Do CRP Gene Variants and Smoking Elevate Recurrent Stroke Risk in Minor Ischemic Stroke Patients?

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Xiaofeng Li, Shina Song, Wenhui Jia, Lihua Xie, Meilin Fan, Changxin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5034450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 12 You are reading this latest preprint version Abstract Background Minor ischemic strokes, though initially presenting with limited symptoms, carry a significant risk of recurrence, potentially leading to severe disability. However, the association of CRP gene variations in predicting the risk for recurrent minor stroke, especially how genetic susceptibility interacts with poor health habits like smoking, still needs to be established. This study investigates the relationships of single-nucleotide polymorphisms (SNPs) in CRP gene with minor stroke recurrence. Furthermore, this research proceeds to explore the potential interactions between these genetic variants and smoking status. Methods A total of 2,032 first-time minor stroke patients were retrospectively recruited from January 2019 to December 2022 in Linfen People's Hospital. Genomic DNA was extracted for genotyping four SNPs of the CRP gene: rs1130864, rs1800947, rs2808632, and rs3093059. We scrutinized the association of these SNPs with the risk of stroke recurrence in an additive, dominant, and recessive genetic model. To further explore this complicated interaction of the CRP gene SNPs with the status of smoking, the tool of Generalized Multifactor Dimensionality Reduction (GMDR), was employed. Besides, multivariate logistic regression was used to estimate the strength of these associations with the risk of recurrence. The patients were followed by a team of three trained rehabilitators, making evaluations every three months for one year, in a very thorough follow-up. Results Our study recruited 260 patients who suffered recurrent minor strokes and 264 age- and sex-matched controls without recurrence. The A allele of rs2808632 (P = 0.002) and C allele of rs3093059 (P = 0.009) were found to be significantly associated with high risk of stroke recurrence by analysis. Those patients with the combined genotypes rs2808632 CA + AA and rs3093059 TC + CC revealed 2.325 times more risk for recurrence when compared to those with the genotypes rs2808632 CC and rs3093059 TT (P = 0.002). Furthermore, in the rs3093059 TC + CC genotypes versus the TT genotype among the smokers, an associated 3.467-fold increased risk for recurrence had been confirmed. Conclusion Our results confirmed that rs2808632 and rs3093059 together are pivotal factors in contributing to heightened minor stroke recurrence. Besides, this significantly affects the interaction between rs3093059 SNP and smoking status. C-reactive protein Gene variants minor ischemic stroke smoking interaction Figures Figure 1 Figure 2 Figure 3 Introduction Stroke continues to be one of the leading causes of morbidity and mortality, with significant burden in China [ 1 , 2 ] . Every year, nearly 3 million new stroke cases are identified in China, with around 30% of these being classified as minor ischemic strokes (minor strokes) [ 3 ] . Their high likelihood for reoccurrence makes this a worrisome phenomenon [ 4 ] . Alarmingly, the recurrence rates in the first year after stroke are as high as 17.7% in Chinese patients [ 5 ] . While recent advances in acute stroke management have improved outcomes from ischemic stroke (IS), there exists a considerable risk of recurrence for IS survivors [ 6 ] , with a high mortality rate. This further underpins the importance of a deeper understanding of the underlying risk factors [ 7 ] . Although hypertension, diabetes mellitus, and tobacco use are known risk factors for IS, the role of genetics in the recurrence of stroke is an emerging area of research [ 8 ] . CRP is the most important biomarker in systemic inflammation that has been associated with cardiovascular diseases [ 9 ] . In particular, elevated CRP levels have been shown to associate with the occurrence of initial stroke and also with its recurrence [ 10 – 12 ] . Single nucleotide polymorphisms (SNPs) within the CRP gene can modulate CRP expression and hence contribute to the predisposition of an individual to the atherosclerotic and thrombotic events leading to stroke [ 13 – 16 ] . Work that has been directed in this critical area, however, is limited. Our study endeavors to bridge this knowledge gap by exploring the potential correlation. We propose that certain CRP gene SNPs could predispose individuals to recurrent strokes, with this risk potentially being exacerbated by environmental factors like smoking—a well-documented risk factor for stroke [ 17 , 18 ] . We tested this hypothesis by selecting cases of recurrent minor stroke along with age and sex-matched controls from a hospital-based stroke registry. We therefore sought to explore complex gene-environment interactions that likely act to modulate the risk of stroke recurrence using the Generalized Multifactor Dimensionality Reduction (GMDR) algorithm. Identification of these interactions is crucial to develop more personalized and hence effective prevention strategies, which might be essential in lowering the burden of recurrent strokes. Methods Study group Our study was approved by the Ethics Committee of Linfen City People's Hospital, and all participants provided informed written consent. Our research, a single-center, hospital-based stroke registry at Linfen People's Hospital, spanned from January 2019 to December 2022. Initially, we identified a cohort of 2,032 patients experiencing their first minor stroke, defined by an NIH Stroke Scale (NIHSS) score ≤ 5 within 7 days of onset. Our diagnosis strictly followed the World Health Organization's criteria, which stipulate that symptoms must persist beyond 24 hours or lead to death, excluding non-vascular causes [ 19 ] . All patients underwent a comprehensive radiographic assessment, including MRI or CT, to confirm ischemic strokes and rule out hemorrhagic or non-ischemic events. The TOAST classification system was meticulously applied to categorize IS patients, specifically focusing on the small-vessel occlusion (SVO) and large-artery atherosclerosis (LAA) subtypes [ 20 ] . Clinical information was collected via in-person interviews performed by professional neurologists at the time of admission and included general information and medical history, hypertension, diabetes, coronary heart disease, lifestyle (smoking and alcohol). Blood samples were taken for biochemical analysis, focusing on triglycerides, total cholesterol, LDL-C, and HDL-C levels. Follow-Up and Outcome Assessment Regular follow-up assessments were carefully planned every three months, continuing until the recurrence of ischemic stroke (IS) or the study's completion in December 2023, with data being gathered through standardized questionnaires during telephone interviews or clinical visits. A recurrent IS was distinctly defined as a novel episode of cerebral infarction manifesting at least 21 days post the initial stroke, characterized by the abrupt emergence of neurological deficits indicative of ischemic pathology. Participants were categorized into two groups based on outcomes: (1) the recurrence group, consisting of those who experienced a recurrent stroke within the first year of their initial stroke, and (2) the non-recurrence group, comprising those who did not experience a recurrent stroke during the study period. From the stroke registry, we identified a cohort of 2,032 individuals who had experienced minor IS within 7 days of onset. Following a rigorous screening process, we selected a study population of 1,516 participants who met the eligibility criteria. Among them, 298 patients experienced recurrence, while 1,148 did not. Ultimately, we collected 260 blood samples from individuals with a history of IS recurrence by the end of the follow-up period. We then selected a control group from the remaining 264 patients who had not experienced a recurrent stroke, resulting in a control group of 264 blood samples. Definition of smoking status Participants were categorized based on their smoking status into two groups: current smokers, defined as those who used tobacco regularly in the 12 months before the end of the follow-up period, and never or former smokers, who had not used tobacco during this time. SNP selection We identified tagging SNPs within the CRP gene using two criteria: (1) SNPs previously associated with ischemic stroke (IS) and (2) genotype data from the International HapMap Project. We accessed the HapMap website ( http://hapmap.ncbi.nlm.nih.gov/ ) and downloaded SNP genotype data from the Han Chinese population in Beijing, China. The data were analyzed using Haploview 3.0 software ( http://www.broad.mit.edu/mpg/haploview/ ) in HapMap format. Tagging SNPs were selected based on a minor allele frequency (MAF) > 0.05 and linkage disequilibrium (r² >0.8). Finally, four specific SNPs within the CRP gene were chosen for this study: rs1130864, rs1800947, rs2808632, and rs3093059. Primer design and genotyping Fasting venous blood samples (2 mL) were obtained from each participant in the early morning and promptly stored at -80°C for subsequent analysis. Genomic DNA was meticulously extracted from peripheral white blood cells utilizing the AxyPrep Blood Genomic DNA Miniprep Kit (Axygen Biosciences), adhering to the manufacturer's protocol. Primers for PCR-RFLP genotyping were designed utilizing Primer 5.0 software (Premier Biosoft International). The PCR amplification was conducted in a 20 µL reaction mixture containing 10 ng of genomic DNA, 1.5 U of Taq DNA polymerase, 0.2 mmol/L of each dNTP, 10 µL of 2× PCR Master Mix, and 1 µL of each primer. The thermal cycling profile included an initial denaturation at 95°C for 5 minutes, followed