Abstract
Background
The association of genetic single-nucleotide polymorphisms (SNPs) related to endothelial function, inflammation, and their outcomes remains poorly studied.
Objectives
To evaluate the occurrence of ischemic stroke (IS) and other vascular events, and relationships between 19 SNPs in genes associated with endothelial function and inflammation with outcomes in a population at high risk of stroke.
Design
A prospective cohort study and multi-center community-based sectional survey.
Methods
As a part of the China National Stroke Screening Survey program, the investigation was carried out in southern China from May 2015 to January 2020. Participants from 8 randomly selected. In people who were determined to be at high risk of stroke, 19 SNPs were examined. Over an average follow-up period of 4.7 years, the results of these subjects were monitored using a longitudinal method. A new IS was the primary outcome assessed, and a combination of new vascular events was the secondary outcome.
Results
In total, 2893 participants were classified as high-risk for stroke, and 2698 were monitored for 4.7 years, resulting in 192 participants (7.1%) experiencing various outcomes. Out of these, 118 individuals (4.4%) had a novel IS, 24 (0.9%) suffered a hemorrhagic stroke (HS), 53 (2.0%) developed myocardial infarction (MI), and 33 (1.2%) passed away. Significant variations have been found in the genotype distributions of TLR4 rs752998, IL6R rs4845625, and TNF rs3093662 among participants with adverse outcomes compared to those without. Generalized multifactor dimensionality reduction (GMDR) analysis identified a substantial SNP-SNP interaction involving HABP2 rs932650, TLR4 rs1927911, and IL6R rs4845625 (P = .004). The high-risk genotypes of these 3 SNPs were linked to an increased risk of IS (OR = 2.186, 95% CI: 1.247-5.426, P < .001) and total vascular events (OR = 2.367, 95% CI: 1.433-5.798, P < .001), according to multivariate logistic regression adjusted for covariates.
Conclusion
The incidence of IS and other vascular events was significantly greater among participants who were categorized as being at high risk for stroke. The interacting high-risk genotypes of HABP2 rs932650, TLR4 rs1927911 and IL6R rs4845625 were independently associated with an increased risk of new IS and other vascular events.
Keywords: High-risk stroke population, prognosis, outcome, ischemic stroke, inflammation, endothelial function, polymorphism
Introduction
Stroke continues to be a major global contributor to disability and mortality, with a significant rise in the annual incidence of cases and stroke-related fatalities.1,2 Whereas the incidence of stroke has declined in developed countries over recent decades due to effective risk factor management and improved healthcare services, the burden of stroke continues to grow in China.3-6 Thus, it is necessary to conduct stroke-related studies and improve stroke prevention strategies in the general Chinese population.5,6 Stroke is a complicated, multifaceted condition impacted by hereditary and environmental variables. 7 However, the specific genetic risks associated with stroke remain poorly understood. Enhancing understanding of the genetic determinants of stroke onset may provide critical insights into its pathophysiology and assist in formulating preventative and treatment measures.
Several genetic investigations have been performed on stroke patients in recent decades, such as genome-wide association studies (GWAS).8,9 Ikram et al 9 identified noteworthy potential loci, including 12p13.33. Still, independent replication is needed to confirm these findings. Despite examining several possible genes for stroke through case-control analysis, few relationships have been consistently validated. 8 According to the results, single nucleotide polymorphisms (SNPs) may have an effect unique to or restricted to particular populations or subtypes of stroke. The Mendelian inheritance paradigm may not apply to stroke because the impact of particular loci may not be enough to identify. 10 Furthermore, interactions between gene variants may play a significant role.11,12 Therefore, the genetic reasons for stroke may not be fully revealed by single-gene linkage analysis. A more comprehensive approach, such as examining SNP-SNP interactions employing generalized multifactor dimensionality reduction (GMDR), might substantially improve our understanding of the genetic risk of stroke. 13 However, limited research has examined the influence of SNP-SNP interactions among genes associated with endothelial function and inflammation on stroke risk.
Atherosclerosis serves as a preclinical indicator of impending stroke and other cardiovascular disorders attributed to luminal narrowing or plaque rupture. 14 It is a chronic immune-inflammatory disease that involves basic mechanisms like endothelial damage, smooth muscle cell proliferation, immune-inflammatory cell and mediator activation, and lipoprotein inflow into atherosclerotic lesions. 15 As a result, endothelial injury and inflammation are pivotal elements in the etiology of atherosclerosis. 15 Numerous investigations have examined the connection between carotid atherosclerosis, endothelial function, and gene variants linked to inflammation.16-18 Gardener et al 16 investigated the correlation between 197 SNPs and carotid plaque across 43 genes associated with inflammation and endothelial function, revealing that SNPs in 10 genes were linked to carotid plaque in the Northern Manhattan population. According to earlier studies, a higher risk of carotid stenosis, 18 carotid plaque, 17 and carotid atherosclerosis in Chinese populations has also been linked to high-risk SNP interactions in genes associated with inflammation and endothelial function. Furthermore, a recent study discovered that during follow-up, the incidence of ischemic stroke (IS) was higher in people with carotid atherosclerosis than in those without. In the high-risk stroke cohort identified using carotid ultrasonography, some genetic SNPs related to endothelial function and inflammation, together with carotid atherosclerosis, were independently linked to an elevated risk of IS. 19 However, neither Gardener et al nor our prior study examined the associations between these SNPs and their prospective connections to possible stroke and other vascular events. Only a few studies have examined the relationships between SNPs in genes linked to endothelial function, inflammation, and IS or other vascular events. Interestingly, no information is available on the association between these SNPs and the frequency of new IS and other vascular events in high-risk stroke groups throughout follow-up. Thus, it has been hypothesized that a higher risk of eventual IS and other vascular events is associated with certain SNPs and high-risk interactions among these SNPs in genes connected to inflammation and endothelial function. Currently, no research has been published on the possible connections between inflammation-related genetic SNPs and endothelial function and outcomes in the high-risk stroke group.
Based on the China National Stroke Screening Survey (CNSSS) program, 20 a community-based survey in 8 communities in Sichuan, southwestern China, focusing on high-risk stroke populations was conducted.21,22 In order to examine the incidence of IS and other vascular events during the follow-up period in this high-risk cohort, an observational analysis was also performed after this survey. The research sought to determine the relationships between 19 SNPs in genes associated with endothelial function and inflammation and the interactions among these SNPs concerning various outcomes. These findings are expected to yield novel insights into the genetic pathways that underpin stroke and other vascular events, presenting viable techniques for their prevention.
Materials and methods
Study design and participants
In Sichuan, southwestern China, a multi-center, community-based cross-sectional survey and prospective cohort study was conducted between May 2015 and January 2020 as part of the CNSSS program, which was approved by the Chinese Stroke Screening and Prevention Commission (Approval No. 2011BAI08B01). The Ethics Committees of the participating hospitals, which included the Affiliated Hospital of Southwest Medical University, Suining Central Hospital, and the People’s Hospital of Deyang City, examined and approved the study protocol (IRB approval number: 2015-024). Before recruitment, all participants gave their informed consent in Chinese (the questionnaire is in Supplemental files 1 and 2).
