Postoperative Blood Glucose Trajectories and Inflammatory Markers: Prognostic Implications in Acute Ischemic Stroke Treated by Thrombectomy

Postoperative Blood Glucose Trajectories and Inflammatory Markers: Prognostic Implications in Acute Ischemic Stroke Treated by Thrombectomy | 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 Postoperative Blood Glucose Trajectories and Inflammatory Markers: Prognostic Implications in Acute Ischemic Stroke Treated by Thrombectomy Yunpeng Liu, Jumei Huang, Yang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6875883/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hyperglycemia frequently occurs after acute ischemic stroke and is associated with worse neurological outcomes. However, the impact of postoperative blood glucose trends, especially in patients with large vessel occlusion (LVO) stroke undergoing mechanical thrombectomy (MT), remains unclear. In this retrospective cohort study of 150 patients with LVO stroke treated with MT between March 2023 and September 2024, we assessed the association between postoperative blood glucose trajectories and 90-day functional outcomes, as well as the potential inflammatory response underlying this association. Daily fingerstick capillary blood glucose levels (fasting and postprandial) were measured for the first seven days post-procedure, and linear regression was used to calculate the slope of the postoperative glucose trend for each patient. Patients were divided into Glucose-Increasing (n = 75) and Glucose-Decreasing (n = 75) groups based on the median slope. The primary outcome was 90-day functional status, assessed by the modified Rankin Scale (mRS) through outpatient clinic visits or structured telephone interviews; secondary outcomes included postoperative day 1 levels of C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10). Patients in the Glucose-Increasing Group had significantly higher median 90-day mRS scores (4.0 vs. 3.0; P = 0.030) and higher postoperative CRP (5.2 vs. 4.1 mg/L; P = 0.022), IL-6 (6.9 vs. 5.7 pg/mL; P = 0.015), and IL-10 (134 vs. 104 pg/mL; P = 0.0017) levels. Multivariate logistic regression adjusting for potential confounders did not identify glucose trend group as an independent predictor of poor outcome (mRS ≥ 3; odds ratio 0.67, 95% CI 0.34–1.37; P = 0.272). These findings suggest that an increasing postoperative blood glucose trend is associated with higher inflammatory markers and poorer functional outcomes in LVO stroke patients undergoing thrombectomy, although it may not be an independent predictor when adjusted for other factors, underscoring the need for future prospective studies. Acute ischemic stroke Mechanical thrombectomy Postoperative hyperglycemia Inflammatory response Functional outcome Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute ischemic stroke (AIS) frequently triggers stress hyperglycemia, even in patients without pre-existing diabetes. Elevated blood glucose in the acute phase of stroke is consistently associated with worse neurological outcomes and higher mortality( 1 ). Hyperglycemia can exacerbate ischemic brain injury by disrupting the blood–brain barrier, amplifying thrombo-inflammatory cascades, and increasing oxidative stress( 2 ). As a result, glycemic control is considered an important aspect of acute stroke care, although clinical trials of intensive insulin therapy, such as the SHINE trial, have not demonstrated improved functional outcomes and have raised concerns about hypoglycemia( 3 ). Mechanical thrombectomy (MT) has become the standard of care for large vessel occlusion (LVO) strokes, dramatically improving recanalization rates and outcomes( 4 ). However, up to half of patients with successful recanalization still experience poor functional recovery, highlighting the need to identify modifiable predictors of outcome. While admission hyperglycemia has been linked to poor prognosis in several studies, its predictive value in thrombectomy patients remains debated. More recently, researchers have turned their attention to postoperative blood glucose trends, recognizing that a single admission glucose measurement may not adequately capture patient risk( 5 ). Limited evidence suggests that postoperative glucose increases could independently predict worse functional outcomes even in patients with successful recanalization, yet comprehensive studies specifically investigating postoperative glycemic trajectories in LVO stroke patients remain