by 25 cycles of 95°C for 30 seconds, 60°C for 30 seconds, and 72°C for 90 seconds, and a final extension at 72°C for 7 minutes. The PCR products were resolved on 3% agarose gels and visualized under ultraviolet light. Subsequently, the PCR amplicons were digested with 2 U of restriction enzymes (New England Biolabs) at 65°C for 1 hour, and the digested fragments were separated on a 3% agarose gel containing ethidium bromide to identify the genotypes, as detailed in Table 1 . Table 1 Primer Sequences, Restriction Enzymes, and Fragment Lengths of SNPs in the CRP Gene SNPSNP Polymorphism Primer Restriction enzyme Fragment length (bp) rs1130864 C > T F: 5′- GGCAGAAGCAAGCATCATCTT-3′ HpyCH4III C: 346 + 183 + 69 + 66 T: 346 + 249 + 69 R: 5′- GGTCATGGGTGGGGAATTAAA-3′ rs1800947 G > C F: 5′- GGCAGAAGCAAGCATCATCTT-3′ Tsp45I G: 530 + 104 C: 663 R: 5′- GGTCATGGGTGGGGAATTAAA-3′ rs2808632 G > T F: 5′-ACTCCCGTTTCCAATAAGTTCCT-3′ BfaI A:381 C:196 + 189 R: 5′-GCCATGCAGAACTGTGAGTCAA-3′ rs3093059 T > C F: 5′-GACTCCTGCCTGAAGCTTTACATAT-3′ Tsp509I T: 178 + 64 + 53 C: 242 + 53 R: 5′-TTCCCCTTCCTGTGTCCAAGT-3′ Statistical analysis Statistical analyses were performed with SPSS software (version 25.0) and GMDR software (version 0.9). The Shapiro-Wilk test was used to check the normality of data distribution of continuous variables, which are expressed as mean ± SD or median (interquartile range). Descriptive statistics are given as counts (%) for categorical data, compared using Pearson chi-square and Fisher's exact tests. Baseline demographics and clinical features between the recurrence and nonrecurrence groups were compared. The quantitative variables with a normal distribution were compared by Student's t-test and those without a normal distribution by the Mann-Whitney U test. In the controls, Hardy-Weinberg equilibrium (HWE) was tested for the distribution of CRP SNPs by chi-square test. The GMDR method was employed to explore gene-gene and gene-environment interactions, identifying the best predictive model for IS recurrence from the combinations of selected SNP genotypes. The best model was chosen based on the highest testing balanced accuracy and cross-validation consistency (CVC). Significant interactions between SNPs and between SNPs and smoking status were visualized using interaction dendrograms and hierarchical graphs, with the MDR software (version 2.0). Interaction effects were quantified using entropy percentages, and the strongest synergistic interactions were highlighted. Multiple logistic regression analysis was conducted to evaluate the genotypes and combinations identified by GMDR. Results are presented as odds ratios (OR) with 95% confidence intervals (CI), indicating the strength and precision of associations. A two-tailed p-value of less than 0.05 was considered to indicate statistical significance. Results Demographic Characteristics A total of 535 mild IS patients were included in the study and categorized into two distinct groups according to r the occurrence of recurrent stroke: the Recurrence group (n=261) and the Nonrecurrence group (n=264) ( Figure 1 ). The demographic and clinical characteristics of the participants are detailed in Table 2 . The Recurrence group had higher rates of male prevalence, hypertension, diabetes, coronary heart disease, family history of stroke, smoking, and alcohol consumption (P 0.05). Table 2 . Baseline characteristics of patients with Recurrence group and Nonrecurrence group Variables Recurrence (n=261) Nonrecurrence (n=264) P values Age (years) 66.93±9.63 66.76±6.51 0.817 Male, N (%) 151 (57.9) 147 (55.7) 0.615 BMI (kg/m 2 ) 24.67±2.18 24.27±2.95 0.083 Systolic BP (mmHg) 148.35±23.91 135.94±19.22 <0.001 Diastolic BP (mmHg) 85.49±12.44 80.40±12.49 <0.001 Lipids (mmol/L) Total cholesterol(mmol/L) 5.27±0.92 4.89±1.22 <0.001 Triglycerides(mmol/L) 1.63±0.90 1.34±0.70 <0.001 HDL‐cholesterol 1.01±0.41 1.12±0.31 <0.001 LDL‐cholesterol 3.48±0.90 3.27±0.85 0.006 Hypertension, N (%) 159 (60.9) 96 (36.4) <0.001 Diabetes, N (%) 65 (24.9) 37 (14.0) 0.002 Coronary heart disease, N (%) 75(28.7) 42 (15.9) <0.001 Family history of stroke, N (%) 65 (24.9) 33 (12.5) <0.001 Smoking, N (%) 104 (39.8) 64 (24.2) <0.001 Alcohol drinking, , N (%) 70 (26.8) 44 (16.7) 0.005 Abbreviations: Mean±standard deviation values for quantitative variables and n (%) for qualitative variables. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure. TC, total cholesterol; TG, triglyceride; HDL, high density lipoprotein cholesterol; LDL, low density lipoprotein. SNPs and recurrent IS susceptibility The genotype frequencies of CRP SNPs (rs1130864, rs1800947, rs2808632, and rs3093059), in the Nonrecurrence group adhered to HWE, as shown in Table 3. The CC genotype for rs1130864 and the GG genotype for rs1800947 were more frequently observed in both the Recurrence and Nonrecurrence groups. However, there was no statistically significant difference in the distribution of genotype or allele frequency distribution when comparing the two groups. The rs2808632 showed a significant difference in genotype distribution, with the AA genotype being more prevalent in the Recurrence group (P = 0.004). This association was further supported by both dominant and recessive models, which indicated a higher risk of IS recurrence for AA genotype carriers (Dominant: OR 1.550, 95% CI 1.092-2.119, P = 0.014; Recessive: OR 1.737, 95% CI 1.108-2.723, P = 0.015). The A allele was also significantly more frequent in the Recurrence group compared to the Nonrecurrence group (P = 0.002). Similarly, rs3093059 was significantly associated with IS recurrence, particularly for the CC genotype (P = 0.003), and this association was consistent across both models (Dominant: OR 1.571, 95% CI 1.113-2.217, P = 0.010; Recessive: OR 1.920, 95% CI 1.172-3.146, P = 0.009). The C allele was significantly more frequent in the Recurrence group (P = 0.001). Table 3 . Association of genotypes and alleles of CRP SNPs with mild IS recurrence SNPs Genotypes and Alleles Recurrence Nonrecurrence OR (95%CI) P value N = 261 N = 264 rs1130864 Additive CC 230（88.1） 225（85.2） 1.00(ref) CT 31（11.9） 39（14.8） 0.802 (0.469-1.290) 0.329 Allele C 491（94.1） 489（92.8） 1.00(ref) T 31（5.9） 39（7.2） 0.792（0.486-1.290） 0.347 rs1800947 Additive GG 228（87.4） 237（89.8） 1.00(ref) GC 33（12.6） 27（10.2） 1.270（0.740-2.180） 0.384 Allele G 489 501 1.00(ref) C 33 27 1.252(0.742-2.114) 0.399 rs2808632 Additive CC 94 123 1.00(ref) CA 108 103 1.372(0.938-2.008) 0.103 AA 59 38 2.032(1.247-3.310) 0.004 Dominant CC 94 123 1.00(ref) CA+AA 167 141 1.550(1.092-2.119) 0.014 Recessive CC+CA 202 226 1.00(ref) AA 59 38 1.737(1.108-2.723) 0.015 Allele C 296 348 1.00(ref) A 226 179 1.484(1.156-1.906) 0.002 rs3093059 Additive TT 114 145 1.00(ref) TC 97 90 1.371(0.940-1.999) 0.101 CC 50 29 1.193(1.305-3.685) 0.003 Dominant TT 114 145 1.00(ref) TC+CC 147 119 1.571（1.113-2.217） 0.010 Recessive TT+TC 211 235 1.00(ref) CC 50 29 1.920（1.172-3.146） 0.009 Allele T 325 380 1.00(ref) C 197 148 1.556(1.201-2.018) 0.001 GMDR analysis of SNP- SNP and gene-smoking interactions The SNP-SNP and SNP-smoking interactions on IS recurrence were analyzed using GMDR ( Table 4 ). In the one-locus model, rs3093059 showed the highest attribute for predicting recurrence, with a Testing Balanced Accuracy of 52.90% and a Cross-Validation Consistency (CVC) of 7/10 (P = 0.0547), indicating a modest association. The two-locus model, considering the interaction between rs3093059 and rs2808632, demonstrated the highest Testing Balanced Accuracy at 58.65% and achieved a perfect CVC score of 10/10 (P = 0.0010), suggesting a synergistic effect between these two genetic variants in predicting stroke recurrence. Figure 2A illustrates the genotypes associated with high and low risks of IS recurrence, as determined by the best two-locus model from the GMDR analysis, focusing on the interaction between rs2808632 and rs3093059. The genotype combinations most strongly associated with high recurrence risk were AA × CC, AA × TC, CA × CC, and CA × TC. In contrast, the genotypes indicating a lower risk of recurrence were CC × TC and CA × TT. Regarding SNP-smoking interactions, incorporating rs3093059 into the smoking model resulted in a slight decrease in Testing Balanced Accuracy to 55.93% but maintained a high CVC of 7/10 (P=0.0107), indicating a significant predictive synergy between the genetic variant and smoking exposure as risk factors for recurrence. Figure 2B presents the risk genotypes identified in the best two-locus model, which includes the interaction between rs3093059 and smoking status, as revealed by GMDR analysis. The genotypes with the highest recurrence risk in the presence of smoking were CC × smoking and TC × smoking, whereas the genotypes associated with a reduced risk of recurrence under non-smoking conditions were TC × non-smoking and TT × non-smoking. Table 4 . Multifactor dimensionality reduction results in the Recurrence group and Nonrecurrence group Locus No. Best model Training accuracy (%) Testing accuracy (%) Sign test (P) CVC Gene–gene interactions 1 rs3093059 55.82 52.90 8(0.0547) 7/10 2 rs3093059, rs2808632 60.10 58.65 10(0.0010) 10/10 3 rs1800947, rs2808632, rs3093059 60.90 54.30 6(0.3770) 6/10 4 rs1130864, rs1800947, rs2808632, rs3093059 61.88 54.88 8(0.0547) 10/10 Gene–smoking interaction 1 Smoking 57.80 57.90 9(0.0107) 10/10 2 Smoking, rs3093059 60.75 55.93 9(0.0107) 7/10 3 Smoking, rs3093059, rs2808632 62.22 56.10 8(0.0547) 8/10 4 Smoking, rs1800947, rs2808632, rs3093059 64.12 54.87 6(0.3770) 6/10 5 Smoking, rs1130864, rs1800947, rs2808632, rs3093059 66.39 56.52 7(0.1719) 10/10 CVC = Cross-validation consistency. MDR Analysis of SNP- SNP and gene-smoking interactions We leveraged information gain theory to elucidate the complex interplay among these genetic variants. This approach led to the creation of an interaction dendrogram and hierarchical interaction graphs