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. 2025 Dec 21;61(3):319–329. doi: 10.1002/lipd.70018

Impact of CYP2C19 Genotype Variants on PCSK9 Inhibitor Efficacy in Lipid‐Lowering Among Patients With Symptomatic Intracranial Atherosclerotic Stenosis

Chao Zhao 1,2,3, Nuan Wang 2, Di Shi 2, Hao Zhou 2, Dan Chen 2, Guofang Chen 1,4,✉
PMCID: PMC13144705  PMID: 41423442

ABSTRACT

Ischemic stroke is frequently associated with symptomatic intracranial atherosclerotic stenosis (sICAS), is a leading cause of global disability and mortality. Current guidelines recommend dual antiplatelet and intensive statin therapies. Proprotein convertase subtilisin 9/kexin type 9 (PCSK9) inhibitors have emerged as a potent lipid‐lowering therapy, potentially influenced by genetic variations, particularly in the CYP2C19 gene. This study at Xuzhou Central Hospital from January 2021 to December 2023 included 151 patients divided into a statin group (n = 73) and a PCSK9 inhibitor (PCSK9i) group (n = 78). It evaluated lipid profiles, inflammatory markers, neurological function, and clinical outcomes over a 180‐day follow‐up period, with additional analysis stratified by CYP2C19 genotype. The PCSK9i group demonstrated significant improvements in lipid parameters compared to the statin group, including greater reductions in low‐density lipoprotein cholesterol (LDL‐C) (p = 0.008), total cholesterol (TC) (p < 0.001), and triacylglycerols (TAG) (p = 0.041), along with apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB) (both p < 0.001). Inflammatory markers, particularly interleukin‐6 (IL‐6), significantly reduced in the PCSK9i group (p < 0.001). In the PCSK9i group, CYP2C19 rapid metabolizers achieved greater reductions in LDL‐C (p = 0.021), ApoB (p = 0.003), and IL‐6 levels (p = 0.041) compared to slow metabolizers. Post‐treatment modified Rankin Scale (mRS) scores were significantly lower in rapid metabolizers compared to slow metabolizers (p = 0.018), though clinical events occurred infrequently in both subgroups. This study demonstrates that PCSK9 inhibitor therapy combined with statins provides enhanced lipid‐lowering and anti‐inflammatory effects compared to statin monotherapy in sICAS patients. While the CYP2C19 genotype may influence specific treatment responses, particularly lipid parameters, its impact on clinical outcomes requires further investigation.

Keywords: CYP2C19, intracranial atherosclerotic stenosis, LDL‐C, PCSK9 inhibitor, statins

1. Introduction

Intracranial atherosclerotic stenosis (ICAS) represents a significant cause of ischemic stroke and is associated with elevated incidence and mortality rates of cerebrovascular diseases (Broderick et al. 2017; Mac Grory et al. 2022; Cheng et al. 2025). Patients with ICAS frequently have multiple chronic diseases, especially dyslipidemia. Dyslipidemia, especially low‐density lipoprotein cholesterol (LDL‐C) levels, constitutes a key risk factor for the progression of atherosclerosis and the occurrence of stroke (Abdul‐Rahman et al. 2022). Statins, as the core therapy for regulating lipid levels, have been proven to effectively lower LDL‐C levels and prevent stroke occurrence (Lavecchia and Cerchia 2019; Gallego‐Colon et al. 2020). However, it is noteworthy that individual responses to statin therapy exhibit considerable variability. In a subset of patients, LDL‐C levels remain refractory to effective control despite intensive statin treatment. Consequently, identifying rational strategies to ameliorate hyperlipidemia and atherosclerosis presents an urgent therapeutic challenge.

The advent of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors has provided new lipid‐lowering therapeutic options for patients with elevated LDL‐C levels and those currently receiving statin therapy, demonstrating considerable therapeutic potential (Zhao et al. 2021). The combination of PCSK9 inhibitors with statins yields synergistic effects, further lowering LDL‐C levels and improving cardiovascular risk profiles in patients with familial hypercholesterolemia and atherosclerotic cardiovascular disease (Shafabakhsh et al. 2021). In recent years, the significant influence of cytochrome P450 2C19 on the efficacy of statin therapy has garnered attention. The CYP2C19 gene belongs to the cytochrome P450 family and can influence the metabolism of statins by regulating enzyme activity, thereby affecting drug efficacy and safety (Tanaka et al. 2019). CYP2C19 genotypes have traditionally been used to guide the selection of antiplatelet therapy (Haj Saleh and Youssef 2025); recent studies (Kirchheiner et al. 2003) suggest that CYP2C19 genotypes may indirectly influence the LDL cholesterol‐lowering effects of PCSK9 inhibitors by regulating inflammatory responses and oxidative stress pathways. However, the precise relationship between CYP2C19 genotypes and the efficacy of PCSK9 inhibitors requires further clinical studies for validation.

