Residual cholesterol as a predictor of early functional outcome after endovascular treatment for acute large-vessel occlusion ischemic stroke

Residual cholesterol as a predictor of early functional outcome after endovascular treatment for acute large-vessel occlusion ischemic stroke | 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 Residual cholesterol as a predictor of early functional outcome after endovascular treatment for acute large-vessel occlusion ischemic stroke Liyang Feng, Zhi Zhang, Zhaotao Wen, Yunpeng Liu, Yang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9123147/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Residual cholesterol, an important marker of lipid metabolism, has been increasingly used in recent years as a monitoring indicator for insulin resistance and for prognosis surveillance after major cardiovascular interventions. However, evidence regarding its utility for functional prognostication in acute occlusive ischemic stroke—particularly among patients undergoing endovascular therapy—remains limited. This study aimed to evaluate whether residual cholesterol can predict functional outcomes after endovascular treatment. Methods We retrospectively analyzed 254 consecutive patients with acute ischemic stroke who underwent endovascular therapy at Beijing Chaoyang Hospital, Capital Medical University, between October 2022 and October 2023. Patients were divided into two groups according to functional outcome. Baseline clinical characteristics and preprocedural biochemical indices were collected to calculate residual cholesterol, and correlation analyses were performed. Independent prognostic factors were identified using logistic regression, and receiver operating characteristic (ROC) curves were generated to assess the predictive performance of residual cholesterol. Results Residual cholesterol (RC) was significantly higher in the poor-outcome group than in the good-outcome group (0.86 [0.49, 1.27] vs 0.64 [0.43, 1.12], p = 0.033). After adjustment in multivariable logistic regression, RC remained an independent predictor of 90-day functional independence after EVT (OR, 1.911; 95% CI, 1.039–3.585; p = 0.040). Conclusion RC has important value in predicting 3-month functional outcomes in patients with AIS due to large-vessel occlusion undergoing EVT, and it improves the predictive performance of models based solely on clinical variables. residual cholesterol acute ischemic stroke large-vessel occlusion endovascular therapy modified Rankin Scale lipid metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Among AIS subtypes, stroke attributable to large-vessel occlusion (LVO)—typically involving the internal carotid artery or the middle cerebral artery—is characterized by abrupt onset, rapid expansion of the ischemic core, and consequently poorer clinical outcomes (1, 2). In recent years, multiple randomized controlled trials and real-world studies have demonstrated that endovascular therapy (EVT), when performed within the recommended time window, significantly increases reperfusion rates and improves neurological functional outcomes. EVT has therefore become the standard of care for anterior-circulation LVO(3-5). However, in clinical practice, the phenomenon of “successful recanalization yet poor functional outcome” is frequently observed. Even among patients achieving high-grade reperfusion (e.g., modified Thrombolysis in Cerebral Infarction [mTICI] 2b/3), a substantial proportion still experience moderate-to-severe disability or death at 90 days. These observations indicate that long-term outcomes after EVT are not determined solely by macrovascular recanalization, but are also influenced by multiple factors, including baseline metabolic status, inflammatory responses, endothelial dysfunction, microcirculatory perfusion, and ischemia–reperfusion injury (6, 7), there is an urgent need to identify accessible, stable, and biologically plausible prognostic biomarkers to facilitate postprocedural risk stratification and to inform individualized strategies for secondary prevention. Dyslipidemia is a classic risk factor for atherosclerosis and ischemic stroke; however, conventional lipid indices such as low-density lipoprotein cholesterol (LDL-C) do not fully account for the residual risk of vascular events. In recent years, increasing attention has been directed toward remnant cholesterol (RC). RC refers to the cholesterol content carried within remnants of triglyceride-rich lipoproteins (TRLs), including chylomicron remnants, very-low-density lipoprotein (VLDL) remnants, and intermediate-density lipoproteins (IDL). Compared with LDL particles, TRL remnants exhibit greater atherogenic potential by more readily being retained within the arterial wall and taken up by macrophages to form foam cells, and are closely associated with endothelial dysfunction, inflammatory activation, and a prothrombotic milieu (8). RC can be readily derived from routine lipid profiles using a simple calculation (RC = total cholesterol − LDL-C − HDL-C), which confers high clinical feasibility and scalability. Prior studies have suggested that RC is associated with the risk of coronary artery disease, peripheral arterial disease, and ischemic stroke; however, evidence regarding its prognostic value in patients undergoing EVT remains relatively limited (9). On the one hand, patients undergoing EVT often have a higher atherosclerotic burden and are exposed to substantial periprocedural stress responses. On the other hand, post-reperfusion microcirculatory dysfunction, blood–brain barrier disruption, and secondary inflammation may interact with lipid metabolic disturbances, potentially modulating neurological recovery (2, 6). Therefore, investigating the impact of RC on functional outcomes after EVT is of clear clinical relevance. Based on this rationale, we aimed to systematically evaluate the association between baseline RC levels and 3-month functional outcomes measured by the modified Rankin Scale (mRS) in patients with AIS-LVO undergoing EVT, and to determine whether RC provides incremental predictive value beyond established clinical prognostic models. Methods 2.1. Study population This retrospective cohort study aimed to evaluate the value of remnant cholesterol in predicting functional outcomes in patients with acute ischemic stroke due to large-vessel occlusion (AIS-LVO) undergoing endovascular therapy (EVT). A total of 254 consecutive patients with acute large-vessel occlusive ischemic stroke who received EVT in the emergency department of Beijing Chaoyang Hospital, Capital Medical University, between October 2022 and October 2023 were enrolled. Inclusion criteria were: (1) acute ischemic stroke with large-vessel occlusion confirmed by imaging (CT/CTA); (2) EVT performed within 24 h of symptom onset; (3) complete preprocedural fasting lipid data, including total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol; and (4) complete clinical information, including baseline National Institutes of Health Stroke Scale (NIHSS) score and 3-month modified Rankin Scale (mRS) score. Exclusion criteria were: (1) severe comorbidities, such as advanced cardiopulmonary disease, hepatic or renal insufficiency, or malignancy; (2) inability to undergo EVT within 24 h of onset; (3) refusal to participate or loss to follow-up; (4) missing imaging data precluding accurate assessment; and (5) any other conditions not meeting the inclusion criteria or compromising study integrity. The study protocol was approved by the Ethics Office of Beijing Chaoyang Hospital, Capital Medical University. 2.2. Data collection and processing All patient data were obtained from Beijing Chaoyang Hospital, Capital Medical University. We systematically collected demographic characteristics of enrolled patients, including age and sex; preprocedural blood biochemical parameters, including total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and lipoprotein(a); and clinical assessments, including neurological deficit severity evaluated on admission using the National Institutes of Health Stroke Scale (NIHSS) and post-thrombectomy reperfusion status assessed using the Thrombolysis in Cerebral Infarction (TICI) grading system. The 3-month modified Rankin Scale (mRS) score was recorded through outpatient visits or telephone follow-up. An mRS score of 0–2 was defined as a favorable outcome, whereas 3–6 indicated an unfavorable outcome. To examine the impact of metabolic status on patient outcomes, remnant cholesterol (RC) was calculated using the following formula: RC=total cholesterol−high-density lipoprotein cholesterol−low-density lipoprotein cholesterol. All data underwent rigorous quality control and timely cleaning to ensure accuracy and compliance with ethical standards, with patient privacy protected throughout. The National Institutes of Health Stroke Scale (NIHSS), Thrombolysis in Cerebral Infarction (TICI), and modified Rankin Scale (mRS) used in this study were previously published assessment tools and were not developed specifically for the present study. Baseline stroke severity was assessed using the NIHSS at admission. Angiographic reperfusion after endovascular treatment was evaluated according to the TICI grading system. Functional outcome was assessed using the mRS. (10-12) 2.3. Statistical analysis Normality of continuous variables was assessed using the Shapiro–Wilk test in GraphPad Prism (version 10.4, USA). Normally distributed data are presented as mean ± standard deviation (mean ± SD) and were compared using the independent-samples t test. Non-normally distributed continuous variables are presented as median (interquartile range [IQR]) and were compared using the Mann–Whitney U test. Categorical variables are summarized as counts and percentages and were compared between groups using the chi-square test. Multivariable logistic regression was performed in R to quantify the association between each variable and outcome after adjustment for potential confounders, and the independent predictive value of each variable was subsequently evaluated. All statistical analyses were conducted using GraphPad Prism, R (4.52.2173.0), and RStudio (2025.9.1.0), with the following packages: readxl , dplyr , corrplot , pROC , rms , randomForest , ggplot2 , reshape2 , and logistf . A two-sided P value < 0.05 was considered statistically significant. Results 1. Baseline clinical characteristics Regarding baseline characteristics, the median age was 62 (53, 72) years in one group and 71 (62.5, 78) years in the other, with a significant between-group difference (p < 0.001). Overall, 175 patients (66.2%) were male, and the sex distribution did not differ significantly between groups. Baseline NIHSS scores differed significantly between the two groups (p=0.006), with higher scores observed in the unfavorable-outcome group. With respect to comorbidities, the prevalence of atrial fibrillation and prior cerebral infarction was significantly higher in the unfavorable-outcome