derived from the MDR analysis (Figure 3). The interaction dendrogram showed that rs3093059 and smoking had the strongest synergy, indicated visually by the red line (Figure 3A). The hierarchical interaction graphs (Figure 3B) demonstrated significant interactions between rs2808632 and rs3093059, with an interaction entropy of 1.06%, suggesting a positive interaction effect. Additionally, the interaction between rs3093059 and smoking status exhibited a notable entropy change of 1.42%, indicating a substantial impact on IS recurrence. Notably, rs3093059 showed the strongest synergy with smoking status, as denoted by the red line, highlighting the potential influence of environmental factors on genetic risk. Combined Genetic Variants and Environmental Factors in IS Recurrence The logistic regression analysis in Table 5 reveals significant combined effects of rs3093059 and rs2808632 within CRP gene on IS recurrence risk. Notably, the rs3093059 TC+CC genotypes, combined with the rs2808632 CA+AA genotypes, were associated with an increased risk of IS recurrence, with an adjusted OR of 2.325 (95% CI 1.341–4.032, P=0.003), compared to those with the rs3093059 TT genotype and rs2808632 CC genotype. Table 6 extends this analysis to the interaction between rs3093059 and smoking status. Smokers with the rs3093059 TC+CC genotypes had a significantly higher risk of IS compared to never smokers with the TT genotype, with an adjusted OR of 3.467 (95% CI 2.040–5.923, P<0.001). Table 5 . Combined effects of rs3093059 and rs2808632 on the risk of IS Recurrence rs3093059 rs2808632 Crude OR (95% CI) Crude P Adjusted OR (95% CI) Adjusted P TT CC 1 a 1 a TC+CC CC 0.777(0.472~1.279) 0.321 0.865(0.502~1.489) 0.600 TT CA+AA 0.705 (0.411~1.208) 0.203 0.812(0.451~1.460) 0.486 TC+CC CA+AA 2.196 (1.326~3.638) 0.002 2.325 (1.341~4.032) 0.003 a The non-risk genotype for each genetic factor was used as the reference OR. The Adjusted P value was calculated by multivariate logistic regression analysis with adjustment for Sex, Smoking, Alcohol consumption, Hypertension, Diabetes, Coronary heart disease and Family history of stroke. Table 6 . Association between CRP SNPs-environment interactions and IS Recurrence rs3093059 Smoking Crude OR (95% CI) Crude P Adjusted OR (95% CI) Adjusted P TT No 1 a 1 a TC+CC No 0.803 (0.525~1.231) 0.314 0.830(0.525~1.313) 0.426 TT Yes 0.619 (0.324~1.183) 0.147 0.537(0.276~1.187) 0.134 TC+CC Yes 3.167 (1.087~3.206) <0.001 3.467(2.040~5.923) <0.001 a The non-risk genotype for each genetic factor was used as the reference OR. The Adjusted P value was calculated by multivariate logistic regression analysis with adjustment for Sex, Smoking, Alcohol consumption, Hypertension, Diabetes, Coronary heart disease and Family history of stroke. Discussion Our study focuses on recurrent minor stroke because stroke survivors are at an increased risk of subsequent strokes, which tend to be more fatal and disabling than the initial event [ 21 , 22 ] . To our current understanding, this research represents the inaugural investigation into examining the relationship between four SNPs of the CRP gene and the 12-month risk of recurrent events in among patients with a history of minor stroke. Our findings reveal significant associations between specific CRP gene polymorphisms, particularly rs2808632 and rs3093059, and heightened susceptibility to recurrence. Moreover, an interplay between rs3093059 and smoking was detected, indicating that smoking amplifies the genetic risk associated with this SNP. These findings emphasize the significance of taking into account genetic as well as lifestyle elements in the treatment of IS patients. CRP is a key biomarker of systemic inflammation, and elevated CRP levels associated with Inflammation-related atherosclerosis and increased thrombogenic potential [ 23-25 ] . Several studies have shown that CRP levels are associated with the prognosis of ischemic stroke [ 26-28 ] . It is worth noting that CRP SNPs is not only related to its serum level, but also related to inflammatory reaction and the progress of atherosclerotic plaque, thus leading to the occurrence of ischemic stroke [ 16 , 29 , 30 ] . While few studies have specifically examined the relationship between CRP gene polymorphisms and the likelihood of minor stroke, existing literature has identified a correlation between the rs3093059 polymorphism and adverse outcomes within three months for patients with large artery atherosclerosis IS [ 31 ] . This finding suggests a possible association between the rs3093059 CC genotype and the propensity for stroke recurrence. So far, research on rs2808632 in CRP gene is notably limited. To date, only one study has reported a link between rs2808632 and rheumatoid arthritis [ 32 ] . In our study, minor stroke patients with the AA genotype of rs2808632 exhibited significantly higher risk of recurrence compared to those with the CC genotype. We also identified a significant interaction between rs2808632 and rs3093059 within CRP gene. Patients with the AA genotype of rs2808632 and the CC genotype of rs3093059 had a markedly higher risk of stroke recurrence compared to other genotype combinations. This suggests a synergistic effect where the presence of both high-risk genotypes exacerbates the likelihood of recurrence. Smoking is a well-established risk factor for IS. Previous reports have shown that patients who smoked at the time of their stroke or had a history of smoking faced a higher risk of death or recurrent vascular events compared to those who never smoked [ 33-35 ] . Our study delved into the relationship between smoking status and CRP SNPs, particularly focusing on rs3093059. Individuals with the rs3093059 CC genotype may experience exacerbated negative effects of smoking, possibly through mechanisms involving increased oxidative stress [ 36 , 37 ] and endothelial dysfunction [ 38 , 39 ] , thus increasing susceptibility to recurrent ischemic events [ 15 ] . This study acknowledges several limitations that merit consideration. Firstly, the retrospective design could introduce selection bias and constrain the ability to determine causality. Secondly, the single-center nature may restrict the generalizability of the findings to broader populations. Additional multi-center studies are needed to emphasize statistical differences. Finally, while we focused on four SNPs, other SNPs may also contribute to stroke recurrence and should be investigated in future research. Conclusion Our research illustrates that certain CRP gene variations, specifically rs2808632 and rs3093059, have a connection with the susceptibility to recurrent minor strokes. We also shed light on the relationships between distinct CRP gene mutations and behaviors like smoking, although additional validation through larger studies is necessary. The creation of effective preventive strategies to minimize the negative impacts of recurrent minor strokes requires precise biomarkers for these genetic and lifestyle-related variations. Declarations Data availability statement The underlying data for this study are available upon request, contingent upon ethical approval and participant confidentiality, through the corresponding author. Ethics statement This study was approved by the Ethics Committee of Linfen City People's Hospital, and all participants provided informed written consent. Statement of author contributions XL formulated and developed the research, secured financial support and ethical clearance, evaluated the findings, and authored the document in its entirety or partially. SS provided valuable assistance in the study design and contributed significantly to the preparation and review of the manuscript. WJ conducted data analysis and performed statistical analysis. LX was responsible for data collection and acquisition. MF and CL provided critical revisions to the manuscript and supervised the study. Manuscript preparation, editing, and review were collaboratively conducted by all authors. Funding Statement This research was supported by the Linfen City Science and Technology Plan Project (Project No. 2014) and the Shanxi Applied Basic Research Program (No. 202403021212232). Acknowledgments We would like to convey our appreciation to each patient who engaged in the process of gathering medical history, assessing scales, and undergoing additional examinations. 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DOI: 10.1016/j.jacc.2021.07.016 . Guo J, Su W, Fang J, Chen N, Zhou M, Zhang Y, He L. Elevated CRP at admission predicts post-stroke cognitive impairment in Han Chinese patients with intracranial arterial stenosis [J]. Neurol Res. 2018, 40(4): 292–296. DOI: 10.1080/01616412.2018.1438224 . Su BJ, Dong Y, Tan CC, Hou XH, Xu W, Sun FR, Cui M, Dong Q, Tan L, Yu JT. Elevated Hs-CRP Levels Are Associated with Higher Risk of Intracranial Arterial Stenosis [J]. Neurotox Res. 2020, 37(2): 425–432. DOI: 10.1007/s12640-019-00108-9 . Hou D, Liu J, Feng R, Gao Y, Wang Y, Wu J. The role of high-sensitivity C-reactive protein levels in functional outcomes in patients with large-artery atherosclerosis and small-artery occlusion [J]. Neurol Res. 2017, 39(11): 981–987. DOI: 10.1080/01616412.2017.1358937 . Wang Q, Ding H, Tang JR, Zhang L, Xu YJ, Yan JT, Wang W, Hui RT, Wang CY, Wang DW. C-reactive protein polymorphisms and genetic susceptibility to ischemic stroke and hemorrhagic stroke in the Chinese Han population [J]. Acta Pharmacol Sin. 2009, 30(3): 291–298. DOI: 10.1038/aps.2009.14 . Chen W, Zhu X, Hu Y, Hong H, Kuang L, Liang N, Zhu J, Jiang L, Wu L. Association of C-reactive protein gene polymorphisms with the risk of ischemic stroke: A systematic review and meta-analysis [J]. Brain Behav. 