2. Methods

2.1. Study Design

This retrospective pilot clinical trial was conducted as a single‐center study at Xuzhou Central Hospital, China. The study was conducted from January 2021 to December 2023. Comprehensive clinical data, including age, gender, vascular risk factors (e.g., hypertension, hyperlipidemia, diabetes mellitus, smoking history, coronary heart disease history, prior stroke, and transient ischemic attack), and relevant medication details (such as statins, antiplatelet agents, antihypertensive drugs, and antidiabetic medications), were extracted from medical records. Regular blood tests were performed for all patients, and parameters were measured, such as glycosylated hemoglobin, C‐reactive protein, and lipid panel, which included measurements of total cholesterol (TC), triacylglycerols (TAG), high‐density lipoprotein cholesterol (HDL‐C), and LDL‐C. Certified neurologists assessed the National Institutes of Health Stroke Scale (NIHSS), Glasgow Coma Scale (GCS), and Modified Rankin Scale (mRS) scores at baseline and during the 6‐month follow‐up period (Broderick et al. 2017). The lipid panel was analyzed 3 months after the baseline assessment. All procedures performed in studies involving human participants were in accordance with the standards upheld by the Ethics Committee of Xuzhou Central Hospital (IRB ID:XZXY‐LK‐20210610‐006), and with those of the 1964 Helsinki Declaration and its later amendments for ethical research involving human subjects. And informed consent was obtained from all participants or their respective patient guardians.

The inclusion criteria were as follows: (1) age ≤ 80 years; (2) one of the following conditions: transient ischemic attack within 14 days (ABCD2 baseline score ≥ 4); nonsevere stroke within 14 days (mRS ≤ 3); (3) stroke event caused by intracranial arterial stenosis (stenosis degree of 50%–99% in cervical internal carotid artery C6‐7, MCA M1 segment, vertebral artery, or basilar artery); diagnosis with magnetic resonance angiography (MRA) or coronary computed tomography angiography (CTA); (4) MRA or CTA confirming atherosclerosis as the cause of intracranial arterial stenosis; (5) patients willing and able to complete all required follow‐up visits as per the treatment protocol; (6) patients receiving information about the purpose and requirements of the study and signing informed consent forms. The exclusion criteria were: (1) uncertain culprit vessels; (2) nonatherosclerotic arterial stenosis; (3) thrombolytic therapy within 24 h; (4) recent or planned endovascular intervention; (5) intracranial bleeding within 90 days; (6) intracranial tumors, aneurysms, or arteriovenous malformation; (7) cardiac sources of embolism; (8) abnormal laboratory parameters; (9) recent hemorrhage or coagulation dysfunction; (10) recent or planned surgery; (11) severe neurological deficits; (12) pregnancy; (13) use of PCSK9 or cholesteryl ester transfer protein inhibitors (CETP) inhibitors; (14) use of specific medications; (15) use of high‐dose niacin or other lipid‐lowering agents; (16) use of CYP3A4 inhibitors; (17) statin allergy; (18) uncontrolled hypertension; (19) heart failure.

The eligible participants were randomly assigned to two cohorts: the PCSK9 inhibitor and statin cohorts. In the PCSK9 inhibitor cohort, participants were given subcutaneous injections of either alirocumab (75 mg) or evolocumab (140 mg) every 2 weeks in addition to daily statin therapy (either 40 mg of atorvastatin or 20 mg of rosuvastatin). Participants in the statin cohort only received daily statin therapy (either 40 mg of atorvastatin or 20 mg of rosuvastatin). Based on CYP2C19 genotyping, patients were stratified into two groups: rapid CYP2C19 genotyping (carriers of at least one CYP2C19*17 allele) and slow CYP2C19 genotyping (carriers of one or two reduced‐function alleles CYP2C192, *3, *4, *5, *6, *7, or *8).

2.2. Clinical Data

Clinical data collected included demographic information, vascular risk factors, and laboratory indicators. Vascular risk factors included hypertension, diabetes, previous ischemic stroke, coronary artery disease, smoking, coronary stent, history of cerebral infarction, cerebral hemorrhage history, cerebrovascular stent history, family medical history, use of antiplatelet drugs before the event, use of statins before the event, onset time, and blood pressure. On admission and follow‐up, the following laboratory data were collected: alanine aminotransferase (ALT), aspartate aminotransferase (AST), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), HDL‐C, LDL‐C, homocysteine, glycosylated hemoglobin, interleukin‐6 (IL‐6), high‐sensitivity C‐reactive protein (hs‐CRP), D‐dimer, lipoprotein(a) (Lp(a)) and lipoprotein‐associated phospholipase A2 (Lp‐PLA2). In addition, scores were obtained for all patients using the GCS, NIHSS, and mRS scores.