group than in the favorable-outcome group (p=0.016 and p=0.043, respectively). There were no significant between-group differences in the prevalence of hypertension, coronary artery disease, diabetes mellitus, or hyperlipidemia. Clinically, postprocedural TICI grades were comparable between the two groups, with no statistically significant difference (Figure 1) . Accordingly, baseline demographic and clinical characteristics were generally comparable between groups, with significant differences observed primarily in age and baseline NIHSS score (Table 1) . The distribution of 3-month modified Rankin Scale (mRS) scores for the entire cohort is presented in Figure 2 . Table 1. Baseline clinical characteristics Baseline clinical characteristics Favorable outcome (mRS 0-2) Unfavorable outcome (mRS 3-6) P value age 62.00 (53.00, 72.00) 71.00 (62.50, 78.00) <0.001 Male gender（68.89%） 95 (34.7%) 80 (31.5%) 0.065 Hypertension 93 (72.7%) 92 (73.0%) 0.949 Old infarct 61 (47.7%) 76 (60.3%) 0.043 Diabetes mellitus 43 (33.6%) 40 (31.7%) 0.754 Atrial fibrillation 11 (8.6%) 24 (19.0%) 0.016 NIHSS pre 11.00 (6.00, 13.25) 12.00 (8.00, 15.00) 0.006 TICI 1.00 (1.00, 1.00) 1.00 (1.00, 1.00) 0.705 Coronary artery disease 26 (20.3%) 26 (20.6%) 0.949 Hyperlipidemia 14 (10.9%) 16 (12.7%) 0.664 2. Biochemical parameters The analysis showed that serum triglyceride levels and fasting blood glucose on postoperative day 1 were significantly higher in the unfavorable-outcome group than in the favorable-outcome group (p=0.046 and p=0.031, respectively). In addition, remnant cholesterol was also elevated in the unfavorable-outcome group (p<0.001). No significant differences were observed for the other biochemical parameters. No significant differences were observed in the other biochemical parameters. (Table 2) Table 2. Biochemical parameters Favorable outcome (n=128) Unfavorable outcome (n=126) P value Total cholesterol（TC） 4.43 (3.51, 5.03) 4.12 (3.41, 4.92) 0.230 High-density lipoprotein cholesterol（HDL） 1.02 (0.83, 1.21) 1.01 (0.88, 1.22) 0.437 Low-density lipoprotein cholesterol（LDL） 2.78 (1.88, 3.40) 2.51 (1.96, 3.19) 0.299 Serum triglycerides（TG） 1.51 (1.08, 2.12) 1.19 (0.88, 1.95) 0.046 Lipoprotein(a)（Lp(a)） 16.00 (9.47, 30.73) 19.05 (9.00, 42.45) 0.249 Blood glucose 7.17 (5.75, 10.07) 7.92 (6.29, 10.47) 0.031 RC 0.69 (0.43, 1.12) 1.01 (0.62, 1.32) ＜0.001 Table 3. Multivariable logistic regression analysis Variables OR(95%CI) aOR(95%CI) Pvalue gender 1.065(0.657, 1.922) 1.037 (0.566, 1.893) 0.905 age 1.034 (1.016, 1.068) 1.028 (1.025, 1.078) 0.020 Old infarct 1.670 (1.017, 2.755) 1.298 (0.757, 2.224) 0.342 Atrial fibrillation 2.503 (1.194, 5.551) 1.982 (0.891, 4.618) 0.100 NIHSS pre 1.072 (1.024, 1.125) 1.066 (1.015, 1.123) 0.013 RC 2.877 (1.635, 5.181) 2.891 (1.590, 5.393) 0.001 3. Multivariable analysis of predictors of 3-month outcomes Variables that were statistically significant in univariable logistic regression were entered into the multivariable logistic regression model. Multicollinearity was assessed by examining tolerance values > 0.1 and variance inflation factors (VIFs) well below 10, indicating no substantial multicollinearity within the model. Table 3 presents the unadjusted and adjusted odds ratios (ORs) for the associations between candidate variables and unfavorable 3-month outcomes after EVT, showing significant associations in both univariable and multivariable analyses. In the adjusted analysis, RC remained significantly associated with unfavorable outcome. In univariable analysis, the OR was 2.877 (95%CI, 1.635–5.181; p=0.002); after adjustment for potential confounders, the adjusted OR (aOR) was 2.891 (95% CI, 1.590–5.393; p=0.001). These findings suggest that higher RC levels are associated with an increased risk of poor functional outcome at 3 months following EVT. 4. Diagnostic performance of remnant cholesterol based on ROC curve analysis In this study, receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of remnant cholesterol (RC) for predicting unfavorable 3-month functional outcomes after EVT in patients with AIS-LVO ( Figure3 ). The optimal RC cutoff for predicting unfavorable outcome was 0.91, yielding a sensitivity of 59.2% and a specificity of 66.4%, with an AUC of 0.715 (95% CI, 0.653–0.780). After incorporating RC, the AUC increased from 0.671 to 0.715. To assess the goodness-of-fit of the logistic regression model incorporating remnant cholesterol (RC) for predicting unfavorable 3-month outcomes, the Hosmer–Lemeshow (H–L) test was performed. For the model including RC, the H–L chi-square statistic was 7.539 (df=8, p=0.479), indicating good calibration, with predicted probabilities of unfavorable outcome consistent with the observed outcomes (p > 0.05). By calculating the net reclassification improvement (NRI), we further quantified the extent to which adding RC improved risk reclassification. For RC, the continuous NRI was 0.5186 (95%CI, 0.2837–0.7610; p<0.001), whereas the categorical NRI, stratified by the optimal cutoff of 0.91, was 0.0625 (95%CI, −0.0634 to 0.1942; p=0.37). These results indicate a modest trend toward improved risk reclassification after incorporating RC; however, the improvement did not reach statistical significance (NRI close to 0, 95% CI crossing 0, p>0.05). Subgroup analyses were performed based on variables that remained significantly different between the favorable- and unfavorable-outcome groups after multivariable adjustment, as well as on several previously reported independent risk factors for poor 3-month outcomes in AIS (Figure 4). Compared with RC<0.91, elevated RC remained an independent risk factor for unfavorable outcomes at 3 months after EVT in patients with AIS-LVO. Baseline characteristics, such as sex and age, did not materially alter this association. In the subgroup with NIHSS>14, the odds ratio was 2.811 (95% CI, 0.939–9.497; p=0.07), and the 95% CI included the null value, indicating a non-significant result. The test for interaction across NIHSS strata was not significant (p=0.747). This finding may be attributable to reduced statistical power due to the small sample size in the NIHSS>14 subgroup rather than a true differential effect. Nevertheless, the consistent direction of effect suggests that the overall trend may still apply to this subgroup, warranting further investigation. Similar patterns were observed in other small subgroups. For example, among patients with atrial fibrillation (n=35), the association was directionally consistent but did not reach statistical significance (OR=4.64, 95%CI,0.677–4.688;p=0.132). Comparable findings were also noted in subgroups defined by hyperlipidemia (n=30), lower-grade reperfusion (n=48), absence of hypertension (n=69), and diabetes mellitus (n=83). In addition, although the subgroup without prior infarction had relatively balanced group sizes, the effect estimate did not achieve statistical significance (p=0.08), likely reflecting a limited number of events and increased uncertainty within the subgroup; interaction testing did not suggest significant effect modification by prior infarction (p>0.05).Overall, these findings support the robustness and generalizability of the association between elevated RC and unfavorable outcomes across different patient subgroups, with no significant evidence of effect modification. Discussion This study focused on the relationship between remnant cholesterol (RC) and 3-month functional outcomes, as assessed by the modified Rankin Scale (mRS), in patients with acute ischemic stroke undergoing mechanical thrombectomy. RC is typically calculated as RC = total cholesterol−LDL-C−HDL-C, and reflects the cholesterol burden carried by triglyceride-rich lipoproteins (TRLs) and their remnants. In recent years, RC has emerged as an important metabolic phenotype within the “residual risk despite achieving LDL-C targets” paradigm (13, 14). In this study, analyses based on 3-month mRS outcomes indicated a statistically significant association between RC and unfavorable prognosis, and suggested that RC may provide incremental information for prognostic modeling; however, its predictive utility is best interpreted within a multifactorial framework. On the one hand, RC remained significantly associated with unfavorable outcomes after adjustment for potential confounders, including age, sex, baseline NIHSS score, and comorbidities, in multivariable logistic regression. On the other hand, ROC analysis demonstrated that RC as a single marker had a moderate discriminative performance for unfavorable outcome (AUC=0.715), with sensitivity and specificity that were not particularly high. These findings imply that RC is unlikely to be a stand-alone predictor; rather, its prognostic contribution is probably jointly modulated by stroke severity, reperfusion quality, metabolic comorbidities, and acute-phase stress responses, and should therefore be understood within an integrated, multivariable and mechanistic context. In recent years, studies on RC in the field of stroke have increased; however, the overall conclusions remain not entirely consistent. On the one hand, population-based studies and genetic evidence support a robust association between RC and atherosclerotic cardiovascular and cerebrovascular events. Several stroke cohorts have also reported that RC is associated with poor functional outcomes or an increased risk of mortality, and under certain circumstances may serve as a prognostic marker (15). On the other hand, some studies suggest that the association between RC and short-term functional outcomes may be influenced by metabolic conditions (e.g., dysglycemia/diabetes and metabolic dysfunction–associated fatty liver disease), nutritional status, and even therapeutic strategies, resulting in heterogeneous, unstable, or non-linear relationships and substantial between-subgroup variability. Such context-dependent effects may contribute to paradoxical associations between lipid levels and outcomes, as well as effect modification across clinical phenotypes (16-18).Therefore, our finding that RC demonstrates a moderate AUC and an independent association with outcome—yet does not exhibit “highly robust” predictive performance—is not inconsistent with prior evidence. Rather, it underscores that the impact of RC on short-term functional outcomes is contingent on cohort composition, the timing of biomarker measurement, and the covariates included for adjustment. Accordingly, RC should not be interpreted as a single, linear, universally strong stand-alone predictor. Moreover, from the perspective of atherosclerosis and thrombosis, TRLs and their remnants are considered to be distinctly pro-atherogenic. Remnant particles can penetrate the arterial wall and be taken up by macrophages to form foam cells, while also inducing endothelial dysfunction, oxidative stress, and vascular inflammation, thereby promoting plaque progression and destabilization(19).Consensus statements from the European Atherosclerosis Society (EAS), together