Between May and September 2015, eight communities in Sichuan participated in this community-based cross-sectional survey. Our earlier papers have provided comprehensive instructions for participant recruiting, risk factor assessment, and sample size computations. 21 In short, cluster randomization was used in Sichuan to choose the 8 communities. A standardized, in-person questionnaire given by qualified interviewers was used to assess residents in each town who were 40 years of age or older. The questionnaire consisted of demographic data, personality characteristics, history of chronic diseases, personal and family medical histories of stroke, and a physical examination.
Assessment of risk variables and identification of stroke high-risk groups
Along with demographic information, the 8 conventional risk variables and a history of stroke were assessed. These 8 risk factors included being overweight or obese, smoking, not exercising, having a family history of stroke, having high blood pressure, having diabetes, having dyslipidemia, and having atrial fibrillation. Our earlier study provided comprehensive diagnostic criteria for these stroke risk factors. 21 If a participant had a history of stroke or satisfied at least 3 of the 8 risk factors, they were considered high-risk for stroke and were enrolled. 21 Residents with immune system diseases, malignant tumors, acute or chronic inflammation, blood system disorders, liver or renal disease, or severe cardiovascular, as well as those with a history of endarterectomy or carotid artery stenting, were excluded, as were those who chose not to participate or who did not finish the 4.7-year follow-up following the in-person survey. For some participants in the high-risk stroke population, evidence-based preventive medications were prescribed, including hypoglycemics for diabetes (oral agents or insulin), antihypertensives for hypertension (such as ACE inhibitors, diuretics, angiotensin receptor blockers, calcium-channel blockers, and beta-blockers), lipid-lowering medications (statins or fibrates), and anti-thrombotic treatments for stroke (antiplatelets or anticoagulants). No other long-term medications, including venotonic drugs, were used.
Genotyping
Based on specific criteria in our earlier articles, 19 SNPs from ten genes associated with endothelial function and inflammation were selected.17,18 In summary, the following criteria were used to select these SNPs from the NCBI database (https://www.ncbi.nlm.nih.gov/SNP): (1) each SNP had a minor allele frequency of more than 0.05; (2) they functioned as labeling SNPs across several human groups; (3) the SNPs were predicted to cause changes in amino acids; and (4) they had been evaluated in previous studies.16-18
For high-risk stroke participants, 3 mL of peripheral blood was collected from an arm vein. A modified phenol/chloroform procedure was used to extract the genomic DNA, and a UNIQ-10 kit (Sangon Biotech Co, Ltd, Shanghai, China) was used to purify it.11,17 As previously reported, the genotypes of the 19 SNPs were determined by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS).11,12,17,18 The investigators doing the analysis were unaware of the participant data.
Study outcomes
Under the CNSs program, every member of the high-risk stroke population was followed up with every 6 months by phone interviews or a review of their medical data. The outcomes, including both primary and secondary, were detailed in our recent publication. 19 The primary outcome was the incidence of a new IS within 4.7 years after the in-person survey. An immediate localized neurological dysfunction that affects the brain or retina and lasts 24 h or longer is known as an IS. The secondary outcome was a combination of new vascular events, which included mortality, hemorrhagic stroke (HS), myocardial infarction (MI), and IS. According to neuroimaging, an HS occurs when there is an immediate extravasation of blood into the brain parenchyma. Electrocardiographic indications, increased creatine kinase levels, and Prolonged angina (>30 min) of myocardial infarction were used to identify MI. Death was attributed to cardio-cerebral vascular causes (Figure 1).
Figure 1.
Flow chart of the study methodology.
Quality control
Figure 1 outlines the detailed quality control and data cleaning procedures, following the CNSSS guidelines. The study’s interviewers were physicians or neurologists. Uniform training and standardized protocols were implemented to ensure consistent and accurate data collection. All staff involved in the study received training through the CNSSS program and were required to pass an examination upon completion. This approach helped maintain the quality of measurements and data throughout the study.
Statistical analysis
SPSS 17.0 (SPSS Inc, New York, USA) was used for statistical analysis. Analysis of variance or Student's t-test was used to evaluate intergroup differences and continuous variables were represented as mean ± standard deviation (SD) if normally distributed. The Wilcoxon rank-sum test was applied to variables that were not regularly distributed. The Chi-square or Fisher’s exact test was used to compare intergroup differences, and categorical variables were displayed as percentages or frequencies.
Genotype frequencies were assessed for Hardy-Weinberg equilibrium by applying the Chi-square test. As previously mentioned, SNP-SNP interactions among the 19 SNPs under diverse circumstances were analyzed using the GMDR approach.11-13,17,18 Moreover, considering confounders, multivariate logistic regression was used to analyze the independent influence of high-risk interacting genotypes among the 19 SNPs on primary and secondary outcomes. Regression models included variables with a P-value <.2 in the univariate comparison comparing subjects with and without outcomes. A P-value <.05 was deemed statistically significant after calculating odds ratios (OR) with 95% confidence intervals (CI).
Results
The baseline characteristics of the high-risk stroke population and their outcomes
Comprehensive data regarding the high-risk stroke population can be found in Table 1 of our earlier publications 23 and Figure 1. In total, 2893 high-risk individuals for stroke were enrolled, with 93.3% (2698 participants) completing the 4.7-year follow-up following the initial face-to-face study. A total of 195 participants (6.7%) were lost to follow-up due to incorrect contact information (124 cases) or relocation to other regions (71 cases, Figure 1). Among the 2698 participants who completed the follow-up, 721 (26.7%) had diabetes mellitus, 1949 (72.2%) had hypertension, and 751 (27.8%) had dyslipidemia. Moreover, 487 individuals (18.1%) reported a history of stroke, including 88 cases of hemorrhagic stroke and 399 cases of IS, at the time of the initial study. 23
Table 1.
Stroke outcomes by stratified analysis in the high-risk group [n %].