2.3. Statistics

Data was analyzed using SPSS software version 26.0. For quantitative data conforming to a normal distribution, the mean ± standard deviation (x̄ ± s) was used for representation. Within‐group comparisons were conducted using paired sample t‐tests, whereas between‐group comparisons were conducted using independent samples t‐tests. For data not following a normal distribution, the median (interquartile range; M [Q1, Q3]) was presented, and within‐group comparisons were carried out using paired Wilcoxon rank‐sum tests, and between‐group comparisons using Mann–Whitney U tests. Categorical data were expressed as numbers (percentage; n [%]), and between‐group comparisons were performed using chi‐square (χ 2) tests and within‐group comparisons using paired χ 2 tests. A p value of < 0.05 was considered statistically significant.

3. Results

3.1. Baseline Clinical and Laboratory Characteristics of Statin and PCSK9i Groups

A total of 151 consecutive patients with symptomatic intracranial atherosclerotic stenosis were enrolled in this study, comprising 78 patients in the PCSK9i group and 73 patients in the statin group. Baseline demographic and clinical characteristics were collected and compared between the two groups (Table 1). The median age of patients was comparable between groups (PCSK9i: 68.0 [60.0–74.0] years vs. statin: 68.0 [58.0–72.0] years, p = 0.746). Similarly, there was no significant difference in sex distribution, with male patients accounting for 62.8% and 60.3% in the PCSK9i and statin groups, respectively (p = 0.749).

TABLE 1.

Baseline characteristics of patients with symptomatic intracranial artery stenosis who received PCSK9 inhibitors or statin therapy according to randomized treatment group.

Variables Statin group (n = 73) PCSK9i group (n = 78) Statistic p
Age, M (Q1, Q3) 68.0 (58.0, 72.0) 68.0 (60.0, 74.0) Z = −0.32 0.746
Men (Example [%]) 44 (60.3) 49 (62.8) Z = −0.320 0.749
CYP2C19 genotype‐ Medium rapid metabolism (%) a 32 (47.8) 35 (52.2) Z = 0.000 1.000
CYP2C19 genotype‐ Medium slow metabolism (example [%]) 41 (56.2) 43 (55.1) Z = −0.128 0.898
Smoking (Example [%]) 18 (24.7) 18 (23.1) Z = −0.227 0.820
Duration of aortic cross‐clamp (Example [%]) 31 (42.5) 32 (41.0) Z = −0.179 0.858
Hypertension (Example [%]) 51 (69.9) 54 (69.2) Z = −0.084 0.933
Diabetes (Example [%]) 20 (27.4) 20 (25.6) Z = −0.244 0.808
Coronary heart disease (CHD) (Example [%]) 11 (15.1) 22 (28.2) Z = −1.946 0.052
Percutaneous coronary intervention (PCI) (Example [%]) 4 (5.5) 5 (6.4) Z = −0.241 0.810
Cerebral infarction (CI) (Example [%]) 19 (26.0) 24 (30.8) Z = −0.643 0.520
Intracerebral hemorrhage (ICH) (Example [%]) 3 (4.1) 2 (2.6) Z = −0.529 0.597
Cerebrovascular stent (Example [%]) 6 (8.2) 10 (12.8) Z = −0.915 0.360
Family history (Example [%]) 1 (1.4) 0 (0) Z = −1.034 0.301
Onset time (days) (Q1, Q3) 3.0 (1.0, 7.0) 2.0 (1.0, 7.0) Z = −1.710 0.477
NIHSS (Q1, Q3) a 5.0 (2.0, 8.0) 5.0 (2.0, 6.0) Z = −0.608 0.543
MRS (Q1, Q3) b 2.0 (1.0, 3.5) 2.0 (1.0, 3.0) Z = −0.033 0.974
ICA (C6–7) (Example [%]) 16 (21.9) 20 (25.6) Z = −0.535 0.593
MCA (M1) (Example [%]) 37 (50.7) 28 (35.9) Z = −1.828 0.068
VA (Example [%]) 16 (21.9) 15 (19.2) Z = −0.407 0.684
BA (Example [%]) 5 (6.8) 5 (6.4) Z = −0.108 0.914
ACA (Example [%]) 9 (12.3) 8 (10.3) Z = −0.401 0.688
SCA (Example [%]) 4 (5.5) 2 (2.6) Z = −0.913 0.361
PCA (Example [%]) 17 (23.3) 13 (16.7) Z = −1.016 0.310
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Note: Data are n (%), median (IQR).

a

Scores on the National Institutes of Health stroke scale (NIHSS) range from 0 to 42, with higher scores indicating a more severe neurological deficit.

b

Scores on the Improved Rankin scale (mRS) range from 0 (normal) to 6 (death).

