Abstract
Stroke is a health problem all over the world. It is a primary cause of disability and ranked number two death cause. In kingdom of Saudi Arabia KSA, the prevalence of stroke in the KSA estimated to be more than 40 per 100 thousand in 2021. The incidence of stroke is increasing in KSA. The risk factors for stroke are grouped into modifiable and nonmodifiable. The modifiable risk factors include diabetes, hyperlipidemia physical inactivity, and diet, whereas the nonmodifiable include sex, age, and race/ethnicity. Decreasing the modifiable risk factors reduces the burden of stroke in population. The long noncoding RNAs (LncRNAs) ANRIL is suggested as a biomarker and treatment target for stroke. The Transcription factor 7-like 2 (TCF7L2) has crucial roles in biological and pathological processes such as inflammation, metabolism, and atherosclerosis. In this study, we examined the associations of ANRIL rs1333045 C>T, CYP2C19*17 (C806T, rs12248560C>T, and TCF7L2 rs12255372 G>T with stroke in 100 stroke cases and 100 matched healthy controls from Tabuk population using the amplification refractory mutation system PCR (ARMS-PCR). Results indicated that the T allele of the ANRIL rs1333048 C>T was associated with stroke with Odd ratio (OR) = 1.73, P value-0.0067. Likewise, the GT genotype and the T allele of the TCF7L2 rs12255372 G>T were associated with stroke with OR = 2.14, P value = 0.01, and 1.9, P value = 0.004, respectively. In addition, the CT genotype and T allele of the CYP2C19*17 (rs12248560) C>T were also associated stroke with OR = 2, P value = 0.02 and OR = 2.3, P value = 0.002, respectively. We conclude that ANRIL rs1333045 C>T, CYP2C19*17 (C806T, rs12248560C>T, and TCF7L2 rs12255372 G>T are potential loci for susceptibility to stroke. This will assist in treatment and/or prevention of cerebrovascular disease.
Keywords: Stroke, The Transcription factor 7-like 2 (TCF7L2), Long noncoding RNAs (lncRNA), ANRIL (antisense noncoding RNA in the INK4 locus), ARMS-amplification refractory mutation system
Introduction
Cerebrovascular disease is a significant cause of morbidity and is recognized as the second leading cause of mortality worldwide (Tong et al. 2019). This category includes stroke (also known as cerebrovascular accident), carotid artery stenosis, spinal stenosis, intracranial artery stenosis, brain aneurysms, and brain vascular malformations. Stroke risk factors can be classified as modifiable and nonmodifiable (Boehme et al. 2017). Nonmodifiable risk factors include age, gender, and race (Boehme et al. 2017). In contrast, modifiable risk factors encompass high blood pressure, diabetes mellitus, cigarette smoking, diet, reduced physical activity, inflammatory disorders, infections, pollution, and coronary artery disease, independent of arrhythmia (Boehme et al. 2017; Wang et al. 2022). Studies of genome wide association revealed specific loci associated with stroke, cardiovascular diseases (CVDs), cancers, and diabetes (Cai et al. 2020; Jalal et al. 2023; Sato et al. 2023).
Long noncoding (lnc) RNAs are RNA molecules longer than 200 nucleotides that do not code for proteins (Zhang et al. 2019). They interact with messenger RNA, microRNA, DNA, and proteins, thereby regulating gene expression (Zhang et al. 2019). They play vital roles in cellular processes, including chromatin remodeling, transcription activation, transcription interference, RNA processing, and messenger RNA translation (Zhang et al. 2019). ANRIL is a long noncoding RNA found in the chromosome 9p21 region (Sanchez et al. 2023). Defective ANRIL gene expression has been associated with injury to the endothelial layer of blood vessels, as well as the proliferation, migration, and apoptosis of vascular smooth muscle cells, and the proliferation of monocytes and their adhesion to endothelial cells. Moreover, ANRIL has been proposed to accelerate atherosclerosis (Chen et al. 2020). In inflammatory conditions such as CVDs and ulcerative colitis, ANRIL expression is associated with the overexpression of pro-inflammatory cytokines, including interleukin 6 (IL-6), CCL2, and periostin (POSTN) (Mehta-Mujoo et al. 2019). Atherosclerosis is an inflammatory disease and is a significant cause of stroke (Wang et al. 2019). Gene variations in ANRIL gene have been associated with stroke, CVDs, cancers, diabetes mellitus, and glaucoma (Sanchez et al. 2023; Zhang et al. 2020; Liu et al. 2022; Kong et al. 2018; Bai et al. 2022). For example, the ANRIL rs1333045 C>T SNP were reported to be associated with coronary heart disease in Chinese population (Hua et al. 2020).
Cytochrome P450 (CYP450) enzymes catalyze the metabolism of endogenous (bile acids, steroid hormones, prostaglandins, and arachidonic acid) and exogenous substrates (e.g., drugs, other xenobiotics, and carcinogens). CYP2C19 is primarily expressed in hepatocytes (Deguchi et al. 2019) and is responsible for metabolizing drugs such as omeprazole (a proton pump inhibitor that suppresses gastric acid secretion) and clopidogrel (an antiplatelet medication) (Sanford et al. 2013). The CYP2C19*17 single nucleotide variant (SNV) enhances CYP2C19 gene transcription, leading to an ultra-rapid metabolizer phenotype (Payan et al. 2015). The distribution of CYP2C19*17 SNV has not been well characterized globally. Furthermore, gene polymorphisms of CYP450s have been associated with cancers, stroke, CVDs, and diabetes mellitus (Hossam Abdelmonem et al. 2024; Elfaki et al. 2018; Deng et al. 2010; Biswas et al. 2024). For instance, CYP2C19 *2/*2 genotype is risk factor for atherosclerosis (Xie et al. 2023), which is a major cause of ischemic stroke (Tsivgoulis et al. 2018).
The Transcription Factor 7-Like 2 (TCF7L2), previously known as T-cell factor 4 (TCF4), belongs to the T-cell factor/lymphoid enhancer factor (TCF/LEF) family. It is a transcription factor located on chromosome 10q25.2-q25.3, consisting of 217,432 base pairs and composed of 17 exons and 596 amino acid residues (Geoghegan et al. 2019; Li et al. 2021). TCF7L2 is a component of the Wnt signaling pathway and plays roles in lipid and carbohydrate metabolism, atherosclerosis, inflammatory response, and regulation of adipocytes (Geoghegan et al. 2019; Li et al. 2021). TCF7L2 exhibits protective properties against atherosclerosis through various molecular mechanisms (Li et al. 2021), including anti-inflammatory effects, suppression of vascular smooth muscle cell proliferation and migration, and reduction of foam cell and myofibroblast formation (Li et al. 2021). Moreover, associations were reported between the TCF7L2 gene polymorphisms and stroke, CVDs type 2 diabetes (T2D) (Amoli et al. 2010; Choi et al. 2014; Javid et al. 2024). The TCF7L2 rs12255372 G>T SNP was associated with diabetes and coronary artery disease (Javid et al. 2024; Dabla et al. 2025).