2023, 13(6): e2976. DOI: 10.1002/brb3.2976 . Ye Z, Zhang H, Sun L, Cai H, Hao Y, Xu Z, Zhang Z, Liu X. GWAS-Supported CRP Gene Polymorphisms and Functional Outcome of Large Artery Atherosclerotic Stroke in Han Chinese [J]. Neuromolecular Med. 2018, 20(2): 225–232. DOI: 10.1007/s12017-018-8485-y . Rhodes B, Merriman ME, Harrison A, Nissen MJ, Smith M, Stamp L, Steer S, Merriman TR, Vyse TJ. A genetic association study of serum acute-phase C-reactive protein levels in rheumatoid arthritis: implications for clinical interpretation [J]. PLoS Med. 2010, 7(9): e1000341. DOI: 10.1371/journal.pmed.1000341 . Chen J, Li S, Zheng K, Wang H, Xie Y, Xu P, Dai Z, Gu M, Xia Y, Zhao M, Liu X, Xu G. Impact of Smoking Status on Stroke Recurrence [J]. J Am Heart Assoc. 2019, 8(8): e011696. DOI: 10.1161/jaha.118.011696 . Kim J, Gall SL, Dewey HM, Macdonell RA, Sturm JW, Thrift AG. Baseline smoking status and the long-term risk of death or nonfatal vascular event in people with stroke: a 10-year survival analysis [J]. Stroke. 2012, 43(12): 3173–3178. DOI: 10.1161/strokeaha.112.668905 . Yao Q, Zhang BY, Lin YD, Hu MJ, Jiang M, Zhou MK, Zhu CR. Association between post-stroke smoking and stroke recurrence in first-ever ischemic stroke survivors: based on a 10-year prospective cohort [J]. Neurol Sci. 2023, 44(10): 3595–3605. DOI: 10.1007/s10072-023-06873-y . Maio R, Perticone M, Suraci E, Sciacqua A, Sesti G, Perticone F. Endothelial dysfunction and C-reactive protein predict the incidence of heart failure in hypertensive patients [J]. ESC Heart Fail. 2021, 8(1): 399–407. DOI: 10.1002/ehf2.13088 . Jan MI, Khan RA, Fozia, Ahmad I, Khan N, Urooj K, Shah A, Khan AU, Ali T, Ishtiaq A, Shah M, Ullah A, Murtaza I, Ullah R, Alotaibi A, Murthy HCA. C-Reactive Protein and High-Sensitive Cardiac Troponins Correlate with Oxidative Stress in Valvular Heart Disease Patients [J]. Oxid Med Cell Longev. 2022, 2022: 5029853. DOI: 10.1155/2022/5029853 . Hein TW, Singh U, Vasquez-Vivar J, Devaraj S, Kuo L, Jialal I. Human C-reactive protein induces endothelial dysfunction and uncoupling of eNOS in vivo [J]. Atherosclerosis. 2009, 206(1): 61–68. DOI: 10.1016/j.atherosclerosis.2009.02.002 . Münzel T, Hahad O, Kuntic M, Keaney JF, Deanfield JE, Daiber A. Effects of tobacco cigarettes, e-cigarettes, and waterpipe smoking on endothelial function and clinical outcomes [J]. Eur Heart J. 2020, 41(41): 4057–4070. DOI: 10.1093/eurheartj/ehaa460 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 23 Dec, 2024 Reviews received at journal 16 Dec, 2024 Reviewers agreed at journal 16 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviewers agreed at journal 11 Dec, 2024 Reviewers agreed at journal 08 Dec, 2024 Reviews received at journal 28 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers invited by journal 18 Sep, 2024 Editor assigned by journal 08 Sep, 2024 Submission checks completed at journal 05 Sep, 2024 First submitted to journal 04 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5034450","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360045559,"identity":"8438bf6f-84f4-4d54-a1e3-174e3d5f62fb","order_by":0,"name":"Xiaofeng Li","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Li","suffix":""},{"id":360045560,"identity":"7e0d6a63-dd91-4fcd-baf6-a824a7156069","order_by":1,"name":"Shina Song","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shina","middleName":"","lastName":"Song","suffix":""},{"id":360045561,"identity":"a48f7d01-6d5b-4241-ae14-04915413a107","order_by":2,"name":"Wenhui Jia","email":"","orcid":"","institution":"Shanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Jia","suffix":""},{"id":360045563,"identity":"6ed0d022-006d-4c44-9288-a88d9733829c","order_by":3,"name":"Lihua Xie","email":"","orcid":"","institution":"Linfen City People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Xie","suffix":""},{"id":360045565,"identity":"79e66c7c-94e8-4c39-b1a1-44a0e243b206","order_by":4,"name":"Meilin Fan","email":"","orcid":"","institution":"Linfen City People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meilin","middleName":"","lastName":"Fan","suffix":""},{"id":360045566,"identity":"416aa460-22f0-4608-abec-88d9bc5d56d6","order_by":5,"name":"Changxin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPmYGNjADRB748MOGgUGCgBY2JC2MB2f2pBGhhQGqBQiYD/OwHSZCCzvzswc/d9Qm9kk3MBzg4Tlvzy/dY8Dwo2IbHoexmRv2njluzCZzgOGAhMXtxJlzzhgw9py5jUcLD5sEb9sxOTaJBIYDBjy3Ewxu5BgwM7bh1yL5t+0YD1hLAts5e6K0SPO21UBsOcB2gHEDYS1sZtKybQeMQVoONvYkJ86ckVZwEJ9f+PkPP5N821aXOH9GAvPnPz/s7Pklkjc++FGBWwsUHAbp/gDnHiCkHgjqiFAzCkbBKBgFIxYAAPsSTiOAb0EyAAAAAElFTkSuQmCC","orcid":"","institution":"First Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Changxin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-09-05 01:42:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5034450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5034450/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-02355-3","type":"published","date":"2025-03-18T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67175076,"identity":"d0779c1a-335f-4e15-914d-97cfbb67783a","added_by":"auto","created_at":"2024-10-22 04:48:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5210825,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchat of participant selection\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5034450/v1/d3016f61021ca261ad70c8b4.jpg"},{"id":67175074,"identity":"3b250fd1-f0b5-4d78-9edd-f48a4274c80b","added_by":"auto","created_at":"2024-10-22 04:48:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":409014,"visible":true,"origin":"","legend":"\u003cp\u003eGMDR for the distribution of high-risk and low-risk genotypes in the best two-factor model. The left bars correspond to positive interaction scores, whereas the right bars signify negative scores. Dark-shaded cells represent high-risk associations for IS recurrence, whereas light shading denotes low-risk associations. (A) The figure illustrates the interaction scores for two SNPs. (B) The figure illustrates the interaction scores for rs3093059 and smoking status (0=no smoking, 1=smoking history).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5034450/v1/873885851ced3a9bc748d408.jpg"},{"id":67175962,"identity":"d2a7ed64-1e3b-4043-87ce-467861f6ae27","added_by":"auto","created_at":"2024-10-22 04:56:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68161,"visible":true,"origin":"","legend":"\u003cp\u003eGene-environment Interaction Analysis Diagram. (A) The interaction dendrogram provides a visual hierarchy of interactions, with the thickness of the red lines representing the strength of synergistic interactions, and thinner orange lines indicating weaker synergistic effects. The arrangement from left to right corresponds to increasing intensity of interaction. (B) The hierarchical interaction graph illustrates the interaction between SNPs and smoking status, where the percentage at the base of each SNP denotes its individual entropy percentage. The percentages on the connecting lines signify the interaction entropy percentage between paired SNPs and along with smoking status. A red line indicates a synergistic interaction with significant redundancy, while a blue line suggests a less pronounced redundancy interaction.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5034450/v1/1bb7d78f60b75ef07afba5d7.png"},{"id":79120481,"identity":"85ea6eee-3733-4879-8cb3-5f5c9c835749","added_by":"auto","created_at":"2025-03-24 16:08:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6656189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5034450/v1/824bfa5c-7d9e-4210-bf0d-8c7bab7fb114.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do CRP Gene Variants and Smoking Elevate Recurrent Stroke Risk in Minor Ischemic Stroke Patients?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke continues to be one of the leading causes of morbidity and mortality, with significant burden in China\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Every year, nearly 3\u0026nbsp;million new stroke cases are identified in China, with around 30% of these being classified as minor ischemic strokes (minor strokes)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Their high likelihood for reoccurrence makes this a worrisome phenomenon\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Alarmingly, the recurrence rates in the first year after stroke are as high as 17.7% in Chinese patients\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. While recent advances in acute stroke management have improved outcomes from ischemic stroke (IS), there exists a considerable risk of recurrence for IS survivors\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, with a high mortality rate. This further underpins the importance of a deeper understanding of the underlying risk factors\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough hypertension, diabetes mellitus, and tobacco use are known risk factors for IS, the role of genetics in the recurrence of stroke is an emerging area of research\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. CRP is the most important biomarker in systemic inflammation that has been associated with cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In particular, elevated CRP levels have been shown to associate with the occurrence of initial stroke and also with its recurrence\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Single nucleotide polymorphisms (SNPs) within the CRP gene can modulate CRP expression and hence contribute to the predisposition of an individual to the atherosclerotic and thrombotic events leading to stroke \u003csup\u003e[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Work that has been directed in this critical area, however, is limited. Our study endeavors to bridge this knowledge gap by exploring the potential correlation. We propose that certain CRP gene SNPs could predispose individuals to recurrent strokes, with this risk potentially being exacerbated by environmental factors like smoking\u0026mdash;a well-documented risk factor for stroke \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. We tested this hypothesis by selecting cases of recurrent minor stroke along with age and sex-matched controls from a hospital-based stroke registry. We therefore sought to explore complex gene-environment interactions that likely act to modulate the risk of stroke recurrence using the Generalized Multifactor Dimensionality Reduction (GMDR) algorithm. Identification of these interactions is crucial to develop more personalized and hence effective prevention strategies, which might be essential in lowering the burden of recurrent strokes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy group\u003c/h2\u003e \u003cp\u003e Our study was approved by the Ethics Committee of Linfen City People's Hospital, and all participants provided informed written consent.