| Variables | Ischemic stroke | Hemorrhagic stroke | Myocardial infarction | Death |
|---|---|---|---|---|
| Sex | ||||
| Male (n = 1288) | 58 (4.5%) | 10 (0.8%) | 25 (1.9%) | 12 (0.9%) |
| Female (n = 1410) | 60 (4.3%) | 14 (1.0%) | 28 (2.0%) | 21 (1.5%) |
| P value | .753 | .550 | .933 | .188 |
| Age, y | ||||
| 40-49 (n = 302) | 11 (3.6%) | 4 (1.3%) | 4 (1.3%) | 3 (1.0%) |
| 50-59 (n = 641) | 17 (2.7%) | 7 (1.1%) | 14 (2.2%) | 6 (0.9%) |
| 60-69 (n = 1040) | 61 (5.9%) | 11 (1.1%) | 24 (2.3%) | 12 (1.2%) |
| 70-79 (n = 590) | 19 (3.2%) | 2 (0.3%) | 10 (1.7%) | 7 (1.2%) |
| ≥80 (n = 125) | 10 (8.0%) | 0 (0.0%) | 1 (0.8%) | 5 (4.0%) |
| P value | .003 | .363 | .745 | .145 |
| Overweight/obesity | ||||
| Yes (n = 1436) | 63 (4.4) | 16 (1.1) | 32 (2.2) | 22 (1.5) |
| No (n = 1262) | 55 (4.4) | 8 ( 0.6) | 21 (1.7) | 11 (0.9) |
| P value | .971 | .185 | .292 | .111 |
| Smoking | ||||
| Yes (n = 714) | 36 (5.0) | 7 (1.0) | 18 (2.5) | 6 (0.8) |
| No (n = 1984) | 82 (4.1) | 17 (0.9) | 35 (1.8) | 27 (1.4) |
| P value | .308 | .763 | .219 | .286 |
| Physical inactivity | ||||
| Yes (n = 1647) | 74 (4.5) | 10 (0.6) | 28 (1.7) | 23 (1.4) |
| No (n = 1051) | 44 (4.2) | 14 (1.3) | 25 (2.4) | 10 (1.0) |
| P value | .704 | .051 | .215 | .307 |
| Hypertension | ||||
| Yes (n = 1949) | 86 (4.4) | 17 (0.9) | 34 (1.7) | 27 (1.4) |
| No (n = 749) | 32 (4.3) | 7 (0.9) | 19 (2.5) | 6 (0.8) |
| P value | .873 | .877 | .184 | .216 |
| Non-treatment (n = 953) | 55 (5.8) | 11 (1.2) | 20 (2.1) | 14 (1.5) |
| Treatment (n = 996) | 31 (3.1) | 6 (0.6) | 14 (1.4) | 13 (1.3) |
| P value | .004 | .190 | .243 | .757 |
| Non-persistence (n = 618) | 26 (4.2) | 5 (0.8) | 11 (1.8) | 10 (1.6) |
| Persistence (n = 378) | 5 (1.3) | 1 (0.3) | 3 (0.8) | 3 (0.8) |
| P value | .013 | .417 | .271 | .390 |
| Diabetes | ||||
| Yes (n = 721) | 37 (5.1) | 6 (0.8) | 16 (2.2) | 11 (1.5) |
| No (n = 1977) | 81 (4.1) | 18 (0.9) | 37 (1.9) | 22 (1.1) |
| P value | .245 | .848 | .565 | .388 |
| Dyslipidemia | ||||
| Yes (n = 751) | 37 (4.9) | 6 (0.8) | 17 (2.3) | 10 (1.3) |
| No (n = 1947) | 81 (4.2) | 18 (0.9) | 36 (1.8) | 23 (1.2) |
| P value | .383 | .756 | .487 | .751 |
| Atrial fibrillation | ||||
| Yes (n = 71) | 2 (2.8) | 1 (1.4) | 0 (0.0) | 1 (1.4) |
| No (n = 2627) | 116 (4.4) | 23 (0.9) | 53 (2.0) | 32 (1.2) |
| P value | .518 | 1.000 | .438 | 1.000 |
| Family history | ||||
| Yes (n = 479) | 25 (5.2) | 5 (1.0) | 8 (1.7) | 9 (1.9) |
| No (n = 2219) | 93 (4.2) | 19 (0.9) | 45 (2.0) | 24 (1.1) |
| P value | .318 | .898 | .609 | .150 |
| Ischemic stroke history, n (%) | ||||
| Yes (n = 399) | 37 (9.3) | 4 (1.0) | 16 (4.0) | 11 (2.8) |
| No (n = 2299) | 81 (3.5) | 20 (0.9) | 37 (1.6) | 22 (1.0) |
| P value | <.001 | 1.000 | .001 | .006 |
| History of hemorrhagic stroke | ||||
| Yes (n = 88) | 11 (12.5) | 7 (8.0) | 7 (8.0) | 3 (3.4) |
| No (n = 2610) | 107 (4.1) | 17 (0.7) | 46 (1.8) | 30 (1.1) |
| P value | <.001 | <.001 | <.001 | .160 |
Out of the 2698 participants, 192 (7.1%) experienced adverse outcomes, comprising 118 (4.4%) new IS cases, 24 (0.9%) HS cases, 53 (2.0%) MI cases, and 33 (1.2%) fatalities. Regarding the deaths, 11 were attributed to cardiovascular causes, 10 to new hemorrhagic strokes, and 12 to new IS. Compared to those without results, those with results were older and had a larger percentage of past stroke history (P < .05). Other risk factors did not significantly differ between the 2 groups (P > .05) during the face-to-face survey. Detailed outcome data are available in Table 3 of previously published articles by our group. 23
Table 3.
GMDR analysis of the top models, P values for results, cross-validation consistency, and prediction accuracy.
| Best model a | Training balanced accuracy | Testing balanced accuracy | Cross-validation consistency | Sign test (P value) |
|---|---|---|---|---|
| 1 | 0.475 | 0.547 | 6/10 | 6 (.142) |
| 1, 2 | 0.551 | 0.562 | 8/10 | 8 (.061) |
| 1, 2, 3 | 0.584 | 0.598 | 9/10 | 10 (.004) |
| 1, 2, 3, 4 | 0.542 | 0.565 | 8/10 | 8 (.058) |
| 1, 2, 3, 4, 5 | 0.587 | 0.512 | 9/10 | 5 (.456) |
| 1, 2, 3, 4, 5, 6 | 0.712 | 0.623 | 7/10 | 8 (.312) |
| 1, 2, 3, 4, 5, 6, 7 | 0.568 | 0.487 | 10/10 | 9 (.072) |
| 1, 2, 3, 4, 5, 6, 7, 8 | 0.672 | 0.537 | 9/10 | 7 (.387) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9 | 0.612 | 0.697 | 9/10 | 8 (.134) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 | 0.438 | 0.497 | 7/10 | 10 (.412) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 | 0.629 | 9 (0.086) | 9/10 | 9 (.086) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 | 0.502 | 0.493 | 8/10 | 5 (.452) |
| 1, 2.3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 | 0.567 | 0.624 | 5/10 | 7 (.216) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 | 0.623 | 0.603 | 7/10 | 8 (.487) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 | 0.647 | 0.586 | 7/10 | 8 (.521) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 | 0.596 | 0.493 | 9/10 | 9 (.118) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 | 0.712 | 0.623 | 8/10 | 10 (.268) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 | 0.612 | 0.597 | 9/10 | 6 (.425) |
| 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 | 0.475 | 0.517 | 7/10 | 7 (.538) |
aNumbers 1 through 19 of GMDR, which stand for generalized multifactor dimensionality reduction, are rs4845625, rs1927911, rs932650, rs8081248, rs4253778, rs2297518, rs1609682, rs7923349, rs1991013, rs2392221, rs11811788, rs4253655, rs3093662, rs1386821, rs1234313, rs1800587, rs3783615, rs752998, and rs4865756 respectively.
Age, IS history, and HS history during the in-person survey have been attributed to new IS or other vascular events over the 4.7-year follow-up period, according to a stratified analysis of risk factors (Table 1). Among the 1949 high-risk stroke participants with hypertension, 996 (51.1%) received antihypertensive treatment, including 378 with persistent use and 618 with non-persistent use. Persistent use was defined as continuous antihypertensive treatment from the initial face-to-face survey through the 4.7-year follow-up. Non-consistent use was defined as survey respondents who were prescribed antihypertensives but stopped taking them throughout the follow-up period. 23 The findings indicated that patients with persistent antihypertensive use over 4.7 years showed a lower incidence of new IS (persistent vs non-persistent: 1.3% vs 4.2%, P = .013, Table 1) and composite vascular events (persistent vs non-persistent: 3.2% vs 8.4%, P = .001, Table 1). Participants with and without outcomes did not significantly differ in other risk variables (Table 1).