scarce. Emerging data also indicate that hyperglycemia after stroke may amplify systemic inflammatory responses, further compromising neurological recovery. Elevated blood glucose can promote cytokine release, oxidative stress, and endothelial dysfunction, creating a pro-inflammatory environment that exacerbates secondary brain injury. In turn, inflammation itself can worsen glucose dysregulation, establishing a vicious cycle between metabolic and immune stress( 6 – 8 ). This underscores the need to explore postoperative glucose trajectories in the context of concurrent inflammatory markers to better understand their collective impact on functional outcomes. Therefore, this study aims to investigate the prognostic significance of postoperative blood glucose trends in patients with LVO stroke treated with MT, while also assessing whether an enhanced early postoperative inflammatory response (e.g., elevated CRP, IL-6, and IL-10 levels) may mediate these associations. Methods Study Design and Population This was a retrospective cohort study conducted at one stroke center in Beijing, China, between March 2023 and September 2024. Consecutive patients who underwent mechanical thrombectomy for acute ischemic stroke were enrolled. Inclusion criteria were: ( 1 ) age ≥ 18 years; ( 2 ) acute ischemic stroke due to large vessel occlusion confirmed by imaging; ( 3 ) successful mechanical thrombectomy within 24 hours of symptom onset; and ( 4 ) available blood glucose and inflammatory marker data within the first week post-procedure. Patients with incomplete medical records or early in-hospital mortality were excluded. This study was approved by the Ethics Committee of Beijing Chao-Yang Hospital, Capital Medical University (approval number: 2022-Ke-522). Data Collection Demographic and clinical data, including age, sex, vascular risk factors (hypertension, diabetes mellitus, coronary artery disease, prior stroke), baseline National Institutes of Health Stroke Scale (NIHSS) scores, admission blood glucose levels, and body mass index (BMI), were collected from electronic medical records. Postoperative laboratory tests, including daily blood glucose levels for the first seven days, C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10) levels measured on postoperative day 1, were also recorded. Blood Glucose Trend Analysis Postoperative blood glucose levels were monitored at least once daily for the first seven days following mechanical thrombectomy, typically measured as fasting capillary blood glucose in the morning and 2-hour postprandial capillary blood glucose levels after each meal. The daily mean blood glucose level was calculated as the arithmetic mean of these measurements. To quantify postoperative glycemic trends, linear regression analysis was performed for each patient, treating the postoperative day number as the independent variable and the daily mean blood glucose level as the dependent variable. The slope of this regression line represented the rate of change in postoperative blood glucose over the first week. Patients were then stratified into two groups based on the median slope value within the cohort: the Glucose-Increasing Group (slope ≥ median, indicating a stable or rising trend) and the Glucose-Decreasing Group (slope < median, indicating a declining trend). This approach enabled a standardized classification of postoperative glycemic trajectories to assess their impact on inflammatory markers and functional outcomes. Outcomes The primary outcome was the 90-day functional outcome, assessed using the modified Rankin Scale (mRS) via outpatient clinic visits or structured telephone interviews. Poor functional outcome was defined as mRS ≥ 3. Secondary outcomes included postoperative inflammatory marker levels (CRP, IL-6, IL-10) measured on postoperative day 1. Statistical Analysis All statistical analyses were performed using GraphPad Prism version 10.0 (GraphPad Software, San Diego, CA, USA). Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range [IQR]) as appropriate, and compared using the independent samples t-test or Mann-Whitney U test. Categorical variables were expressed as counts and percentages, and compared using the chi-square test or Fisher's exact test. Multivariate logistic regression was performed to identify independent predictors of poor functional outcome (mRS ≥ 3), adjusting for potential confounders (age, baseline NIHSS score, diabetes, hypertension, coronary artery disease, and prior stroke). A two-sided P-value < 0.05 was considered statistically significant. Results Baseline Characteristics of the Patients A total of 150 patients who underwent mechanical thrombectomy for acute ischemic stroke were included in this study. Patients were stratified into two groups based on the median slope of their 7-day postoperative blood glucose levels: the Glucose-Increasing Group (n = 75) and the Glucose-Decreasing Group (n = 75). The baseline characteristics of the two groups are summarized in Table 1 . There were no statistically significant differences in age, gender distribution, baseline NIHSS scores, admission blood glucose levels, body mass index (BMI), or the prevalence of hypertension, diabetes, coronary artery disease (CAS), and prior stroke history between the two groups (all P > 0.05). Table 1 Baseline characteristics of the study patients according to the blood glucose trend group (Glucose-Decreasing Group vs. Glucose-Increasing Group). Variable Glucose-Decreasing Group (n = 75) Glucose-Increasing Group (n = 75) P-value Age, years 67.0 ± 9.2 66.2 ± 9.3 0.585 Male, n (%) 53 (71%) 52 (69%) 0.767 NIHSS score 13.1 ± 7.0 13.3 ± 6.7 0.839 Admission glucose, mmol/L 11.2 ± 4.1 12.2 ± 3.7 0.139 BMI, kg/m² 24.7 ± 2.5 24.3 ± 3.0 0.435 Hypertension, n (%) 44 (59%) 44 (59%) 1 Diabetes, n (%) 17 (23%) 13 (17%) 0.412 CAD, n (%) 11 (15%) 13 (17%) 0.667 Prior stroke, n (%) 12 (16%) 11 (15%) 0.814 Comparison of 90-Day Functional Outcomes According to Blood Glucose Trend The primary outcome of this study was the 90-day functional status, assessed using the mRS. The Glucose-Increasing Group had a higher median 90-day mRS score of 4.0 (IQR, 2.0–5.0) compared to 3.0 (IQR, 2.0–4.0) in the Glucose-Decreasing Group. This difference was statistically significant (P = 0.030), indicating that a increasing postoperative blood glucose trend was associated with poorer functional outcomes at 90 days (Fig. 1 ). Moreover, when analyzing the distribution of mRS categories (0–2 vs. 3–6), the proportion of patients with good functional outcomes (mRS ≤ 2) was higher in the Glucose-Decreasing Group (40%) compared to the Glucose-Increasing Group (32%). However, this difference was not statistically significant (P = 0.395). Multivariate Analysis of Blood Glucose Trend Group and Functional Outcomes To further assess whether the blood glucose trend group independently predicted poor functional outcomes (defined as mRS ≥ 3 at 90 days), we performed a multivariate logistic regression analysis adjusting for potential confounders, including age, baseline NIHSS score, diabetes, hypertension, coronary artery disease (CAD), and prior stroke. As shown in Table 2 and illustrated in the forest plot (Fig. 2 ), the blood glucose trend group (Glucose-Increasing vs. Glucose-Decreasing) was not an independent predictor of poor outcome (odds ratio [OR], 0.67; 95% CI, 0.34–1.37; P = 0.272). Other baseline factors, including age (OR, 0.99; P = 0.709), baseline NIHSS (OR, 0.98; P = 0.368), diabetes (OR, 1.00; P = 0.998), hypertension (OR, 1.21; P = 0.595), CAD (OR, 1.03; P = 0.951), and prior stroke (OR, 0.75; P = 0.529), were also not significantly associated with poor functional outcomes in this cohort. Table 2 Results of multivariate logistic regression analysis for predicting poor functional outcome (mRS ≥ 3). OR are presented with 95% confidence intervals (CI). Variable Odds Ratio (OR) 95% CI Lower 95% CI Upper P-value Blood Glucose Trend Group (Increasing vs. Decreasing) 0.67 0.33 1.37 0.272 Age 0.99 0.96 1.03 0.709 NIHSS 0.98 0.93 1.03 0.368 Diabetes 1 0.42 2.36 0.998 Hypertension 1.21 0.6 2.45 0.595 CAD 1.03 0.4 2.69 0.951 Prior Stroke 0.75 0.3 1.85 0.529 Comparison of Postoperative Day 1 C-Reactive Protein (CRP) Levels Between Blood Glucose Trend Groups To evaluate the inflammatory response in the early postoperative period, CRP levels were measured on postoperative day 1 and compared between the two blood glucose trend groups. The Glucose-Increasing Group demonstrated a slightly higher median CRP level of 5.2 mg/L (IQR, 3.2–7.0 mg/L), compared to a median of 4.1 mg/L (IQR, 2.8–5.3 mg/L) in the Glucose-Decreasing Group. This difference was statistically significant (P = 0.022), suggesting a modestly greater early postoperative inflammatory response in patients whose blood glucose tended to increase over the first week. Figure 3 illustrates the distribution of CRP levels in the two groups. Comparison of Postoperative Day 1 IL-6 and IL-10 Levels Between Blood