With respect to CYP2C19 genotype distribution, the proportion of patients with rapid metabolism was comparable between groups (p = 1.000), as was the proportion with slow metabolism (p = 0.898). The prevalence of traditional cardiovascular risk factors, including current smoking (p = 0.820), duration of aortic cross‐clamp (p = 0.858), hypertension (p = 0.933), and diabetes mellitus (p = 0.808), showed no significant differences between the PCSK9i and statin groups. Notably, there was a trend toward higher prevalence of coronary heart disease in the PCSK9i group compared to the statin group (p = 0.052). Furthermore, baseline neurological function was comparable between groups as assessed by NIHSS score (p = 0.543) and mRS score (p = 0.974). These findings indicated that the baseline characteristics were generally well‐balanced between the two treatment groups.

3.2. Superior Lipid‐Lowering and Anti‐Inflammatory Effects With PCSK9 Inhibitor Treatment Compared to Statin Monotherapy

To evaluate the efficacy of different treatment strategies, we compared the changes in lipid parameters, and inflammatory markers between baseline and after 180 days of treatment in both groups (Table 2). At baseline, all parameters were comparable between the PCSK9i and statin groups.

TABLE 2.

Changes in lipid parameters and inflammatory indexes after treatment.

Variables Statin group (n = 73) PCSK9i group (n = 78) p
Baseline LDL‐C, M (Q1, Q3) (mmol/L) 3 (2.43, 3.62) 3.16 (2.27, 3.88) 0.786
After treatment (180 days) 2.14 (1.38, 2.69) 1.71 (1.12, 2.00) 0.004
Difference −1.5 (−1.74, −1.05) −1.95 (−2.52, −0.96) 0.008
Baseline TC, M (Q1, Q3) (mmol/L) 4.14 (3.48, 5.14) 4.09 (3.17, 5.39) 0.844
After treatment (180 days) 3.94 (3.28, 4.84) 3.39 (2.56, 4.48) 0.010
Difference −0.2 (−0.2, −0.2) −0.85 (−1.53, −0.2) 0.000
Baseline TAG, M (Q1, Q3) (mmol/L) 2.33 (1.78, 2.95) 2.24 (1.75, 2.98) 0.712
After treatment (180 days) 1.89 (1.17, 2.64) 1.62 (1.04, 1.91) 0.007
Difference −0.46 (−1.13, −0.29) −0.75 (−1.30, −0.26) 0.041
Baseline ApoA1, M (Q1, Q3) (g/L) 1.22 (1.01, 1.42) 1.26 (1.01, 1.43) 0.603
After treatment (180 days) 1.34 (1.08, 1.67) 2.12 (1.38, 2.54) 0.000
Difference −0.11 (−0.11, 0.44) 0.78 (0.02, 1.33) 0.000
Baseline ApoB, M (Q1, Q3) (g/L) 0.83 (0.65, 1.05) 0.81 (0.56, 1.03) 0.387
After treatment (180 days) 0.75 (0.57, 0.98) 0.60 (0.50, 0.73) 0.003
Difference −0.12 (−0.41, 0.22) −0.17 (−0.39, −0.03) 0.000
Baseline HDL‐C, M (Q1, Q3) (mmol/L) 1.04 (0.89, 1.27) 1.00 (0.85, 1.20) 0.139
After treatment (180 days) 1.05 (0.82, 1.19) 1.17 (0.88, 1.33) 0.015
Difference −0.10 (−0.24, 0.11) 0.06 (−0.05, 0.25) 0.000
Baseline Lp(a), M (Q1, Q3) (nmol/L) 227 (119.5, 481) 247.50 (126.75, 442.75) 0.867
After treatment (180 days) 150 (68.5, 408) 115.00 (35.98, 237.00) 0.040
Difference −48.27 (−129.47, 260.15) −78.50 (−176.75, 18.50) 0.008
Baseline IL‐6, M (Q1, Q3) (pg/mL) 11.5 (9.9, 12.45) 11.59 (9.49, 12.90) 0.736
After treatment (180 days) 6.53 (5.95, 7.4) 5.04 (4.69, 5.45) 0.000
Difference −4.79 (−6.00, −3.10) −6.44 (−8.02, −4.39) 0.000
Baseline hs‐CRP, M (Q1, Q3) (mg/L) 9.04 (3.89, 17.25) 10.96 (7.98, 12.69) 0.838
After treatment (180 days) 6.73 (6.12, 7.31) 5.28 (4.59, 5.75) 0.000
Difference −1.41 (−9.58, 2.51) −5.36 (−7.52, 0.00) 0.163
Baseline Lp‐PLA2, M (Q1, Q3) (nmol/min/mL) 192.84 (175.67, 210.55) 195.00 (173.00, 284.75) 0.243
After treatment (180 days) 185 (182, 243.5) 174.09 (157.22, 202.17) 0.000
Difference 0.00 (−7.63, 25.89) −20.01 (−57.59, −0.16) 0.000
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Abbreviations: ApoA1, apolipoprotein A1; ApoB, apolipoprotein b; HDL‐C, high‐density lipoprotein cholesterol; hs‐CRP, hypersensitive C‐reactive protein; IL‐6, interleukin‐6; LDL‐C, low‐density lipoprotein cholesterol; Lp‐pla2, lipoprotein‐associated phospholipase a2; Lp(a), lipoprotein a; PCSK9i, a proprotein convertase subtilisin/kexin type 9 inhibitor; TAG, triglyceride; TC, total cholesterol.

