Serum High-Density Lipoprotein Levels and Risk of In-Stent Restenosis Following Carotid Artery Stenting: A Multivariate Analysis-Based Retrospective Cohort Study

Serum High-Density Lipoprotein Levels and Risk of In-Stent Restenosis Following Carotid Artery Stenting: A Multivariate Analysis-Based Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum High-Density Lipoprotein Levels and Risk of In-Stent Restenosis Following Carotid Artery Stenting: A Multivariate Analysis-Based Retrospective Cohort Study Yuting Tang, Lingjia Dong, Feifan Sun, Zisheng Liu, Maohua Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6277872/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective: To investigate the relationship between serum high-density lipoprotein (HDL) levels and the risk of in-stent restenosis (ISR) following carotid artery stenting (CAS), aiming to provide potential targets for ISR prevention and treatment. Methods: A retrospective analysis was conducted on 132 patients who underwent CAS in the Department of Neurosurgery at Wenzhou Central Hospital between March 2019 and January 2024 and had 6-month (±1 month) follow-up data available. Patients were divided into ISR group (96 cases) and non-ISR group (36 cases) based on the occurrence of ISR. Demographic, clinical, and laboratory data were collected for both groups. Independent predictors of ISR were identified using t-test, Mann-Whitney U test, chi-square test, and logistic regression analysis. The predictive performance of the model was evaluated using ROC curve analysis. Results: Serum HDL levels were significantly lower in the ISR group than in the non-ISR group (p < 0.001). Univariate logistic regression analysis showed that male gender, smoking, hypertension, high BMI, and elevated serum globulin levels were positively associated with ISR risk (p < 0.05), while higher serum HDL levels were negatively associated with ISR risk (p < 0.05). Multivariate logistic regression analysis further confirmed that smoking, hypertension, and elevated serum globulin levels were independent risk factors for ISR (p < 0.05), while higher serum HDL levels were an independent protective factor for ISR (p < 0.05). ROC curve analysis indicated that the multivariable model had good predictive performance for ISR, with an AUC of 0.84. At the optimal cutoff point, the model demonstrated a sensitivity of 92% and a specificity of 75%. Conclusion: Serum high-density lipoprotein (HDL) plays a crucial protective role in the development of ISR. Monitoring and increasing serum HDL levels may help prevent ISR and improve patient prognosis. Serum high-density lipoprotein Carotid artery stenting In-stent restenosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Carotid artery stenosis is one of the major causes of ischemic stroke [1] . The current surgical treatments for carotid artery stenosis primarily include carotid endarterectomy (CEA) and carotid artery stenting (CAS). However, the incidence of in-stent restenosis (ISR) after CAS is higher than that after CEA [2] . Currently, ISR is often defined as a vascular stenosis rate > 50% within 5 mm of the stent or at both ends of the stent [3] . ISR increases the risk of recurrent stroke [4] , thereby significantly affecting surgical outcomes and the quality of life of patients. Therefore, early detection and prevention of ISR are of significant practical importance. Multiple studies have indicated that factors such as female sex, advanced age, smoking, diabetes, hypertension, serum albumin levels, dyslipidemia, and cardiovascular diseases are risk factors for ISR following CAS [5–8] . Epidemiological research has shown that in patients with acute coronary syndrome and low HDL levels, the risk of death and adverse cardiac events increases during both short-term and long-term follow-up after drug-eluting stent implantation [9] . The objective of this study is to investigate whether serum HDL levels are associated with ISR and to explore their significance. 2 Methods 2.1 Study Design and Population This retrospective analysis was performed on 163 patients who underwent their first CAS in the Department of Neurosurgery at Wenzhou Central Hospital from March 2019 to January 2024 and received a follow-up carotid and cerebrovascular angiography at 6 months (± 1 month). Inclusion criteria were: 1) The first diagnosis of moderate-to-severe internal carotid artery stenosis (stenosis ≥ 50% according to NASCET criteria) confirmed by digital subtraction angiography (DSA); 2) Successful CAS with complete coverage of the lesion by the stent and a post-procedural residual stenosis of < 30%; 3) Completion of a DSA re-examination within 6 months (± 1 month) after the procedure, with confirmed in-stent restenosis (ISR, stenosis ≥ 50%); 4) Regular dual antiplatelet therapy following stent implantation, along with the use of statins and other agents for secondary prevention; 5) Complete clinical, imaging, and laboratory data. Exclusion criteria encompassed: 1) Chronic total arterial occlusion or developmental abnormalities; 2) Concomitant moderate-to-severe stenosis or occlusion of other intracranial/extracranial large arteries; 3) Acute/chronic inflammation or wasting diseases at the time of admission; 4) Severe liver or renal dysfunction; 5) A history of or current malignancy or autoimmune disease. The final analysis included only patients meeting these criteria to ensure data integrity and reliability. This study was conducted in accordance with the Declaration of Helsinki and received ethical approval from the institutional review board, which waived the requirement for informed consent due to its retrospective nature. A total of 132 patients were ultimately included in this study, with 96 patients in the ISR group and 36 patients in the non-ISR group, after excluding 31 cases(Fig. 1 ). 2.2 Intervention Patients received oral aspirin (100 mg/day) and clopidogrel (75 mg/day) 3 days prior to surgery. Carotid artery stenting was performed by neurosurgical experts specializing in interventional procedures. After surgery, both groups were administered long-term aspirin (100 mg/day) and a three-month course of clopidogrel (75 mg/day). 2.3 Data Collection Data were extracted from electronic medical records and encompassed a comprehensive array of variables, including gender, age, body mass index (BMI), smoking status, alcohol consumption, history of hypertension (HTN History), history of diabetes mellitus (DM History), history of coronary heart disease (CHD History), white blood cell count (WBC), red blood cell count (RBC), lymphocyte count (LY), neutrophil count (NEUT), platelet count (PLT), serum albumin level (Serum ALB), serum globulin level (Serum GLOB), serum triglyceride level (Serum TG), serum high-density lipoprotein level (Serum HDL), serum low-density lipoprotein level (Serum LDL), LDL/HDL, serum homocysteine level (Serum HCY).Demographic variables included age, sex, BMI, smoking, and alcohol consumption, recorded as categorical variables. Clinical characteristics encompassed comorbidities such as hypertension, diabetes mellitus, and coronary heart disease. Laboratory parameters included hematological indicators (WBC, RBC, LY, NEUT, PLT), and biochemical markers (Serum ALB, Serum GLOB, Serum TG, Serum HDL, Serum LDL, LDL/HDL, Serum HCY). All measurements were performed using standardized and calibrated equipment to ensure accuracy and consistency.In order to capture each patient’s baseline status, all laboratory and urinalysis parameters were obtained from the first blood and urine samples collected upon admission and prior to any endovascular or surgical intervention, thereby minimizing the potential influence of subsequent therapeutic measures on these values. 2.4 Follow-up Angiography and Evaluation for ISR All patients included in the analysis underwent follow-up angiography at 6 months (± 1 month) after successful CAS, with the angiographic results interpreted by at least two experienced clinicians. Based on the follow-up angiography results, the patients were divided into the non-ISR group or the ISR group. 