IntroductionModern medicine faces a steady increase in the use of drugs, the expansion of pharmacological possibilities, and the growing number of patients receiving multicomponent pharmacotherapy. Given these circumstances, the issue of rationality and safety of pharmacotherapy becomes paramount [1]. The individual response to administered drugs is largely determined by genetic factors. Pharmacodynamics and pharmacokinetics of each drug depend on polymorphisms of specific genes [2]. It is known that cytochrome P450 isoenzymes participate in phase I biotransformation reactions, among which the CYP2C9 gene makes a significant contribution [3]. The CYP2C9 gene is highly polymorphic. According to numerous studies, the most common CYP2C9 alleles are CYP2C9*2 (R144C, rs1799853) and CYP2C9*3 (I359L, rs1057910). The presence of these alleles leads to reduced function of the cytochrome P450 isoenzyme [4]. Currently, it is known that the frequencies of these polymorphisms vary across populations. According to research data, the reduced‑function allele CYP2C9*2 is most frequent in European (13%) and Latin American populations (8%), as well as among Central/South Asian (11%) and Middle Eastern (13%) populations. The CYP2C9*3 allele is most frequent in Central/South Asia (11%), Europe (7.6%), and the Middle East (8.3%) [5].

It has been established that individuals with altered enzyme activity show substantial fluctuations in plasma drug concentrations [1]. Thus, genetic polymorphism not only affects the individual pharmacological response to a particular drug but can also lead to unfavorable drug–drug interactions. Among the negative consequences of drug interactions, the most significant are the development of adverse reactions, reduced efficacy of pharmacotherapy, increased frequency and duration of hospitalizations, and increased mortality [6].

Today, stroke remains one of the leading causes of mortality, disability, and long‑term incapacity worldwide [7]. Pharmacotherapy is an important component of the comprehensive treatment of post‑stroke patients, and the lack of an individualized approach to its selection may lead to severe complications [8].

ObjectiveAnalysis of the frequency distribution of alleles and genotypes of the CYP2C9 gene (rs1057910, rs1799853) in stroke patients, taking into account possible drug interactions in real clinical practice in Arkhangelsk.

Materials and methodsA cross‑sectional study was conducted on a sample of patients diagnosed with stroke, hospitalized in the neurology department of the regional vascular center of the First City Clinical Hospital named after E.E. Volosevich of the Arkhangelsk Region. The work was carried out at the Department of Clinical Pharmacology and Pharmacotherapy of the Northern State Medical University and the Regional Center for Antithrombotic Therapy of the First City Clinical Hospital named after E.E. Volosevich in Arkhangelsk. The study design was approved by the local ethics committee of the Northern State Medical University (meeting protocol No. 10/12‑25 dated 24.12.2025).

The study included 81 patients. Inclusion criteria: patients of both sexes hospitalized in the neurology department of the regional vascular center of the First City Clinical Hospital named after E.E. Volosevich; age over 18 years; diagnosis of stroke (ICD‑10: I60.2, I63.3, I61.4, I63.8, I63.9, I63.4, I61.0, I60.1, I63.5); voluntary written informed consent to participate in the study.

Non‑inclusion criteria: absence of clinical diagnosis of stroke (ICD‑10 codes as above); absence of voluntary written informed consent; age under 18 years.

Exclusion criteria: withdrawal of consent at any stage of the study.

The fact of drug intake was established based on electronic medical records. Drug–drug interactions were assessed using the available platform https://mediqlab.com. In accordance with clinical guidelines, laboratory tests were performed: complete blood count (Mindray Bc5380 hematology analyzer), biochemical blood test (ILab Taurus biochemical analyzer), coagulogram (Sysmex CS2000i automatic coagulometer).

All study participants underwent molecular genetic analysis to determine the frequency of genotypes and alleles of the CYP2C9 gene, followed by analysis of possible drug–drug interactions. DNA was extracted from venous blood collected in Acti‑Fine tubes with ethylenediaminetetraacetic acid using the RealBest‑Genetics DNA‑Express reagent kits. Genotyping of single nucleotide polymorphic allelic variants of the CYP2C9 gene was performed by real‑time polymerase chain reaction on a Bio‑Rad CFX96 Touch amplifier using SNP‑Screen reagent kits (Sintol LLC, Russian Federation) at the centralized bacteriological laboratory of the First City Clinical Hospital named after E.E. Volosevich. The polymorphic variants rs1057910 (Ile359Leu) and rs1799853 (Arg144Cys) of the CYP2C9 gene were studied. The laboratory performing the study participates in external quality assessment. The conformity of allele frequencies in the studied groups to the Hardy–Weinberg equilibrium was tested using the available online calculator https://converterok.com/calculator/hardy‑weinberg‑calculator/.