Regarding lipid profiles, both groups showed significant improvements after treatment, with the PCSK9i group demonstrating superior efficacy. The reductions in LDL‐C (p = 0.008), TC (p < 0.001), and TAG (p = 0.041) were significantly more significant in the PCSK9i group compared to the statin group. A notable difference was observed in the ApoA1 response, where the PCSK9i group showed a significant increase while the statin group showed minimal change (p < 0.001). Similarly, HDL‐C levels improved significantly in the PCSK9i group (p < 0.001). ApoB levels decreased more substantially in the PCSK9i group (p < 0.001), and Lp(a) levels showed a more pronounced reduction in the PCSK9i group (p = 0.008).

In inflammatory markers, several parameters showed significant improvements in the PCSK9i group compared to the statin group. IL‐6 levels demonstrated a significantly more substantial reduction in the PCSK9i group (p < 0.001). hs‐CRP after treatment was significantly lower in the PCSK9i group (p < 0.001). Similarly, Lp‐PLA2 showed a more significant decrease in the PCSK9i group (p < 0.001). Additionally, Lp(a) levels exhibited a more pronounced reduction in the PCSK9i group compared to the statin group (p = 0.008) (Table 2). These findings suggest that PCSK9i treatment may provide comprehensive anti‐inflammatory benefits beyond its lipid‐lowering effects.

These findings demonstrate that while both treatment strategies improved multiple cardiovascular risk parameters, the PCSK9i treatment substantially improved more across most lipid parameters and inflammatory markers. This comprehensive analysis suggests enhanced therapeutic efficacy of the PCSK9i approach in managing multiple aspects of cardiovascular risk.

3.3. Impact of CYP2C19 Genotypes on PCSK9 Inhibitor‐Mediated Lipid Profile Changes

To investigate whether the CYP2C19 genotype influences the lipid‐lowering efficacy of PCSK9 inhibitor therapy, we analyzed lipid parameter changes stratified by metabolizer status in the PCSK9i group. At baseline, there were no significant differences in lipid parameters between rapid (n = 35) and slow (n = 43) metabolizers.

After 180 days of treatment, differences emerged in several lipid parameters between the metabolizer groups (Figure 1 and Tables 3 and 4). Rapid metabolizers showed significantly lower LDL‐C levels compared to slow metabolizers (p = 0.013), with the magnitude of reduction from baseline being significantly different between groups (p = 0.021) (Table 3). Total cholesterol levels also demonstrated a more pronounced decrease in rapid metabolizers (p = 0.029), although baseline levels were comparable (p = 0.053).

FIGURE 1.

FIGURE 1

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Differential effects of PCSK9 inhibitor therapy on lipid parameters stratified by CYP2C19 metabolizer status. This study examined the association between CYP2C19 metabolizer status and lipid parameter modulation in response to PCSK9 inhibitor therapy. Box plots illustrate the distribution of key lipid parameters in CYP2C19 rapid (n = 35) and slow (n = 43) metabolizers at baseline and following 180 days of treatment. The plots depict median values (horizontal lines within boxes), interquartile ranges (boxes), and individual data points (black dots). Statistical significance is indicated by asterisks (*p < 0.05, ***p < 0.001). ApoA1, apolipoprotein A1; ApoB, apolipoprotein b; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; TC, total cholesterol; TG, triacylglycerols.

TABLE 3.

PCSK9i‐mediated reduction in low‐density lipoprotein levels stratified by CYP2C19 genotype.

CYP2C19 metabolizer status n (%) LDL‐C at hospitalization (mmol/L), median (min–max) LDL‐C 6 months after hospitalization (mmol/L), median (min–max) ∆LDL‐C (mmol/L), median (min–max) p
Rapid metabolism 35 (45) 3.24 (2.05, 4.03) 0.89 (0.68, 1.51) −1.92 (−2.77, −1.33) 0.021
Slow metabolism 43 (55) 3.14 (2.27, 3.85) 1.33 (1.05, 1.74) −1.97 (−2.26, −0.94)
Total 78 (100) 3.16 (2.27, 3.88) 1.71 (1.12, 2.00)
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Note: CYP2C19 *2, *3, *17; the LDL‐C values between two medians differed, p = 0.01; p value between medians of ∆LDL‐C.

TABLE 4.