This study aims to examine the relationship between ANRIL rs1333045 C>T, CYP2C19*17 (C806T, rs12248560), and TCF7L2 rs12255372 G>T with stroke occurrence in the Saudi population (see Fig. 1).
Fig. 1.
Diagnostic tools and flowchart for ischemic stroke
Methodology
Study Population
In order to conduct a study on ethnically preserved genetic variation, only Arabs (Saudis) were included; non-Arabs, non-Saudis, or recently neutralized Arabs were not included to participate as participants. 200 participants were enrolled in the study; 100 of them were patients (stroke), while the remaining hundred fifteen (100) were their healthy counterparts. The control group samples were taken from individuals who were there for a regular checkup at the hospitals, while the specimens were taken from the King Salman Military Hospital in Tabuk and outpatient section of the neurology departments at King Khaled Hospital. The informed consent form and questionnaire were filled out by the research participants.
The study received ethical approval (UT-91-23-2020) from the University of Tabuk's Ethics Committee. All patients and control subjects gave their informed consent prior to sample collection. The study complied with the Helsinki Declaration's basic guidelines and standard operating procedures for employing human participants in research.
Criteria for Inclusion and Exclusion of Patients
We included cases with clinically confirmed stroke from Tabuk population (Table 1). The diagnosis of the stroke was with the D-dimer blood test, computed tomograthy (CT) scan, stroke Magnetic resonance imaging (MRI) scan, and cerebral angiography (Sazonova et al. 2014). All cases with other chronic diseases (e.g., cancer, diabetes, CVDs) were excluced from this study.
Table 1.
The Comparative demographic and clinical features of the study subjects under investigation
| Feature | Controlsa | Patients | Pb |
|---|---|---|---|
| Age | 35.39 ± 12.93 | 57.33 ± 10.39 | < 0.001 |
| Gender | 28.77 ± 11.10 | 35.01 ± 80.44 | < 0.010 |
| HbA1c | 5.10 ± 0.550 | 6.07 ± 0.429 | < 0.001 |
| Blood sugar fasting | 98.89 ± 5.58 | 98.90 ± 6.09 | < 0.001 |
| Lipid biomarkers | |||
| Cholesterol | 117.36 ± 7.30 | 178.16 ± 54.07 | < 0.001 |
| HDL | 48.18 ± 11.82 | 26.87 ± 4.33 | < 0.001 |
| Triglyceride | 125.09 ± 8.70 | 162.16 ± 38.17 | < 0.001 |
| VLDL | 27.12 ± 5.89 | 43.10 ± 15.72 | < 0.001 |
| LDL | 118.19 ± 8.93 | 148.8 ± 35.8 | < 0.001 |
| Other markers | |||
| ALT | 206 ± 11.29 | 290 ± 27.61 | 0.504 |
| Platelet count | 233.8 ± 72.4 | 255.4 ± 84.39 | 0.159 |
| AST | 23.19 ± 12.15 | 27.89 ± 16.8 | 0.179 |
astudent’s t test-continuous variables, bValues are expressed as mean ± standard deviation
Inclusion Criteria for Healthy Controls
The inclusion criteria of healthy control group (Table 1) included participants visiting for routine checkup to King Khaled Hospital and King Salman Military Hospital, Tabuk, KSA. Healthy controls were ethnic Saudi men and women with no history of chronic diseases and were apparently in good health as confirmed by routine checkup.
Collection of Specimens
Each healthy control participant received a written informed consent form and completed a questionnaire. Blood samples (approximately 3 mL) were collected during routine blood draws to minimize additional phlebotomy, while samples from stroke patients were collected in lavender-top tubes. Blood samples were stored immediately at temperatures between − 20 °C and − 30 °C.
Diagnostic Markers of Stroke
Stroke, which most commonly occurs when a blood clot obstructs blood flow to the brain, starving it of oxygen and quickly killing brain cells, is a leading and largely preventable cause of death and disability in the United States. Physical examinations and analysis of brain scan pictures are typically used to diagnose strokes. The most frequent stroke mimics include seizures, somatoform disorders (conversion), migraine headaches, and hypoglycemia. Stroke diagnosis typically involves physical examinations such as taking blood pressure readings and listening to the heart along with neurological examination to determine the impact of a possible stroke in the nervous system. Additional tests include Hematologic testing, Magnetic Resonance Imaging (MRI) or magnetic resonance angiography (Sazonova et al. 2014), Carotid ultrasound (Nezu and Hosomi 2020), computed tomography (CT) scan (Byrne et al. 2020), and Cerebral angiogram.
Data were collected from tests conducted in the hospital and from a standardized questionnaire that includes demographic information such as Systemic hypertension (blood pressure measures above 140/90 mmHg or by past anti-hypertensive medication usage for this indication), age and gender, and other cerebrovascular risk factors include these.
Dyslipidemia is defined as total serum Cholesterol > 200 mg/dl, LDL, > 100 mg/dl, HDL < 50 mg/dl, or Triglycerides > 150 mg/dl; it can also be caused by the use of lipid-lowering medications or a previous diagnosis of diabetes mellitus; it can also be caused by the use of current medications, atrial fibrillation detected on any prior monitoring, and current smoking status.
DNA Isolation
Peripheral blood DNA was isolated using a standard DNeasy Blood K (Qiagen, Germany) from healthy controls and stroke patient following the manufacturer’s instructions. Isolated genomic DNA was dissolved in elution buffer followed by storing at 4 °C. Its integrity and purity were verified using NanoDropTM (ThermoScientific,Waltham,Massachusetts,USA). By calculating the OD (optical density) at A260/A280, the extracted DNA samples were screened for purity. Good-quality DNA is indicated by an A260/A280 ratio between 1.80 and 1.96.
Genotyping of ANRIL-rs1333045A>C, TCF7L2 rs12255372G>T, and CYP2C19*17 C>T
ARMS-PCR was used to determine the genotypes of ANRIL-rs1333045A > C, TCF7L2-rs12255372G > T, and CYP2C19*17 rs12248560 C > T gene variations. The primers used for the genotyping are shown in Table 2 and were designed using primer3 software.
Table 2.
