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

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 al⁹ 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 al¹⁶ 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 publications²³ 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

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.

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.

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 al¹⁶ 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 - 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

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

Supplemental Material - 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

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.

Footnotes

Declaration of conflicting interests:

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.

Ethical statement

ORCID iD

Xingyang Yi

Supplemental Material

Supplemental material for this article is available online.

Appendix

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