Distributions of genotypes in patients with and without results
The genotype distribution of the 19 SNPs followed the Hardy-Weinberg equilibrium principle (P > .05). According to univariate analysis, the genotype distribution of TLR4 rs752998, IL6R rs4845625, and TNF rs3093662 differed significantly among individuals with and without outcomes. There were no discernible variations between the 2 groups in the genotype distribution of the other 16 SNPs (P > .05, Table 2).
Table 2.
Distribution of genotypes among subjects with and without outcomes [(n, %)].
| Subjects with outcomes (n = 192) | Subjects without outcomes (n = 2506) | Wald χ2 value | P-value | |
|---|---|---|---|---|
| TNFSF4 (rs11811788) | ||||
| CG 413 | 23 (12.0) | 390 (15.6) | 1.848 a | .367 a |
| GG | 2 (1.0) | 24 (1.0) | ||
| CC | 167 (87.0) | 2092 (83.5) | ||
| TNFSF4 (rs1234313) | ||||
| AG | 96 (50.0) | 1124 (44.9) | 1.934 | .380 |
| GG | 20 (10.4) | 298 (11.9) | ||
| AA | 76 (39.6) | 1084 (43.3) | ||
| IL6R (rs1386821) | ||||
| GT | 19 (9.9) | 193 (7.7) | 1.266 a | .451 a |
| GG | 0 (0.0) | 8 (0.3) | ||
| TT | 173 (90.1) | 2305 (92.0) | ||
| IL1A (rs1609682) | ||||
| GG | 101 (52.6) | 1178 (47.0) | 2.319 | .314 |
| GT | 76 (39.6) | 1093 (43.6) | ||
| TT | 15 (7.8) | 235 (9.4) | ||
| IL1A (rs1800587) | ||||
| AG | 23 (12.0) | 325 (13.0) | 0.616 a | .732 a |
| GG | 169 (88.0) | 2165 (86.4) | ||
| AA | 0 (0.0) | 16 (0.6) | ||
| TLR4 (rs1927911) | ||||
| GG | 63 (32.8) | 921 (36.8) | 3.783 | .151 |
| AG | 106 (55.2) | 1206 (48.1) | ||
| AA | 23 (12.0) | 379 (15.1) | ||
| ITGA2 (rs1991013) | ||||
| GG | 21 (10.9) | 231 (9.2) | 0.950 | .622 |
| AA | 92 (47.9) | 1172 (46.8) | ||
| AG | 79 (41.1) | 1103 (44.0) | ||
| NOS2A (rs2297518) | ||||
| AG | 52 (27.1) | 680 (27.1) | 0.502 a | .797 a |
| AA | 5 (2.6) | 51 (2.0) | ||
| GG | 135 (70.3) | 1775 (70.8) | ||
| VCAM1 (rs2392221) | ||||
| CT | 46 (24.0) | 600 (23.9) | 0.142 a | .941 a |
| CC | 142 (74.0) | 1858 (74.1) | ||
| TT | 4 (2.1) | 48 (1.9) | ||
| TNF (rs3093662) | ||||
| AG | 15 (7.8) | 116 (4.6) | 3.913 | .048 |
| AA | 177 (92.2) | 2390 (95.4) | ||
| VCAM1 (rs3783615) | ||||
| AA | 192 (100.0) | 2506 (100.0) | — | — |
| PPARA (rs4253655) | ||||
| AG | 0 (0.0) | 8 (0.3) | 0.009 a | .924 a |
| GG | 192 (100.0) | 2498 (99.7) | ||
| PPARA (rs4253778) | ||||
| CG | 1 (0.5) | 8 (0.3) | 0.000 a | 1.000 a |
| GG | 191 (99.5) | 2498 (99.7) | ||
| IL6R (rs4845625) | ||||
| TT | 63 (32.8) | 676 (27.0) | 6.152 | .046 |
| CC | 33 (17.2) | 611 (24.4) | ||
| CT | 96 (50.0) | 1219 (48.6) | ||
| ITGA2 (rs4865756) | ||||
| AG | 76 (39.6) | 934 (37.3) | 2.953 | .228 |
| GG | 99 (51.6) | 1416 (56.5) | ||
| AA | 17 (8.9) | 156 (6.2) | ||
| TLR4 (rs752998) | ||||
| TT | 0 (0.0) | 55 (2.2) | 5.945 a | .048 a |
| GG | 133 (69.3) | 1771 (70.7) | ||
| GT | 59 (30.7) | 680 (27.1) | ||
| HABP2 (rs7923349) | ||||
| TT | 20 (10.4) | 272 (10.9) | 0.975 | .614 |
| GT | 72 (37.5) | 852 (34.0) | ||
| GG | 100 (52.1) | 1382 (55.1) | ||
| NOS2A (rs8081248) | ||||
| AG | 86 (44.8) | 1117 (44.6) | 0.012 | .994 |
| AA | 21 (10.9) | 270 (10.8) | ||
| GG | 85 (44.3) | 1119 (44.7) | ||
| HABP2 (rs932650) | ||||
| CT | 92 (47.9) | 1092 (43.6) | 2.198 | .333 |
| CC | 13 (6.8) | 235 (9.4) | ||
| TT | 87 (45.3) | 1179 (47.0) | ||
aFisher’s exact test.
Interaction between the 19 SNPs
The GMDR method assessed SNP-SNP interactions among the 19 SNPs across different scenarios. The analysis revealed a significant interaction between HABP2 rs932650, TLR4 rs1927911, and IL6R rs4845625, which was determined to be the optimal model for predicting outcomes. The model attained a cross-validation consistency score of 9/10 and a sign test score 10 (P = .004, Table 3). Furthermore, permutation testing produced an empirical P-value of .021 for prediction error in the GMDR model. The findings indicate that the observed outcomes arise from the synergistic effects of the 3 SNPs rather than the influence of individual loci in isolation.
Relationship between various genetic combinations and outcomes
The relationships between the outcomes and different genotype combinations of the 3 interacting SNPs were next examined. The relative risks of different genotype combinations were evaluated using the wild-type genotypes (HABP2 rs932650TT, TLR4 rs1927911AA, IL6R rs4845625CC) as the reference. Four specific genotype combinations significantly contributed to the outcomes: (1) HABP2 rs932650TC, TLR4 rs1927911AG, and IL6R rs4845625CT (OR = 2.03, 95% CI: 1.22-5.23, P = .011); (2) HABP2 rs932650CC, TLR4 rs1927911AG, and IL6R rs4845625CT (OR = 1.62, 95% CI: 1.02-2.48, P = .033); (3) HABP2 rs932650CC, TLR4 rs1927911AG, and IL6R rs4845625TT (OR = 1.86, 95% CI: 1.12-3.96, P = .024), and (4) HABP2 rs932650CC, TLR4 rs1927911GG, and IL6R rs4845625TT (OR = 1.92, 95% CI: 1.18-4.02, P = .013) (Table 4). These genotype combinations were classified as interacting genotypes with high risk. The remaining 3 SNP combinations were classified as low-risk interaction genotypes due to their lack of statistical significance (P > .05; Table 4).