\u003c/p\u003e \u003cp\u003eOur research, a single-center, hospital-based stroke registry at Linfen People's Hospital, spanned from January 2019 to December 2022. Initially, we identified a cohort of 2,032 patients experiencing their first minor stroke, defined by an NIH Stroke Scale (NIHSS) score\u0026thinsp;\u0026le;\u0026thinsp;5 within 7 days of onset. Our diagnosis strictly followed the World Health Organization's criteria, which stipulate that symptoms must persist beyond 24 hours or lead to death, excluding non-vascular causes\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. All patients underwent a comprehensive radiographic assessment, including MRI or CT, to confirm ischemic strokes and rule out hemorrhagic or non-ischemic events. The TOAST classification system was meticulously applied to categorize IS patients, specifically focusing on the small-vessel occlusion (SVO) and large-artery atherosclerosis (LAA) subtypes\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Clinical information was collected via in-person interviews performed by professional neurologists at the time of admission and included general information and medical history, hypertension, diabetes, coronary heart disease, lifestyle (smoking and alcohol). Blood samples were taken for biochemical analysis, focusing on triglycerides, total cholesterol, LDL-C, and HDL-C levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFollow-Up and Outcome Assessment\u003c/h2\u003e \u003cp\u003eRegular follow-up assessments were carefully planned every three months, continuing until the recurrence of ischemic stroke (IS) or the study's completion in December 2023, with data being gathered through standardized questionnaires during telephone interviews or clinical visits. A recurrent IS was distinctly defined as a novel episode of cerebral infarction manifesting at least 21 days post the initial stroke, characterized by the abrupt emergence of neurological deficits indicative of ischemic pathology. Participants were categorized into two groups based on outcomes: (1) the recurrence group, consisting of those who experienced a recurrent stroke within the first year of their initial stroke, and (2) the non-recurrence group, comprising those who did not experience a recurrent stroke during the study period.\u003c/p\u003e \u003cp\u003eFrom the stroke registry, we identified a cohort of 2,032 individuals who had experienced minor IS within 7 days of onset. Following a rigorous screening process, we selected a study population of 1,516 participants who met the eligibility criteria. Among them, 298 patients experienced recurrence, while 1,148 did not. Ultimately, we collected 260 blood samples from individuals with a history of IS recurrence by the end of the follow-up period. We then selected a control group from the remaining 264 patients who had not experienced a recurrent stroke, resulting in a control group of 264 blood samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of smoking status\u003c/h2\u003e \u003cp\u003eParticipants were categorized based on their smoking status into two groups: current smokers, defined as those who used tobacco regularly in the 12 months before the end of the follow-up period, and never or former smokers, who had not used tobacco during this time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSNP selection\u003c/h2\u003e \u003cp\u003eWe identified tagging SNPs within the CRP gene using two criteria: (1) SNPs previously associated with ischemic stroke (IS) and (2) genotype data from the International HapMap Project. We accessed the HapMap website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hapmap.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://hapmap.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and downloaded SNP genotype data from the Han Chinese population in Beijing, China. The data were analyzed using Haploview 3.0 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broad.mit.edu/mpg/haploview/\u003c/span\u003e\u003cspan address=\"http://www.broad.mit.edu/mpg/haploview/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in HapMap format. Tagging SNPs were selected based on a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and linkage disequilibrium (r\u0026sup2; \u0026gt;0.8). Finally, four specific SNPs within the CRP gene were chosen for this study: rs1130864, rs1800947, rs2808632, and rs3093059.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePrimer design and genotyping\u003c/h2\u003e \u003cp\u003eFasting venous blood samples (2 mL) were obtained from each participant in the early morning and promptly stored at -80\u0026deg;C for subsequent analysis. Genomic DNA was meticulously extracted from peripheral white blood cells utilizing the AxyPrep Blood Genomic DNA Miniprep Kit (Axygen Biosciences), adhering to the manufacturer's protocol. Primers for PCR-RFLP genotyping were designed utilizing Primer 5.0 software (Premier Biosoft International). The PCR amplification was conducted in a 20 \u0026micro;L reaction mixture containing 10 ng of genomic DNA, 1.5 U of Taq DNA polymerase, 0.2 mmol/L of each dNTP, 10 \u0026micro;L of 2\u0026times; PCR Master Mix, and 1 \u0026micro;L of each primer. The thermal cycling profile included an initial denaturation at 95\u0026deg;C for 5 minutes, followed by 25 cycles of 95\u0026deg;C for 30 seconds, 60\u0026deg;C for 30 seconds, and 72\u0026deg;C for 90 seconds, and a final extension at 72\u0026deg;C for 7 minutes. The PCR products were resolved on 3% agarose gels and visualized under ultraviolet light. Subsequently, the PCR amplicons were digested with 2 U of restriction enzymes (New England Biolabs) at 65\u0026deg;C for 1 hour, and the digested fragments were separated on a 3% agarose gel containing ethidium bromide to identify the genotypes, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer Sequences, Restriction Enzymes, and Fragment Lengths of SNPs in the CRP Gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolymorphism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRestriction enzyme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFragment length (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers1130864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: 5\u0026prime;- GGCAGAAGCAAGCATCATCTT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHpyCH4III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC: 346\u0026thinsp;+\u0026thinsp;183\u0026thinsp;+\u0026thinsp;69\u0026thinsp;+\u0026thinsp;66\u003c/p\u003e \u003cp\u003eT: 346\u0026thinsp;+\u0026thinsp;249\u0026thinsp;+\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: 5\u0026prime;- GGTCATGGGTGGGGAATTAAA-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers1800947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: 5\u0026prime;- GGCAGAAGCAAGCATCATCTT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTsp45I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eG: 530\u0026thinsp;+\u0026thinsp;104\u003c/p\u003e \u003cp\u003eC: 663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: 5\u0026prime;- GGTCATGGGTGGGGAATTAAA-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers2808632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: 5\u0026prime;-ACTCCCGTTTCCAATAAGTTCCT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBfaI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eA:381\u003c/p\u003e \u003cp\u003eC:196\u0026thinsp;+\u0026thinsp;189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: 5\u0026prime;-GCCATGCAGAACTGTGAGTCAA-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ers3093059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: 5\u0026prime;-GACTCCTGCCTGAAGCTTTACATAT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTsp509I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT: 178\u0026thinsp;+\u0026thinsp;64\u0026thinsp;+\u0026thinsp;53\u003c/p\u003e \u003cp\u003eC: 242\u0026thinsp;+\u0026thinsp;53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: 5\u0026prime;-TTCCCCTTCCTGTGTCCAAGT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed with SPSS software (version 25.0) and GMDR software (version 0.9). The Shapiro-Wilk test was used to check the normality of data distribution of continuous variables, which are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range). Descriptive statistics are given as counts (%) for categorical data, compared using Pearson chi-square and Fisher's exact tests. Baseline demographics and clinical features between the recurrence and nonrecurrence groups were compared. The quantitative variables with a normal distribution were compared by Student's t-test and those without a normal distribution by the Mann-Whitney U test. In the controls, Hardy-Weinberg equilibrium (HWE) was tested for the distribution of CRP SNPs by chi-square test.