Glucose Trend Groups To further characterize the postoperative inflammatory and anti-inflammatory responses, we measured interleukin-6 (IL-6) and interleukin-10 (IL-10) levels on postoperative day 1. The Glucose-Increasing Group demonstrated a moderately higher median IL-6 level of 6.9 pg/mL (IQR, 5.3–8.2 pg/mL), compared to 5.7 pg/mL (IQR, 4.6–7.4 pg/mL) in the Glucose-Decreasing Group. This difference was statistically significant (P = 0.015), indicating a modestly enhanced early postoperative inflammatory response in patients whose blood glucose tended to increase over the first week (Fig. 4 a). Similarly, the Glucose-Increasing Group exhibited a higher median IL-10 concentration of 134 pg/mL (IQR, 96–177 pg/mL), compared to 104 pg/mL (IQR, 84–135 pg/mL) in the Glucose-Decreasing Group. This difference was also statistically significant (P = 0.0017), indicating a moderately enhanced early postoperative anti-inflammatory response in patients with a postoperative increase in blood glucose (Fig. 4 b). Discussion Maintaining optimal glycemic levels in the acute period after ischemic stroke has important clinical implications, particularly in patients with large vessel occlusion (LVO). Hyperglycemia is a common stress response in acute stroke and has long been associated with worse neurological outcomes( 9 ). In the context of mechanical thrombectomy for LVO, our findings reinforce that elevated postoperative blood glucose correlates with poorer functional recovery. This aligns with prior study showing that even when successful recanalization is achieved, patients who exhibit high glucose levels in the hours to days following thrombectomy have higher rates of “futile recanalization” – meaning they fail to attain good functional outcomes despite restored blood flow( 10 ). The clinical relevance is underscored by evidence that post-thrombectomy hyperglycemia is linked to increased 3-month mortality and disability( 11 ). Taken together, these observations highlight that glycemic trends after stroke are more than a benign epiphenomenon; rather, they likely reflect the severity of the injury and may actively contribute to secondary brain damage. Current stroke guidelines acknowledge the detrimental association of hyperglycemia with outcome and recommend avoiding extreme hyperglycemia, although the optimal target range remains debated. Notably, while aggressive glucose control might intuitively seem beneficial, randomized trials in acute ischemic stroke have not shown improved outcomes with intensive insulin therapy compared to standard management. The SHINE trial, for example, demonstrated that tightly controlling blood glucose (80–130 mg/dL via IV insulin) conferred no functional benefit over a more moderate insulin regimen targeting < 180 mg/dL, while carrying added risk of hypoglycemia( 3 ). These findings suggest that uncontrolled hyperglycemia is harmful, but simply lowering glucose without addressing the underlying ischemic injury or stress response may not reverse the damage. Our study reinforces the importance of avoiding sustained postoperative hyperglycemia in LVO stroke patients, yet it also prompts careful consideration of how and when to intervene. Achieving a euglycemic state through safe methods is still an essential supportive goal, but overly aggressive correction can be counterproductive. Clinicians should aim for prudent glycemic control (e.g. keeping glucose ~ 140–180 mg/dL) in the post-thrombectomy period while monitoring closely for hypoglycemia( 12 ). The robust association between post-stroke hyperglycemia and poor outcomes in our cohort emphasizes that these patients are a high-risk population where vigilant metabolic management and supportive care are warranted. The observation that many patients developed significant hyperglycemia after mechanical thrombectomy is biologically plausible given the profound stress of a large stroke. Acute ischemic stroke triggers a cascade of neuroendocrine stress responses. Critical illness and tissue injury activate the hypothalamic–pituitary–adrenal axis and sympathetic nervous system, leading to surges in counterregulatory hormones (catecholamines, cortisol) that antagonize insulin and drive up blood glucose levels( 12 ). In parallel, severe stroke induces a systemic inflammatory reaction that further disrupts glucose homeostasis. Pro-inflammatory cytokines such as IL-6 and tumor necrosis factor (TNF-α) are released during the acute phase and can impair insulin