Primers sequences used for genotyping of the SNPs
| ARMS primers for LncRNA in the INK4 locus-rs1333045 C>T | ||||
| LNCR45-Fo | 5´-CGAAGAGCAATAATATATAGTACACTGGGC-3´ | 442 bp | 55 °C | |
| LNCR45-Ro | 5´-TTAATGAATGCTTACTAGATGCCTGA-3´ | |||
| LNCR45-FI-A | T | 5´-TGAAACTTCTTATTTAGTGGTGCATACC-3´ | 298 bp | |
| LNCR45-RI-C | C | 5´-GCAGTTCAAAGGAAGTACCATAAAAAG-3´ | 200 bp | |
| ARMS primers for CYP2C19*17 C>T rs12248560 C>T Genotyping | ||||
| CYP2C19*17 Fo | 5′-GAG ATCA GCTCTT CCTTC AGTT ACAC‐3′ | 462 bp | 56 °C | |
| CYP2C19*17 Ro | 5′-CAC CTT TAC CAT TT AA CC CC CTA AA AA‐3′ | |||
| CYP2C19*17 FI | T | 5′-TTT TTC AAA TTTG TGT CTT CTG TTC TCA AA TT‐3′ | 227 bp | |
| CYP2C19*17 RI | C | 5′-GCG CAT TAT CT CTT ACA TCA GAG CTG‐3′ | 292 bp | |
| AR.MS-PCR primers for TCF7L2 rs12255372G>T variation | ||||
| TCF7L2-Fo | 5′-GGGCAATAGATACATTTTAAGA‐3′ | 760 bp | 53 °C | |
| TCF7L2-Ro | 5′-GAGATAGATGATAGGCTGTT‐3′ | |||
| TCF7L2- FI | G | 5′-GGAATATCCAGGCAAGAATG‐3′ | 494 bp | |
| TCF7L2-RI | T | 5′-CCTGAGTAATTATCAGAATATGGTA‐3′ | 310 bp | |
Gel Electrophoresis
Gel Electrophoresis of Long Coding RNA-ANRIL-rs1333045 C>T Gene Polymorphism
The amplicons were subjected to safe dye staining (prime safe Dye, GeNet Bio) and 2% agarose gel electrophoresis (Cleaver, UK). The amplification of the ANRIL-rs1333045 C>T gene using the primers Fo and Ro resulted in a band measuring 442 bp, which served as a control band to show the quantity and quality of DNA. FI and Ro amplification results in a band of 298 bp (the A allele), while amplification of Fo and RI primers results in a band of 200 bp (the C allele) (Table 2), as depicted in Fig. 2.
Fig. 2.
Genotyping of the ANRIL rs1333045 C>T polymorphism. An agarose gel image shows the results of genotyping the ANRIL rs1333045 C>T polymorphism. The 200 bp band represents the homozygous CC genotype, while the 298 bp band corresponds to the homozygous AA genotype. A 442 bp band is also visible, which is the uncut PCR product. Lane M contains a 100 bp DNA ladder. The gel illustrates the genotypes for samples S1-S12
Statistical Analysis
Data analysis was done by using SPSS 16.0 software program (Chicago, IL, USA) to evaluate the comparison data for healthy controls and stroke patients. The genotyping frequency and biochemical properties of ANRIL-rs1333045A>C, TCF7L2 rs12255372G>T, and CYP2C19*17-rs12248560 C>T were compared using Fisher’s exact test and chi-squared analysis. Furthermore, the χ2 test was employed to compare the genotype frequencies observed in the case and control patients in order to evaluate the HWE. When the P value for each observation was less than 0.05, it was considered significant. The odds ratios (ORs), risk ratios (RRs), and risk differences (RDs) are compared with 95% confidence intervals (CIs) by regression analysis. The association between the genotypes of TCF7L2-rs12255372G>T, ANRIL-rs1333045A>C, and CYP2C19*17-rs12248560 C>T and risk of stroke was examined by using multivariate analysis (see Figs. 3, 4).
Fig. 3.
Genotyping of the CYP2C19*17 C>T polymorphism. An agarose gel image shows the results of genotyping the CYP2C19*17 C>T polymorphism. The 292 bp band represents the homozygous CC genotype, while the 227 bp band corresponds to the homozygous TT genotype. A 462 bp band is also visible, which is the uncut PCR product. Lane M contains a 100 bp DNA ladder. The gel illustrates the genotypes for samples S1-S16
Fig. 4.
Genotyping of the TCF7L2 rs12255372 G>T polymorphism. This figure displays an agarose gel showing the genotyping results for the TCF7L2 rs12255372 G>T polymorphism. The 494 bp band represents the homozygous G allele, while the 310 bp band corresponds to the homozygous T allele. A 760 bp band, representing the uncut PCR product, is also visible. Lane M contains a 100 bp DNA ladder. The gel illustrates the genotypes for samples S1-S13
To better model the association between the selected genotypes and stroke risk, a multivariate logistic regression was performed. The presence of stroke (case vs. control) was the primary outcome. The model incorporated several independent variables, including genetic variants, age, gender, and lipid profile parameters. To account for potential confounding effects, age and key lipid profile components were included as covariates. The analysis yielded odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). A False Discovery Rate (FDR) correction was also applied to adjust for multiple comparisons, and a P value of less than 0.05 was considered statistically significant.
Power Analysis
We conducted a post hoc power analysis to assess the statistical power of our study. Using a two-sided approach with the pwr package (Version 1.3-0.3) in R (Version 4.3.2), we estimated the required sample size based on the allele frequencies of each genotype in both cases and controls. The analysis used a standard alpha level of 0.05.
Results
Demographic Charateristics of Study Participients
The study has characterized various biochemical parameters in a comparative assessment between the healthy control (mean age = 36) and the stroke patients (mean age = 57). The majority of the biochemical markers examined in this investigation showed statistically significant changes in stroke patients. As reported in Table 2, there exist significant variations in serum lipid profile and blood glucose status between the two groups. Accordingly, higher values were observed in stroke patients for blood sugar (fasting), glycated hemoglobin, cholesterol, LDL, VLDL, and triglycerides compared to the healthy controls. There were no significant variations for platelet count and liver enzymes (ALT/AST). The significance of gene variations of different allelic forms was also examined and statistically correlated with the gender and age within the stroke patients.
Statistical Power
Our analysis confirmed that the study had enough statistical power to detect a significant effect for each of the chosen genetic markers. Using a two-sided test and a standard alpha level of 0.05, the statistical power for Lnc-RNA rs1333045 C>T was 0.52, for TCF7L2 rs12255372 G>T, it was 0.60, and for Cytochrome CYP2C19*17, it was 0.61. Although our power values were below the usual threshold of 0.80, they were still acceptable for this study considering the sample size limits. The power analysis, based on the specific allele frequencies of each SNP in our case and control groups, shows a reasonable probability of correctly identifying a true effect if one exists.
Genotypic and Alleles Distribution of TCF7L2-rs12255372G>T, Long Noncoding RNA (ANRIL-rs1333045A>C), and CYP2C19*17 C>T Gene Variations in Cases and Controls
Long Noncoding RNA ANRIL-rs1333045 C>T
Our result reported the frequencies of long noncoding RNA-ANRILrs1333045 C>T in stroke are CC (24%), CT (26%), and TT (50%), in comparison to the controls, who were having TT (30%), CT (39%), and CC (31%), in that order. (Table 3). The majority of the biochemical markers examined in this investigation showed statistically significant changes in stroke patients. Additionally, it was indicated that stroke cases have higher incidence of T allele (0.63 vs. 0.49) than did healthy persons.
Table 3.