2.5 Statistical Analysis Descriptive statistics were employed to summarize the demographic, clinical and laboratory data, with continuous variables presented as means ± standard deviations or medians with interquartile ranges, and categorical variables as frequencies and percentages. Comparative analyses between the ISR and non-ISR groups utilized t-tests or Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables. Univariate logistic regression was conducted to identify potential predictors of ISR, with variables showing a p-value < 0.05 being selected for multivariate analysis. Multivariate logistic regression, adjusted for confounders, was used to determine independent predictors of ISR, presenting adjusted odds ratios (AOR) with 95% confidence intervals (CI). The model’s predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, calculating the area under the curve (AUC) to assess discrimination. All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 27.0, R software version 4.1.3, and Python software version 3.9.7, with a significance level set at p < 0.05. 3 Results 3.1 Baseline Characteristics The baseline characteristics of the 132 patients, including 96 with ISR and 32 with non-ISR, are detailed in Table 1 . As shown in Fig. 2 : Differences in gender, smoking history, and hypertension history were observed between the ISR and non-ISR groups. The proportion of males in the ISR group was significantly higher than in the non-ISR group (84.38% vs. 66.67%, p = 0.025), and the proportions of patients with a history of smoking (75.00% vs. 50.00%, p = 0.006) and hypertension (84.38% vs. 66.67%, p = 0.025) were also significantly higher, further supporting these factors as predictors of ISR risk. Box plot analysis (Fig. 3 ) clearly demonstrated that serum HDL levels in the ISR group were significantly lower than in the non-ISR group (0.93 ± 0.19 vs. 1.14 ± 0.29 mmol/L, p < 0.001), whereas serum globulin levels (29.31 ± 4.10 vs. 25.84 ± 5.07 mmol/L, p < 0.001) and BMI (23.00 ± 2.46 vs. 21.68 ± 3.38 kg/m², p = 0.037) were significantly higher in the ISR group. These differences suggest that serum HDL may play a protective role against ISR, while elevated serum globulin levels and BMI could be potential risk factors for ISR. With respect to platelet count, the ISR group also exhibited higher levels (238.34 ± 86.15 vs. 213.67 ± 51.19, p = 0.047). No statistically significant differences were observed between the two groups in terms of age, diabetes prevalence, white blood cell count, neutrophil count, or LDL/HDL ratio (p > 0.05). Data are expressed as mean ± standard deviation or frequency (percentage). Continuous variables were compared using the t-test, and categorical variables were compared using the chi-square test. Table 1 Comparison of Baseline Characteristics Variables Non-ISR(n = 36) ISR(n = 96) P Age(years) 70.67 ± 5.76 72.38 ± 7.27 0.163 Male, n(%) 24 (66.67) 81 (84.38) 0.025 Smoking, n(%) 18 (50.00) 72 (75.00) 0.006 Alcohol, n(%) 6 (16.67) 33 (34.38) 0.047 HTN History, n(%) 24 (66.67) 81 (84.38) 0.025 DM History, n(%) 18 (50.00) 48 (50.00) 1.000 CHD History, n(%) 12 (33.33) 21 (21.88) 0.176 BMI(kg/m 2 ) 21.68 ± 3.38 23.00 ± 2.46 0.037 WBC(10 9 /L) 7.90 ± 3.07 7.97 ± 2.31 0.908 RBC(10 9 /L) 4.27 ± 0.59 4.09 ± 0.41 0.051 LY(10 9 /L) 1.59 ± 0.67 1.68 ± 0.70 0.515 MEUT(10 9 /L) 5.49 ± 2.94 5.24 ± 2.16 0.647 PLT(10 9 /L) 213.67 ± 51.19 238.34 ± 86.15 0.047 Serum ALB(g/L) 37.97 ± 3.20 36.85 ± 3.26 0.081 Serum GLOB(g/L) 25.84 ± 5.07 29.31 ± 4.10 < .001 Serum TG(mmol/L) 1.77 ± 1.62 1.54 ± 0.62 0.406 Serum HDL(mmol/L) 1.14 ± 0.29 0.93 ± 0.19 < .001 Serum LDL(mmol/L) 2.62 ± 0.68 2.41 ± 0.72 0.136 LDL/HDL 2.39 ± 0.75 2.66 ± 0.88 0.105 Serum HCY(µmol/L) 13.13 ± 3.56 14.80 ± 6.62 0.155 3.2 Univariate Analysis Univariate logistic regression identified several predictors of intracranial aneurysm rupture, as shown in Table 2 . Among these, male sex (OR: 2.70, 95% CI: 1.11–6.54, p = 0.028), smoking (OR: 3.00, 95% CI: 1.35–6.68, p = 0.007), hypertension (OR: 2.70, 95% CI: 1.11–6.54, p = 0.028), and an elevated BMI (OR: 1.19, 95% CI: 1.03–1.38, p = 0.017) were significantly associated with the risk of ISR. Increased serum globulin levels (OR: 1.20, 95% CI: 1.09–1.32, p < 0.001) raised the likelihood of ISR, whereas higher serum HDL levels (OR: 0.01, 95% CI: 0.00–0.11, p < 0.001) were protective against ISR. Furthermore, variables such as age, alcohol consumption, diabetes, coronary artery disease, red blood cell count, white blood cell count, neutrophil count, lymphocyte count, platelet count, serum LDL levels, serum albumin levels, serum triglyceride levels, and serum homocysteine levels did not exhibit statistically significant associations (p > 0.05). Table 2 Analysis for ISR Risk Univariate and Multivariate Logistic Regression Analysis Variables Univariate Analysis Multivariate Analysis P_value OR (95%CI) P_value OR (95%CI) Age 0.207 1.04 (0.98 ~ 1.10) Male vs. Female 0.028 2.70 (1.11 ~ 6.54) Smoking 0.007 3.00 (1.35 ~ 6.68) 0.007 3.84(1.44 ~ 10.25) Alcohol 0.052 2.62 (0.99 ~ 6.93) HTN History 0.028 2.70 (1.11 ~ 6.54) 0.042 3.35 (1.04 ~ 10.73) DM History 1.000 1.00 (0.46 ~ 2.15) CHD History 0.179 0.56 (0.24 ~ 1.30) BMI 0.017 1.19 (1.03 ~ 1.38) WBC 0.894 1.01 (0.87 ~ 1.18) RBC 0.054 0.44 (0.19 ~ 1.02) LY 0.513 1.21 (0.68 ~ 2.14) MEUT 0.595 0.96 (0.82 ~ 1.12) PLT 0.112 1.00 (1.00 ~ 1.01) Serum ALB 0.083 0.90 (0.79 ~ 1.01) Serum GLOB < .001 1.20 (1.09 ~ 1.32) 0.008 1.16 (1.04 ~ 1.29) Serum TG 0.238 0.81 (0.56 ~ 1.15) Serum HDL < .001 0.01 (0.00 ~ 0.11) 0.002 0.01 (0.00 ~ 0.20) Serum LDL 0.142 0.68 (0.40 ~ 1.14) LDL/HDL 0.112 1.58 (0.90 ~ 2.77) Serum HCY 0.172 1.09 (0.96 ~ 1.23) OR: Odds Ratio, CI: Confidence Interval 3.3 Multivariate Analysis, ROC Curve Analysis, and Logistic Regression Scatter Plot Multivariate logistic regression was performed to identify independent predictors of intracranial aneurysm rupture, adjusting for potential confounders, as summarized in Table 2 . Smoking (OR: 3.84, 95% CI: 1.44–10.25, p = 0.007), hypertension (OR: 3.35, 95% CI: 1.04–10.73, p = 0.042), and increased serum globulin levels (OR: 1.16, 95% CI: 1.04–1.29, p = 0.008) were closely associated with the risk of ISR. In contrast, elevated serum HDL levels (OR: 0.01, 95% CI: 0.00–0.20, p = 0.002) exhibited a protective effect. ROC curve analysis was further employed to evaluate the predictive accuracy of the multivariable model (Fig. 4 ), yielding an AUC of 0.84, which indicates excellent discrimination between ISR and non-ISR. At the optimal cutoff point, the model demonstrated a sensitivity of 92% and a specificity of 75%, underscoring its robustness in predicting ISR risk. Figure 5 presents the logistic regression fit curve depicting the relationship between serum HDL levels and the probability of ISR occurrence. The results revealed a significant negative correlation between HDL levels and ISR occurrence; as HDL levels increased, the risk of ISR significantly decreased, emphasizing HDL’s protective role and further supporting that HDL may be an independent protective factor against ISR. 3.4 Heatmap The heatmap (Fig. 6 ) visually illustrates the strength and direction of correlations between ISR and various clinical variables. In the heatmap, the rows represent demographic characteristics, clinical features, and laboratory indices, while the columns represent ISR status. The color gradient ranges from dark red (strong positive correlation) to dark blue (strong negative correlation). Analysis revealed a significant negative correlation between serum HDL levels and ISR (color-coded dark blue, correlation coefficient r = -0.62, p < 0.001). The protective effect of HDL remained robust after multivariable adjustment (adjusted OR: 0.01, 95% CI: 0.00–0.20, p = 0.002), underscoring HDL’s pivotal role as an independent protective factor against ISR. In contrast, smoking (r = + 0.45, p = 0.007), hypertension (r = + 0.38, p = 0.042), and elevated serum globulin levels (r = + 0.41, p = 0.008) showed significant positive correlations with ISR (color-coded red), further confirming their roles as independent risk factors. Notably, the strength of the negative correlation between HDL and ISR was markedly greater than that of other variables, highlighting HDL’s distinct biological value in predicting ISR risk. In summary, the heatmap provides multidimensional evidence supporting the pivotal role of serum HDL levels in ISR prevention, offering visual evidence to guide targeted interventions. 