with multiple review articles, have emphasized that remnant cholesterol represents a major contributor to residual atherosclerotic cardiovascular disease (ASCVD) risk and may constitute a potentially modifiable therapeutic target(20-22).In this study, the observed statistical association between RC and unfavorable outcomes suggests that RC may, to some extent, capture an underlying risk milieu related to atherosclerotic burden, metabolic dysregulation, and vascular inflammation. However, this association should be further interpreted and validated in conjunction with more comprehensive clinical and imaging information, such as infarct core and penumbra characteristics, collateral status, and hemorrhagic transformation. More importantly, the relationship between RC and inflammation may be relevant to stroke prognosis. Some studies have suggested an association between non-fasting RC and other inflammatory markers, such as elevated C-reactive protein (CRP), indicating that RC may not only reflect lipid burden but also correlate with heightened inflammatory activity. Inflammation contributes throughout the entire course of secondary injury and recovery after stroke, including expansion of the ischemic penumbra, blood–brain barrier disruption, reperfusion injury, post-stroke immunosuppression and infectious complications, as well as processes of neural repair (23-25). Therefore, mechanistically, RC is more likely to influence outcomes through an integrated “metabolism–inflammation–vascular” pathway; however, its effect is often neither isolated nor strictly linear, and may be modulated by stroke severity, reperfusion quality, and acute-phase stress responses. During the acute phase of stroke, marked systemic stress responses occur, and inflammation and infection can rapidly alter the lipid profile—manifesting as reductions in HDL-C, elevations in triglycerides, impaired clearance of TRLs, and hepatic remodeling of lipoprotein metabolism—rendering a single lipid measurement more susceptible to acute stress effects rather than reflecting long-term exposure (26). This may help explain why, in acute stroke cohorts, the relationship between RC and outcomes is more likely to be influenced by the timing of blood sampling, the magnitude of physiological stress, intercurrent infection, and overall inflammatory burden, thereby appearing non-linear or unstable across different subgroups. Moreover, in the context of malnutrition, hypoalbuminemia, or catabolic states, lipid parameters may increasingly reflect systemic consumption or inflammatory activity rather than baseline metabolic risk, giving rise to an apparent “lipid paradox,” in which lower lipid levels are associated with worse outcomes(27, 28).Therefore, even though RC showed a statistically significant association in our study, it should be recognized that RC may partly reflect acute-phase metabolic–inflammatory status rather than solely long-term lipid exposure; accordingly, its clinical significance is better assessed within an integrated, comprehensive risk framework. In patients undergoing mechanical thrombectomy, 90-day functional outcomes are typically driven by a limited number of strong clinical determinants, such as baseline NIHSS score and successful reperfusion (mTICI grade), which explain a substantial proportion of the variability in mRS. Even with successful recanalization, poor outcomes may still occur, indicating that prognosis is jointly determined by multiple factors. In this context, the independent effect of RC as a metabolic marker is more likely to be masked by dominant predictors or modified by other factors, resulting in an apparently unstable association. Consistent with this notion, RC remained significant in our multivariable model, whereas in subgroup analyses, several small subgroups (e.g., NIHSS > 14, atrial fibrillation, hyperlipidemia, or lower mTICI grades) showed markedly widened confidence intervals and borderline non-significant estimates, which more likely reflects limited statistical power rather than a true loss of effect or a change in direction. From a predictive modeling perspective, we further compared the baseline model with the model incorporating RC. The results showed that adding RC increased the AUC from 0.671 to 0.715, suggesting that RC provides incremental discriminative information. The optimal RC cutoff for predicting unfavorable outcome was 0.91 (sensitivity, 59.2%; specificity, 66.4%), indicating a certain stratification utility; however, the moderate sensitivity and specificity imply that RC is better suited as an adjunct variable within a multivariable model rather than as a stand-alone marker for clinical screening or decision-making. In terms of model fit, the Hosmer–Lemeshow test for the model including RC yielded a p value of 0.479 ( p > 0.05), indicating acceptable agreement between predicted probabilities and observed outcomes and an overall good model fit. Notably, reclassification analyses showed a pattern in which the continuous NRI was significant, whereas the categorical NRI was not. Specifically, the continuous NRI was 0.5186 (95% CI, 0.2837–0.7610; p < 0.001), while the categorical NRI based on the 0.91 cutoff was 0.0625 (95% CI, −0.0634 to 0.1942; p = 0.37). This “significant continuous NRI but non-significant categorical NRI” pattern is not uncommon in prediction modeling studies. Potential explanations include the high sensitivity of categorical NRI to threshold selection; when risk-group boundaries are not aligned with clinically meaningful decision thresholds, the ability to demonstrate improved risk-group assignment may be limited. Thus, even if predicted probabilities improve at the continuous scale, this may not translate into statistically significant changes in categorical risk classification. Accordingly, we interpret our findings as indicating that incorporation of RC yields meaningful improvements in probability updating and overall reclassification on the continuous scale, whereas reclassification improvement based on the 0.91 threshold is modest and did not reach statistical significance. This suggests that the incremental value of RC may primarily manifest as subtle refinements in estimated risk probabilities rather than substantial shifts of large numbers of individuals between predefined risk categories. In subgroup analyses, the direction of the association between RC ≥ 0.91 and an increased risk of unfavorable outcome was largely consistent across most subgroups, and tests for interaction did not indicate significant effect modification (all P for interaction > 0.05), suggesting that the current evidence does not support the notion that the effect of RC is confined to a specific subgroup. For certain subgroups in which the 95% CI crossed 1 or the p value approached but did not reach 0.05, a more plausible explanation is that reductions in subgroup sample size and event counts increased estimation uncertainty. In particular, in the subgroup without prior cerebral infarction, although the sample sizes between groups were relatively comparable, the reduced number of events may have limited statistical power, resulting in borderline non-significant p values (e.g., p = 0.08) and widened confidence intervals. Meanwhile, the non-significant interaction test further indicates that prior cerebral infarction did not materially modify the strength of the association between RC and outcome. Therefore, subgroup findings should be interpreted as exploratory, serving primarily to demonstrate consistency and stability in the direction of association rather than to support definitive subgroup-specific conclusions. Integrating our findings with the existing literature, we propose that the potential value of RC in the stroke field may primarily lie in two aspects: 1.As a metabolic marker reflecting TRL remnant burden and atherosclerotic risk, RC may help refine vascular risk profiling in patients with stroke, particularly among those who achieve adequate LDL-C control yet continue to exhibit residual risk. 2.For acute-phase prognostication, RC is more likely to yield a stable incremental predictive benefit when jointly modeled with inflammatory markers and metabolic comorbidities, rather than being used as a stand-alone screening index. If future studies confirm that RC or RC-based composite measures can consistently improve model performance in external cohorts, RC could potentially be applied for early risk stratification, identification of high-risk individuals, and to inform secondary prevention strategies after stroke. Limitations and future directions This study has several limitations. First, the retrospective single-center design may introduce selection bias. Second, acute-phase laboratory measurements are susceptible to the timing of blood sampling and physiological stress responses. Third, outcomes after thrombectomy are strongly driven by reperfusion status, treatment time window, and baseline stroke severity; therefore, the effect of RC should be further examined in larger cohorts with formal assessments of interaction, non-linearity, and external validation. Future studies could integrate longitudinal metabolic monitoring with inflammatory biomarkers to explore potential effect modification of RC across clinical subgroups and to validate its robustness and prognostic utility in predictive models. Abbreviations RC , remnant cholesterol; AIS-LVO , acute ischemic stroke with large-vessel occlusion; EVT , endovascular treatment; mRS , modified Rankin Scale; NIHSS , National Institutes of Health Stroke Scale; TICI , Thrombolysis in Cerebral Infarction. Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Office of Beijing Chaoyang Hospital, Capital Medical University. Due to the retrospective nature of the study and the use of de-identified clinical data, the requirement for informed consent was waived by the Ethics Office of Beijing Chaoyang Hospital, Capital Medical University. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analised during the current study were derived from the electronic medical record system and physician order entry system of Beijing Chaoyang Hospital, Capital Medical University. The data are not publicly available due to patient privacy and institutional restrictions but are available from the corresponding authors on reasonable request and with permission from the hospital. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions Liyang Feng contributed to the study conception and design, data collection, data analysis, and manuscript drafting. Zhi Zhang and Zhaotao Wen contributed to data collection, data interpretation, and revision of the manuscript. Yunpeng Liu and Yang Wang supervised the study, contributed to the study design, critically revised the manuscript, and served as corresponding authors. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the staff of Beijing Chaoyang Hospital, Capital Medical University, for their support in data collection and clinical management. We also sincerely appreciate all healthcare professionals involved in the diagnosis, treatment, and follow-up of the patients included in this study. Authors Liyang Feng was the primary contributor to this study. Zhi Zhang and Zhaotao Wen contributed equally and were designated as co-first authors. Yunpeng Liu and Yang Wang served as the corresponding authors. Liyang Feng： [email protected] Master of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China Zhi Zhang： [email protected] Master of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China Zhaotao Wen: [email protected] Master of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China Yunpeng Liu： [email protected] Doctor of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China Yang wang： [email protected] Doctor of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China References Diseases GBD, Injuries C. 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Triglyceride-rich lipoproteins and their remnants: metabolic insights, role in atherosclerotic cardiovascular disease, and emerging therapeutic strategies-a consensus statement from the European Atherosclerosis Society. Eur Heart J. 2021;42(47):4791-806. Wadstrom BN, Wulff AB, Pedersen KM, Jensen GB, Nordestgaard BG. Elevated remnant cholesterol increases the risk of peripheral artery disease, myocardial infarction, and ischaemic stroke: a cohort-based study. Eur Heart J. 2022;43(34):3258-69. Brott T, Adams HP Jr, Olinger CP, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20(7):864-870. Higashida RT, Furlan AJ, Roberts H, et al. Trial design and reporting standards for intra-arterial cerebral thrombolysis for acute ischemic stroke. Stroke. 2003;34(8):e109-e137. van Swieten JC, Koudstaal PJ, Visser MC, et al. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604-607. Castaner O, Pinto X, Subirana I, Amor AJ, Ros E, Hernaez A, et al. Remnant Cholesterol, Not LDL Cholesterol, Is Associated With Incident Cardiovascular Disease. J Am Coll Cardiol. 2020;76(23):2712-24. Delialis D, Georgiopoulos G, Aivalioti E, Konstantaki C, Oikonomou E, Bampatsias D, et al. Remnant cholesterol in atherosclerotic cardiovascular disease: A systematic review and meta-analysis. Hellenic J Cardiol. 2023;74:48-57. Wang K, Wang R, Yang J, Liu X, Shen H, Sun Y, et al. Remnant cholesterol and atherosclerotic cardiovascular disease: Metabolism, mechanism, evidence, and treatment. Front Cardiovasc Med. 2022;9:913869. Tan Z, Zhang Q, Liu Q, Meng X, Wu W, Wang L, et al. Relationship between remnant cholesterol and short-term prognosis in acute ischemic stroke patients. Brain Behav. 2024;14(5):e3537. Jiang S, Jin A, Xing W, Jing J. Impact of remnant cholesterol on acute ischemic stroke prognosis: a nationwide cohort analysis stratified by non-alcoholic fatty liver disease status. Front Neurol. 2025;16:1472871. Andone S, Farczadi L, Imre S, Balasa R. Fatty Acids and Lipid Paradox-Neuroprotective Biomarkers in Ischemic Stroke. Int J Mol Sci. 2022;23(18). Heo JH, Jo SH. Triglyceride-Rich Lipoproteins and Remnant Cholesterol in Cardiovascular Disease. J Korean Med Sci. 2023;38(38):e295. Nordestgaard BG, Langlois MR, Langsted A, Chapman MJ, Aakre KM, Baum H, et al. Quantifying atherogenic lipoproteins for lipid-lowering strategies: Consensus-based recommendations from EAS and EFLM. Atherosclerosis. 2020;294:46-61. Gugliucci A. Beyond LDL: Understanding Triglyceride-Rich Lipoproteins to Tackle Residual Risk. J Clin Med. 2023;12(12). Duran EK, Pradhan AD. Triglyceride-Rich Lipoprotein Remnants and Cardiovascular Disease. Clin Chem. 2021;67(1):183-96. Doi T, Langsted A, Nordestgaard BG. Dual elevated remnant cholesterol and C-reactive protein in myocardial infarction, atherosclerotic cardiovascular disease, and mortality. Atherosclerosis. 2023;379:117141. Qiu YM, Zhang CL, Chen AQ, Wang HL, Zhou YF, Li YN, et al. Immune Cells in the BBB Disruption After Acute Ischemic Stroke: Targets for Immune Therapy? Front Immunol. 2021;12:678744. Westendorp WF, Dames C, Nederkoorn PJ, Meisel A. Immunodepression, Infections, and Functional Outcome in Ischemic Stroke. Stroke. 2022;53(5):1438-48. Berberich AJ, Hegele RA. A Modern Approach to Dyslipidemia. Endocr Rev. 2022;43(4):611-53. Zhou H, Wang A, Meng X, Lin J, Jiang Y, Jing J, et al. Low serum albumin levels predict poor outcome in patients with acute ischaemic stroke or transient ischaemic attack. Stroke Vasc Neurol. 2021;6(3):458-66. Hoshino T. Prognostic Implications of the Acute Lipid Levels in Stroke Patients. J Atheroscler Thromb. 2024;31(8):1133-4. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Editor invited by journal 23 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 21 Mar, 2026 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-9123147","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617356935,"identity":"90cd8c18-bdb9-4abb-9a2b-50cc3198b911","order_by":0,"name":"Liyang Feng","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liyang","middleName":"","lastName":"Feng","suffix":""},{"id":617356939,"identity":"5c2e9632-2f20-45c2-a62a-7cdb3f3205a5","order_by":1,"name":"Zhi Zhang","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Zhang","suffix":""},{"id":617356942,"identity":"a171339e-8beb-4e2d-9a59-45d9fb0d69ff","order_by":2,"name":"Zhaotao Wen","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaotao","middleName":"","lastName":"Wen","suffix":""},{"id":617356943,"identity":"aedd9866-918b-4ec2-a3a9-37abc752c828","order_by":3,"name":"Yunpeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACPmY4k/ngAwkeGx5+9gb8WtgQWtiSDSxk0mQkew4Q0IJg8qgJVNgctjG44UBACzuPmTTvjjty5vxr2Bhu5JznYbjBwPjhYw4+h4G0nHlmbDnj7bGHM87c5mGc3cAsOXMbIS1thxM33DiXbizZc5uHWeYAGzMvEVrqN9w4Yyb99985HjaJBOK0JBic7zGTkOA5wMNDWAtbseXctmeGG24AA1mCJ5lHgudgM16/8PMf3njjbdsdeYPzh0FRaWdvf7z54IePeLQAAYsEA8MBBgaJBJgAYwNe9UDA/AGshf8AIYWjYBSMglEwUgEA6rFN0m76JCEAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yunpeng","middleName":"","lastName":"Liu","suffix":""},{"id":617356945,"identity":"dfed684f-d4e3-4cea-bb3c-884c54c62312","order_by":4,"name":"Yang Wang","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-14 14:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9123147/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9123147/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106300711,"identity":"30f8a6a7-2b33-41f9-aedc-bb8acabfa456","added_by":"auto","created_at":"2026-04-07 09:15:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28372,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection and follow-up.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9123147/v1/c899e41ea201658f41177516.png"},{"id":106403969,"identity":"73a2b6bc-d244-4bf9-a770-4b979ec42d67","added_by":"auto","created_at":"2026-04-08 09:15:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30059,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the distribution of modified Rankin Scale (mRS) scores at 3 months. The mRS was used to assess functional status, ranging from 0 (no symptoms) to 6 (death). The distribution was as follows: mRS = 0 (89 patients, 35.01%), mRS = 1 (31 patients, 12.20%), mRS = 2 (8 patients, 3.15%), mRS = 3 (22 patients, 8.67%), mRS = 4 (54 patients, 21.26%), mRS = 5 (14 patients, 5.51%), and mRS = 6 (36 patients, 14.17%).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9123147/v1/30080018ea3a864d4a8672d9.png"},{"id":106404644,"identity":"65cf2f44-5dfd-4f08-b6f3-4c93dac29058","added_by":"auto","created_at":"2026-04-08 09:16:26","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":287076,"visible":true,"origin":"","legend":"\u003cp\u003epresents the ROC curves. After incorporating remnant cholesterol (RC), the area under the curve (AUC) increased from 0.671 to 0.715.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9123147/v1/82f3e2656f624e1c03bd2067.jpeg"},{"id":106403200,"identity":"7a466ea5-2db9-4d13-ab91-6ca2bdcd1ed7","added_by":"auto","created_at":"2026-04-08 09:13:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159319,"visible":true,"origin":"","legend":"\u003cp\u003eshows the forest plot of the subgroup analysis, presenting the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the association between RC strata (RC≥0.91 vs RC\u0026lt;0.91) and unfavorable outcomes across different clinical subgroups, along with tests for interaction (P for interaction).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9123147/v1/dc3a9b2f6041d1023d801214.png"},{"id":106405683,"identity":"95fa94c7-ea79-40b4-85dc-17a56b9b6fa3","added_by":"auto","created_at":"2026-04-08 09:28:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1259727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9123147/v1/be7deaff-198d-46e7-82d5-06d3fec97b10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eResidual cholesterol as a predictor of early functional outcome after endovascular treatment for acute large-vessel occlusion ischemic stroke\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Among AIS subtypes, stroke attributable to large-vessel occlusion (LVO)\u0026mdash;typically involving the internal carotid artery or the middle cerebral artery\u0026mdash;is characterized by abrupt onset, rapid expansion of the ischemic core, and consequently poorer clinical outcomes\u0026nbsp;(1, 2).\u0026nbsp;In recent years, multiple randomized controlled trials and real-world studies have demonstrated that endovascular therapy (EVT), when performed within the recommended time window, significantly increases reperfusion rates and improves neurological functional outcomes. EVT has therefore become the standard of care for anterior-circulation LVO(3-5).\u0026nbsp;However, in clinical practice, the phenomenon of \u0026ldquo;successful recanalization yet poor functional outcome\u0026rdquo; is frequently observed. Even among patients achieving high-grade reperfusion (e.g., modified Thrombolysis in Cerebral Infarction [mTICI] 2b/3), a substantial proportion still experience moderate-to-severe disability or death at 90 days. These observations indicate that long-term outcomes after EVT are not determined solely by macrovascular recanalization, but are also influenced by multiple factors, including baseline metabolic status, inflammatory responses, endothelial dysfunction, microcirculatory perfusion, and ischemia\u0026ndash;reperfusion injury\u0026nbsp;(6, 7),\u0026nbsp;there is an urgent need to identify accessible, stable, and biologically plausible prognostic biomarkers to facilitate postprocedural risk stratification and to inform individualized strategies for secondary prevention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDyslipidemia is a classic risk factor for atherosclerosis and ischemic stroke; however, conventional lipid indices such as low-density lipoprotein cholesterol (LDL-C) do not fully account for the residual risk of vascular events. In recent years, increasing attention has been directed toward remnant cholesterol (RC). RC refers to the cholesterol content carried within remnants of triglyceride-rich lipoproteins (TRLs), including chylomicron remnants, very-low-density lipoprotein (VLDL) remnants, and intermediate-density lipoproteins (IDL). Compared with LDL particles, TRL remnants exhibit greater atherogenic potential by more readily being retained within the arterial wall and taken up by macrophages to form foam cells, and are closely associated with endothelial dysfunction, inflammatory activation, and a prothrombotic milieu\u0026nbsp;(8).