Frequency of TCF7L2 rs12255372 G>T, CYP2C19*17C>T, Lnc-NArs1333045 C>T genotypes in controls and cases
| Genotype | Subjects | N = | CC | CT | TT | C | T | Df | X2 | P value | FDR |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Lnc-RNA rs1333045 C>T | Cases | 100 | 24 (24%) | 26 (26%) | 50 (50%) | 0.37 | 0.63 | 2 | 8.49 | 0.0143 | 0.015 |
| Controls | 100 | 31 (31%) | 39 (39%) | 30 (30%) | 0.51 | 0.49 | |||||
| Cytochrome CYP2C19*17 | Cases | 100 | 61 (61%) | 30 (30%) | 9 (9%) | 0.76 | 0.24 | 2 | 9.05 | 0.010 | 0.015 |
| Controls | 100 | 79 (79%) | 18 (18%) | 3 (3%) | 0.88 | 0.12 | |||||
| TCF7L2 rs12255372 G>T | Cases | 101 | 40 (39.60%) | 50 (49.50%) | 11 (10.89%) | 0.64 | 0.36 | 2 | 8.31 | 0.015 | 0.015 |
| Controls | 100 | 60 (60%) | 35 (35%) | 5 (5%) | 0.78 | 0.22 |
CYP2C19*17 C>T
In contrast to controls, who were more likely to have frequencies of CC (31%), CT (39%), and TT (30%), respectively, the frequencies of CYP2C19*17 C>T in stroke are CC (61%), CT (30%), and TT (9%). (Table 3).The CYP2C19*17-C>T genotypes differed statistically significantly (P < 0.014) between stroke patients and healthy controls. Furthermore, a higher frequency of the C allele was seen in stroke patients compared to healthy controls (0.63 vs. 0.49). (Table 3).
Transcription Factor 7-like 2 (TCF7L2) rs12255372 G>T
In contrast to controls, who were more likely to have frequencies of GG (60%), CT (35%), and TT (5%), respectively, the frequencies of TCF7L2 rs12255372 G>T in stroke are GG (39.60%), GT (49.50%), and TT (10.89%). (Table 3). There was a significant statistical difference (P < 0.015) in the TCF7L2 rs12255372 G>T genotypes between stroke patients and healthy controls. Furthermore, a higher frequency of the T allele was seen in stroke patients compared to healthy controls (0.36 vs. 0.22). (Table 3).
Logistic Regression Analysis of Long Noncoding RNA rs1333045 C>T Genotypes to Predict the Risk of Stroke
According to Table 4, results indicated a significant correlation between the codominant model’s stroke susceptibility and the LNC RNA ANRIL rs1333045-TT genotype, with an OR of 2.15 (95%) CI = (1.069 to 4.331), RR = 1.50 (95%) CI = (11.0421 to 2.1678), and P < 0.031, (Table 4) However, no correlation between the LNCRNA-CC and CT genotypes was found. In the Recessive model, a substantial correlation was seen between the ANRIL (CC+CT) and TT genotypes, indicating an increased susceptibility to stroke (OR = 2.33(95%) CI (1.3062 to 4.1681), RR = 1.55(01.1287 to 2.143), and P < 0.004).
Table 4.
Association of ANRIL rs1333048 C>T genotypes with the stroke risk
| Genotypes | Healthy controls | Stroke cases | OR (95% CI) | Risk ratio (RR) | P value | FDR |
|---|---|---|---|---|---|---|
| Codominant model | ||||||
| ANRIL-CC | 31 | 24 | 1 (reference) | 1 (reference) | ||
| ANRIL-CT | 39 | 26 | 0.86 (0.415 to 1.783) | 0.93 (0.6919 to 1.2753) | 0.68 | 0.68 |
| ANRIL-TT | 30 | 50 | 2.15 (1.069 to 4.331) | 1.50 (1.0421 to 2.1678) | 0.031 | 0.062 |
| Dominant inheritance model | ||||||
| ANRIL-CC | 31 | 24 | 1 (reference) | 1 (reference) | ||
| ANRIL-(CT+TT) | 69 | 76 | 1.42 (0.7618 to 2.6571) | 1.18 (0.8876 to 1.5806) | 0.268 | 0.322 |
| Recessive model | ||||||
| ANRIL (CC+CT) | 70 | 50 | 1 (reference) | 1 (reference) | ||
| ANRIL-TT | 30 | 50 | 2.33 (1.3062 to 4.1681) | 1.55 (01.1287 to 2.143) | 0.004 | 0.02 |
| Allele | ||||||
| ANRIL-C | 101 | 74 | 1 (reference) | 1 (reference) | ||
| ANRIL-T | 99 | 126 | 1.73 (1.1655 to 2.5890) | 1.31 (1.0799 to 1.5932) | 0.0067 | 0.02 |
| Overdominant model | ||||||
| ANRIL-CT | 39 | 26 | 1 (reference) | 1 (reference) | ||
| ANRIL-CC+TT | 61 | 74 | 1.81 (0.9977 to 3.3188) | 1.32 (1.0118 to 1.7427) | 0.050 | 0.075 |
However, under the dominant model, there was no correlation found between the stroke susceptibility and the LNCRNA-CC vs. (CT+TT) genotypes. With an OR = 1.81, (95%) CI (0.9977 to 3.3188), RR = 1.32, and P < 0.050, there was no association found between the LNCRNA-CT and ANRIL −CC+TT genotypes and stroke in the over dominant model. (Table 4). The allelic comparison revealed a substantial correlation between the LNCRNA-T allele and stroke susceptibility, with an OR of 1.73, 95% CI (1.1655 to 2.5890), RR 1.31, and P 0.0067.
Relationship Between the Genotypes of LncRNArs1333045 and the Clinical and Demographic Characteristics of Stroke Patients
Our results showed a significant relationship (p 0.014) between the gender of the stroke patients and the lncRNArs1333045 C>T genotypes. (Table 5). However, it was discovered that compared to male stroke patients, female patients had a higher rate of heterozygosity. The findings additionally showed a significant association (P < 0.0064) between the age of the stroke patients and their long noncoding RNA ANRILrs1333045 C>T genotypes. Advanced age cases were shown to have a higher frequency of heterozygosity than younger individuals. There was a noteworthy association (p 0.0053) observed between the blood cholesterol levels and the genotypes of long noncoding RNA rs1333045 C>T in stroke patients. Our findings showed that there was no significant correlation between the long noncoding RNA rs1333045 C>T genotype and LDL-C (mg/dL) and HDL ≤ 40 (mg/dL) in stroke patients. Triglycerides (mg/dL) and long noncoding RNA rs1333045 C>T genotypes were shown to be strongly correlated in stroke patients (P 0.002). (Table 5).
Table 5.