signaling, exacerbating insulin resistance( 9 , 12 ). The net result is “stress hyperglycemia,” a state of transient hyperglycemia that occurs despite previously normal glycemic control. This mechanism explains why even non-diabetic patients with a large stroke can experience marked blood glucose elevations. In our study, the postoperative glucose rise likely reflects this neuroendocrine and inflammatory surge following reperfusion therapy. Reperfusion of ischemic brain tissue, while necessary for salvage, can itself augment metabolic stress through reperfusion injury and oxidative stress, potentially contributing to hyperglycemia. Moreover, mechanical thrombectomy often involves general anesthesia or sedatives and periprocedural dextrose-containing fluids, which might also influence perioperative glucose levels. It should be noted that stress hyperglycemia is usually defined as a transient phenomenon resolving as the acute illness abates( 2 ). By contrast, chronic hyperglycemia due to diabetes is a long-standing condition that can worsen stroke outcomes through pre-existing vascular damage. Distinguishing stress-induced hyperglycemia from uncontrolled diabetes is challenging in retrospective data, but both are relevant. Importantly, the pathophysiological impact of acute hyperglycemia on the injured brain is thought to be detrimental: excess glucose in acute stroke can augment lactic acidosis in ischemic tissue, increase neuronal calcium influx, promote free radical formation, and compromise the blood–brain barrier, thereby enlarging infarct size and increasing risk of hemorrhagic transformation( 13 ). These mechanisms offer a plausible explanation for the worse outcomes observed in patients with higher postoperative glucose levels. Our findings also revealed a relationship between elevated postoperative blood glucose and heightened inflammation, as evidenced by higher CRP and cytokine levels (IL-6 and IL-10) in these patients. This intersection of hyperglycemia and inflammation is well-recognized in critical illness and stroke. IL-6, a prototypical pro-inflammatory cytokine, rises early after ischemic stroke and correlates with stroke severity and poor prognosis( 14 , 15 ). CRP, an acute-phase reactant largely driven by IL-6, typically peaks within 2–6 days after stroke and is also a marker of systemic inflammatory burden( 16 ). In our cohort, patients with pronounced hyperglycemia tended to have higher IL-6 and CRP levels, suggesting a more intense systemic inflammatory response accompanying the stress hyperglycemia. This is consistent with the concept that severe strokes (which often produce greater inflammatory responses) are also the ones most likely to precipitate significant stress hyperglycemia. Inflammatory cytokines like IL-6 can create a vicious cycle by worsening insulin resistance and contributing to further glucose elevation( 17 ). Conversely, hyperglycemia itself may amplify inflammation; acute spikes in glucose have been shown to increase circulating cytokine concentrations via oxidative stress mechanisms( 18 ). Thus, hyperglycemia and inflammation can be mutually reinforcing, each exacerbating the other’s deleterious effects on the injured brain. It is noteworthy that IL-10 was also elevated in patients with high glucose. IL-10 is an anti-inflammatory cytokine, often rising as a compensatory response to counteract excessive inflammation. Elevated IL-10 levels in the hyperglycemic group might reflect the body’s attempt to modulate the heightened inflammatory state. The exact role of IL-10 in stroke is complex; while it generally conveys neuroprotective and anti-inflammatory effects, higher IL-10 has been paradoxically associated with worse outcomes in some analyses, possibly because it signifies a reaction to severe initial injury( 19 ). In our study, the parallel rise of IL-10 with IL-6 and CRP in hyperglycemic patients likely indicates a more substantial immune activation overall in those individuals. These findings support a pathophysiological link between metabolic stress and inflammation after stroke: patients with greater stress (larger infarcts, more tissue injury) develop both higher glucose and a stronger inflammatory response. This milieu – hyperglycemia coupled with inflammation – can potentiate secondary brain damage by promoting endothelial dysfunction, edema, and hemorrhagic complications( 20 ). Our results therefore underscore the importance of considering inflammatory markers alongside