\u0026nbsp;RC can be readily derived from routine lipid profiles using a simple calculation (RC = total cholesterol \u0026minus; LDL-C \u0026minus; HDL-C), which confers high clinical feasibility and scalability. Prior studies have suggested that RC is associated with the risk of coronary artery disease, peripheral arterial disease, and ischemic stroke; however, evidence regarding its prognostic value in patients undergoing EVT remains relatively limited\u0026nbsp;(9).\u0026nbsp;On the one hand, patients undergoing EVT often have a higher atherosclerotic burden and are exposed to substantial periprocedural stress responses. On the other hand, post-reperfusion microcirculatory dysfunction, blood\u0026ndash;brain barrier disruption, and secondary inflammation may interact with lipid metabolic disturbances, potentially modulating neurological recovery\u0026nbsp;(2, 6).\u0026nbsp;Therefore, investigating the impact of RC on functional outcomes after EVT is of clear clinical relevance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Based on this rationale, we aimed to systematically evaluate the association between baseline RC levels and 3-month functional outcomes measured by the modified Rankin Scale (mRS) in patients with AIS-LVO undergoing EVT, and to determine whether RC provides incremental predictive value beyond established clinical prognostic models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study aimed to evaluate the value of remnant cholesterol in predicting functional outcomes in patients with acute ischemic stroke due to large-vessel occlusion (AIS-LVO) undergoing endovascular therapy (EVT). A total of 254 consecutive patients with acute large-vessel occlusive ischemic stroke who received EVT in the emergency department of Beijing Chaoyang Hospital, Capital Medical University, between October 2022 and October 2023 were enrolled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e were: (1) acute ischemic stroke with large-vessel occlusion confirmed by imaging (CT/CTA); (2) EVT performed within 24 h of symptom onset; (3) complete preprocedural fasting lipid data, including total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol; and (4) complete clinical information, including baseline National Institutes of Health Stroke Scale (NIHSS) score and 3-month modified Rankin Scale (mRS) score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e were: (1) severe comorbidities, such as advanced cardiopulmonary disease, hepatic or renal insufficiency, or malignancy; (2) inability to undergo EVT within 24 h of onset; (3) refusal to participate or loss to follow-up; (4) missing imaging data precluding accurate assessment; and (5) any other conditions not meeting the inclusion criteria or compromising study integrity.\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Office of Beijing Chaoyang Hospital, Capital Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Data collection and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patient data were obtained from Beijing Chaoyang Hospital, Capital Medical University. We systematically collected demographic characteristics of enrolled patients, including age and sex; preprocedural blood biochemical parameters, including total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and lipoprotein(a); and clinical assessments, including neurological deficit severity evaluated on admission using the National Institutes of Health Stroke Scale (NIHSS) and post-thrombectomy reperfusion status assessed using the Thrombolysis in Cerebral Infarction (TICI) grading system.\u003c/p\u003e\n\u003cp\u003eThe 3-month modified Rankin Scale (mRS) score was recorded through outpatient visits or telephone follow-up. An mRS score of 0\u0026ndash;2 was defined as a favorable outcome, whereas 3\u0026ndash;6 indicated an unfavorable outcome. To examine the impact of metabolic status on patient outcomes, remnant cholesterol (RC) was calculated using the following formula: RC=total cholesterol\u0026minus;high-density lipoprotein cholesterol\u0026minus;low-density lipoprotein cholesterol. All data underwent rigorous quality control and timely cleaning to ensure accuracy and compliance with ethical standards, with patient privacy protected throughout. The National Institutes of Health Stroke Scale (NIHSS), Thrombolysis in Cerebral Infarction (TICI), and modified Rankin Scale (mRS) used in this study were previously published assessment tools and were not developed specifically for the present study. Baseline stroke severity was assessed using the NIHSS at admission. Angiographic reperfusion after endovascular treatment was evaluated according to the TICI grading system. Functional outcome was assessed using the mRS. (10-12)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test in GraphPad Prism (version 10.4, USA). Normally distributed data are presented as mean \u0026plusmn; standard deviation (mean \u0026plusmn; SD) and were compared using the independent-samples \u003cem\u003et\u003c/em\u003e test. Non-normally distributed continuous variables are presented as median (interquartile range [IQR]) and were compared using the Mann\u0026ndash;Whitney U test. Categorical variables are summarized as counts and percentages and were compared between groups using the chi-square test.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression was performed in R to quantify the association between each variable and outcome after adjustment for potential confounders, and the independent predictive value of each variable was subsequently evaluated.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using GraphPad Prism, R (4.52.2173.0), and RStudio (2025.9.1.0), with the following packages: \u003cem\u003ereadxl\u003c/em\u003e, \u003cem\u003edplyr\u003c/em\u003e, \u003cem\u003ecorrplot\u003c/em\u003e, \u003cem\u003epROC\u003c/em\u003e, \u003cem\u003erms\u003c/em\u003e, \u003cem\u003erandomForest\u003c/em\u003e, \u003cem\u003eggplot2\u003c/em\u003e, \u003cem\u003ereshape2\u003c/em\u003e, and \u003cem\u003elogistf\u003c/em\u003e. A two-sided \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Baseline clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding baseline characteristics, the median age was 62 (53, 72) years in one group and 71 (62.5, 78) years in the other, with a significant between-group difference (p \u0026lt; 0.001). Overall, 175 patients (66.2%) were male, and the sex distribution did not differ significantly between groups. Baseline NIHSS scores differed significantly between the two groups (p=0.006), with higher scores observed in the unfavorable-outcome group. With respect to comorbidities, the prevalence of atrial fibrillation and prior cerebral infarction was significantly higher in the unfavorable-outcome group than in the favorable-outcome group (p=0.016 and p=0.043, respectively). There were no significant between-group differences in the prevalence of hypertension, coronary artery disease, diabetes mellitus, or hyperlipidemia. Clinically, postprocedural TICI grades were comparable between the two groups, with no statistically significant difference \u003cstrong\u003e(Figure 1)\u003c/strong\u003e. Accordingly, baseline demographic and clinical characteristics were generally comparable between groups, with significant differences observed primarily in age and baseline NIHSS score \u003cstrong\u003e(Table 1)\u003c/strong\u003e. The distribution of 3-month modified Rankin Scale (mRS) scores for the entire cohort is presented in \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBaseline clinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFavorable outcome (mRS 0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnfavorable outcome (mRS 3-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e62.00 (53.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e71.00 (62.50, 78.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eMale gender（68.89%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e95 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e80 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e93 (72.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e92 (73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eOld infarct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e61 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e76 (60.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e43 (33.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e40 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e11 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e24 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eNIHSS pre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e11.00 (6.00, 13.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e12.00 (8.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eTICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e26 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e26 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e14 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e16 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Biochemical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis showed that serum triglyceride levels and fasting blood glucose on postoperative day 1 were significantly higher in the unfavorable-outcome group than in the favorable-outcome group (p=0.046 and p=0.031, respectively). In addition, remnant cholesterol was also elevated in the unfavorable-outcome group (p\u0026lt;0.001). No significant differences were observed for the other biochemical parameters. No significant differences were observed in the other biochemical parameters. \u003cstrong\u003e(Table 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBiochemical parameters\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"562\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFavorable outcome (n=128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnfavorable outcome (n=126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eTotal cholesterol（TC）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e4.43 (3.51, 5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e4.12 (3.41, 4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eHigh-density lipoprotein cholesterol（HDL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.02 (0.83, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.01 (0.88, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eLow-density lipoprotein cholesterol（LDL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.78 (1.88, 3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.51 (1.96, 3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eSerum triglycerides（TG）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.51 (1.08, 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.19 (0.88, 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eLipoprotein(a)（Lp(a)）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e16.00 (9.47, 30.