\u003c/p\u003e \u003cp\u003eThe GMDR method was employed to explore gene-gene and gene-environment interactions, identifying the best predictive model for IS recurrence from the combinations of selected SNP genotypes. The best model was chosen based on the highest testing balanced accuracy and cross-validation consistency (CVC). Significant interactions between SNPs and between SNPs and smoking status were visualized using interaction dendrograms and hierarchical graphs, with the MDR software (version 2.0). Interaction effects were quantified using entropy percentages, and the strongest synergistic interactions were highlighted. Multiple logistic regression analysis was conducted to evaluate the genotypes and combinations identified by GMDR. Results are presented as odds ratios (OR) with 95% confidence intervals (CI), indicating the strength and precision of associations. A two-tailed p-value of less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eDemographic Characteristics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA total of 535 mild IS patients were included in the study and categorized into two distinct groups according to r\u0026nbsp;the occurrence of recurrent stroke: the Recurrence group (n=261) and the Nonrecurrence group (n=264) (\u003cstrong\u003eFigure 1\u003c/strong\u003e). The demographic and clinical characteristics of the participants are detailed in \u003cstrong\u003eTable 2\u003c/strong\u003e. The Recurrence group had higher rates of male prevalence, hypertension, diabetes, coronary heart disease, family history of stroke, smoking, and alcohol consumption (P \u0026lt; 0.05). However, no significant disparities were observed in terms of age and BMI between the two groups (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 2\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eBaseline characteristics of patients with Recurrence group and Nonrecurrence group\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eRecurrence (n=261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003eNonrecurrence (n=264)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eP values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e66.93\u0026plusmn;9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e66.76\u0026plusmn;6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eMale,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e151 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e147 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e24.67\u0026plusmn;2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e24.27\u0026plusmn;2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e148.35\u0026plusmn;23.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e135.94\u0026plusmn;19.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e85.49\u0026plusmn;12.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e80.40\u0026plusmn;12.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eLipids (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eTotal cholesterol(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e5.27\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e4.89\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e1.63\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e1.34\u0026plusmn;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eHDL‐cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eLDL‐cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e3.48\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e3.27\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eHypertension,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e159 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e96 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eDiabetes,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e65 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e37 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eCoronary heart disease,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e75(28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e42 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eFamily history of stroke,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e65 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e33 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eSmoking,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e104 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e64 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35.7143%;\"\u003e\n \u003cp\u003eAlcohol drinking,\u0026nbsp;,\u0026nbsp;N\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003e70 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e44 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: Mean\u0026plusmn;standard deviation values for quantitative variables and n (%) for qualitative variables. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure. TC, total cholesterol; TG, triglyceride; HDL, high density lipoprotein cholesterol; LDL, low density lipoprotein.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eSNPs and recurrent IS susceptibility\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe genotype frequencies of CRP SNPs (rs1130864, rs1800947, rs2808632, and rs3093059), in the Nonrecurrence group adhered to HWE, as shown in Table 3. The CC genotype for rs1130864 and the GG genotype for rs1800947 were more frequently observed in both the Recurrence and Nonrecurrence groups. However, there was no statistically significant difference in the distribution of genotype or allele frequency distribution when comparing the two groups.\u003c/p\u003e\n\u003cp\u003eThe rs2808632 showed a significant difference in genotype distribution, with the AA genotype being more prevalent in the Recurrence group (P = 0.004). This association was further supported by both dominant and recessive models, which indicated a higher risk of IS recurrence for AA genotype carriers (Dominant: OR 1.550, 95% CI 1.092-2.119, P = 0.014; Recessive: OR 1.737, 95% CI 1.108-2.723, P = 0.015). The A allele was also significantly more frequent in the Recurrence group compared to the Nonrecurrence group (P = 0.002). Similarly, rs3093059 was significantly associated with IS recurrence, particularly for the CC genotype (P = 0.003), and this association was consistent across both models (Dominant: OR 1.571, 95% CI 1.113-2.217, P = 0.010; Recessive: OR 1.920, 95% CI 1.172-3.146, P = 0.009). The C allele was significantly more frequent in the Recurrence group (P = 0.001).\u003c/p\u003e\n\u003cp\u003eTable 3\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eAssociation of genotypes and alleles of CRP SNPs with mild IS recurrence\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eGenotypes and Alleles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eNonrecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u0026nbsp;\u003c/em\u003e=\u0026nbsp;261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eN\u0026nbsp;=\u0026nbsp;264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003ers1130864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAdditive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e230（88.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e225（85.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e31（11.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e39（14.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e0.802 (0.469-1.290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e491（94.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e489（92.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e31（5.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e39（7.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e0.792（0.486-1.290）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003ers1800947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAdditive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e228（87.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e237（89.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e33（12.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e27（10.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.270（0.740-2.180）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.252(0.742-2.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003ers2808632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAdditive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.372(0.938-2.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e2.032(1.247-3.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eDominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.550(1.092-2.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eRecessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCC+CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.737(1.108-2.723)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.484(1.156-1.906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003ers3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAdditive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.371(0.940-1.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.193(1.305-3.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eDominant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eTC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.571（1.113-2.217）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eRecessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eTT+TC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.920（1.172-3.146）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.00(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4639%;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.556(1.201-2.