Association of clinical features of Stroke patients with lncRNA ANRILrs1333045 C>T genotypes
| Parameters | N = | CC | CT | TT | Df | X2 | P value | FDR |
|---|---|---|---|---|---|---|---|---|
| Stroke patients | 100 | 24 | 26 | 50 | ||||
| Association of ANRILrs1333045 C>T with gender | ||||||||
| Male | 62 | 16 | 10 | 36 | 2 | 8.46 | 0.014 | 0.021 |
| Female | 38 | 08 | 16 | 14 | ||||
| Association of ANRILrs1333045 C>T with age | ||||||||
| Age < 50 | 30 | 13 | 08 | 09 | 2 | 10.11 | 0.0064 | 0.0128 |
| Age > 50 | 70 | 11 | 18 | 41 | ||||
| Association of ANRILrs1333045 C>T with Cholesterol | ||||||||
| Cholesterol ≤ 200 (mg/dL) | 68 | 17 | 12 | 41 | 2 | 10.48 | 0.0053 | 0.0128 |
| Cholesterol > 200 (mg/dL) | 32 | 07 | 14 | 09 | ||||
| Association of ANRILrs1333045 C>T with LDL | ||||||||
| LDL ≤ 100 (mg/dL) | 68 | 14 | 18 | 36 | 2 | 1.42 | 0.491 | 0.589 |
| LDL > 100 (mg/dL) | 32 | 10 | 8 | 14 | ||||
| Association of ANRILrs1333045 C>T with HDL | ||||||||
| HDL ≤ 40 (mg/dL) | 40 | 11 | 9 | 20 | 2 | 0.65 | 0.722 | 0.722 |
| HDL > 40 (mg/dL) | 60 | 13 | 17 | 30 | ||||
| Association of ANRILrs1333045 C>T with TGL | ||||||||
| TGL ≤ 200 (mg/dL) | 70 | 13 | 14 | 43 | 2 | 12.19 | 0.002 | 0.012 |
| TGL > 200 (mg/dL) | 30 | 11 | 12 | 07 | ||||
Regression Analysis of CYP2C19*17C > T Genotypes to Predict the Risk of Stroke
In the codominant inheritance model, the CYP2C19*17 CT genotype was found to be significantly associated with stroke susceptibility with an OR of 2.19 (95% CI) (1.11 to 4.305) and a RR 1.51(1.023 to 2.2450) and P−0.020, respectively (Table 6). Similarly, CYP2C19*17-TT genotype was also significantly associated with stroke risk with ORs of 3.95(95% CI) (1.024 to 15.222) and 2.27 (0.844 to 6.122) and P < 0.046, respectively. In case of the dominant inheritance model, the CYP2C19*17-CC vs. CYP2C19*17 (CT+TT) genotypes were strongly associated with stroke susceptibility, with odd ratios of 2.44(95% CI) (1.3052 to 4.5810) and 1.62 (1.1171 to 2.3604) and P < 0.005, respectively. The CYP2C19*17-(CC+CT) genotype was also not associated to stroke susceptibility in the recessive inheritance model, with ORs of 3.23 (95% CI) (0.8485 to 12.3209) and 2.07(0.7712 to 5.5821) and P < 0.085. The allelic comparison revealed a substantial association between the CYP2C19*17-C allele and stroke susceptibility, with ORs of 2.34 (95% CI) (1.372 to 4.011), RRs of 1.61, and P−0.002 (Table 6).
Table 6.
Estimation of association of CYP2C19*17 C → T genotypes with stroke risk
| Genotypes | Healthy controls | Stroke cases | OR (95% CI) | Risk ratio (RR) | P-Val | FDR |
|---|---|---|---|---|---|---|
| (N = 100) | (N = 100 | |||||
| Codominant model | ||||||
| CYP 2C19*17-CC | 79 | 60 | 1 (ref.) | 1 (ref.) | ||
| CYP 2C19*17-CT | 18 | 31 | 2.19 (1.1186 to 4.3052) | 1.51 (1.0232 to 2.2450) | 0.020 | 0.04 |
| CYP 2C19*17 -TT | 03 | 09 | 3.95 (1.0249 to 15.2229) | 2.27 (0.8442 to 6.1222) | 0.046 | 0.0552 |
| Dominant model | ||||||
| CYP 2C19*17-CC | 79 | 60 | 1 (ref.) | 1 (ref.) | ||
| CYP 2C19*17-(CT+TT) | 21 | 39 | 2.44 (1.3052 to 4.5810) | 1.62 (1.1171 to 2.3604) | 0.005 | 0.015 |
| Recessive model | ||||||
| CYP2 C19*17-(CC+CT) | 97 | 90 | 1 (ref.) | 1 (ref.) | ||
| CYP 2C19*17-TT | 03 | 09 | 3.23 (0.8485 to 12.3209) | 2.07 (0.7712 to 5.5821) | 0.085 | 0.085 |
| Allele | ||||||
| CYP 2C19*17-C | 176 | 150 | 1 (ref.) | 1 (ref.) | ||
| CYP 2C19*17-T | 24 | 48 | 2.34 (1.3727 to 4.0117) | 1.61 (1.1509 to 2.2794) | 0.0018 | 0.0108 |
| Over dominant model | ||||||
| CYP2C19*17-CC+TT | 82 | 69 | 1 (ref.) | 1 (ref.) | ||
| CYP2C19*17-CT | 18 | 30 | 1.98 (1.0173 to 3.8565) | 1.44 (0.9771 to 2.1462) | 0.044 | 0.0552 |
Relationship Between the Clinical and Demographic Characteristics of Stroke Patients and the CYP2C19*17C>T Genotypes
A significant association was indicated between the CYP2C19*17 C>T genotypes and the gender of the stroke patients (P 0.014) (Table 7). It was discovered, nevertheless, that compared to female stroke patients, male stroke patients had a higher rate of heterozygosity. Furthermore, the findings showed a significant association (P < 0.0064) between the age of the stroke patients and their CYP 2 C 19*17 C>T genotypes. Advanced age cases were shown to have a higher frequency of heterozygosity than younger individuals. The CYP2C19*17 genotypes and blood cholesterol expression in stroke patients showed a significant connection (P 0.004). Our results showed a non-significant correlation between the CYP2C19*17 C>T genotype with stroke patients' LDL-C (mg/dL) and HDL ≤ 40 (mg/dL) levels. Stroke patients’ triglycerides (mg/dL) and CYP2C19*17 C>T genotypes were shown to be highly associated (P 0.004) (Table 7).
Table 7.