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e19.05 (9.00, 42.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBlood glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e7.17 (5.75, 10.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e7.92 (6.29, 10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.69 (0.43, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.01 (0.62, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariable logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"562\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eaOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e1.065(0.657, 1.922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.037 (0.566, 1.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e1.034 (1.016, 1.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.028 (1.025, 1.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eOld infarct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e1.670 (1.017, 2.755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.298 (0.757, 2.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e2.503 (1.194, 5.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.982 (0.891, 4.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNIHSS pre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e1.072 (1.024, 1.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.066 (1.015, 1.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e2.877 (1.635, 5.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.891 (1.590, 5.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMultivariable analysis of predictors of 3-month outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariables that were statistically significant in univariable logistic regression were entered into the multivariable logistic regression model. Multicollinearity was assessed by examining tolerance values \u0026gt; 0.1 and variance inflation factors (VIFs) well below 10, indicating no substantial multicollinearity within the model. Table 3 presents the unadjusted and adjusted odds ratios (ORs) for the associations between candidate variables and unfavorable 3-month outcomes after EVT, showing significant associations in both univariable and multivariable analyses. In the adjusted analysis, RC remained significantly associated with unfavorable outcome. In univariable analysis, the OR was 2.877 (95%CI, 1.635\u0026ndash;5.181; p=0.002); after adjustment for potential confounders, the adjusted OR (aOR) was 2.891 (95% CI, 1.590\u0026ndash;5.393; p=0.001). These findings suggest that higher RC levels are associated with an increased risk of poor functional outcome at 3 months following EVT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Diagnostic performance of remnant cholesterol based on ROC curve analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of remnant cholesterol (RC) for predicting unfavorable 3-month functional outcomes after EVT in patients with AIS-LVO (\u003cstrong\u003eFigure3\u003c/strong\u003e). The optimal RC cutoff for predicting unfavorable outcome was 0.91, yielding a sensitivity of 59.2% and a specificity of 66.4%, with an AUC of 0.715 (95% CI, 0.653\u0026ndash;0.780). After incorporating RC, the AUC increased from 0.671 to 0.715.\u003c/p\u003e\n\u003cp\u003eTo assess the goodness-of-fit of the logistic regression model incorporating remnant cholesterol (RC) for predicting unfavorable 3-month outcomes, the Hosmer\u0026ndash;Lemeshow (H\u0026ndash;L) test was performed. For the model including RC, the H\u0026ndash;L chi-square statistic was 7.539 (df=8, p=0.479), indicating good calibration, with predicted probabilities of unfavorable outcome consistent with the observed outcomes (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eBy calculating the net reclassification improvement (NRI), we further quantified the extent to which adding RC improved risk reclassification. For RC, the continuous NRI was 0.5186 (95%CI, 0.2837\u0026ndash;0.7610; p\u0026lt;0.001), whereas the categorical NRI, stratified by the optimal cutoff of 0.91, was 0.0625 (95%CI, \u0026minus;0.0634 to 0.1942; p=0.37). These results indicate a modest trend toward improved risk reclassification after incorporating RC; however, the improvement did not reach statistical significance (NRI close to 0, 95% CI crossing 0, p\u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were performed based on variables that remained significantly different between the favorable- and unfavorable-outcome groups after multivariable adjustment, as well as on several previously reported independent risk factors for poor 3-month outcomes in AIS (Figure 4). Compared with RC\u0026lt;0.91, elevated RC remained an independent risk factor for unfavorable outcomes at 3 months after EVT in patients with AIS-LVO. Baseline characteristics, such as sex and age, did not materially alter this association. In the subgroup with NIHSS\u0026gt;14, the odds ratio was 2.811 (95% CI, 0.939\u0026ndash;9.497; p=0.07), and the 95% CI included the null value, indicating a non-significant result. The test for interaction across NIHSS strata was not significant (p=0.747). This finding may be attributable to reduced statistical power due to the small sample size in the NIHSS\u0026gt;14 subgroup rather than a true differential effect. Nevertheless, the consistent direction of effect suggests that the overall trend may still apply to this subgroup, warranting further investigation. Similar patterns were observed in other small subgroups. For example, among patients with atrial fibrillation (n=35), the association was directionally consistent but did not reach statistical significance (OR=4.64, 95%CI,0.677\u0026ndash;4.688;p=0.132). Comparable findings were also noted in subgroups defined by hyperlipidemia (n=30), lower-grade reperfusion (n=48), absence of hypertension (n=69), and diabetes mellitus (n=83). In addition, although the subgroup without prior infarction had relatively balanced group sizes, the effect estimate did not achieve statistical significance (p=0.08), likely reflecting a limited number of events and increased uncertainty within the subgroup; interaction testing did not suggest significant effect modification by prior infarction (p\u0026gt;0.05).Overall, these findings support the robustness and generalizability of the association between elevated RC and unfavorable outcomes across different patient subgroups, with no significant evidence of effect modification.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focused on the relationship between remnant cholesterol (RC) and 3-month functional outcomes, as assessed by the modified Rankin Scale (mRS), in patients with acute ischemic stroke undergoing mechanical thrombectomy. RC is typically calculated as RC = total cholesterol\u0026minus;LDL-C\u0026minus;HDL-C, and reflects the cholesterol burden carried by triglyceride-rich lipoproteins (TRLs) and their remnants. In recent years, RC has emerged as an important metabolic phenotype within the \u0026ldquo;residual risk despite achieving LDL-C targets\u0026rdquo; paradigm\u0026nbsp;(13, 14).\u0026nbsp;In this study, analyses based on 3-month mRS outcomes indicated a statistically significant association between RC and unfavorable prognosis, and suggested that RC may provide incremental information for prognostic modeling; however, its predictive utility is best interpreted within a multifactorial framework. On the one hand, RC remained significantly associated with unfavorable outcomes after adjustment for potential confounders, including age, sex, baseline NIHSS score, and comorbidities, in multivariable logistic regression. On the other hand, ROC analysis demonstrated that RC as a single marker had a moderate discriminative performance for unfavorable outcome (AUC=0.715), with sensitivity and specificity that were not particularly high. These findings imply that RC is unlikely to be a stand-alone predictor; rather, its prognostic contribution is probably jointly modulated by stroke severity, reperfusion quality, metabolic comorbidities, and acute-phase stress responses, and should therefore be understood within an integrated, multivariable and mechanistic context.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, studies on RC in the field of stroke have increased; however, the overall conclusions remain not entirely consistent. On the one hand, population-based studies and genetic evidence support a robust association between RC and atherosclerotic cardiovascular and cerebrovascular events. Several stroke cohorts have also reported that RC is associated with poor functional outcomes or an increased risk of mortality, and under certain circumstances may serve as a prognostic marker\u0026nbsp;(15).\u0026nbsp;On the other hand, some studies suggest that the association between RC and short-term functional outcomes may be influenced by metabolic conditions (e.g., dysglycemia/diabetes and metabolic dysfunction\u0026ndash;associated fatty liver disease), nutritional status, and even therapeutic strategies, resulting in heterogeneous, unstable, or non-linear relationships and substantial between-subgroup variability. Such context-dependent effects may contribute to paradoxical associations between lipid levels and outcomes, as well as effect modification across clinical phenotypes\u0026nbsp;(16-18).Therefore, our finding that RC demonstrates a moderate AUC and an independent association with outcome\u0026mdash;yet does not exhibit \u0026ldquo;highly robust\u0026rdquo; predictive performance\u0026mdash;is not inconsistent with prior evidence. Rather, it underscores that the impact of RC on short-term functional outcomes is contingent on cohort composition, the timing of biomarker measurement, and the covariates included for adjustment. Accordingly, RC should not be interpreted as a single, linear, universally strong stand-alone predictor.