Table 4.
Relationships between outcome risk and genotype combinations.
| rs4845625 | CC | TT | TT | CT | CT | TT, CT | TT | TT, CT |
|---|---|---|---|---|---|---|---|---|
| rs1927911 | AA | GG | AG | AG | AG | GG | GG, AG | GG, AG |
| rs932650 | TT | CC | CC | CC | TC | CC, TC | CC | CC, TC |
| OR | 1 a | 1.92 | 1.86 | 1.62 | 2.03 | 1.18 | 1.24 | 1.23 |
| 95% CI | — | 1.18-4.02 | 1.12-3.96 | 1.02-2.48 | 1.22-5.23 | 0.87-1.53 | 0.92-2.12 | 0.81-1.89 |
| P Value | — | .013 | .024 | .033 | .011 | .312 | .478 | .621 |
OR, odds ratio; CI, confidence interval.
aEach variant’s wild-type genotype served as the standard.
Examination of multivariate logistic regression evaluating the impact of high-risk interactive genotypes on outcomes
The independent effects of high-risk interaction genotype on the primary outcome (new IS) and the secondary outcome (a composite of vascular events including IS, HS, MI, and death) were assessed using multivariate logistic regression. Values of 0 and 1 were given to those with low-risk and high-risk interactive genotypes, respectively. The regression model was supplemented with other variables that indicated a potential correlation (P < .2) with the primary and secondary outcomes identified in the univariate analysis.
The results showed that the IL6R rs4845625TT genotype (OR = 1.294, 95% CI: 1.027-2.206, P = .041), high-risk interactive genotypes (OR = 2.186, 95% CI: 1.247-5.426, P < .001), and stroke history (OR = 2.263, 95% CI: 1.328-5.876, P < .001) were all independently linked to an increased risk of new SI (Table 5). Similarly, stroke history (OR = 2.538, 95% CI: 1.576-6.235, P < .001), the IL6R rs4845625TT genotype (OR = 1.472, 95% CI: 1.054-2.015, P = .042), and high-risk interactive genotypes (OR = 2.367, 95% CI: 1.433-5.798, P < .001) were significant independent risk factors for the composite vascular events during follow-up (Table 6).
Table 5.
Key risk variables for new IS: multivariate analysis.
| Risk factor | OR | 95% CI | P value |
|---|---|---|---|
| Age | 1.062 | 0.924-1.312 | .237 |
| History of stroke | 2.263 | 1.328-5.876 | <.001 |
| Antihypertensive treatment | 0.991 | 0.842-1.358 | .473 |
| Persistence of antihypertensives | 0.832 | 0.728-0.971 | <.001 |
| TLR4 rs1927911AG | 1.208 | 0.967-2.113 | .153 |
| TNF rs3093662 AG | 1.179 | 0.934-1.724 | .286 |
| IL6R rs4845625TT | 1.294 | 1.027-2.206 | .041 |
| TLR4 rs752998GT | 1.149 | 0.898-1.623 | .279 |
| High-risk interactive genotypes | 2.186 | 1.247-5.426 | <.001 |
OR, odds ratios; CI, confidence interval.
Table 6.
Key risk variables for composite vascular events: multivariate analysis.
| Risk factor | OR | 95% CI | P value |
|---|---|---|---|
| Age | 1.098 | 0.932-1.432 | .181 |
| Diabetes | 1.061 | 0.879-2.063 | .238 |
| History of stroke | 2.538 | 1.576-6.235 | <.001 |
| Antihypertensive treatment | 0.942 | 0.823-1.225 | .305 |
| Persistence of antihypertensives | 0.788 | 0.681-0.918 | <.001 |
| TLR4 rs1927911AG | 1.336 | 0.979-2.317 | .139 |
| TNF rs3093662 AG | 1.267 | 0.917-1.724 | .235 |
| IL6R rs4845625TT | 1.472 | 1.054-2.015 | .042 |
| TLR4 rs752998GT | 1.201 | 0.921-1.616 | .314 |
| High-risk interactive genotypes | 2.367 | 1.433-5.798 | <.001 |
OR, odds ratios; CI, confidence interval.
Furthermore, continuing antihypertensive medication was linked to a lower risk of composite vascular events (OR = 0.788, 95% CI: 0.681-0.918, P < .001, Table 6) and new IS (OR = 0.832, 95% CI: 0.728-0.971, P < .001, Table 5).
Discussion
The present study demonstrated a high prevalence of IS and other vascular events among individuals categorized as high-risk for stroke during the follow-up period. Particular SNPs, such as TLR4 rs752998, IL6R rs4845625, and TNF rs3093662, demonstrated significant associations with these outcomes. A significant interaction was also noted between HABP2 rs932650, TLR4 rs1927911, and IL6R rs4845625. The high-risk interactive genotypes of these SNPs were associated with a higher risk of IS and a composite of vascular events throughout the 4.7-year follow-up period.
The prevalence of stroke in China has steadily increased over the last 10 years, with conventional risk factors significantly contributing to its onset.1,4,20 High sodium intake, low intake of fruits and vegetables, being overweight, physical inactivity, hypertension, dyslipidemia, diabetes, and smoking are major risk factors for stroke in the Chinese population. Evidence-based therapies addressing these factors could considerably decrease stroke incidence.4,20 It has been demonstrated that taking antihypertensives, lipid-lowering, hypoglycemic, and anti-thrombotic drugs reduces the risk of several vascular events and stroke. 20 However, compared to countries with high incomes, China continues to have poorer rates of treatment adherence and goal attainment for these risk variables. 24
In line with earlier studies, the present research discovered that a history of stroke was independently linked to an increased risk of having a new IS and composite vascular events. However, no significant correlation was found between the incidence of new IS or composite vascular events and other traditional risk variables.2,5,20 Given that the current investigation mainly targeted a high-risk stroke population defined by the presence of at least 3 risk variables listed above, this divergence may result from variations in study design.