Statistical analysis of the data obtained was performed using STATA 2014. Qualitative data were described using absolute numbers and percentages. The normality of the distribution of quantitative data was assessed using the Shapiro–Wilk test. A distribution was considered different from normal if the statistical significance level (p) was less than 0.05. Data with non‑normal distribution are presented as median and first and third quartiles (Me (Q1–Q3)). Categorical variables were compared using the χ² test (Pearson's chi‑square test). Comparisons of means were performed using the Mann–Whitney test. Differences were considered statistically significant at a significance level (p) less than 0.05.

Primary genetic data were not and will not be transferred outside the Russian Federation in accordance with Federal Law No. 86‑FZ.

ResultsThe study included 81 patients aged 43 to 95 years with a diagnosed stroke, of whom 40 were women (49.38%) and 41 were men (50.62%). Patients were divided into two groups: those initially hospitalized for stroke and those readmitted with recurrent stroke. The primary hospitalization group (age Me = 70 (65–80)) consisted of 29 men (44.62%) and 36 women (55.38%). The recurrent stroke group (age Me = 73 (63–83)) comprised 12 men (75%) and 4 women (25%).

The main clinical and laboratory parameters are presented in Tables 1 and 2. The groups did not differ statistically in comorbidities and risk factors (Table 1). Analysis of laboratory blood parameters revealed no statistically significant differences except for potassium level (p = 0.022) (Table 2). In the primary hospitalization group, blood potassium was 4.2 (3.865–4.615), while in the readmission group it was 4.575 (4.31–4.845).

Table 1. Characteristics of patients included in the study

ParameterPatients without readmission (n=65)Patients with readmission (n=16)χ², pHigh‑risk pulmonary embolism, n (%)13 (20%)4 (25%)χ² = 0.1936, p = 0.660Arterial hypertension, n (%)64 (98.46%)16 (100%)χ² = 0.2492, p = 0.618Ischemic heart disease, n (%)41 (63.08%)11 (68.75%)χ² = 0.1798, p = 0.672Diabetes mellitus, n (%)20 (30.77%)4 (25%)χ² = 0.2050, p = 0.651Cardiac arrhythmias, n (%)29 (44.62%)5 (31.25%)χ² = 0.9417, p = 0.332Atherosclerosis, n (%)58 (89.23%)16 (100%)χ² = 1.8861, p = 0.170Chronic kidney disease, n (%)11 (16.92%)2 (12.5%)χ² = 0.1864, p = 0.666Obesity, n (%)2 (3.08%)1 (6.25%)χ² = 0.3625, p = 0.547Smoking, n (%)8 (12.31%)1 (6.25%)χ² = 0.4770, p = 0.490Alcohol abuse, n (%)4 (6.15%)2 (12.5%)χ² = 0.7539, p = 0.385

Table 2. Analysis of clinical and laboratory parameters

ParameterPatients without readmission (n=65)Patients with readmission (n=16)z, pSystolic blood pressure (Me (Q1–Q3)), mmHg140 (140–170)155 (140–160)z = –0.711, p = 0.477Diastolic blood pressure (Me (Q1–Q3)), mmHg90 (80–90)90 (90–90)z = –1.382, p = 0.167Heart rate (Me (Q1–Q3)), bpm80 (73–80)80 (75.5–80)z = –0.061, p = 0.951Hemoglobin (Me (Q1–Q3)), g/L135.5 (122–145)133.5 (113.5–149.5)z = 0.168, p = 0.866Platelets (Me (Q1–Q3)), 10⁹/L201.5 (170.5–249)204 (160–246.5)z = –0.247, p = 0.805Prothrombin time (Me (Q1–Q3)), s10.8 (10–11.4)10.75 (10.25–11.3)z = 0.038, p = 0.970INR (Me (Q1–Q3))1.02 (0.98–1.12)1.025 (0.99–1.14)z = –0.214, p = 0.831APTT (Me (Q1–Q3)), s33.2 (28.2–36.4)33.95 (29.1–35.95)z = –0.329, p = 0.742Fibrinogen (Me (Q1–Q3)), g/L3.25 (2.65–4.02)3.165 (2.715–4.05)z = –0.019, p = 0.985Creatinine (Me (Q1–Q3)), µmol/L91 (79.5–109.5)107 (89.5–114)z = –1.865, p = 0.062Urea (Me (Q1–Q3)), mmol/L6.665 (5.35–8.515)7.205 (5.595–9.29)z = –0.752, p = 0.452ALT (Me (Q1–Q3)), U/L17 (13–28)17 (14–25)z = –0.061, p = 0.951AST (Me (Q1–Q3)), U/L26 (22–30)25 (21–36.5)z = 0.061, p = 0.951Total bilirubin (Me (Q1–Q3)), µmol/L13 (8.7–19.9)12.5 (8.95–17.65)z = 0.402, p = 0.688Cholesterol (Me (Q1–Q3)), mmol/L5.32 (4.18–6.36)5.585 (4.335–6.885)z = –0.685, p = 0.493Triglycerides (Me (Q1–Q3)), mmol/L1.44 (0.91–2.1)1.47 (0.87–1.64)z = 0.767, p = 0.443LDL (Me (Q1–Q3)), mmol/L3.41 (2.42–4.41)3.415 (2.365–4.68)z = 0.167, p = 0.868HDL (Me (Q1–Q3)), mmol/L1.18 (1.03–1.43)1.25 (1.07–1.53)z = –0.757, p = 0.449Sodium (Me (Q1–Q3)), mmol/L140.35 (137.55–142.5)140.6 (139.75–141.9)z = –0.301, p = 0.764Potassium (Me (Q1–Q3)), mmol/L4.2 (3.865–4.615)4.575 (4.31–4.845)z = –2.298, p = 0.022