\u003c/p\u003e\n\u003cp\u003eMoreover, from the perspective of atherosclerosis and thrombosis, TRLs and their remnants are considered to be distinctly pro-atherogenic. Remnant particles can penetrate the arterial wall and be taken up by macrophages to form foam cells, while also inducing endothelial dysfunction, oxidative stress, and vascular inflammation, thereby promoting plaque progression and destabilization(19).Consensus statements from the European Atherosclerosis Society (EAS), together with multiple review articles, have emphasized that remnant cholesterol represents a major contributor to residual atherosclerotic cardiovascular disease (ASCVD) risk and may constitute a potentially modifiable therapeutic target(20-22).In this study, the observed statistical association between RC and unfavorable outcomes suggests that RC may, to some extent, capture an underlying risk milieu related to atherosclerotic burden, metabolic dysregulation, and vascular inflammation. However, this association should be further interpreted and validated in conjunction with more comprehensive clinical and imaging information, such as infarct core and penumbra characteristics, collateral status, and hemorrhagic transformation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore importantly, the relationship between RC and inflammation may be relevant to stroke prognosis. Some studies have suggested an association between non-fasting RC and other inflammatory markers, such as elevated C-reactive protein (CRP), indicating that RC may not only reflect lipid burden but also correlate with heightened inflammatory activity. Inflammation contributes throughout the entire course of secondary injury and recovery after stroke, including expansion of the ischemic penumbra, blood\u0026ndash;brain barrier disruption, reperfusion injury, post-stroke immunosuppression and infectious complications, as well as processes of neural repair\u0026nbsp;(23-25).\u0026nbsp;Therefore, mechanistically, RC is more likely to influence outcomes through an integrated \u0026ldquo;metabolism\u0026ndash;inflammation\u0026ndash;vascular\u0026rdquo; pathway; however, its effect is often neither isolated nor strictly linear, and may be modulated by stroke severity, reperfusion quality, and acute-phase stress responses. During the acute phase of stroke, marked systemic stress responses occur, and inflammation and infection can rapidly alter the lipid profile\u0026mdash;manifesting as reductions in HDL-C, elevations in triglycerides, impaired clearance of TRLs, and hepatic remodeling of lipoprotein metabolism\u0026mdash;rendering a single lipid measurement more susceptible to acute stress effects rather than reflecting long-term exposure\u0026nbsp;(26).\u0026nbsp;This may help explain why, in acute stroke cohorts, the relationship between RC and outcomes is more likely to be influenced by the timing of blood sampling, the magnitude of physiological stress, intercurrent infection, and overall inflammatory burden, thereby appearing non-linear or unstable across different subgroups. Moreover, in the context of malnutrition, hypoalbuminemia, or catabolic states, lipid parameters may increasingly reflect systemic consumption or inflammatory activity rather than baseline metabolic risk, giving rise to an apparent \u0026ldquo;lipid paradox,\u0026rdquo; in which lower lipid levels are associated with worse outcomes(27, 28).Therefore, even though RC showed a statistically significant association in our study, it should be recognized that RC may partly reflect acute-phase metabolic\u0026ndash;inflammatory status rather than solely long-term lipid exposure; accordingly, its clinical significance is better assessed within an integrated, comprehensive risk framework.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn patients undergoing mechanical thrombectomy, 90-day functional outcomes are typically driven by a limited number of strong clinical determinants, such as baseline NIHSS score and successful reperfusion (mTICI grade), which explain a substantial proportion of the variability in mRS. Even with successful recanalization, poor outcomes may still occur, indicating that prognosis is jointly determined by multiple factors. In this context, the independent effect of RC as a metabolic marker is more likely to be masked by dominant predictors or modified by other factors, resulting in an apparently unstable association. Consistent with this notion, RC remained significant in our multivariable model, whereas in subgroup analyses, several small subgroups (e.g., NIHSS \u0026gt; 14, atrial fibrillation, hyperlipidemia, or lower mTICI grades) showed markedly widened confidence intervals and borderline non-significant estimates, which more likely reflects limited statistical power rather than a true loss of effect or a change in direction.\u003c/p\u003e\n\u003cp\u003eFrom a predictive modeling perspective, we further compared the baseline model with the model incorporating RC. The results showed that adding RC increased the AUC from 0.671 to 0.715, suggesting that RC provides incremental discriminative information. The optimal RC cutoff for predicting unfavorable outcome was 0.91 (sensitivity, 59.2%; specificity, 66.4%), indicating a certain stratification utility; however, the moderate sensitivity and specificity imply that RC is better suited as an adjunct variable within a multivariable model rather than as a stand-alone marker for clinical screening or decision-making. In terms of model fit, the Hosmer\u0026ndash;Lemeshow test for the model including RC yielded a \u003cem\u003ep\u003c/em\u003e value of 0.479 (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), indicating acceptable agreement between predicted probabilities and observed outcomes and an overall good model fit.\u003c/p\u003e\n\u003cp\u003eNotably, reclassification analyses showed a pattern in which the continuous NRI was significant, whereas the categorical NRI was not. Specifically, the continuous NRI was 0.5186 (95% CI, 0.2837\u0026ndash;0.7610; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while the categorical NRI based on the 0.91 cutoff was 0.0625 (95% CI, \u0026minus;0.0634 to 0.1942; \u003cem\u003ep\u003c/em\u003e = 0.37). This \u0026ldquo;significant continuous NRI but non-significant categorical NRI\u0026rdquo; pattern is not uncommon in prediction modeling studies. Potential explanations include the high sensitivity of categorical NRI to threshold selection; when risk-group boundaries are not aligned with clinically meaningful decision thresholds, the ability to demonstrate improved risk-group assignment may be limited. Thus, even if predicted probabilities improve at the continuous scale, this may not translate into statistically significant changes in categorical risk classification. Accordingly, we interpret our findings as indicating that incorporation of RC yields meaningful improvements in probability updating and overall reclassification on the continuous scale, whereas reclassification improvement based on the 0.91 threshold is modest and did not reach statistical significance. This suggests that the incremental value of RC may primarily manifest as subtle refinements in estimated risk probabilities rather than substantial shifts of large numbers of individuals between predefined risk categories.\u003c/p\u003e\n\u003cp\u003eIn subgroup analyses, the direction of the association between RC \u0026ge; 0.91 and an increased risk of unfavorable outcome was largely consistent across most subgroups, and tests for interaction did not indicate significant effect modification (all \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05), suggesting that the current evidence does not support the notion that the effect of RC is confined to a specific subgroup. For certain subgroups in which the 95% CI crossed 1 or the \u003cem\u003ep\u003c/em\u003e value approached but did not reach 0.05, a more plausible explanation is that reductions in subgroup sample size and event counts increased estimation uncertainty. In particular, in the subgroup without prior cerebral infarction, although the sample sizes between groups were relatively comparable, the reduced number of events may have limited statistical power, resulting in borderline non-significant \u003cem\u003ep\u003c/em\u003e values (e.g., \u003cem\u003ep\u003c/em\u003e = 0.08) and widened confidence intervals. Meanwhile, the non-significant interaction test further indicates that prior cerebral infarction did not materially modify the strength of the association between RC and outcome. Therefore, subgroup findings should be interpreted as exploratory, serving primarily to demonstrate consistency and stability in the direction of association rather than to support definitive subgroup-specific conclusions.\u003c/p\u003e\n\u003cp\u003eIntegrating our findings with the existing literature, we propose that the potential value of RC in the stroke field may primarily lie in two aspects:\u003c/p\u003e\n\u003cp\u003e1.As a metabolic marker reflecting TRL remnant burden and atherosclerotic risk, RC may help refine vascular risk profiling in patients with stroke, particularly among those who achieve adequate LDL-C control yet continue to exhibit residual risk.\u003c/p\u003e\n\u003cp\u003e2.For acute-phase prognostication, RC is more likely to yield a stable incremental predictive benefit when jointly modeled with inflammatory markers and metabolic comorbidities, rather than being used as a stand-alone screening index. If future studies confirm that RC or RC-based composite measures can consistently improve model performance in external cohorts, RC could potentially be applied for early risk stratification, identification of high-risk individuals, and to inform secondary prevention strategies after stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the retrospective single-center design may introduce selection bias. Second, acute-phase laboratory measurements are susceptible to the timing of blood sampling and physiological stress responses. Third, outcomes after thrombectomy are strongly driven by reperfusion status, treatment time window, and baseline stroke severity; therefore, the effect of RC should be further examined in larger cohorts with formal assessments of interaction, non-linearity, and external validation. Future studies could integrate longitudinal metabolic monitoring with inflammatory biomarkers to explore potential effect modification of RC across clinical subgroups and to validate its robustness and prognostic utility in predictive models.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eRC\u003c/strong\u003e, remnant cholesterol; \u003cstrong\u003eAIS-LVO\u003c/strong\u003e, acute ischemic stroke with large-vessel occlusion; \u003cstrong\u003eEVT\u003c/strong\u003e, endovascular treatment; \u003cstrong\u003emRS\u003c/strong\u003e, modified Rankin Scale; \u003cstrong\u003eNIHSS\u003c/strong\u003e, National Institutes of Health Stroke Scale; \u003cstrong\u003eTICI\u003c/strong\u003e, Thrombolysis in Cerebral Infarction.