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eGMDR analysis of SNP- SNP and gene-smoking interactions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe SNP-SNP and SNP-smoking interactions on IS recurrence were analyzed using GMDR (\u003cstrong\u003eTable 4\u003c/strong\u003e). In the one-locus model, rs3093059 showed the highest attribute for predicting recurrence, with a Testing Balanced Accuracy of 52.90% and a Cross-Validation Consistency (CVC) of 7/10 (P = 0.0547), indicating a modest association. The two-locus model, considering the interaction between rs3093059 and rs2808632, demonstrated the highest Testing Balanced Accuracy at 58.65% and achieved a perfect CVC score of 10/10 (P = 0.0010), suggesting a synergistic effect between these two genetic variants in predicting stroke recurrence. \u003cstrong\u003eFigure 2A\u003c/strong\u003e illustrates the genotypes associated with high and low risks of IS recurrence, as determined by the best two-locus model from the GMDR analysis, focusing on the interaction between rs2808632 and rs3093059. The genotype combinations most strongly associated with high recurrence risk were AA \u0026times; CC, AA \u0026times; TC, CA \u0026times; CC, and CA \u0026times; TC. In contrast, the genotypes indicating a lower risk of recurrence were CC \u0026times; TC and CA \u0026times; TT.\u003c/p\u003e\n\u003cp\u003eRegarding SNP-smoking interactions, incorporating rs3093059 into the smoking model resulted in a slight decrease in Testing Balanced Accuracy to 55.93% but maintained a high CVC of 7/10 (P=0.0107), indicating a significant predictive synergy between the genetic variant and smoking exposure as risk factors for recurrence. \u003cstrong\u003eFigure 2B\u003c/strong\u003e presents the risk genotypes identified in the best two-locus model, which includes the interaction between rs3093059 and smoking status, as revealed by GMDR analysis. The genotypes with the highest recurrence risk in the presence of smoking were CC \u0026times; smoking and TC \u0026times; smoking, whereas the genotypes associated with a reduced risk of recurrence under non-smoking conditions were TC \u0026times; non-smoking and TT \u0026times; non-smoking.\u003c/p\u003e\n\u003cp\u003eTable 4\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eMultifactor dimensionality reduction results in the Recurrence group and Nonrecurrence group\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocus No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBest model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraining accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTesting accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSign test (P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCVC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene\u0026ndash;gene interactions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ers3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8(0.0547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ers3093059, rs2808632\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10(0.0010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ers1800947, rs2808632, rs3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6(0.3770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ers1130864, rs1800947, rs2808632, rs3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8(0.0547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGene\u0026ndash;smoking interaction\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9(0.0107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking, rs3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9(0.0107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking, rs3093059, rs2808632\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8(0.0547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking, rs1800947, rs2808632, rs3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6(0.3770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking, rs1130864, rs1800947, rs2808632, rs3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7(0.1719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCVC = Cross-validation consistency.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eMDR Analysis of SNP- SNP and gene-smoking interactions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe leveraged information gain theory to elucidate the complex interplay among these genetic variants. This approach led to the creation of an interaction dendrogram and hierarchical interaction graphs derived from the MDR analysis (Figure 3). The interaction dendrogram showed that rs3093059 and smoking had the strongest synergy, indicated visually by the red line (Figure 3A). The hierarchical interaction graphs (Figure 3B) demonstrated significant interactions between rs2808632 and rs3093059, with an interaction entropy of 1.06%, suggesting a positive interaction effect. Additionally, the interaction between rs3093059 and smoking status exhibited a notable entropy change of 1.42%, indicating a substantial impact on IS recurrence. Notably, rs3093059 showed the strongest synergy with smoking status, as denoted by the red line, highlighting the potential influence of environmental factors on genetic risk.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCombined Genetic Variants and Environmental Factors in IS Recurrence\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe logistic regression analysis in Table 5 reveals significant combined effects of rs3093059 and rs2808632 within CRP gene on IS recurrence risk. Notably, the rs3093059 TC+CC genotypes, combined with the rs2808632 CA+AA genotypes, were associated with an increased risk of IS recurrence, with an adjusted OR of 2.325 (95% CI 1.341\u0026ndash;4.032, P=0.003), compared to those with the rs3093059 TT genotype and rs2808632 CC genotype.\u003c/p\u003e\n\u003cp\u003eTable 6 extends this analysis to the interaction between rs3093059 and smoking status. Smokers with the rs3093059 TC+CC genotypes had a significantly higher risk of IS compared to never smokers with the TT genotype, with an adjusted OR of 3.467 (95% CI 2.040\u0026ndash;5.923, P\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eCombined effects of rs3093059 and rs2808632 on the risk of IS Recurrence\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003ers3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003ers2808632\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003eCrude \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eAdjusted \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eTC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e0.777(0.472~1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e0.865(0.502~1.489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eCA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e0.705 (0.411~1.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e0.812(0.451~1.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eTC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eCA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e2.196 (1.326~3.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e2.325 (1.341~4.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea The non-risk genotype for each genetic factor was used as the reference OR. The Adjusted P value was calculated by multivariate logistic regression analysis with adjustment for Sex, Smoking, Alcohol consumption, Hypertension, Diabetes, Coronary heart disease and Family history of stroke.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eAssociation between CRP SNPs-environment interactions and IS Recurrence\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003ers3093059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eCrude \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eAdjusted \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003eTC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e0.803 (0.525~1.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e0.830(0.525~1.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003eTT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e0.619 (0.324~1.183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e0.537(0.276~1.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003eTC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4694%;\"\u003e\n \u003cp\u003e3.167 (1.087~3.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e3.467(2.040~5.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ea The non-risk genotype for each genetic factor was used as the reference OR. The Adjusted P value was calculated by multivariate logistic regression analysis with adjustment for Sex, Smoking, Alcohol consumption, Hypertension, Diabetes, Coronary heart disease and Family history of stroke.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study focuses on recurrent minor stroke because stroke survivors are at an increased risk of subsequent strokes, which tend to be more fatal and disabling than the initial event\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. To our current understanding, this research represents the inaugural investigation into examining the relationship between four SNPs of the CRP gene and the 12-month risk of recurrent events in among patients with a history of minor stroke. Our findings reveal significant associations between specific CRP gene polymorphisms, particularly rs2808632 and rs3093059, and heightened susceptibility to recurrence. Moreover, an interplay between rs3093059 and smoking was detected, indicating that smoking amplifies the genetic risk associated with this SNP. These findings emphasize the significance of taking into account genetic as well as lifestyle elements in the treatment of IS patients.