4 Discussion This study identified serum HDL levels as an independent protective factor against ISR. HDL levels in patients with ISR were significantly lower than those without restenosis, with a statistically significant difference (p < 0.05). This finding suggests that HDL may play a crucial protective role in the pathophysiological process of ISR. As the primary carrier for reverse cholesterol transport, HDL’s anti-atherosclerotic properties are well established; however, its specific mechanism in ISR requires further investigation. Most studies [10] suggest that intimal hyperplasia is one of the central mechanisms underlying ISR, highlighting the crucial role of endothelial protection in the prevention and treatment of cardiovascular and cerebrovascular diseases. The functional integrity of endothelial cells is essential for maintaining vascular wall stability. Cytokines secreted by endothelial cells precisely regulate the phenotypic transition of smooth muscle cells, thereby influencing pathological processes such as cell proliferation, migration, and extracellular matrix remodeling. As a major component of human lipoproteins, HDL plays a significant role in maintaining the normal physiological function of endothelial cells [11] . Previous study [12] has shown that HDL can significantly reverse oxidized low-density lipoprotein (ox-LDL)-induced endothelial-dependent arterial relaxation dysfunction. This effect is primarily achieved by HDL removing lysophosphatidylcholine (LPC) from ox-LDL and preventing its accumulation in endothelial cells. Experimental results indicate that HDL not only prevents LPC-induced endothelial dysfunction but also restores normal endothelial cell function by promoting LPC release following LPC exposure. Therefore, this protective mechanism of HDL not only reveals its potential role in the pathophysiological process of atherosclerosis but also underscores its significance in maintaining endothelial integrity. Stent implantation damages the vascular endothelium, triggering inflammatory cell infiltration, platelet aggregation, growth factor release, and the accumulation of smooth muscle cells and extracellular matrix, which gradually form plaques and increase the risk of ISR. [13] Therefore, restoring endothelial integrity is crucial. Seetharam et al. [14] revealed that HDL promotes endothelial cell migration and re-endothelialization through the scavenger receptor B type I (SR-BI)-mediated signaling pathway, underscoring the critical roles of HDL and SR-BI in maintaining endothelial integrity and preventing vascular diseases. Tmmagaki et al. [15] found that HDL induces a biphasic change in intracellular pH in endothelial cells — a transient acidification followed by rapid alkalization — which promotes cell proliferation and the repair of damaged endothelial cells, thereby exerting anti-atherosclerotic effects. This effect is dependent on extracellular calcium ions and the sodium-hydrogen exchange mechanism. In contrast, LDL induces intracellular acidification and inhibits cell proliferation. HDL also plays a role in inhibiting vascular intimal hyperplasia. Study has shown [16] that HDL can significantly inhibit the expression of chemokines CCL2, CCL5, and CX3CL1 in vascular smooth muscle cells (SMCs). These chemokines have been confirmed to directly promote the proliferation of vascular smooth muscle cells. Additionally, HDL inhibits the expression of chemokine receptors CCR2 and CX3CR1. By reducing the expression of chemokines and their receptors, HDL weakens their interactions, thereby inhibiting SMC proliferation signaling pathways. Moreover, HDL activates the Akt signaling pathway through its carried sphingosylphosphorylcholine and sphingosine sulfate, inhibiting the mitochondrial apoptosis pathway and thereby protecting endothelial cells from apoptosis [17] . Post-stent implantation pharmacological management also plays a significant role in ISR occurrence. Regular use of antiplatelet agents after the procedure is one of the key strategies for preventing ISR. HDL can inhibit platelet activation through multiple mechanisms. Chen et al. [18] found that HDL can inhibit platelet aggregation by enhancing nitric oxide synthase activity in platelets. Nofer et al. [19] demonstrated that HDL, at physiological concentrations, can significantly inhibit thrombin-induced platelet aggregation and fibrinogen binding by modulating the phosphoinositide signaling pathway and activating PKC. In conclusion, this study, along with numerous previous reports, suggests that HDL plays a crucial role in the development and progression of ISR. HDL exerts protective effects through various mechanisms, including endothelial cell protection, promotion of re-endothelialization, inhibition of intimal hyperplasia, and suppression of platelet activation.Although current evidence provides some understanding of HDL's role in ISR, its precise mechanisms require further investigation. Uncovering these pathways may offer novel insights and potential therapeutic targets for ISR prevention and management, ultimately improving patient outcomes. Future research should focus on elucidating the intricate molecular signaling pathways involving HDL and exploring the impact of clinical interventions on HDL function, with the aim of gaining a more comprehensive understanding of HDL’s protective role in ISR. Limit This study has several limitations that warrant consideration. First, the single-center retrospective design inherently restricts the generalizability of the findings, as patient demographics, procedural protocols, and postoperative management may vary across institutions. Second, the relatively small sample size, particularly the imbalanced distribution between the ISR (n = 96) and non-ISR (n = 36) groups, may reduce statistical power and increase the risk of type II errors in multivariate analyses. Third, the exclusion of 31 patients due to incomplete data or predefined criteria introduces potential selection bias, which could compromise the external validity of the results. Fourth, the retrospective nature of data collection limits the ability to account for unmeasured confounders, such as genetic predispositions, lifestyle factors, or temporal variations in laboratory parameters (e.g., single baseline HDL measurements may not reflect dynamic changes post-stent implantation). Finally, while the study adjusted for multiple clinical variables, residual confounding from unrecorded factors cannot be entirely ruled out. Future multi-center prospective studies with larger cohorts, extended follow-up periods, and serial biomarker assessments are warranted to validate these findings and elucidate causal relationships. Declarations Acknowledgements Not applicable. Authors’ contributions YT contributed to the study design, data collection, statistical analysis, and manuscript preparation. LD ,FS and ZL were involved in the data collection. MC contributed to the study concept and design, as well as manuscript preparation and review. All authors contributed to the article and approved the submitted version. Funding This study was supported by the National Clinical Key Specialty Construction Project of the National Health Commission of China and Wenzhou Panvascular Disease Management Center, Wenzhou Central Hospital. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Ethics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration. All experimental protocols were approved by the Human Ethics Committee of Wenzhou Central Hospital. All methods were carried out in accordance with relevant guidelines and regulations in the ethical approval and consent to participate. 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Suppression of Endothelial Cell Apoptosis by High Density Lipoproteins (HDL) and HDL-associated Lysosphingolipids[J]. J Biol Chem, 2001, 276(37):34480-34485. DOI: 10.1074/jbc.M103782200. Chen LY, Mehta JL. Inhibitory effect of high-density lipoprotein on platelet function is mediated by increase in nitric oxide synthase activity in platelets[J]. Life Sci, 1994, 55(23):1815-1821. DOI: 10.1016/0024-3205(94)90092-2. Nofer JR, Walter M, Kehrel B, et al. HDL 3 -Mediated Inhibition of Thrombin-Induced Platelet Aggregation and Fibrinogen Binding Occurs via Decreased Production of Phosphoinositide-Derived Second Messengers 1,2-Diacylglycerol and Inositol 1,4,5-tris-Phosphate[J]. Arterioscler Thromb Vasc Biol, 1998, 18(6):861-869. DOI: 10.1161/01.ATV.18.6.861. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor invited by journal 23 May, 2025 Editor assigned by journal 24 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 21 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6277872","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471738791,"identity":"e78af34c-cde9-44de-a475-1e0a2cdb052f","order_by":0,"name":"Yuting Tang","email":"","orcid":"","institution":"Wenzhou Central Hospital Affiliated Dingli Clinical Institute of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Tang","suffix":""},{"id":471738792,"identity":"081bf0ec-7408-4333-97ba-9021ac69bd97","order_by":1,"name":"Lingjia Dong","email":"","orcid":"","institution":"Wenzhou Central Hospital Affiliated Dingli Clinical Institute of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingjia","middleName":"","lastName":"Dong","suffix":""},{"id":471738793,"identity":"cffe1b33-61dc-44c8-828e-0ed3d650584d","order_by":2,"name":"Feifan Sun","email":"","orcid":"","institution":"Wenzhou Central Hospital Affiliated Dingli Clinical Institute of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feifan","middleName":"","lastName":"Sun","suffix":""},{"id":471738794,"identity":"d78af089-691d-4b5d-8962-a44114aa18ba","order_by":3,"name":"Zisheng Liu","email":"","orcid":"","institution":"Wenzhou Central Hospital Affiliated Dingli Clinical Institute of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zisheng","middleName":"","lastName":"Liu","suffix":""},{"id":471738795,"identity":"b07a3360-1b19-4199-9296-8af4776095c8","order_by":4,"name":"Maohua Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYFACHoYDHyok5PhJ0cJ4cMYZC2PJBhK0MB/mbatI3EC0Fv5pZw8cnDlPgnEDA/PDRzeI0SJxOy/hwMdtEszmDGzGxjnEaDGQzjE4OHObBJtlAw+bNNFaDvPOkeAxOECalgYJCeK1SNwGOmzGMQkDyWZi/cI/O8f4w4eauvp+9uaHj4nSggDMpCkfBaNgFIyCUYAPAAC3yi+wP2K/agAAAABJRU5ErkJggg==","orcid":"","institution":"Wenzhou Central Hospital Affiliated Dingli Clinical Institute of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Maohua","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-03-21 13:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6277872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6277872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84919102,"identity":"d45ef35b-28a5-416f-9768-68a549b52ea7","added_by":"auto","created_at":"2025-06-18 19:31:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92959,"visible":true,"origin":"","legend":"\u003cp\u003eThis retrospective cohort study included 132 patients who underwent carotid angiography 6 months (±1 month) after carotid artery stenting between March 1, 2019, and January 1, 2024, out of an initial 163 patients. After applying the inclusion and exclusion criteria, a total of 132 patients were included, with 96 patients assigned to the ISR group and 36 patients assigned to the non-ISR group. The analytical workflow of this study included univariate analysis, multivariate analysis, and model building to identify risk factors and predictors of ISR.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/f39002e148293a7294836b3c.png"},{"id":84919105,"identity":"7701e160-6998-4aa1-b776-3fbc63f08f00","added_by":"auto","created_at":"2025-06-18 19:31:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38474,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart of demographic and clinical characteristics shows the distribution of gender, smoking history, and hypertension (HTN) history between the ISR and non-ISR groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/b161b0343c57d4f9dcda4c85.png"},{"id":84919108,"identity":"e220f7b0-6b5f-4cdd-91d7-0b8329867179","added_by":"auto","created_at":"2025-06-18 19:31:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54345,"visible":true,"origin":"","legend":"\u003cp\u003eThis figure compares serum HDL levels, serum globulin levels, and BMI between the ISR and non-ISR groups. Significant differences in serum HDL levels, serum globulin levels, and BMI were found (p \u0026lt; 0.05), highlighting their potential roles in ISR.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/7cf675a7ce765a0ef9f72410.png"},{"id":84919109,"identity":"31550e86-777c-41d0-b370-32768c2cdfde","added_by":"auto","created_at":"2025-06-18 19:31:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45468,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the multivariate model\u003c/p\u003e\n\u003cp\u003eThe ROC curve shows an AUC of 0.84, indicating excellent discrimination of ISR risk by the multivariate logistic regression model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/4aab2ef2502cdff1bdbb186d.png"},{"id":84920741,"identity":"5da99a7b-7454-4de3-b9d0-9744bc2a8a6c","added_by":"auto","created_at":"2025-06-18 19:39:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68365,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of Serum HDL levels vs. ISR occurrence with Logistic Regression Curve.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/65202917b84e708f2602b000.png"},{"id":84920749,"identity":"d3cced56-ea3f-44c3-b893-af8607e2202b","added_by":"auto","created_at":"2025-06-18 19:39:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":155595,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of Correlation between ISR and Clinical Variables.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/e3701dcd941b5dda51a0e35c.png"},{"id":84921260,"identity":"81db0be7-bb59-4129-bc0a-40d2d56ba232","added_by":"auto","created_at":"2025-06-18 19:47:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1143991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6277872/v1/33421fc2-df07-4247-a4e5-1293221b2b0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum High-Density Lipoprotein Levels and Risk of In-Stent Restenosis Following Carotid Artery Stenting: A Multivariate Analysis-Based Retrospective Cohort Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCarotid artery stenosis is one of the major causes of ischemic stroke\u003csup\u003e[1]\u003c/sup\u003e. The current surgical treatments for carotid artery stenosis primarily include carotid endarterectomy (CEA) and carotid artery stenting (CAS). However, the incidence of in-stent restenosis (ISR) after CAS is higher than that after CEA\u003csup\u003e[2]\u003c/sup\u003e. Currently, ISR is often defined as a vascular stenosis rate\u0026thinsp;\u0026gt;\u0026thinsp;50% within 5 mm of the stent or at both ends of the stent\u003csup\u003e[3]\u003c/sup\u003e. ISR increases the risk of recurrent stroke\u003csup\u003e[4]\u003c/sup\u003e, thereby significantly affecting surgical outcomes and the quality of life of patients. Therefore, early detection and prevention of ISR are of significant practical importance. Multiple studies have indicated that factors such as female sex, advanced age, smoking, diabetes, hypertension, serum albumin levels, dyslipidemia, and cardiovascular diseases are risk factors for ISR following CAS \u003csup\u003e[5\u0026ndash;8]\u003c/sup\u003e. Epidemiological research has shown that in patients with acute coronary syndrome and low HDL levels, the risk of death and adverse cardiac events increases during both short-term and long-term follow-up after drug-eluting stent implantation\u003csup\u003e[9]\u003c/sup\u003e. The objective of this study is to investigate whether serum HDL levels are associated with ISR and to explore their significance.