Notes: Me — median; Q1 — first quartile; Q3 — third quartile; INR — international normalized ratio; APTT — activated partial thromboplastin time; ALT — alanine aminotransferase; AST — aspartate aminotransferase; LDL — low‑density lipoprotein; HDL — high‑density lipoprotein.

In this study, we analyzed the distribution of genotypes at positions Ile359Leu (rs1057910) and Arg144Cys (rs1799853) of the CYP2C9 gene in patients hospitalized for the first time and those readmitted. The genotype distribution at the polymorphic locus Ile359Leu (rs1057910) of the CYP2C9 gene in both the primary hospitalization and readmission groups was consistent with Hardy–Weinberg equilibrium (at p = 0.05, χ² = 2.6214 and χ² = 0.1712, respectively). Similarly, the genotype distribution at the polymorphic locus Arg144Cys (rs1799853) of the CYP2C9 gene in both groups conformed to Hardy–Weinberg equilibrium (at p = 0.05, χ² = 0.6722 and χ² = 0.3265, respectively).

Both the CYP2C9 Ile359Leu (rs1057910) and Arg144Cys (rs1799853) polymorphism analyses showed that the majority of patients in both groups carried the homozygous wild‑type allele. Carriers of reduced‑function variants Ile359Leu (rs1057910) and Arg144Cys (rs1799853) of CYP2C9, corresponding to poor metabolizers, were found in both groups. Notably, the homozygous mutant allele at Ile359Leu (rs1057910) was observed only in the primary hospitalization group, while no homozygous mutant allele at Arg144Cys (rs1799853) was detected in either group. Analysis of the frequency of reduced‑function alleles of the studied single nucleotide polymorphisms in the CYP2C9 gene revealed no statistically significant differences between the groups (p > 0.05) (Table 3).

Table 3. Comparative analysis of the frequencies of CYP2C9 polymorphism genotypes in patients hospitalized for the first time and hospitalized again

Genotype / alleleFrequency among first‑time hospitalized patients (n=65)Frequency among readmitted patients (n=16)χ², pIle359Leu CYP2C9 (rs1057910)   A/A89.23% (n=58)81.25% (n=13)χ² = 1.3866, p = 0.500A/C9.23% (n=6)18.75% (n=3) C/C1.54% (n=1)0% (n=0) A93.85% (n=122)90.625% (n=29) C6.15% (n=6)9.375% (n=3) Arg144Cys CYP2C9 (rs1799853)   C/C81.54% (n=53)75% (n=12)χ² = 0.3463, p = 0.556C/T18.46% (n=12)25% (n=4) T/T0% (n=0)0% (n=0) C90.77% (n=118)87.50% (n=28) T9.23% (n=12)12.50% (n=4) 

We further analyzed the combinations of acetylsalicylic acid and clopidogrel (a substrate of CYP2C9) with other drugs that could potentially affect the ongoing pharmacotherapy through drug–drug interactions. The most common combinations were: clopidogrel with atorvastatin (32.10%), clopidogrel with omeprazole (32.10%), acetylsalicylic acid with omeprazole (81.48%), acetylsalicylic acid with bisoprolol (59.26%), and acetylsalicylic acid with perindopril (48.15%) (Table 4).