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Office of Beijing Chaoyang Hospital, Capital Medical University. Due to the retrospective nature of the study and the use of de-identified clinical data, the requirement for informed consent was waived by the Ethics Office of Beijing Chaoyang Hospital, Capital Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analised during the current study were derived from the electronic medical record system and physician order entry system of Beijing Chaoyang Hospital, Capital Medical University. The data are not publicly available due to patient privacy and institutional restrictions but are available from the corresponding authors on reasonable request and with permission from the hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiyang Feng contributed to the study conception and design, data collection, data analysis, and manuscript drafting. Zhi Zhang and Zhaotao Wen contributed to data collection, data interpretation, and revision of the manuscript. Yunpeng Liu and Yang Wang supervised the study, contributed to the study design, critically revised the manuscript, and served as corresponding authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of Beijing Chaoyang Hospital, Capital Medical University, for their support in data collection and clinical management. We also sincerely appreciate all healthcare professionals involved in the diagnosis, treatment, and follow-up of the patients included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiyang Feng was the primary contributor to this study. Zhi Zhang and Zhaotao Wen contributed equally and were designated as co-first authors. Yunpeng Liu and Yang Wang served as the corresponding authors.\u003c/p\u003e\n\u003cp\u003eLiyang Feng：[email protected] Master of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China\u003c/p\u003e\n\u003cp\u003eZhi Zhang：[email protected] Master of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China\u003c/p\u003e\n\u003cp\u003eZhaotao Wen: \u0026nbsp;[email protected] Master of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China\u003c/p\u003e\n\u003cp\u003eYunpeng Liu：[email protected] Doctor of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China\u003c/p\u003e\n\u003cp\u003eYang wang：[email protected] Doctor of Medicine; Beijing Chaoyang Hospital, Capital Medical University, Beijing, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDiseases GBD, Injuries C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133-61.\u003c/li\u003e\n\u003cli\u003ede Havenon A, Zaidat OO, Amin-Hanjani S, Nguyen TN, Bangad A, Abbasi M, et al. Large Vessel Occlusion Stroke due to Intracranial Atherosclerotic Disease: Identification, Medical and Interventional Treatment, and Outcomes. Stroke. 2023;54(6):1695-705.\u003c/li\u003e\n\u003cli\u003eTurc G, Tsivgoulis G, Audebert HJ, Boogaarts H, Bhogal P, De Marchis GM, et al. European Stroke Organisation (ESO)-European Society for Minimally Invasive Neurological Therapy (ESMINT) expedited recommendation on indication for intravenous thrombolysis before mechanical thrombectomy in patients with acute ischemic stroke and anterior circulation large vessel occlusion. J Neurointerv Surg. 2022;14(3):209.\u003c/li\u003e\n\u003cli\u003eSarraj A, Hassan AE, Abraham MG, Ortega-Gutierrez S, Kasner SE, Hussain MS, et al. Trial of Endovascular Thrombectomy for Large Ischemic Strokes. N Engl J Med. 2023;388(14):1259-71.\u003c/li\u003e\n\u003cli\u003eSembill JA, Sprugel MI, Haupenthal D, Kremer S, Knott M, Muhlen I, et al. Endovascular thrombectomy in patients with anterior circulation stroke: an emulated real-world comparison. Neurol Res Pract. 2024;6(1):37.\u003c/li\u003e\n\u003cli\u003eSperring CP, Savage WM, Argenziano MG, Leifer VP, Alexander J, Echlov N, et al. No-Reflow Post-Recanalization in Acute Ischemic Stroke: Mechanisms, Measurements, and Molecular Markers. Stroke. 2023;54(9):2472-80.\u003c/li\u003e\n\u003cli\u003eStoll G, Nieswandt B, Schuhmann MK. Ischemia/reperfusion injury in acute human and experimental stroke: focus on thrombo-inflammatory mechanisms and treatments. Neurol Res Pract. 2024;6(1):57.\u003c/li\u003e\n\u003cli\u003eGinsberg HN, Packard CJ, Chapman MJ, Boren J, Aguilar-Salinas CA, Averna M, et al. Triglyceride-rich lipoproteins and their remnants: metabolic insights, role in atherosclerotic cardiovascular disease, and emerging therapeutic strategies-a consensus statement from the European Atherosclerosis Society. Eur Heart J. 2021;42(47):4791-806.\u003c/li\u003e\n\u003cli\u003eWadstrom BN, Wulff AB, Pedersen KM, Jensen GB, Nordestgaard BG. Elevated remnant cholesterol increases the risk of peripheral artery disease, myocardial infarction, and ischaemic stroke: a cohort-based study. Eur Heart J. 2022;43(34):3258-69.\u003c/li\u003e\n\u003cli\u003eBrott T, Adams HP Jr, Olinger CP, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20(7):864-870.\u003c/li\u003e\n\u003cli\u003eHigashida RT, Furlan AJ, Roberts H, et al. Trial design and reporting standards for intra-arterial cerebral thrombolysis for acute ischemic stroke. Stroke. 2003;34(8):e109-e137.\u003c/li\u003e\n\u003cli\u003evan Swieten JC, Koudstaal PJ, Visser MC, et al. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604-607.\u003c/li\u003e\n\u003cli\u003eCastaner O, Pinto X, Subirana I, Amor AJ, Ros E, Hernaez A, et al. Remnant Cholesterol, Not LDL Cholesterol, Is Associated With Incident Cardiovascular Disease. J Am Coll Cardiol. 2020;76(23):2712-24.\u003c/li\u003e\n\u003cli\u003eDelialis D, Georgiopoulos G, Aivalioti E, Konstantaki C, Oikonomou E, Bampatsias D, et al. Remnant cholesterol in atherosclerotic cardiovascular disease: A systematic review and meta-analysis. Hellenic J Cardiol. 2023;74:48-57.\u003c/li\u003e\n\u003cli\u003eWang K, Wang R, Yang J, Liu X, Shen H, Sun Y, et al. Remnant cholesterol and atherosclerotic cardiovascular disease: Metabolism, mechanism, evidence, and treatment. Front Cardiovasc Med. 2022;9:913869.\u003c/li\u003e\n\u003cli\u003eTan Z, Zhang Q, Liu Q, Meng X, Wu W, Wang L, et al. Relationship between remnant cholesterol and short-term prognosis in acute ischemic stroke patients. Brain Behav. 2024;14(5):e3537.\u003c/li\u003e\n\u003cli\u003eJiang S, Jin A, Xing W, Jing J. Impact of remnant cholesterol on acute ischemic stroke prognosis: a nationwide cohort analysis stratified by non-alcoholic fatty liver disease status. Front Neurol. 2025;16:1472871.\u003c/li\u003e\n\u003cli\u003eAndone S, Farczadi L, Imre S, Balasa R. Fatty Acids and Lipid Paradox-Neuroprotective Biomarkers in Ischemic Stroke. Int J Mol Sci. 2022;23(18).\u003c/li\u003e\n\u003cli\u003eHeo JH, Jo SH. Triglyceride-Rich Lipoproteins and Remnant Cholesterol in Cardiovascular Disease. J Korean Med Sci. 2023;38(38):e295.\u003c/li\u003e\n\u003cli\u003eNordestgaard BG, Langlois MR, Langsted A, Chapman MJ, Aakre KM, Baum H, et al. Quantifying atherogenic lipoproteins for lipid-lowering strategies: Consensus-based recommendations from EAS and EFLM. Atherosclerosis. 2020;294:46-61.\u003c/li\u003e\n\u003cli\u003eGugliucci A. Beyond LDL: Understanding Triglyceride-Rich Lipoproteins to Tackle Residual Risk. J Clin Med. 2023;12(12).\u003c/li\u003e\n\u003cli\u003eDuran EK, Pradhan AD. Triglyceride-Rich Lipoprotein Remnants and Cardiovascular Disease. Clin Chem. 2021;67(1):183-96.\u003c/li\u003e\n\u003cli\u003eDoi T, Langsted A, Nordestgaard BG. Dual elevated remnant cholesterol and C-reactive protein in myocardial infarction, atherosclerotic cardiovascular disease, and mortality. Atherosclerosis. 2023;379:117141.\u003c/li\u003e\n\u003cli\u003eQiu YM, Zhang CL, Chen AQ, Wang HL, Zhou YF, Li YN, et al. Immune Cells in the BBB Disruption After Acute Ischemic Stroke: Targets for Immune Therapy? Front Immunol. 2021;12:678744.\u003c/li\u003e\n\u003cli\u003eWestendorp WF, Dames C, Nederkoorn PJ, Meisel A. Immunodepression, Infections, and Functional Outcome in Ischemic Stroke. Stroke. 2022;53(5):1438-48.\u003c/li\u003e\n\u003cli\u003eBerberich AJ, Hegele RA. A Modern Approach to Dyslipidemia. Endocr Rev. 2022;43(4):611-53.\u003c/li\u003e\n\u003cli\u003eZhou H, Wang A, Meng X, Lin J, Jiang Y, Jing J, et al. Low serum albumin levels predict poor outcome in patients with acute ischaemic stroke or transient ischaemic attack. Stroke Vasc Neurol. 2021;6(3):458-66.\u003c/li\u003e\n\u003cli\u003eHoshino T. Prognostic Implications of the Acute Lipid Levels in Stroke Patients. J Atheroscler Thromb. 2024;31(8):1133-4.\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":"residual cholesterol, acute ischemic stroke, large-vessel occlusion, endovascular therapy, modified Rankin Scale, lipid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-9123147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9123147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eResidual cholesterol, an important marker of lipid metabolism, has been increasingly used in recent years as a monitoring indicator for insulin resistance and for prognosis surveillance after major cardiovascular interventions. However, evidence regarding its utility for functional prognostication in acute occlusive ischemic stroke\u0026mdash;particularly among patients undergoing endovascular therapy\u0026mdash;remains limited. This study aimed to evaluate whether residual cholesterol can predict functional outcomes after endovascular treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 254 consecutive patients with acute ischemic stroke who underwent endovascular therapy at Beijing Chaoyang Hospital, Capital Medical University, between October 2022 and October 2023. Patients were divided into two groups according to functional outcome. Baseline clinical characteristics and preprocedural biochemical indices were collected to calculate residual cholesterol, and correlation analyses were performed. Independent prognostic factors were identified using logistic regression, and receiver operating characteristic (ROC) curves were generated to assess the predictive performance of residual cholesterol.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eResidual cholesterol (RC) was significantly higher in the poor-outcome group than in the good-outcome group (0.86 [0.49, 1.27] vs 0.64 [0.43, 1.12], p\u0026thinsp;=\u0026thinsp;0.033). After adjustment in multivariable logistic regression, RC remained an independent predictor of 90-day functional independence after EVT (OR, 1.911; 95% CI, 1.039\u0026ndash;3.585; p\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRC has important value in predicting 3-month functional outcomes in patients with AIS due to large-vessel occlusion undergoing EVT, and it improves the predictive performance of models based solely on clinical variables.\u003c/p\u003e","manuscriptTitle":"Residual cholesterol as a predictor of early functional outcome after endovascular treatment for acute large-vessel occlusion ischemic stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 09:15:52","doi":"10.21203/rs.3.rs-9123147/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-01T00:29:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T00:28:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T18:06:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T15:16:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-03-21T15:12:55+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":"c9e2f21b-8c63-4180-bc2c-65c95784972a","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T09:15:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 09:15:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9123147","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9123147","identity":"rs-9123147","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}