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective analysis was performed on 163 patients who underwent their first CAS in the Department of Neurosurgery at Wenzhou Central Hospital from March 2019 to January 2024 and received a follow-up carotid and cerebrovascular angiography at 6 months (\u0026plusmn;\u0026thinsp;1 month). Inclusion criteria were: 1) The first diagnosis of moderate-to-severe internal carotid artery stenosis (stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50% according to NASCET criteria) confirmed by digital subtraction angiography (DSA); 2) Successful CAS with complete coverage of the lesion by the stent and a post-procedural residual stenosis of \u0026lt;\u0026thinsp;30%; 3) Completion of a DSA re-examination within 6 months (\u0026plusmn;\u0026thinsp;1 month) after the procedure, with confirmed in-stent restenosis (ISR, stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50%); 4) Regular dual antiplatelet therapy following stent implantation, along with the use of statins and other agents for secondary prevention; 5) Complete clinical, imaging, and laboratory data. Exclusion criteria encompassed: 1) Chronic total arterial occlusion or developmental abnormalities; 2) Concomitant moderate-to-severe stenosis or occlusion of other intracranial/extracranial large arteries; 3) Acute/chronic inflammation or wasting diseases at the time of admission; 4) Severe liver or renal dysfunction; 5) A history of or current malignancy or autoimmune disease. The final analysis included only patients meeting these criteria to ensure data integrity and reliability. This study was conducted in accordance with the Declaration of Helsinki and received ethical approval from the institutional review board, which waived the requirement for informed consent due to its retrospective nature. A total of 132 patients were ultimately included in this study, with 96 patients in the ISR group and 36 patients in the non-ISR group, after excluding 31 cases(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Intervention\u003c/h2\u003e \u003cp\u003ePatients received oral aspirin (100 mg/day) and clopidogrel (75 mg/day) 3 days prior to surgery. Carotid artery stenting was performed by neurosurgical experts specializing in interventional procedures. After surgery, both groups were administered long-term aspirin (100 mg/day) and a three-month course of clopidogrel (75 mg/day).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cp\u003eData were extracted from electronic medical records and encompassed a comprehensive array of variables, including gender, age, body mass index (BMI), smoking status, alcohol consumption, history of hypertension (HTN History), history of diabetes mellitus (DM History), history of coronary heart disease (CHD History), white blood cell count (WBC), red blood cell count (RBC), lymphocyte count (LY), neutrophil count (NEUT), platelet count (PLT), serum albumin level (Serum ALB), serum globulin level (Serum GLOB), serum triglyceride level (Serum TG), serum high-density lipoprotein level (Serum HDL), serum low-density lipoprotein level (Serum LDL), LDL/HDL, serum homocysteine level (Serum HCY).Demographic variables included age, sex, BMI, smoking, and alcohol consumption, recorded as categorical variables. Clinical characteristics encompassed comorbidities such as hypertension, diabetes mellitus, and coronary heart disease. Laboratory parameters included hematological indicators (WBC, RBC, LY, NEUT, PLT), and biochemical markers (Serum ALB, Serum GLOB, Serum TG, Serum HDL, Serum LDL, LDL/HDL, Serum HCY). All measurements were performed using standardized and calibrated equipment to ensure accuracy and consistency.In order to capture each patient\u0026rsquo;s baseline status, all laboratory and urinalysis parameters were obtained from the first blood and urine samples collected upon admission and prior to any endovascular or surgical intervention, thereby minimizing the potential influence of subsequent therapeutic measures on these values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Follow-up Angiography and Evaluation for ISR\u003c/h2\u003e \u003cp\u003eAll patients included in the analysis underwent follow-up angiography at 6 months (\u0026plusmn;\u0026thinsp;1 month) after successful CAS, with the angiographic results interpreted by at least two experienced clinicians. Based on the follow-up angiography results, the patients were divided into the non-ISR group or the ISR group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were employed to summarize the demographic, clinical and laboratory data, with continuous variables presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or medians with interquartile ranges, and categorical variables as frequencies and percentages. Comparative analyses between the ISR and non-ISR groups utilized t-tests or Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables. Univariate logistic regression was conducted to identify potential predictors of ISR, with variables showing a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 being selected for multivariate analysis. Multivariate logistic regression, adjusted for confounders, was used to determine independent predictors of ISR, presenting adjusted odds ratios (AOR) with 95% confidence intervals (CI). The model\u0026rsquo;s predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, calculating the area under the curve (AUC) to assess discrimination. All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 27.0, R software version 4.1.3, and Python software version 3.9.7, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\n \u003cp\u003eThe baseline characteristics of the 132 patients, including 96 with ISR and 32 with non-ISR, are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e: Differences in gender, smoking history, and hypertension history were observed between the ISR and non-ISR groups. The proportion of males in the ISR group was significantly higher than in the non-ISR group (84.38% vs. 66.67%, p\u0026thinsp;=\u0026thinsp;0.025), and the proportions of patients with a history of smoking (75.00% vs. 50.00%, p\u0026thinsp;=\u0026thinsp;0.006) and hypertension (84.38% vs. 66.67%, p\u0026thinsp;=\u0026thinsp;0.025) were also significantly higher, further supporting these factors as predictors of ISR risk. Box plot analysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) clearly demonstrated that serum HDL levels in the ISR group were significantly lower than in the non-ISR group (0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 vs. 1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 mmol/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas serum globulin levels (29.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10 vs. 25.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07 mmol/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BMI (23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46 vs. 21.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.037) were significantly higher in the ISR group. These differences suggest that serum HDL may play a protective role against ISR, while elevated serum globulin levels and BMI could be potential risk factors for ISR. With respect to platelet count, the ISR group also exhibited higher levels (238.34\u0026thinsp;\u0026plusmn;\u0026thinsp;86.15 vs. 213.67\u0026thinsp;\u0026plusmn;\u0026thinsp;51.19, p\u0026thinsp;=\u0026thinsp;0.047). No statistically significant differences were observed between the two groups in terms of age, diabetes prevalence, white blood cell count, neutrophil count, or LDL/HDL ratio (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or frequency (percentage). Continuous variables were compared using the t-test, and categorical variables were compared using the chi-square test.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Baseline Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-ISR(n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eISR(n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.38\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (84.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (34.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHTN History, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (84.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDM History, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHD History, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (21.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEUT(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213.67\u0026thinsp;\u0026plusmn;\u0026thinsp;51.