Table 4. Pharmacotherapy considering possible drug–drug interactions in hospitalized patients with stroke

Drug combinationWithout readmission (n=65)With readmission (n=16)χ², pClopidogrel + Atorvastatin (n=26)21 (32.31%)5 (31.25%)χ² = 0.0066, p = 0.935Clopidogrel + Omeprazole (n=26)21 (32.31%)5 (31.25%)χ² = 0.0066, p = 0.935Acetylsalicylic acid + Omeprazole (n=66)52 (80%)14 (87.5%)χ² = 0.4786, p = 0.489Acetylsalicylic acid + Bisoprolol (n=48)39 (60.0%)9 (56.25%)χ² = 0.0748, p = 0.784Acetylsalicylic acid + Perindopril (n=39)29 (44.62%)10 (62.50%)χ² = 1.6450, p = 0.200Acetylsalicylic acid + Clopidogrel (n=24)19 (29.23%)5 (31.25%)χ² = 0.0251, p = 0.874Acetylsalicylic acid + Spironolactone (n=15)13 (20.0%)2 (12.50%)χ² = 0.4786, p = 0.489Acetylsalicylic acid + Amlodipine (n=20)16 (24.62%)4 (25.0%)χ² = 0.0010, p = 0.975

Additionally, we analyzed the relationship between CYP2C9 genotypes and the frequency of readmissions during stroke pharmacotherapy. No statistically significant differences were found between CYP2C9 allele variants and the frequency of readmissions (p > 0.05) (Table 5).

DiscussionPolypharmacy is a significant risk factor for adverse drug reactions resulting from drug interactions [9]. Complex treatment regimens used for patients with cardiovascular diseases significantly increase the likelihood of dangerous drug interactions [10]. The analysis of pharmacotherapy in the study sample of post‑stroke patients revealed potential undesirable drug–drug interactions between clopidogrel and acetylsalicylic acid.

Co‑administration of atorvastatin and clopidogrel may reduce the conversion of the prodrug clopidogrel to its active form, thereby suppressing its antiplatelet effects. Combination of clopidogrel with acetylsalicylic acid leads to enhanced inhibition of platelet aggregation, increasing the risk of gastrointestinal bleeding [11]. Concurrent use of clopidogrel or acetylsalicylic acid with proton pump inhibitors, such as omeprazole, may reduce their antiplatelet effect [12]. Co‑administration of acetylsalicylic acid with perindopril may reduce the antihypertensive effect of the ACE inhibitor [13]. Simultaneous use of acetylsalicylic acid with bisoprolol may reduce the hypotensive effect of the beta‑blocker [14].

In our study, we identified carriers of the reduced‑function alleles CYP2C9*2 and CYP2C9*3 in the patient sample, with frequencies of 19.75% and 12.34%, respectively, which significantly exceeds the data from a study on the prevalence of these alleles among Korean stroke patients, where the carrier frequency of CYP2C9*3 was only 4%, and carriers of CYP2C9*2 were not detected at all [15]. These differences in results may be due to population genetic characteristics, analytical methods, or ethnic background of participants.

The mutant allele frequencies of CYP2C9*2 and CYP2C9*3 in our study were 9.9% and 6.8%, respectively. These values are comparable with data published for the Russian population. According to available genetic studies, the frequencies of CYP2C9*2 and CYP2C9*3 alleles in the Russian population reach approximately 11% and 5.8%, respectively [16].

Our study did not confirm the hypothesis that carriage of reduced‑function CYP2C9 alleles (rs1057910 and rs1799853) affects the frequency of readmissions, which may be due to insufficient sample size.

Study limitationsThis study has several limitations that should be considered when interpreting the results:

To overcome these limitations, it is advisable to: increase the sample size, especially for rare genotypes; consider additional genetic factors affecting treatment efficacy; conduct multicenter studies to improve data representativeness.

ConclusionIn this study, carriers of reduced‑function variants Ile359Leu (rs1057910) and Arg144Cys (rs1799853) of CYP2C9 were identified among patients in the study sample. Although the study did not reveal a statistically significant association between the presence of reduced‑function CYP2C9 alleles and the frequency of readmissions, it demonstrates the importance of implementing pharmacogenetic testing in stroke patients receiving pharmacotherapy.

In conclusion, it should be noted that genetic allelic variants of the CYP2C9 gene, which cause variability in response to pharmacotherapy, create additional difficulties in achieving the required level of safety and effectiveness of the therapy. Understanding differences in the activity of cytochrome P450 isoenzymes is important for preventing adverse drug reactions that may develop as a result of drug interactions.

The data obtained emphasize the need for further population pharmacogenetic studies with larger sample sizes to assess the contribution of rare but potentially significant polymorphisms.