These findings suggest that continuous use of antihypertensive medications is associated with a decreased risk of new IS and composite vascular events in hypertensive patients. This emphasizes how important it is to continue antihypertensive medication as a means of reducing the risk of stroke and other vascular events in high-risk groups. Non-adherence to antihypertensive therapy is prevalent and linked to adverse outcomes.4,5,23 In China, treatment rates, adherence to antihypertensive medications, and attainment of target blood pressure levels are considerably lower than in high-income countries. 24 Inflammation is a significant factor in the pathogenesis of hypertension. Immune cell infiltration into vessel walls, kidneys, and the central nervous system, in conjunction with oxidative stress, sympathetic tone, and the renal renin-angiotensin system, plays a significant role in developing hypertension and its associated complications. Continuous antihypertensive therapy may mitigate immune cell infiltration, reduce proinflammatory cytokine production, lower vascular tone, relieve endothelial inflammation and oxidative stress, prevent renal interstitial infiltration, and enhance endothelial function. 25 Thus, a deeper comprehension of the connection between antihypertensive therapies and inflammation may facilitate the creation of innovative treatments for hypertension. 26
While the genetic causes of stroke are not fully understood, several genetic variants have been identified as potential risk factors for stroke, suggesting that genetic predisposition may significantly contribute to its development.11,12 Through processes like luminal stenosis or plaque rupture, atherosclerosis, a chronic inflammatory disease, can increase the risk of stroke and other cardiovascular events. 27 Two crucial elements in the pathophysiology of atherosclerosis are endothelial injury and inflammation.14,15 Gardener et al 16 pioneered identifying a correlation among carotid plaque, endothelial function, and inflammation-related gene variants within the Northern Manhattan population. Afterward, our studies showed that gene variants associated with endothelial function and inflammation correlated with stenosis and carotid plaque in high-risk stroke populations.17,18 However, there is limited knowledge regarding the relationship between SNPs in these genes and the occurrence of stroke and other vascular events during the follow-up period in high-risk stroke populations.
Numerous case-control studies have examined the influence of SNPs in genes associated with endothelial function and inflammation on IS. Research indicates a strong association between SNPs in inflammation-related genes (CRP rs1205, NOS3 rs1800779, and rs2257073) and 1 SNP in an endothelial function-related gene (HABP2 rs11196288) with ischemic stroke.28,29 This investigation uncovered significant disparities in the genotype distribution of TLR4 rs752998, IL6R rs4845625, and TNF rs3093662 between those with outcomes and those without, as determined by univariate analysis.
Tumor necrosis factor (TNF) may serve as a biomarker for patient survival rates and an ischemic stroke risk factor. 30 The TNF gene encodes a cytokine from the TNF ligand family to regulate the adhesion of activated T lymphocytes to endothelial cells. According to a meta-analysis, TNF-α polymorphism has been linked to higher plasma levels of TNF-α, indicating a greater vulnerability to IS. 31 Interleukin-6 (IL-6), a cytokine, is crucial in the endothelial cells’ “response to injury” process. 32 The IL-6 receptor (IL6R) gene located on chromosome 1q21 modulates IL-6 concentrations. According to Huang et al. SNPs in IL6R are associated with IS in individuals with metabolic syndrome. 33 Polymorphisms in IL-6R were also connected to the neurological condition of IS patients, according to a Korean study. 34 Cardiovascular disorders, inflammation, and oxidative stress are all significantly impacted by toll-like receptor 4 (TLR4). 35 The innate immune response is associated with the TLR4 gene. While a correlation exists between TLR4 polymorphism and the risk of MI, no such association has been established for IS. 36 A correlation between follow-up outcomes and TLR4 polymorphism was observed. Results from earlier studies investigating the connection between ischemic stroke and variations in genes linked to endothelial function and inflammation have been mixed.29,37 While the C807T polymorphism in ITGA2 raised the incidence of ischemic stroke, 37 Cheng et al. 29 found that polymorphisms in HABP2 and PPARA were strongly linked to IS. However, neither our studies nor others identified such correlations.8-10,36 Given that SNPs vary significantly between ethnic groups, the contradictory findings in association studies across populations may be caused by differences in the genetic backgrounds of the subject groups. Furthermore, certain gene variants may interact with other genetic variables,11,12 and stroke is a complicated disease that does not follow the Mendelian inheritance pattern. 7 Thus, linkage analysis is not appropriate for determining the genetic etiology of stroke, as it is usually employed for single-gene illnesses. 13 The contradictory results may also be because many earlier studies did not evaluate SNP-SNP interactions concerning IS.8-10,29,36,37
The study’s most important discovery was the interaction between HABP2 rs932650, TLR4 rs1927911, and IL6R rs4845625. In the 4.7-year follow-up, the high-risk interactive genotypes of these 3 SNPs were independently linked to an increased risk of IS and a composite of vascular events. The specific molecular processes via which these 3 SNPs collaboratively elevate the risk of stroke and other vascular events remain unidentified. One important risk factor for stroke is atherosclerosis. Atherosclerosis and stroke are primarily caused by the proliferation of smooth muscle cells, endothelial damage, lipoprotein influx, and inflammatory cell activation.28,38
Furthermore, the interplay between inflammation and endothelium damage is pivotal in advancing the disease. Activating immune-inflammatory cells and mediators, oxidative stress and immune cell infiltration into the artery wall and kidneys may result in endothelial damage and atherosclerosis.15,25 Conversely, endothelial injury intensifies the inflammatory response and further exacerbates endothelial damage. 25 Several studies have linked many SNPs in genes, such as IL1A, NOS2A, ITGA2, IL6R, and HABP2, to endothelial function and inflammation.16-18 Hyaluronan-binding protein 2 (HABP2) affects the proliferation of vascular smooth muscle cells and the susceptibility of plaques. 39 A hyaluronan-binding protein that supports cellular adhesion and vascular integrity is encoded by the HABP2 gene. According to earlier studies, HABP2 SNPs are associated with ischemic stroke,28,29 venous thromboembolic disease, 40 and carotid atherosclerosis.16-18 The SNPs in the IL6R gene modulate IL-6 concentrations and are associated with a heightened risk of IS. 33 Variants of TLR4 are implicated in the immunological response and have been correlated with the risk of myocardial infarction. 36 The interplay among the 3 SNPs concerning IS risk may be attributed to their functions in encoding and modulating enzymes that affect the pathophysiology of IS, endothelial function, and inflammation. These processes can result in IS or other vascular events. However, other studies using animal models or cell cultures are required to clarify further the molecular mechanisms underlying the interactions among these 3 variants.
The current study presents several strengths: (1) it employs a multi-center, community-based cross-sectional survey and prospective cohort design targeting a high-risk stroke population; (2) this is the first investigation to examine the relationships of 19 SNPs and their interactions in genes associated with endothelial function and inflammation in relation to IS and other vascular events during follow-up; (3) it uses the GMDR approach for the analysis of gene-gene interactions; and (4) the study includes a follow-up period of 4.7 years after the face-to-face survey. There are numerous limitations to the present study. First, the present research was a multi-center, cross-sectional survey and prospective cohort analysis. Recall bias may be present due to self-reported questionnaires and telephone follow-up. The following measures were implemented during this investigation, along with several active quality control measures: (1) multi-angle observation of subjects; (2) relaxing subjects’ minds and bodies and reducing anxiety through deep breathing and meditation during face-to-face survey; (3) maintaining objectivity, not being influenced by personal emotions and subjective factors as much as possible; (4) seeking family members opinions to gain a more comprehensive understanding of the truth behind the matter. Second, the present study was conducted in Sichuan, southwestern China, where genetic polymorphisms showed significant variation among ethnic groups. The findings may not be generalizable to the broader Chinese population. Future studies should adopt a multi-center design that includes various ethnic groups and a wider demographic to validate our findings. Third, although the present research was concentrated on the role of 19 known SNPs in IS and other vascular events, it did not examine other pertinent genetic markers, potentially limiting its overall contribution to understanding stroke risk. Fourth, the high-risk interactive genotypes were identified in HABP2 rs932650, TLR4 rs1927911, and IL6R rs4845625 that elevate the risk of IS and other vascular events; however, the molecular mechanisms underlying these associations are not yet understood. Future studies should involve multi-center approaches, larger sample sizes, and a broader range of genetic variants to comprehensively identify gene-gene interaction effects on outcomes in the high-risk stroke population. Furthermore, previous research demonstrates that the continuation of drug therapy, including hypoglycemic agents, antiplatelets, anticoagulants, and lipid-lowering medications, is associated with the occurrence of IS and other vascular events in high-risk stroke populations. 23 The current study primarily aimed to evaluate the associations of 19 SNPs and their interactions with outcomes in a high-risk population. Consequently, the analysis of the relationship between drug therapy persistence and stroke occurrence was not revisited in the current study.