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238.34\u0026thinsp;\u0026plusmn;\u0026thinsp;86.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum ALB(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum GLOB(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum TG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum HDL(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum LDL(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL/HDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum HCY(\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Univariate Analysis\u003c/h2\u003e\n \u003cp\u003eUnivariate logistic regression identified several predictors of intracranial aneurysm rupture, as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Among these, male sex (OR: 2.70, 95% CI: 1.11\u0026ndash;6.54, p\u0026thinsp;=\u0026thinsp;0.028), smoking (OR: 3.00, 95% CI: 1.35\u0026ndash;6.68, p\u0026thinsp;=\u0026thinsp;0.007), hypertension (OR: 2.70, 95% CI: 1.11\u0026ndash;6.54, p\u0026thinsp;=\u0026thinsp;0.028), and an elevated BMI (OR: 1.19, 95% CI: 1.03\u0026ndash;1.38, p\u0026thinsp;=\u0026thinsp;0.017) were significantly associated with the risk of ISR. Increased serum globulin levels (OR: 1.20, 95% CI: 1.09\u0026ndash;1.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) raised the likelihood of ISR, whereas higher serum HDL levels (OR: 0.01, 95% CI: 0.00\u0026ndash;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were protective against ISR. Furthermore, variables such as age, alcohol consumption, diabetes, coronary artery disease, red blood cell count, white blood cell count, neutrophil count, lymphocyte count, platelet count, serum LDL levels, serum albumin levels, serum triglyceride levels, and serum homocysteine levels did not exhibit statistically significant associations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis for ISR Risk Univariate and Multivariate Logistic Regression Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP_value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP_value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.98\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale vs. Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70 (1.11\u0026thinsp;~\u0026thinsp;6.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 (1.35\u0026thinsp;~\u0026thinsp;6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84(1.44\u0026thinsp;~\u0026thinsp;10.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (0.99\u0026thinsp;~\u0026thinsp;6.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHTN History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70 (1.11\u0026thinsp;~\u0026thinsp;6.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35 (1.04\u0026thinsp;~\u0026thinsp;10.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDM History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.46\u0026thinsp;~\u0026thinsp;2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHD History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.24\u0026thinsp;~\u0026thinsp;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19 (1.03\u0026thinsp;~\u0026thinsp;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.87\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (0.19\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (0.68\u0026thinsp;~\u0026thinsp;2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.82\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (1.00\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum ALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.79\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum GLOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20 (1.09\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (1.04\u0026thinsp;~\u0026thinsp;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.56\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum HDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0.00\u0026thinsp;~\u0026thinsp;0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0.00\u0026thinsp;~\u0026thinsp;0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum LDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68 (0.40\u0026thinsp;~\u0026thinsp;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL/HDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 (0.90\u0026thinsp;~\u0026thinsp;2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum HCY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.96\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Multivariate Analysis, ROC Curve Analysis, and Logistic Regression Scatter Plot\u003c/h2\u003e\n \u003cp\u003eMultivariate logistic regression was performed to identify independent predictors of intracranial aneurysm rupture, adjusting for potential confounders, as summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Smoking (OR: 3.84, 95% CI: 1.44\u0026ndash;10.25, p\u0026thinsp;=\u0026thinsp;0.007), hypertension (OR: 3.35, 95% CI: 1.04\u0026ndash;10.73, p\u0026thinsp;=\u0026thinsp;0.042), and increased serum globulin levels (OR: 1.16, 95% CI: 1.04\u0026ndash;1.29, p\u0026thinsp;=\u0026thinsp;0.008) were closely associated with the risk of ISR. In contrast, elevated serum HDL levels (OR: 0.01, 95% CI: 0.00\u0026ndash;0.20, p\u0026thinsp;=\u0026thinsp;0.002) exhibited a protective effect. ROC curve analysis was further employed to evaluate the predictive accuracy of the multivariable model (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), yielding an AUC of 0.84, which indicates excellent discrimination between ISR and non-ISR. At the optimal cutoff point, the model demonstrated a sensitivity of 92% and a specificity of 75%, underscoring its robustness in predicting ISR risk. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the logistic regression fit curve depicting the relationship between serum HDL levels and the probability of ISR occurrence. The results revealed a significant negative correlation between HDL levels and ISR occurrence; as HDL levels increased, the risk of ISR significantly decreased, emphasizing HDL\u0026rsquo;s protective role and further supporting that HDL may be an independent protective factor against ISR.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Heatmap\u003c/h2\u003e\n \u003cp\u003eThe heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) visually illustrates the strength and direction of correlations between ISR and various clinical variables. In the heatmap, the rows represent demographic characteristics, clinical features, and laboratory indices, while the columns represent ISR status. The color gradient ranges from dark red (strong positive correlation) to dark blue (strong negative correlation). Analysis revealed a significant negative correlation between serum HDL levels and ISR (color-coded dark blue, correlation coefficient r = -0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The protective effect of HDL remained robust after multivariable adjustment (adjusted OR: 0.01, 95% CI: 0.00\u0026ndash;0.20, p\u0026thinsp;=\u0026thinsp;0.002), underscoring HDL\u0026rsquo;s pivotal role as an independent protective factor against ISR. In contrast, smoking (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.45, p\u0026thinsp;=\u0026thinsp;0.007), hypertension (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.042), and elevated serum globulin levels (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.008) showed significant positive correlations with ISR (color-coded red), further confirming their roles as independent risk factors. Notably, the strength of the negative correlation between HDL and ISR was markedly greater than that of other variables, highlighting HDL\u0026rsquo;s distinct biological value in predicting ISR risk. In summary, the heatmap provides multidimensional evidence supporting the pivotal role of serum HDL levels in ISR prevention, offering visual evidence to guide targeted interventions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study identified serum HDL levels as an independent protective factor against ISR. HDL levels in patients with ISR were significantly lower than those without restenosis, with a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This finding suggests that HDL may play a crucial protective role in the pathophysiological process of ISR. As the primary carrier for reverse cholesterol transport, HDL\u0026rsquo;s anti-atherosclerotic properties are well established; however, its specific mechanism in ISR requires further investigation.