Association of clinical features of stroke patients with CYP2C19*17C >T genotypes
| Parameters | N = | CC | CT | TT | Df | X2 | P value | FDR |
|---|---|---|---|---|---|---|---|---|
| Stroke patients | 100 | 61 | 30 | 09 | ||||
| Association of CYP2C19*17C>T with gender | ||||||||
| Male | 62 | 40 | 16 | 6 | 2 | 1.37 | 0.504 | 0.6 |
| Female | 38 | 21 | 14 | 03 | ||||
| Association of CYP2C19*17C>T with age | ||||||||
| Age < 50 | 30 | 17 | 07 | 06 | 2 | 6.53 | 0.038 | 0.076 |
| Age > 50 | 70 | 44 | 23 | 03 | ||||
| Association of CYP2C19*17C>T with Cholesterol | ||||||||
| Cholesterol ≤ 200 (mg/dL) | 68 | 48 | 14 | 06 | 2 | 9.48 | 0.008 | 0.024 |
| Cholesterol > 200 (mg/dL) | 32 | 13 | 16 | 03 | ||||
| Association of CYP2C19*17C>T with LDL | ||||||||
| LDL ≤ 100 (mg/dL) | 70 | 49 | 16 | 05 | 2 | 7.96 | 0.081 | 0.122 |
| LDL > 100 (mg/dL) | 30 | 12 | 14 | 04 | ||||
| Association of CYP2C19*17C>T with HDL | ||||||||
| HDL ≤ 40 (mg/dL) | 40 | 22 | 14 | 04 | 2 | 1.02 | 0.600 | 0.6 |
| HDL > 40 (mg/dL) | 60 | 39 | 16 | 05 | ||||
| Association of CYP2C19*17C>T with TGL | ||||||||
| TGL ≤ 200 (mg/dL) | 70 | 44 | 23 | 3 | 2 | 10.83 | 0.004 | 0.024 |
| TGL > 200 (mg/dL) | 30 | 17 | 07 | 06 | ||||
Logistic Regression Analysis of Transcription Factor 7-like 2rs12255372 G>T Genotypes to Predict the Risk of Stroke
In the codominant model, the TCF7-L2-rs12255372 GT genotype was found associated with stroke risk with an OR of 2.14 (95% CI) (1.189 to 3.861) and RR 1.45(1.079 to 1.967) and P−0.011, respectively (Table 8). Similarly, TCF7-L2-rs12255372-TT genotype is associated with stroke susceptibility risk with ORs of 3.0 (95% CI) (1.06 to 10.218) and 1.92 (0.912 to 4.04) and P < 0.038, respectively. In case of the dominant inheritance model, the TCF7-L2-rs12255372 (GT+TT) genotypes were strongly associated with stroke susceptibility, with OR of 2.28 (95% CI) (1.30 to 4.02) and 1.51 (0.7763 to 6.9468) and P < 0.004, respectively. In the recessive inheritance model, the TCF7-L2-rs12255372 (GG+GT) genotype did not correlate with the risk of stroke, with ORs of 2.32 (95% CI) (0.7763 to 6.9468) and 1.64 (0.7839 to 3.4448), as well as P < 0.130. The TCF7-L2 rs12255372 T allele and stroke susceptibility were found to be significantly correlated by allelic comparison, with ORs of 1.90 (95% CI) (1.2294 to 2.9603), RRs of 1.41 (95% CI) (1.0983 to 1.8205), and P−0.004 (Table 8). In the over dominant inheritance model, a strong correlation was seen between the TCF7-L2-rs12255372 (GG+TT) vs. GT genotypes and the risk of stroke, with odd ratios of 1.82 (95% CI) (1.033 to 3.208) and 1.36 (1.007 to 1.838) and P < 0.04, respectively (Table 8).
Table 8.
Correlation of the TCF7L2 rs12255372 G>T genotypes with stroke
| Genotypes | Healthy controls | stroke cases | OR (95% CI) | Risk ratio (RR) | P-val | FDR |
|---|---|---|---|---|---|---|
| (N = 100) | (N = 101 | |||||
| Codominant inheritance model | ||||||
| TCF7L2-GG | 60 | 40 | 1 (ref.) | 1 (ref.) | ||
| TCF7L2-GT | 35 | 50 | 2.14 (1.1893 to 3.8610) | 1.45 (1.0792 to 1.9675) | 0.011 | 0.022 |
| TCF7L2-TT | 05 | 11 | 3.00 (1.0657 to 10.2189) | 1.92 (0.9122 to 4.0411) | 0.038 | 0.0528 |
| Dominant model | ||||||
| TCF7L2-GG | 60 | 40 | 1 (ref.) | 1 (ref.) | ||
| TCF7L2-(GT+TT) | 40 | 61 | 2.28 (1.3003 to 4.0242) | 1.51 (1.1346 to 2.0230) | 0.004 | 0.012 |
| Recessive inheritance model | ||||||
| TCF7L2-(GG+GT) | 95 | 90 | 1 (ref.) | 1 (ref.) | ||
| TCF7L2-TT | 05 | 11 | 2.32 (0.7763 to 6.9468) | 1.64 (0.7839 to 3.4448) | 0.130 | 0.130 |
| Allele | ||||||
| TCF7L2-G | 155 | 130 | 1 (ref.) | 1 (ref.) | ||
| TCF7L2-T | 45 | 72 | 1.90 (1.2294 to 2.9603) | 1.41 (1.0983 to 1.8205) | 0.004 | 0.012 |
| Over dominant model | ||||||
| TCF7L2-GG+TT | 65 | 51 | 1 (ref.) | 1 (ref.) | ||
| TCF7L2-GT | 35 | 50 | 1.82 (1.0332 to 3.2086) | 1.36 (1.0072 to 1.8386) | 0.044 | 0.0528 |
Association Between Stroke Patients and TCF7L2-rs12255372 G>T Genotype
Our results showed a significant (p 0.048) association between the genotypes of TCF7-L2 rs12255372 G>T and the subjects’ gender (Table 9). It was discovered, nevertheless, that compared to female stroke patients, male stroke patients had a higher rate of heterozygosity. Furthermore, a non-significant connection (P < 0.192) was found between the age of the stroke patients and their TCF7-L2 rs12255372 G>T genotypes. Advanced age cases were shown to have a higher frequency of heterozygosity than younger individuals. The genotypes of TCF7-L2 rs12255372 G>T and blood cholesterol levels in stroke patients showed a significant connection (p 0.036). Our findings showed a significant (P < 0.031) correlation between TCF7-L2 rs12255372 G>T genotypes and LDL-C (mg/dL) in stroke patients. Our findings showed a non-significant (P < 0.53) correlation between TCF7-L2 rs12255372 G>T genotype and HDL-C (mg/dL) in stroke patients. Our findings showed a significant (P < 0.031) correlation between the TCF7-L2 rs12255372 G>T genotype and stroke patients with LDL-C (mg/dL) and HDL ≤ 40 (mg/dL). Stroke patients’ triglycerides (mg/dL) and TCF7-L2 rs12255372 G>T genotypes were shown to be substantially associated (P 0.024) (Table 9).
Table 9.
Clinical characteristics of stroke patients and their association with TCF7L2 rs12255372 T>G genotypes
| Parameters | N = | GG | GT | TT | Df | X2 | P value | FDR |
|---|---|---|---|---|---|---|---|---|
| Stroke patients | 101 | 40 | 50 | 11 | 2 | |||
| Association of TCF7-L2 rs12255372 G>T with gender | ||||||||
| Male | 62 | 26 | 33 | 03 | 2 | 6.07 | 0.048 | 0.072 |
| Female | 39 | 14 | 17 | 08 | ||||
| Association of TCF7-L2 rs12255372 G>T with age | ||||||||
| Age < 50 | 31 | 11 | 14 | 06 | 2 | 3.3 | 0.192 | 0.230 |
| Age > 50 | 70 | 29 | 36 | 05 | ||||
| Association of TCF7-L2 rs12255372 G>T with Cholesterol | ||||||||
| Cholesterol ≤ 200 (mg/dL) | 68 | 26 | 38 | 04 | 2 | 6.6 | 0.036 | 0.072 |
| Cholesterol > 200 (mg/dL) | 33 | 14 | 12 | 07 | ||||
| Association of TCF7-L2 rs12255372 G>T with LDL | ||||||||
| LDL ≤ 100 (mg/dL) | 70 | 31 | 35 | 04 | 2 | 6.89 | 0.031 | 0.072 |
| LDL > 100 (mg/dL) | 31 | 09 | 15 | 07 | ||||
| Association of TCF7-L2 rs12255372 G>T with HDL | ||||||||
| HDL ≤ 40 (mg/dL) | 40 | 18 | 19 | 03 | 2 | 1.24 | 0.53 | 0.53 |
| HDL > 40 (mg/dL) | 61 | 22 | 31 | 8 | ||||
| Association of TCF7-L2 rs12255372 G>T with TGL | ||||||||
| TGL ≤ 200 (mg/dL) | 70 | 27 | 39 | 04 | 2 | 7.45 | 0.024 | 0.072 |
| TGL > 200 (mg/dL) | 31 | 13 | 11 | 07 | ||||
Multivariate Association with Stroke Risk
A multivariate logistic regression analysis was conducted to investigate the association between select genetic variants, demographic characteristics, and stroke risk. The model was adjusted for age and lipid profiles, which were identified as significant confounding factors. The results, highlighted in Table 10, reveal several key associations.