Conclusion
This cohort analysis showed that among people who were classified as having a high risk of stroke, IS and other vascular events were significantly more common. Specific SNPs in genes linked to inflammation and endothelial function were shown to be connected with the outcomes. Furthermore, a substantial interaction was identified among HABP2 rs932650, TLR4 rs1927911, and IL6R rs4845625. The interacting high-risk genotypes of HABP2 rs932650, TLR4 rs1927911 and IL6R rs4845625 were independently associated with an increased risk of new IS and other vascular events. The results suggest new approaches for protecting high-risk groups against IS and related vascular events.
Supplemental Material
Supplemental Material for Polymorphisms in genes related to inflammation and endothelial function are associated with ischemic stroke and other vascular events in populations at high risk of stroke and other vascular events in populations at high risk of stroke by Hong Chen, Hua Luo, Ju Zhou, Ming Yu, Ting Qing, Yanfen Wang, Minjie Shao, Wei Wei and Xingyang Yi in Journal of Central Nervous System Disease.
Supplemental Material for Polymorphisms in genes related to inflammation and endothelial function are associated with ischemic stroke and other vascular events in populations at high risk of stroke by Hong Chen, Hua Luo, Ju Zhou, Ming Yu, Ting Qing, Yanfen Wang, Minjie Shao, Wei Wei and Xingyang Yi in Journal of Central Nervous System Disease.
Appendix.
Abbreviations
- SNPs
Single nucleotide polymorphisms
- GMDR
Generalized multifactor dimensionality reduction
- OR
Odds ratio
- CI
Confidence interval
- CNSSS
China national stroke screening survey
- MALDI-TOF MS
Matrix-assisted laser desorption/ionization time of flight mass spectrometry
- TNF
Tumor necrosis factor
- IL-6
Interleukin-6
- IL6R
IL-6 receptor
- TLR4
Toll like receptor 4
- HABP2
Hyaluronan-binding protein 2
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was partly supported by grants from the Scientific Research Foundation of Sichuan Provincial Health Department (Grant No. 16ZD046). The funding body did not participate in the study’s design, collection, analysis, and interpretation of data and in writing the manuscript.
Author contributions: HC, XY, HL, and MY designed this study and acquired funding. HL, MY, JZ, TQ, YW, and WW performed face-to-face survey and follow-up. HC, XY, TQ and MS statistical analysis and drafted the figures. HC, XY and MS drafted manuscript and the tables. XY, HL, and MY supervised this project.
Supplemental Material: Supplemental material for this article is available online.
Ethical statement
Consent for publication
All author and participant consent for publication.
ORCID iD
Xingyang Yi https://orcid.org/0000-0002-9254-8182
References
- 1.GBD 2016 Causes of Death Collaborators . Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390:1151-1210. doi: 10.1016/S0140-6736(17)32152-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.GBD 2019 Stroke Collaborators . Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021;20:795-820. doi: 10.1016/S1474-4422(21)00252-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Feigin VL, Nguyen G, Cercy K, et al. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. N Engl J Med. 2018;379:2429-2437. doi: 10.1056/NEJMoa1804492 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Wu S, Wu B, Liu M, et al. Stroke in China: advances and challenges in epidemiology, prevention, and management. Lancet Neurol. 2019;18:394-405. doi: 10.1016/S1474-4422(18)30500-3 [DOI] [PubMed] [Google Scholar]
- 5.Tu WJ, Zhao Z, Yin P, et al. Estimated burden of stroke in China in 2020. JAMA Netw Open. 2023;6:e231455. doi: 10.1001/jamanetworkopen.2023.1455 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Tu WJ, Wang LD, Special Writing Group of China Stroke Surveillance Report . China stroke surveillance report 2021. Mil Med Res. 2023;10:33. doi: 10.1186/s40779-023-00463-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Lindgren A. Stroke genetics: a review and update. J Stroke. 2014;16:114-123. doi: 10.5853/jos.2014.16.3.114 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ekkert A, Šliachtenko A, Grigaitė J, Burnytė B, Utkus A, Jatužis D. Ischemic stroke genetics: what is new and how to apply it in clinical practice? Genes. 2021;13:48. doi: 10.3390/genes13010048 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ikram MA, Seshadri S, Bis JC, et al. Genomewide association studies of stroke. N Engl J Med. 2009;360:1718-1728. doi: 10.1056/NEJMoa0900094 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ott J, Park T. Overview of frequent pattern mining. Genomics Inform. 2022;20:e39. doi: 10.5808/gi.22074 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Yi X, Liao D, Fu X, Zhang B, Wang C. Interaction among CYP2C8, EPHX2, and CYP4A11 gene variants significantly increases the risk for ischemic stroke in Chinese populations. J Atherosclerosis Thromb. 2015;22:1148-1157. doi: 10.5551/jat.29025 [DOI] [PubMed] [Google Scholar]
- 12.Yi X, Lin J, Wang C, Huang R, Liu Y. Interactions among variants in eicosanoid genes increase risk of atherothrombotic stroke in Chinese populations. J Stroke Cerebrovasc Dis. 2017;26:1773-1780. doi: 10.1016/j.jstrokecerebrovasdis.2017.04.005 [DOI] [PubMed] [Google Scholar]
- 13.Chen GB, Liu N, Klimentidis YC, et al. A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence. Hum Genet. 2014;133:139-150. doi: 10.1007/s00439-013-1361-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Adams A, Bojara W, Romanens M. Ultrasound examination of the carotid artery for improved prediction of cardiovascular events and the effect of statin treatment in advanced atherosclerosis: an observational study. Herz. 2024;49:60-68. doi: 10.1007/s00059-023-05197-z [DOI] [PubMed] [Google Scholar]
- 15.Talepoor AG, Fouladseresht H, Khosropanah S, Doroudchi M. Immune-inflammation in atherosclerosis: a new twist in an old tale. Endocr, Metab Immune Disord: Drug Targets. 2020;20:525-545. doi: 10.2174/1871530319666191016095725 [DOI] [PubMed] [Google Scholar]