PCSK9i lipid‐lowering effects stratified by CYP2C19 genotype.

PCSK9i group CYP2C19 rapid metabolism (n = 35) CYP2C19 slow metabolism (n = 43) z p
After treatment (180 days) LDL‐C, M (Q1, Q3) (mmol/L) 0.89 (0.68, 1.51) 1.33 (1.05, 1.74) −2.487 0.013
After treatment (180 days) TC, M (Q1, Q3) (mmol/L) 2.93 (2.01, 4.37) 3.75 (2.90, 4.49) −2.185 0.029
After treatment (180 days) TAG, M (Q1, Q3) (mmol/L) 1.67 (1.03, 1.88) 1.57 (1.23, 1.94) −0.010 0.992
After treatment (180 days) ApoA1, M (Q1, Q3) (g/L) 2.23 (1.67, 2.58) 1.70 (1.06, 2.45) −2.170 0.030
After treatment (180 days) ApoB, M (Q1, Q3) (g/L) 0.52 (0.44, 0.65) 0.67 (0.54, 0.84) −2.995 0.003
After treatment (180 days) HDL‐C, M (Q1, Q3) (mmol/L) 1.14 (0.88, 1.28) 1.18 (0.88, 1.35) −0.482 0.630
After treatment (180 days) Lp(a), M (Q1, Q3) (nmol/L) 166 (36.8, 247) 91.00 (33.50, 195.00) −1.713 0.087
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Regarding other lipid parameters, ApoA1 levels after treatment were significantly higher in rapid metabolizers compared to slow metabolizers (p = 0.030), while ApoB levels were significantly lower (p = 0.003). However, HDL‐C levels showed no significant difference between metabolizer groups after treatment (p = 0.630). Similarly, TAG levels after treatment were comparable between rapid and slow metabolizers (p = 0.992) (Table 4).

These findings suggest that the CYP2C19 genotype may influence certain aspects of the lipid‐lowering response to PCSK9 inhibitor therapy, particularly in terms of LDL‐C, TC, ApoA1, and ApoB modulation. This observed difference in treatment response between CYP2C19 genotypes may have implications for personalized lipid‐lowering therapy in patients with symptomatic intracranial arterial stenosis.

3.4. Impact of CYP2C19 Genotyping on PCSK9 Inhibitor Efficacy in Modulating Inflammatory Markers and Clinical Outcomes

In addition to lipid parameters, we evaluated the impact of the CYP2C19 genotype on inflammatory markers, functional outcomes, and clinical events (Figure 2 and Tables 5 and 6). At baseline, inflammatory markers were comparable between rapid and slow metabolizers, with no significant differences in IL‐6 (p = 0.794), hs‐CRP (p = 0.177), or Lp‐PLA2 (p = 0.988) levels.

FIGURE 2.

FIGURE 2

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Effects of PCSK9 inhibitor treatment on inflammatory markers in CYP2C19 metabolizer groups. This study investigated the relationship between CYP2C19 metabolizer status and inflammatory marker modulation following PCSK9 inhibitor therapy. Box plots demonstrate the distribution of inflammatory markers in CYP2C19 rapid (n = 35) and slow (n = 43) metabolizers at baseline and post‐180‐day treatment intervals. The plots present median values (horizontal lines within boxes), interquartile ranges (boxes), and individual measurements (black dots). Statistical significance is denoted by asterisks (*p < 0.05). hs‐CRP, hypersensitive C‐reactive protein; IL‐6, interleukin‐6; Lp(a), lipoprotein a; Lp‐PLA2, lipoprotein‐associated phospholipase a2.

TABLE 5.

Comparison of inflammatory indexes changes between these two groups by CYP2C19 genotype.

PCSK9i group CYP2C19 rapid metabolism (n = 35) CYP2C19 slow metabolism (n = 43) z p
After treatment (180 days) Hba1c, M (Q1, Q3) (%) 6.02 (5.50, 7.0) 5.99 (5.42, 6.50) −0.447 0.655
After treatment (180 days) IL‐6, M (Q1, Q3) (pg/mL) 4.80 (4.51, 5.44) 5.20 (4.88, 5.46) −2.040 0.041
After treatment (180 days) hs‐CRP, M(Q1, Q3) (mg/L) 5.25 (4.26, 5.48) 5.45 (4.61, 6.04) −1.512 0.130
After treatment (180 days) Lp‐PLA2, M (Q1, Q3) (nmol/min/mL) 171.3 (149.17, 182.66) 178.57 (163.72, 223.00) −2.130 0.033
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TABLE 6.

Changes in mRS scores at day 180 in PCSK9i group.

