, Rodríguez-Galván K, Manzo-Francisco LA, Vargas-Alarcón G, Fragoso JM, Peña-Duque MA, et al. C3435T polymorphism of the ABCB1 gene is associated with poor clopidogrel responsiveness in a Mexican population undergoing percutaneous coronary intervention. Thrombosis research. 2015;136(5):894-8.
Chen Y, Huang X, Tang Y, Xie Y, Zhang Y. Both PON1 Q192R and CYP2C19*2 influence platelet response to clopidogrel and ischemic events in Chinese patients undergoing percutaneous coronary intervention. International journal of clinical and experimental medicine. 2015;8(6):9266-74.
Wójcik T, Szymkiewicz P, Wiśniewski J, Lebioda A, Jonkisz A, Gamian A, et al. Distribution of polymorphisms in the CYP2C19 and ABCB1 genes among patients with acute coronary syndrome in Lower Silesian population. Advances in clinical and experimental medicine : official organ Wroclaw Medical University. 2019;28(12):1621-6.
Taubert D, von Beckerath N, Grimberg G, Lazar A, Jung N, Goeser T, et al. Impact of P-glycoprotein on clopidogrel absorption. Clinical pharmacology and therapeutics. 2006;80(5):486-501.
Carlquist JF, Knight S, Horne BD, Huntinghouse JA, Rollo JS, Muhlestein JB, et al. Cardiovascular risk among patients on clopidogrel anti-platelet therapy after placement of drug-eluting stents is modified by genetic variants in both the CYP2C19 and ABCB1 genes. Thrombosis and haemostasis. 2013;109(4):744-54.
James S, Akerblom A, Cannon CP, Emanuelsson H, Husted S, Katus H, et al. Comparison of ticagrelor, the first reversible oral P2Y(12) receptor antagonist, with clopidogrel in patients with acute coronary syndromes: Rationale, design, and baseline characteristics of the PLATelet inhibition and patient Outcomes (PLATO) trial. American heart journal. 2009;157(4):599-605.
[Chinese Society of Cardiology and Chinese College of Cardiovascular Physicians Expert Consensus statement on dual antiplatelet therapy in patients with coronary artery disease]. Zhonghua xin xue guan bing za zhi. 2021;49(5):432-54.
Shi X, Zhang Y, Zhang Y, Zhang R, Lin B, Han J, et al. Personalized Antiplatelet Therapy Based on CYP2C19 Genotypes in Chinese ACS Patients Undergoing PCI: A Randomized Controlled Trial. Frontiers in cardiovascular medicine. 2021;8:676954.
Notarangelo FM, Maglietta G, Bevilacqua P, Cereda M, Merlini PA, Villani GQ, et al. Pharmacogenomic Approach to Selecting Antiplatelet Therapy in Patients With Acute Coronary Syndromes: The PHARMCLO Trial. Journal of the American College of Cardiology. 2018;71(17):1869-77.
Pereira NL, Farkouh ME, So D, Lennon R, Geller N, Mathew V, et al. Effect of Genotype-Guided Oral P2Y12 Inhibitor Selection vs Conventional Clopidogrel Therapy on Ischemic Outcomes After Percutaneous Coronary Intervention: The TAILOR-PCI Randomized Clinical Trial. Jama. 2020;324(8):761-71.
Kheiri B, Abdalla A, Osman M, Barbarawi M, Zayed Y, Haykal T, et al. Personalized antiplatelet therapy in patients with coronary artery disease undergoing percutaneous coronary intervention: A network meta-analysis of randomized clinical trials. Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions. 2019;94(2):181-6.
Tan K, Lian Z, Shi Y, Wang X, Yu H, Li M, et al. The effect of CYP2C19 genotype-guided antiplatelet therapy on outcomes of selective percutaneous coronary intervention patients: an observational study. Personalized medicine. 2019;16(4):301-12.

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Genetic and Clinical Determinants of MACE and Haemorrhage in Antiplatelet Therapy: Insights from Pharmacogenomic Analysis

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