\u003c/p\u003e\n\u003cp\u003eCRP is a key biomarker of systemic inflammation, and elevated CRP levels associated with Inflammation-related atherosclerosis and increased thrombogenic potential\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e23-25\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Several studies have shown that CRP levels are associated with the prognosis of ischemic stroke\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e26-28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. It is worth noting that CRP SNPs is not only related to its serum level, but also related to inflammatory reaction and the progress of atherosclerotic plaque, thus leading to the occurrence of ischemic stroke\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. While few studies have specifically examined the relationship between CRP gene polymorphisms and the likelihood of minor stroke, existing literature has identified a correlation between the rs3093059 polymorphism and adverse outcomes within three months for patients with large artery atherosclerosis IS\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This finding suggests a possible association between the rs3093059 CC genotype and the propensity for stroke recurrence. So far, research on rs2808632 in CRP gene is notably limited. To date, only one study has reported a link between rs2808632 and rheumatoid arthritis\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In our study, minor stroke patients with the AA genotype of rs2808632 exhibited significantly higher risk of recurrence compared to those with the CC genotype. We also identified a significant interaction between rs2808632 and rs3093059 within CRP gene. Patients with the AA genotype of rs2808632 and the CC genotype of rs3093059 had a markedly higher risk of stroke recurrence compared to other genotype combinations. This suggests a synergistic effect where the presence of both high-risk genotypes exacerbates the likelihood of recurrence. \u003c/p\u003e\n\u003cp\u003eSmoking is a well-established risk factor for IS. Previous reports have shown that patients who smoked at the time of their stroke or had a history of smoking faced a higher risk of death or recurrent vascular events compared to those who never smoked\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33-35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Our study delved into the relationship between smoking status and CRP SNPs, particularly focusing on rs3093059. Individuals with the rs3093059 CC genotype may experience exacerbated negative effects of smoking, possibly through mechanisms involving increased oxidative stress\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e and endothelial dysfunction\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, thus increasing susceptibility to recurrent ischemic events\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study acknowledges several limitations that merit consideration. Firstly, the retrospective design could introduce selection bias and constrain the ability to determine causality. Secondly, the single-center nature may restrict the generalizability of the findings to broader populations. Additional multi-center studies are needed to emphasize statistical differences. Finally, while we focused on four SNPs, other SNPs may also contribute to stroke recurrence and should be investigated in future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research illustrates that certain CRP gene variations, specifically rs2808632 and rs3093059, have a connection with the susceptibility to recurrent minor strokes. We also shed light on the relationships between distinct CRP gene mutations and behaviors like smoking, although additional validation through larger studies is necessary. The creation of effective preventive strategies to minimize the negative impacts of recurrent minor strokes requires precise biomarkers for these genetic and lifestyle-related variations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe underlying data for this study are available upon request, contingent upon ethical approval and participant confidentiality, through the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Linfen City People\u0026apos;s Hospital, and all participants provided informed written consent.\u003c/p\u003e\n\u003cp\u003eStatement of author contributions\u003c/p\u003e\n\u003cp\u003eXL formulated and developed the research, secured financial support and ethical clearance, evaluated the findings, and authored the document in its entirety or partially. SS provided valuable assistance in the study design and contributed significantly to the preparation and review of the manuscript. WJ conducted data analysis and performed statistical analysis. LX was responsible for data collection and acquisition. MF and CL provided critical revisions to the manuscript and supervised the study. Manuscript preparation, editing, and review were collaboratively conducted by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Linfen City Science and Technology Plan Project (Project No. 2014) and the Shanxi Applied Basic Research Program (No. 202403021212232).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe would like to convey our appreciation to each patient who engaged in the process of gathering medical history, assessing scales, and undergoing additional examinations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association [J]. 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DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehaa460\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehaa460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein, Gene variants, minor ischemic stroke, smoking, interaction","lastPublishedDoi":"10.21203/rs.3.rs-5034450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5034450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMinor ischemic strokes, though initially presenting with limited symptoms, carry a significant risk of recurrence, potentially leading to severe disability. However, the association of CRP gene variations in predicting the risk for recurrent minor stroke, especially how genetic susceptibility interacts with poor health habits like smoking, still needs to be established. This study investigates the relationships of single-nucleotide polymorphisms (SNPs) in CRP gene with minor stroke recurrence. Furthermore, this research proceeds to explore the potential interactions between these genetic variants and smoking status.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 2,032 first-time minor stroke patients were retrospectively recruited from January 2019 to December 2022 in Linfen People's Hospital. Genomic DNA was extracted for genotyping four SNPs of the CRP gene: rs1130864, rs1800947, rs2808632, and rs3093059. We scrutinized the association of these SNPs with the risk of stroke recurrence in an additive, dominant, and recessive genetic model. To further explore this complicated interaction of the CRP gene SNPs with the status of smoking, the tool of Generalized Multifactor Dimensionality Reduction (GMDR), was employed. Besides, multivariate logistic regression was used to estimate the strength of these associations with the risk of recurrence. The patients were followed by a team of three trained rehabilitators, making evaluations every three months for one year, in a very thorough follow-up.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study recruited 260 patients who suffered recurrent minor strokes and 264 age- and sex-matched controls without recurrence. The A allele of rs2808632 (P\u0026thinsp;=\u0026thinsp;0.002) and C allele of rs3093059 (P\u0026thinsp;=\u0026thinsp;0.009) were found to be significantly associated with high risk of stroke recurrence by analysis. Those patients with the combined genotypes rs2808632 CA\u0026thinsp;+\u0026thinsp;AA and rs3093059 TC\u0026thinsp;+\u0026thinsp;CC revealed 2.325 times more risk for recurrence when compared to those with the genotypes rs2808632 CC and rs3093059 TT (P\u0026thinsp;=\u0026thinsp;0.002). Furthermore, in the rs3093059 TC\u0026thinsp;+\u0026thinsp;CC genotypes versus the TT genotype among the smokers, an associated 3.467-fold increased risk for recurrence had been confirmed.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur results confirmed that rs2808632 and rs3093059 together are pivotal factors in contributing to heightened minor stroke recurrence. Besides, this significantly affects the interaction between rs3093059 SNP and smoking status.\u003c/p\u003e","manuscriptTitle":"Do CRP Gene Variants and Smoking Elevate Recurrent Stroke Risk in Minor Ischemic Stroke Patients?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 04:48:25","doi":"10.21203/rs.3.rs-5034450/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-23T05:20:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-17T01:42:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258811621372507950320652986794922500832","date":"2024-12-16T18:58:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251949689718173901580437909569930992838","date":"2024-12-13T11:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191756965514847938611741701064794047003","date":"2024-12-11T07:56:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63879032225882547348468433758199443872","date":"2024-12-09T00:27:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-28T11:14:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184950375178080740376696498544888360448","date":"2024-09-20T07:36:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-18T09:09:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-09T01:44:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-05T12:19:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2024-09-05T01:41:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8dfa7a6f-34c7-4d83-aa64-4707141548be","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:01:58+00:00","versionOfRecord":{"articleIdentity":"rs-5034450","link":"https://doi.org/10.1186/s40001-025-02355-3","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-03-18 15:57:44","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-10-22 04:48:25","video":"","vorDoi":"10.1186/s40001-025-02355-3","vorDoiUrl":"https://doi.org/10.1186/s40001-025-02355-3","workflowStages":[]},"version":"v1","identity":"rs-5034450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5034450","identity":"rs-5034450","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}