Parameters Rapid metabolizers (n = 35) Slow metabolizers (n = 43) p
Pre‐mRs 2.0 (1.0, 3.0) 2.0 (1.0, 3.0) 0.091
Post‐mRs 1.0 (0.0, 1.0) 1.0 (1.0, 2.0) 0.018
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After 180 days of treatment, rapid metabolizers showed slightly lower IL‐6 levels than slow metabolizers (p = 0.041). However, the changes in hs‐CRP (p = 0.130) levels were not significantly different between the metabolizer groups (Table 5). Similarly, Lp(a) levels after treatment showed no significant difference between rapid and slow metabolizers (p = 0.087) (Table 4).

Regarding functional outcomes, while baseline mRS scores were comparable between groups (p = 0.091), rapid metabolizers demonstrated significantly better post‐treatment mRS scores than slow metabolizers (p = 0.018) (Table 6).

In the two groups of patients, the number of patients without stroke recurrence at 180 days (recurrence‐free rate) was 34 (97.1%) and 35 (81.4%), respectively (Figure 3). The results of the intergroup comparison using the Log‐rank test for Kaplan–Meier survival analysis showed that there was a statistically significant difference in recurrence time between the two groups (p < 0.05). As the follow‐up period progressed, the number of patients without recurrence (recurrence‐free rate) in the fast metabolism group increased significantly compared to the slow metabolism group, and the recurrence events occurred earlier in the fast metabolism group. The survival status of the two groups is detailed in Figure 4.

FIGURE 3.

FIGURE 3

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ROC curves of CYP2C19 genotype predicting symptomatic intracranial arterial stenosis recurrence.

FIGURE 4.

FIGURE 4

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Survival curve analysis of patients with fast and slow metabolizers of the CYP2C19 gene within the PCSK9 group at Day 180.

4. Discussion

The lipid‐lowering efficacy of the PCSK9 inhibitor added to high‐intensity statin therapy in patients with sICAS represents a critical advancement in personalized cardiovascular medicine(Abdul‐Rahman et al. 2022; Mac Grory et al. 2022). While PCSK9 inhibitor therapy has shown promising results in lipid management, individual response variability remains a significant clinical challenge in patients with sICAS. Recent evidence suggests that CYP2C19 variants may influence cardiovascular medication efficacy through various metabolic pathways. However, the relationship between the CYP2C19 genotype and PCSK9i treatment response has not been well characterized in sICAS patients. This study investigated the impact of CYP2C19 genotype variants on the lipid‐lowering and anti‐inflammatory effects of PCSK9i therapy, aiming to provide insights into potential genetic factors that might influence treatment outcomes. Our analysis focused on comprehensive lipid profile changes, inflammatory marker responses, and clinical outcomes stratified by CYP2C19 metabolizer status, with careful consideration of potential confounding factors through matched baseline characteristics between treatment groups.

In the comparative study assessing the effects of PCSK9 inhibitors combined with high‐intensity statin therapy, considerable changes were observed in lipid profile changes and biochemical parameters, offering insights into the intricate dynamics of cardiovascular risk management. The administration of PCSK9 inhibitors and statins showed pronounced improvements in several lipid parameters, including decreases in TC, LDL‐C, TAG and increases in HDL‐C levels. These comprehensive improvements in the lipid profiles underscored the efficacy of PCSK9 inhibitors in enhancing lipid metabolism and reducing cardiovascular risk, consistent with the literature highlighting the potent lipid‐lowering capabilities of PCSK9 inhibitors (Gallego‐Colon et al. 2020; Lavecchia and Cerchia 2019). Furthermore, the study observed a notable elevation in ALT levels within the PCSK9i group. This phenomenon was not attributed directly to PCSK9 inhibitor usage and may be related to the high‐intensity statin therapy. This finding aligned with existing research suggesting that statins can occasionally elevate liver enzymes (Newman et al. 2019; Alvarez‐Jimenez et al. 2023), but the combined treatment did not considerably enhance this effect. Despite substantial improvements in lipid profile, serum creatinine levels remained unchanged in the PCSK9 inhibitor + statin group, suggesting a neutral effect on renal function. This observation is particularly relevant given the complex relationship between statins and renal function (Zhao et al. 2021), and PCSK9 inhibitors evidently did not worsen kidney function parameters.