\u003c/p\u003e \u003cp\u003eMost studies\u003csup\u003e[10]\u003c/sup\u003e suggest that intimal hyperplasia is one of the central mechanisms underlying ISR, highlighting the crucial role of endothelial protection in the prevention and treatment of cardiovascular and cerebrovascular diseases. The functional integrity of endothelial cells is essential for maintaining vascular wall stability. Cytokines secreted by endothelial cells precisely regulate the phenotypic transition of smooth muscle cells, thereby influencing pathological processes such as cell proliferation, migration, and extracellular matrix remodeling. As a major component of human lipoproteins, HDL plays a significant role in maintaining the normal physiological function of endothelial cells \u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious study\u003csup\u003e[12]\u003c/sup\u003e has shown that HDL can significantly reverse oxidized low-density lipoprotein (ox-LDL)-induced endothelial-dependent arterial relaxation dysfunction. This effect is primarily achieved by HDL removing lysophosphatidylcholine (LPC) from ox-LDL and preventing its accumulation in endothelial cells. Experimental results indicate that HDL not only prevents LPC-induced endothelial dysfunction but also restores normal endothelial cell function by promoting LPC release following LPC exposure. Therefore, this protective mechanism of HDL not only reveals its potential role in the pathophysiological process of atherosclerosis but also underscores its significance in maintaining endothelial integrity.\u003c/p\u003e \u003cp\u003eStent implantation damages the vascular endothelium, triggering inflammatory cell infiltration, platelet aggregation, growth factor release, and the accumulation of smooth muscle cells and extracellular matrix, which gradually form plaques and increase the risk of ISR.\u003csup\u003e[13]\u003c/sup\u003e Therefore, restoring endothelial integrity is crucial. Seetharam et al.\u003csup\u003e[14]\u003c/sup\u003e revealed that HDL promotes endothelial cell migration and re-endothelialization through the scavenger receptor B type I (SR-BI)-mediated signaling pathway, underscoring the critical roles of HDL and SR-BI in maintaining endothelial integrity and preventing vascular diseases. Tmmagaki et al.\u003csup\u003e[15]\u003c/sup\u003e found that HDL induces a biphasic change in intracellular pH in endothelial cells \u0026mdash; a transient acidification followed by rapid alkalization \u0026mdash; which promotes cell proliferation and the repair of damaged endothelial cells, thereby exerting anti-atherosclerotic effects. This effect is dependent on extracellular calcium ions and the sodium-hydrogen exchange mechanism. In contrast, LDL induces intracellular acidification and inhibits cell proliferation.\u003c/p\u003e \u003cp\u003eHDL also plays a role in inhibiting vascular intimal hyperplasia. Study has shown \u003csup\u003e[16]\u003c/sup\u003e that HDL can significantly inhibit the expression of chemokines CCL2, CCL5, and CX3CL1 in vascular smooth muscle cells (SMCs). These chemokines have been confirmed to directly promote the proliferation of vascular smooth muscle cells. Additionally, HDL inhibits the expression of chemokine receptors CCR2 and CX3CR1. By reducing the expression of chemokines and their receptors, HDL weakens their interactions, thereby inhibiting SMC proliferation signaling pathways. Moreover, HDL activates the Akt signaling pathway through its carried sphingosylphosphorylcholine and sphingosine sulfate, inhibiting the mitochondrial apoptosis pathway and thereby protecting endothelial cells from apoptosis \u003csup\u003e[17]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePost-stent implantation pharmacological management also plays a significant role in ISR occurrence. Regular use of antiplatelet agents after the procedure is one of the key strategies for preventing ISR. HDL can inhibit platelet activation through multiple mechanisms. Chen et al.\u003csup\u003e[18]\u003c/sup\u003e found that HDL can inhibit platelet aggregation by enhancing nitric oxide synthase activity in platelets. Nofer et al.\u003csup\u003e[19]\u003c/sup\u003e demonstrated that HDL, at physiological concentrations, can significantly inhibit thrombin-induced platelet aggregation and fibrinogen binding by modulating the phosphoinositide signaling pathway and activating PKC.\u003c/p\u003e \u003cp\u003eIn conclusion, this study, along with numerous previous reports, suggests that HDL plays a crucial role in the development and progression of ISR. HDL exerts protective effects through various mechanisms, including endothelial cell protection, promotion of re-endothelialization, inhibition of intimal hyperplasia, and suppression of platelet activation.Although current evidence provides some understanding of HDL's role in ISR, its precise mechanisms require further investigation. Uncovering these pathways may offer novel insights and potential therapeutic targets for ISR prevention and management, ultimately improving patient outcomes. Future research should focus on elucidating the intricate molecular signaling pathways involving HDL and exploring the impact of clinical interventions on HDL function, with the aim of gaining a more comprehensive understanding of HDL\u0026rsquo;s protective role in ISR.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study has several limitations that warrant consideration. First, the single-center retrospective design inherently restricts the generalizability of the findings, as patient demographics, procedural protocols, and postoperative management may vary across institutions. Second, the relatively small sample size, particularly the imbalanced distribution between the ISR (n\u0026thinsp;=\u0026thinsp;96) and non-ISR (n\u0026thinsp;=\u0026thinsp;36) groups, may reduce statistical power and increase the risk of type II errors in multivariate analyses. Third, the exclusion of 31 patients due to incomplete data or predefined criteria introduces potential selection bias, which could compromise the external validity of the results. Fourth, the retrospective nature of data collection limits the ability to account for unmeasured confounders, such as genetic predispositions, lifestyle factors, or temporal variations in laboratory parameters (e.g., single baseline HDL measurements may not reflect dynamic changes post-stent implantation). Finally, while the study adjusted for multiple clinical variables, residual confounding from unrecorded factors cannot be entirely ruled out. Future multi-center prospective studies with larger cohorts, extended follow-up periods, and serial biomarker assessments are warranted to validate these findings and elucidate causal relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYT contributed to the study design, data collection, statistical analysis, and manuscript preparation. LD ,FS and ZL were involved in the data collection. MC contributed to the study concept and design, as well as manuscript preparation and review. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Clinical Key Specialty Construction Project of the National Health Commission of China and Wenzhou Panvascular Disease Management Center, Wenzhou Central Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration. All experimental protocols were approved by the Human Ethics Committee of Wenzhou Central Hospital. All methods were carried out in accordance with relevant guidelines and regulations in the ethical approval and consent to participate. Written informed consent is waived by Human Ethics Committee of Wenzhou Central Hospital considering the retrospective nature of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHassani S, Fisher M. Management of Atherosclerotic Carotid Artery Disease: A Brief Overview and Update[J]. Am J Med, 2022, 135(4):430-434. DOI: 10.1016/j.amjmed.2021.09.027.