Table 10.
Multivariate logistic regression analysis for associations with stroke risk
| Characteristic | ORa | 95% CIa | P value | FDRb |
|---|---|---|---|---|
| ANRIL rs1333048 C>T | ||||
| CC | 0.53 | 0.18, 1.57 | 0.3 | 0.4 |
| CT | 0.65 | 0.22, 1.94 | 0.4 | 0.5 |
| TT | ||||
| TCF7L2 rs12255372 G>T | ||||
| GG | 0.53 | 0.09, 2.81 | 0.5 | 0.5 |
| GT | 8.59 | 1.45, 53.2 | 0.017 | 0.069 |
| TT | ||||
| CYP2C19*17 C>T | ||||
| CC | 0.14 | 0.02, 0.77 | 0.029 | 0.076 |
| CT | 1.75 | 0.25, 10.9 | 0.6 | 0.6 |
| TT | ||||
| Gender | ||||
| Male | 1.87 | 0.72, 5.06 | 0.2 | 0.4 |
| Age | 1.16 | 1.12, 1.22 | < 0.001 | < 0.001 |
| Lipid Profile | ||||
| Cholesterol | 1.16 | 1.07, 1.31 | 0.002 | 0.027 |
| LDL | 1.11 | 1.04, 1.23 | 0.009 | 0.031 |
| VLDL | 1.28 | 1.09, 1.59 | 0.007 | 0.031 |
| Triglyceride | 1.24 | 1.11, 1.52 | 0.004 | 0.027 |
| Other | ||||
| Blood sugar (fasting) | 1.03 | 0.88, 1.28 | 0.8 | > 0.9 |
| AST | 0.99 | 0.90, 1.12 | 0.8 | > 0.9 |
| ALT | 1.00 | > 0.9 | > 0.9 |
aOR = odds ratio, CI = confidence interval
bFalse discovery rate correction for multiple testing
The analysis identified a strong and significant association between the TCF7L2 rs12255372 GT genotype and an increased risk of stroke. Individuals with this genotype had a remarkably higher odds of experiencing a stroke compared to those with the GG genotype, as indicated by an odds ratio (OR) of 8.59 (95% CI 1.45–53.2; P = 0.017). Although the P value was significant, the false discovery rate (FDR) of 0.069 suggests that this finding may require further validation in a larger cohort. In contrast, the ANRIL rs1333048 and CYP2C19*17 single nucleotide polymorphisms (SNPs) did not show a statistically significant association with stroke risk in this model.
Consistent with established research, our analysis confirmed age as a highly significant and independent risk factor for stroke. The OR of 1.16 (95% CI 1.12–1.22; P < 0.001) suggests that for each year of increasing age, the odds of stroke increase. Similarly, various components of the lipid profile were significantly associated with an increased risk of stroke. Elevated levels of cholesterol (OR = 1.16; 95% CI: 1.07–1.31; P = 0.002), low-density lipoprotein (LDL) (OR = 1.11; 95% CI 1.04–1.23; P = 0.009), very low-density lipoprotein (VLDL) (OR = 1.28; 95% CI 1.09–1.59; P = 0.007), and triglycerides (OR = 1.24; 95% CI 1.11–1.52; P = 0.004) were all significant predictors of stroke. Other factors, including gender and metabolic markers such as fasting blood sugar, AST, and ALT, did not show a statistically significant association with stroke in the adjusted model.
Discussion
Cerebrovascular disease including stroke represents very important causes of mortality and morbidity all over the world (Wang et al. 2021). Early diagnosis and lifestyle and behavioral modification can reduce or delay the incidence of cerebrovascular and cardiovascular diseases (Boehme et al. 2017). This necessitates the identification and characterization of risk loci for detection and stratification of population susceptible to these diseases. In this study, we report the potential associations of the ANRIL gene’s rs1333045 C>T, CYP2C19*17 (rs12248560) C>T, and the TCF7L2 rs12255372 G>T with stroke occurrence in Saudi population; after being confirmed in further studies, these results can be employed in genetic testing for identification of the susceptible individuals for the prevention or delay of the stroke.
The ANRIL gene’s rs1333045 C>T SNV raises the risk of several conditions, including cancer, cardiovascular disease, and neurological illnesses. The ANRIL gene’s rs1333045 C>T polymorphism in cancer has been associated with a higher risk of melanoma, lung and breast cancers (Sanchez et al. 2023). The C allele of the ANRIL gene’s rs1333045 C>T is associated with greater ANRIL expression, which aids in the growth and spread of tumors. In addition, the ANRIL gene’s rs1333045 C>T SNP has been associated with a higher risk of coronary heart disease (CHD) in Chinese population (Hua et al. 2020). ANRIL regulates the apoptosis and vascular cell proliferation and thereby confers protection against atherosclerosis (Razeghian-Jahromi et al. 2022). The dysregulation of ANRIL has been associated with several diseases such as cancers and CVDs (Sanchez et al. 2023), probably because ANRIL regulates several vital physiological processes such as cell proliferation, apoptosis, cell cycle, and inflammation (Razeghian-Jahromi et al. 2022). The ANRIL rs1333045 C>T polymorphism has been associated with an elevated susceptibility to Alzheimer’s disease, Parkinson's disease, and amyotrophic lateral sclerosis (ALS) in neurodegenerative diseases (Morris et al. 2019). Our results indicated that the TT genotype of the ANRIL gene’s rs1333045 C>T SNP was associated with increased risk of stroke (Tables 3 and 4). This result is in line with a report that indicated the association of ANRIL with predisposition to ischemic stroke (Bai et al. 2022). The ANRIL, belongs to the long noncoding RNA family, was reported to exhibit a strong association with the susceptibility to atherosclerotic cerebrovascular and cardiovascular diseases (Bai et al. 2022; Holdt et al. 2010). Moreover, the lncRNA ANRIL genetic variations were also reported to be associated with elevated susceptibility to ischemic stroke in Chinese population (Wang et al. 2021). Our results also showed that ANRIL gene’s rs1333045 C>T SNP genotypes are differently distributed in patients with hypercholesterolemia and hyperlipidemia and patients with normal blood cholesterol and triglyceride levels (Table 5). The dyslipidemia is an established risk for atherosclerosis and vascular diseases such as stroke and cardiovascular disease. It has been reported that abnormal expression of ANRIL promotes induction of atherosclerosis and is a risk factor for coronary artery disease (Chen et al. 2020). The defective expression of ANRIL is associated with injury of the vascular endothelium (Razeghian-Jahromi et al. 2022). In addition, the ANRIL dysregulation is related to proliferation, migration, apoptosis of vascular smooth muscle cell, monocytes adhesion to endothelium, impaired metabolism of glycolipids, and nucleic acid damage (Chen et al. 2020; Cho et al. 2019). It has been proposed ANRIL is involved in CAD development via regulation of the genes CAP-GLY domain containing linker protein 1 (CLIP1), EZR, and Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) expression which initiates the atherosclerosis and the CAD (Cho et al. 2019). This can also be a cause of the stroke occurrence in this population as the atherosclerosis is the major cause of ischemic stroke (Banerjee and Chimowitz 2017). Our results also indicated that ANRIL gene’s rs1333045 C>T genotypes are significantly different between males and female cases (Table 5). This result is in agreement with a study reported that the women have higher risk to stroke than men (Rexrode et al. 2022). Results also showed that ANRIL gene’s rs1333045 C>T genotypes are associated with age (Table 5). This result is consistent with the study reported that 75% of all strokes occur in elderly individuals (aged ≥ 65 years) (Yousufuddin and Young 2019). Our results are inconsistent with studies indicating that the C allele of the ANRIL gene’s rs1333045 C>T is associated with atherosclerosis and CAD (Cunnington et al. 2010; Jarinova et al. 2009). This inconsistency may be due to different sample size or different ethnicity.