A recent study has shown an association between CYP2C19 genetic variations and lipid metabolism in patients with ischemic stroke (Tanaka et al. 2019; Bai et al. 2020). Decreased efficacy of statins increases relapse risk in patients with the CYP2C19 slow metabolizing phenotype (Alhazzani et al. 2021; Vaughan et al. 2020). Patients with a CYP2C19 slow metabolic phenotype are at increased risk of relapse due to the decreased efficacy of statins (Dai et al. 2022). This study demonstrated that individuals with rapid CYP2C19 genotyping showed a considerably more robust response to lipid‐lowering effects in the group that received PCSK9 inhibitors, indicating an interaction between genes and drugs and subsequent enhanced treatment outcomes. This finding emphasized the increasing importance of pharmacogenomics in customizing treatments, especially in the field of cardiovascular medicine. The weakening of the PCSK9 inhibitor's effects in individuals with slow CYP2C19 genotyping further underscored the impact of genetic variation on treatment responses (Sachdeva et al. 2023). This gene‐drug interaction suggests a complex interplay between CYP2C19‐mediated metabolism and PCSK9 inhibition pathways. Several mechanisms may explain this observed association: (1) CYP2C19 rapid metabolizers may more efficiently convert certain statins to their active forms, potentially enhancing the synergistic effect between statins and PCSK9 inhibitors, leading to more pronounced upregulation of LDL receptors and increased LDL‐C clearance; (2)CYP2C19 genotypes influence systemic inflammation and oxidative stress levels, with rapid metabolizers potentially exhibiting lower inflammatory markers (Han et al. 2024), which could favorably impact PCSK9 expression and enhance the efficacy of PCSK9 inhibitors; (3) CYP2C19 genotypes may affect the expression or function of proteins involved in lipid metabolism. Future research should focus on elucidating the exact molecular mechanisms underlying the CYP2C19‐PCSK9 inhibitor interaction and investigating potential interactions with other genetic variants involved in lipid metabolism.

In our safety analysis, we observed differential outcomes between metabolizer groups. Although rapid metabolizers showed more favorable functional outcomes as measured by mRS scores (p = 0.018), the observed difference in recurrent ischemic events (2 events vs. 1 event, p = 0.001) should be interpreted with caution due to the limited sample size and low event rates. The single mortality event documented in the slow metabolizer group (p = 0.001) similarly requires careful interpretation within this context. Notably, neither group experienced myocardial infarction or hemorrhagic complications during the study period. These preliminary findings suggest potential associations between metabolizer status and clinical outcomes; however, larger cohort studies with extended follow‐up periods are essential to validate these observations and establish their clinical significance.

Furthermore, our study identified significant differences in the subgroup analysis based on rapid or slow CYP2C19 metabolizers. Specifically, individuals with slow CYP2C19 genotyping in the PCSK9i group exhibited notable variances compared with the statin group across all markers. CYP2C19 plays a crucial role in the metabolism of various drugs (Wang et al. 2018), including statins and PCSK9 inhibitors (Shatnawi et al. 2023; Li et al. 2023). The slow metabolizers of CYP2C19 may have altered drug metabolism kinetics (Dorne 2004; Chiba et al. 2014; Trenaman et al. 2021), leading to the reduced efficacy of the medications in this subgroup. Additionally, genetic polymorphisms linked to CYP2C19 may influence responses to specific drug therapies (Aga et al. 2020), potentially affecting glycemic control outcomes. Further research is warranted to elucidate the underlying mechanisms driving differential treatment responses in rapid and slow CYP2C19 genotyping subgroups to refine therapeutic strategies for individuals with varying genetic profiles.

The limitations of this study are twofold. First, although the research design meticulously controlled for confounding variables, the small sample size may have limited the generalizability of the findings. The importance of a larger cohort to the validation of these results and exploration of potential subgroups with unique characteristics cannot be emphasized enough. Furthermore, the study duration may not capture long‐term effects or the potential emergence of adverse events associated with the combination therapy.

5. Conclusion

This study demonstrated that PCSK9 inhibitors combined with high‐intensity statin therapy improved lipid profiles and inflammatory markers more than statin monotherapy in patients with symptomatic intracranial atherosclerotic stenosis. The CYP2C19 genotype appeared to influence treatment response, particularly in lipid parameter changes and inflammatory marker levels. However, the clinical significance of these findings requires further investigation due to the limited number of clinical events observed during the follow‐up period. Larger‐scale, multicenter studies with extended follow‐up periods are needed to validate these findings and better understand the relationship between CYP2C19 genotype variants and treatment outcomes in this patient population.

Author Contributions

Chao Zhao and Nuan Wang collected the references, wrote the manuscript and completed the diagrams. Di Shi, Hao Zhou, and Dan Chen provided advice and suggestions for improving the manuscript. Chao Zhao and Guofang Chen edited and revised the manuscript. All authors have reviewed and approved the final version of the full text being submitted.

Funding

This work was supported by the grant from the Development Fund of Affiliated Hospital of Xuzhou Medical University (XYFY202316).

Ethics Statement

This study received ethical approval from the ethics committee of Xuzhou Central Hospital (IRB ID: XZXY‐LK‐20210610‐006), and informed consent was obtained from all participants or their respective patient guardians.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

We wish to extend our thanks to Dr. Guofang Chen and Dr. Nuan Wang, whose expert guidance and constant encouragement were instrumental in the completion of this study. Additionally, this research would not have been possible without the funding provided by the Development Fund of the Affiliated Hospital of Xuzhou Medical University (Grant number: XYFY202316).

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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