\u003c/li\u003e\n \u003cli\u003eArquizan C, Trinquart L, Touboul PJ, et al. Restenosis Is More Frequent After Carotid Stenting Than After Endarterectomy: The EVA-3S Study[J]. Stroke, 2011, 42(4):1015-1020. DOI: 10.1161/STROKEAHA.110.589309.\u003c/li\u003e\n \u003cli\u003eCai X, Chen X, Xiang Y, et al. Balloon-Assisted Angioplasty for the Treatment of In-Stent Restenosis After Vertebral Artery Ostium Stenting: Experiences From One Single Center[J]. The Neurologist, 2022, 27(3):106-110. DOI: 10.1097/NRL.0000000000000383.\u003c/li\u003e\n \u003cli\u003eBonati LH, Gregson J, Dobson J, et al. Restenosis and risk of stroke after stenting or endarterectomy for symptomatic carotid stenosis in the International Carotid Stenting Study (ICSS): secondary analysis of a randomised trial[J]. Lancet Neurol, 2018, 17(7):587-596. DOI: 10.1016/S1474-4422(18)30195-9.\u003c/li\u003e\n \u003cli\u003eSong P, Fang Z, Wang H, et al. Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: a systematic review, meta-analysis, and modelling study[J]. Lancet Glob Health, 2020, 8(5):e721-e729. DOI: 10.1016/S2214-109X(20)30117-0.\u003c/li\u003e\n \u003cli\u003eLal BK, Beach KW, Roubin GS, et al. Restenosis after carotid artery stenting and endarterectomy: a secondary analysis of CREST, a randomised controlled trial[J]. Lancet Neurol, 2012, 11(9):755-763. DOI: 10.1016/S1474-4422(12)70159-X.\u003c/li\u003e\n \u003cli\u003eJakubiak GK, Pawlas N, Cieślar G, et al. Pathogenesis and Clinical Significance of In-Stent Restenosis in Patients with Diabetes[J]. Int J Environ Res Public Health, 2021, 18(22):11970. DOI: 10.3390/ijerph182211970.\u003c/li\u003e\n \u003cli\u003eLin X, Ke F, Chen M. Association of albumin levels with the risk of intracranial atherosclerosis[J]. BMC Neurol, 2023, 23(1):198. DOI: 10.1186/s12883-023-03234-2.\u003c/li\u003e\n \u003cli\u003eWolfram RM, Brewer HB, Xue Z, et al. Impact of Low High-Density Lipoproteins on In-Hospital Events and One-Year Clinical Outcomes in Patients With Non\u0026ndash;ST-Elevation Myocardial Infarction Acute Coronary Syndrome Treated With Drug-Eluting Stent Implantation[J]. Am J Cardiol, 2006, 98(6):711-717. DOI: 10.1016/j.amjcard.2006.04.006.\u003c/li\u003e\n \u003cli\u003eDai Z, Xu G. Restenosis after carotid artery stenting[J]. Vascular, 2017, 25(6):576-586. DOI: 10.1177/1708538117706273.\u003c/li\u003e\n \u003cli\u003eLi XP, Zhao SP, Zhang XY, et al. Protective effect of high density lipoprotein on endothelium-dependent vasodilatation[J]. Int J Cardiol, 2000, 73(3):231-236. DOI: 10.1016/S0167-5273(00)00221-7.\u003c/li\u003e\n \u003cli\u003eMatsuda Y, Hirata K, Inoue N, et al. High density lipoprotein reverses inhibitory effect of oxidized low density lipoprotein on endothelium-dependent arterial relaxation.[J]. Circ Res, 1993, 72(5):1103-1109. DOI: 10.1161/01.RES.72.5.1103.\u003c/li\u003e\n \u003cli\u003eOsherov AB, Gotha L, Cheema AN, et al. Proteins mediating collagen biosynthesis and accumulation in arterial repair: novel targets for anti-restenosis therapy[J]. Cardiovasc Res, 2011, 91(1):16-26. DOI: 10.1093/cvr/cvr012.\u003c/li\u003e\n \u003cli\u003eSeetharam D, Mineo C, Gormley AK, et al. High-Density Lipoprotein Promotes Endothelial Cell Migration and Reendothelialization via Scavenger Receptor-B Type I[J]. Circ Res, 2006, 98(1):63-72. DOI: 10.1161/01.RES.0000199272.59432.5b.\u003c/li\u003e\n \u003cli\u003eTamagaki T, Sawada S, Imamura H, et al. Effects of high-density lipoproteins on intracellular pH and proliferation of human vascular endothelial cells[J]. Atherosclerosis, 1996, 123(1-2):73-82. DOI: 10.1016/0021-9150(95)05774-9.\u003c/li\u003e\n \u003cli\u003eVorst EPC, Vanags LZ, Dunn LL, et al. High‐density lipoproteins suppress chemokine expression and proliferation in human vascular smooth muscle cells[J]. FASEB J, 2013, 27(4):1413-1425. DOI: 10.1096/fj.12-212753.\u003c/li\u003e\n \u003cli\u003eNofer JR, Levkau B, Wolinska I, et al. Suppression of Endothelial Cell Apoptosis by High Density Lipoproteins (HDL) and HDL-associated Lysosphingolipids[J]. J Biol Chem, 2001, 276(37):34480-34485. DOI: 10.1074/jbc.M103782200.\u003c/li\u003e\n \u003cli\u003eChen LY, Mehta JL. Inhibitory effect of high-density lipoprotein on platelet function is mediated by increase in nitric oxide synthase activity in platelets[J]. Life Sci, 1994, 55(23):1815-1821. DOI: 10.1016/0024-3205(94)90092-2.\u003c/li\u003e\n \u003cli\u003eNofer JR, Walter M, Kehrel B, et al. HDL\u003csub\u003e3\u003c/sub\u003e -Mediated Inhibition of Thrombin-Induced Platelet Aggregation and Fibrinogen Binding Occurs via Decreased Production of Phosphoinositide-Derived Second Messengers 1,2-Diacylglycerol and Inositol 1,4,5-tris-Phosphate[J]. Arterioscler Thromb Vasc Biol, 1998, 18(6):861-869. DOI: 10.1161/01.ATV.18.6.861.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Serum high-density lipoprotein, Carotid artery stenting, In-stent restenosis","lastPublishedDoi":"10.21203/rs.3.rs-6277872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6277872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the relationship between serum high-density lipoprotein (HDL) levels and the risk of in-stent restenosis (ISR) following carotid artery stenting (CAS), aiming to provide potential targets for ISR prevention and treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective analysis was conducted on 132 patients who underwent CAS in the Department of Neurosurgery at Wenzhou Central Hospital between March 2019 and January 2024 and had 6-month (±1 month) follow-up data available. Patients were divided into ISR group (96 cases) and non-ISR group (36 cases) based on the occurrence of ISR. Demographic, clinical, and laboratory data were collected for both groups. Independent predictors of ISR were identified using t-test, Mann-Whitney U test, chi-square test, and logistic regression analysis. The predictive performance of the model was evaluated using ROC curve analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSerum HDL levels were significantly lower in the ISR group than in the non-ISR group (p \u0026lt; 0.001). Univariate logistic regression analysis showed that male gender, smoking, hypertension, high BMI, and elevated serum globulin levels were positively associated with ISR risk (p \u0026lt; 0.05), while higher serum HDL levels were negatively associated with ISR risk (p \u0026lt; 0.05). Multivariate logistic regression analysis further confirmed that smoking, hypertension, and elevated serum globulin levels were independent risk factors for ISR (p \u0026lt; 0.05), while higher serum HDL levels were an independent protective factor for ISR (p \u0026lt; 0.05). ROC curve analysis indicated that the multivariable model had good predictive performance for ISR, with an AUC of 0.84. At the optimal cutoff point, the model demonstrated a sensitivity of 92% and a specificity of 75%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSerum high-density lipoprotein (HDL) plays a crucial protective role in the development of ISR. Monitoring and increasing serum HDL levels may help prevent ISR and improve patient prognosis.\u003c/p\u003e","manuscriptTitle":"Serum High-Density Lipoprotein Levels and Risk of In-Stent Restenosis Following Carotid Artery Stenting: A Multivariate Analysis-Based Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 19:31:46","doi":"10.21203/rs.3.rs-6277872/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-06-21T08:52:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272437036334073806951852990853192104945","date":"2025-06-13T10:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T08:08:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-23T11:22:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-24T07:16:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T07:16:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-03-21T13:02:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c188887e-9fdb-427e-b8a0-93678aa57f5c","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T19:31:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 19:31:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6277872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6277872","identity":"rs-6277872","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}