- 16.Gardener H, Beecham A, Cabral D, et al. Carotid plaque and candidate genes related to inflammation and endothelial function in hispanics from northern Manhattan. Stroke. 2011;42:889-896. doi: 10.1161/STROKEAHA.110.591065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Yi X, Zhu L, Sui G, et al. Inflammation and endothelial function relevant genetic polymorphisms and carotid plaque in Chinese population. J Atherosclerosis Thromb. 2020;27:978-994. doi: 10.5551/jat.53074 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Liu L, Yi X, Luo H, Yu M. Inflammation and endothelial function relevant genetic polymorphisms in carotid stenosis in southwestern China. Front Neurol. 2023;13:1076898. doi: 10.3389/fneur.2022.1076898 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chen H, Qing T, Luo H, et al. Inflammation and endothelial function relevant genetic polymorphisms, carotid atherosclerosis, and vascular events in high-risk stroke population. Front Neurol. 2024;15:1405183. doi: 10.3389/fneur.2024.1405183 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Guan T, Ma J, Li M, et al. Rapid transitions in the epidemiology of stroke and its risk factors in China from 2002 to 2013. Neurology. 2017;89:53-61. doi: 10.1212/WNL.0000000000004056 [DOI] [PubMed] [Google Scholar]
- 21.Yi X, Luo H, Zhou J, et al. Prevalence of stroke and stroke related risk factors: a population based cross sectional survey in southwestern China. BMC Neurol. 2020;20:5. doi: 10.1186/s12883-019-1592-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Yi X, Chen H, Wang Y, et al. Prevalence and risk factors of high-risk population for stroke: a population-based cross-sectional survey in southwestern China. Front Neurol. 2022;13:693894. doi: 10.3389/fneur.2022.693894 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Yi X, Chen H, Yu M, et al. Persistenceof drug therapy is associated with ischemic stroke and other vascular events in high-risk stroke population. Front Neurol. 2022;13:925061. doi: 10.3389/fneur.2022.925061 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wang Z, Chen Z, Zhang L, et al. Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation. 2018;137:2344-2356. doi: 10.1161/CIRCULATIONAHA.117.032380 [DOI] [PubMed] [Google Scholar]
- 25.Zhang RM, McNerney KP, Riek AE, Bernal-Mizrachi C. Immunity and hypertension. Acta Physiol. 2021;231:e13487. doi: 10.1111/apha.13487 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Robles-Vera I, Toral M, Romero M, et al. Antihypertensive effects of probiotics. Curr Hypertens Rep. 2017;19:26. doi: 10.1007/s11906-017-0723-4 [DOI] [PubMed] [Google Scholar]
- 27.Kumar A, Kumar P, Kathuria P, et al. Genetics of ischemic stroke: an Indian scenario. Neurol India. 2016;64:29-37. doi: 10.4103/0028-3886.173645 [DOI] [PubMed] [Google Scholar]
- 28.Muiño E, Krupinski J, Carrera C, Gallego-Fabrega C, Montaner J, Fernández-Cadenas I. An inflammatory polymorphisms risk scoring system for the differentiation of ischemic stroke subtypes. Mediat Inflamm. 2015;2015:569714. doi: 10.1155/2015/569714 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Cheng YC, Stanne TM, Giese AK, et al. Genome-wide association analysis of young onset stroke identifies a locus on chromosome 10q25 near HABP2. Stroke. 2016;47:307-316. doi: 10.1161/STROKEAHA.115.011328 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Clausen BH, Wirenfeldt M, Høgedal SS, et al. Characterization of the TNF and IL-1 systems in human brain and blood after ischemic stroke. Acta Neuropathol Commun. 2020;8:81. doi: 10.1186/s40478-020-00957-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Duan R, Wang N, Shang Y, et al. TNF-A (G-308A) polymorphism, circulating levels of TNF-a andIGF-1: risk factors for ischemic stroke-an updated meta-analysis. Front Aging Neurosci. 2022;14:831910. doi: 10.3389/fnagi.2022.831910 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Choi BR, Johnson KR, Maric D, McGavern DB. Monocyte-derived IL-6 programs microglia to rebuild damaged brain vasculature. Nat Immunol. 2023;24:1110-1123. doi: 10.1038/s41590-023-01521-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Huang X, Ye Q, Zhu Z, et al. Polymorphism of IL6 receptor gene is associated with ischaemic stroke in patients with metabolic syndrome. Brain Res. 2020;1728:146594. doi: 10.1016/j.brainres.2019.146594 [DOI] [PubMed] [Google Scholar]
- 34.Kim DH, Yoo SD, Chon J, et al. Interleukin-6 receptor polymorphisms contribute to the neurological status of Korean patients with ischemic stroke. J Kor Med Sci. 2016;31:430-434. doi: 10.3346/jkms.2016.31.3.430 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Jin M, Fang J, Wang JJ, et al. Regulation of toll-like receptor (TLR) signaling pathways in atherosclerosis: from mechanisms to targeted therapeutics. Acta Pharmacol Sin. 2023;44:2358-2375. doi: 10.1038/s41401-023-01123-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Enquobahrie DA, Smith NL, Bis JC, et al. Cholesterol ester transfer protein, interleukin-8, peroxisome proliferator activator receptor alpha, and Toll-like receptor 4 genetic variations and risk of incident nonfatal myocardial infarction and ische-mic stroke. Am J Cardiol. 2008;101:1683-1688. doi: 10.1016/j.amjcard.2008.02.052 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Jalel A, Midani F, Fredj SH, Messaoud T, Hentati F, Soualmia H. Association of BglII polymorphism in ITGA2 and (894G/T and -786T/C) polymorphisms in eNOS gene with stroke susceptibility in Tunisian patients α2 gene polymorphism in α2β1 integrin and eNOS gene variants and stroke. Biol Res Nurs. 2021;23:1099800420977685. doi: 10.1177/1099800420977685 [DOI] [PubMed] [Google Scholar]
- 38.Frąk W, Wojtasińska A, Lisińska W, Młynarska E, Franczyk B, Rysz J. Pathophysiology of cardiovascular diseases: new insights into molecular mechanisms of atherosclerosis, arterial hypertension, and coronary artery disease. Biomedicines. 2022;10:1938. doi: 10.3390/biomedicines10081938 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Zeerleder S. Factor VII-activating protease: hemostatic protein or immune regulator? Semin Thromb Hemost. 2018;44:151-158. doi: 10.1055/s-0037-1607431 [DOI] [PubMed] [Google Scholar]
- 40.Sundquist K, Wang X, Svensson PJ, et al. Plasminogen activator inhibitor-1 4G/5G polymorphism, factor V Leiden, prothrombin mutations and the risk of VTE recurrence. ThrombHaemost. 2015;114:1156-1164. doi: 10.1160/TH15-01-0031 [DOI] [PubMed] [Google Scholar]
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Supplementary Materials
Supplemental Material for Polymorphisms in genes related to inflammation and endothelial function are associated with ischemic stroke and other vascular events in populations at high risk of stroke and other vascular events in populations at high risk of stroke by Hong Chen, Hua Luo, Ju Zhou, Ming Yu, Ting Qing, Yanfen Wang, Minjie Shao, Wei Wei and Xingyang Yi in Journal of Central Nervous System Disease.
Supplemental Material for Polymorphisms in genes related to inflammation and endothelial function are associated with ischemic stroke and other vascular events in populations at high risk of stroke by Hong Chen, Hua Luo, Ju Zhou, Ming Yu, Ting Qing, Yanfen Wang, Minjie Shao, Wei Wei and Xingyang Yi in Journal of Central Nervous System Disease.