Our results showed that the T allele of the CYP2C19*17 (rs12248560) C>T was associated with stroke (Tables 3 and 6), and that the CYP2C19*17 (rs12248560) C>T genotype distribution was significantly different in patients with hypercholesterolemia and hyperlipidemia and patients with normal lipid profile (Table 7). The Hyperlipidemia is a potent risk factor for atherosclerosis (Navar-Boggan et al. 2015), which is a cause of ischemic stroke (Kim 2021). Our results are consistent with the study by Bai et al., which reported an association between CYP2C19 gene variations with ischemic stroke and metabolism of lipids in Chinese population (Bai et al. 2020). In general, CYP2C19 is reported to be involved in atherosclerosis via its metabolic and biological roles in vascular endothelial cells (Xie et al. 2023). The CYP2C gene family members metabolize the arachidonic acid into the metabolites such as endodermal hyperpolarized factor (EDHF) (Cai et al. 2023). These metabolites dilate blood vessels (Cai et al. 2023) and regulate tone of vasculatures, kidney function and involved in elevated blood pressure and cardiovascular disease (Cai et al. 2023). It has been reported that the sound expression of the CYP450 in endothelial cells is required for the activity of the endothelial NO synthase and its impairment leads to increased activity of the prostanoid vasoconstrictor (Cai et al. 2023). This would result in dysfunction of endothelium and increased blood pressure (Cai et al. 2023; Malacarne et al. 2022). The activity of the CYP2C is important in regulation of blood pressure. Hypertension is an important risk factor for stroke (Wajngarten and Silva 2019). Our result is in disagreement with a study reported that the CYP2C19 Loss-Of-Function is associated with increased susceptibility to ischemic stroke in a study from USA (Patel et al. 2021). This disagreement may be due to different sample size or different ethnicity.
The results indicated that the GT and the T allele of the TCF7L2 rs12255372 G > T were associated with stroke (Tables 3 and 8). Our results also indicated that the TCF7L2 rs12255372 G>T genotype distribution differed significantly between male and female cases (Table 9). This result is consistent with a study demonstrated that the women are more susceptible for stroke than men (Rexrode et al. 2022). Results also showed the TCF7L2 rs12255372 G>T genotype distribution was different between patients aged ≤ 50 and patients aged > 50 years old (Table 9). This result is consistent with a study indicated that the risk of stoke increases in cases aged ≥ 65. Results showed that there is a significant difference in the genotype of the TCF7L2 rs12255372 G>T in patients with normal and patients with dyslipidemia (Table 9). Hyperlipidemia is a risk factor for cerebrovascular events (Alloubani et al. 2021). Studies also reported that silencing of TCF7L2 results in inadequate secretion of insulin, defective adipogenesis, dysplasia of vasculature, and accumulation of lipid (Li et al. 2021). However, the increased expression of the TCF7L2 enhances polarization of the macrophage and suppresses neointimal hyperplasia. These studies indicated the anti-atherosclerotic properties of TCF7L2 (Li et al. 2021). Our results are in line with a study reported the association of the TCF7L2 gene variations with metabolic syndrome (Delgado-Lista et al. 2011). Metabolic syndrome is associated with ischemic stroke (Zhan et al. 2022). The T allele of the TCF7L2 rs12255372 G>T is reported to decrease the expression of TCF7L2 in adipose tissue in the Finnish population. This is probably the reason why the TCF7L2 rs12255372 G>T with T allele loses its anti-atherosclerotic properties and leads to atherosclerosis and ischemic stroke GT genotype and T allele carriers.
The limitations of the present study include the single hospital-based recruitment of the stroke patients, the cross-sectional design, the small sample size, and the absence of environmental and lifestyle adjustments. Further protein functional and large-scale case–control longitudinal studies taking into account these limitations are recommended to verify these findings.
Conclusion
In summary, this research sheds more light on the possible association of LncRNA-ANRIL locus, TCF7-L2, and CYP2C19*17 genes with stroke occurrence. In addition, the ARMS-PCR reported to be rapid, inexpensive, and is highly applicable to genotyping of these single nucleotide variations in these loci. Further well-designed studies with increased sample sizes are necessary to confirm our findings before being considered for genetic testing.
Acknowledgements
The authors extend their appreciation to Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (0020-1442-S). We also would like to thank all stroke patients who participate in this study.
Author Contributions
A.H. and R.M. did conceptualization, formal analysis, funding acquisition, methodology, resources, project administration, supervision and writing—review and editing. O.M.A. IE and M.A did data curation, formal analysis, methodology and writing—original draft. M.M and R.H did investigation, resources, software, formal analysis and writing—original draft. A.A.A.O, IE. M. A. did formal analysis, software, writing—original draft and writing—review & editing. J.B, K.H,. IE. H.A did investigation, formal analysis, resources, validation, visualization and writing—review and editing.
Funding
This study was supported by funding from Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, the project number (0020-1442-S).
Data Availability
We have included the data associated with the study in the manuscript. In case of specific queries corresponding, authors can be contacted.
Declarations
Conflict of interest
The authors declare no competing interests.
Informed Consent
Informed consent was obtained from all subjects involved in the study.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Abdullah Hamadi, Email: a.aldhafri@ut.edu.sa.
Rashid Mir, Email: rashid@ut.edu.sa.
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Data Availability Statement
We have included the data associated with the study in the manuscript. In case of specific queries corresponding, authors can be contacted.