glucose levels when assessing post-stroke patients. Future studies might explore whether anti-inflammatory strategies or insulin therapy modulate these intertwined pathways to improve outcomes. Our results are in line with a growing body of literature highlighting post-stroke hyperglycemia as a predictor of poor outcome, especially in LVO stroke treated with thrombectomy. Numerous prior studies have documented that an elevated admission glucose impairs recovery after endovascular therapy. For instance, Kim et al. reported that patients presenting with blood glucose > 140 mg/dL had significantly lower chances of functional independence at 3 months( 21 ). More recent analyses have shifted focus from a single glucose value to the trajectory and dynamics of glucose post-stroke. Merlino et al. introduced the concept of “persistent hyperglycemia,” finding that patients who remained hyperglycemic from admission through 24 hours had higher mortality and hemorrhagic transformation rates despite successful thrombectomy( 5 ). Similarly, a systematic review and meta-analysis by Perez-Vega et al. confirmed that higher peri-procedural glucose levels are associated with worse functional outcomes after mechanical thrombectomy across multiple studies( 22 ). Our study’s finding of a strong association between postoperative glucose rise and poor 90-day outcomes echoes these reports, and further supports that stress hyperglycemia indices—such as the glucose-to-A1c ratio (GAR) or stress hyperglycemia ratio (SHR)—which adjust for chronic glycemic status, have been shown to be robust predictors of stroke outcomes( 23 , 24 ). These results reinforce and expand upon existing observations in a retrospective clinical cohort, underscoring the significant prognostic value of postoperative glycemic trends. This study has several limitations that warrant consideration and also highlight opportunities for future research. First, as a single-center, retrospective analysis, the ability to infer causality is limited, and unmeasured confounding factors may have influenced both glycemic trends and outcomes. Our reliance on intermittent fingerstick capillary blood glucose measurements—rather than continuous glucose monitoring—could have missed significant glycemic fluctuations, suggesting the need for future studies to incorporate continuous or more frequent glucose assessments. Furthermore, the inflammatory markers (CRP, IL-6, and IL-10) were measured only at specific postoperative time points, which might not fully capture the dynamic inflammatory response. Future investigations could employ serial measurements to better map the trajectory of inflammation and its interplay with stress hyperglycemia. We also focused on early postoperative glucose trends within the first week after thrombectomy, without tracking longer-term glucose control or delayed metabolic effects. Prospective, multicenter studies with larger sample sizes and standardized glucose monitoring protocols are needed to confirm our findings and to explore whether active interventions—such as targeted insulin therapy or anti-inflammatory treatments—can improve functional outcomes in patients exhibiting stress hyperglycemia after LVO stroke. Finally, incorporating newer metrics like the stress hyperglycemia ratio or glucose variability indices may provide more nuanced risk stratification, guiding tailored therapeutic approaches in this vulnerable population. Conclusion In conclusion, our findings suggest that an increasing trend in postoperative blood glucose during the first week after mechanical thrombectomy for large vessel occlusion stroke is associated with higher inflammatory marker levels and poorer 90-day functional outcomes. These results underscore the clinical importance of monitoring postoperative glucose trajectories and highlight the complex interplay between stress hyperglycemia, inflammation, and stroke recovery. While causality cannot be definitively established in this retrospective analysis, our study reinforces the need for vigilant glycemic management and suggests that future prospective investigations incorporating continuous glucose monitoring and targeted metabolic interventions may help improve outcomes in this high-risk population. Declarations No potential conflicts of interest were reported by the authors. Funding This study was supported by the Multidisciplinary Clinical Research Innovation Team Program of Beijing Chao-Yang Hospital (Project Number: CYDXK202204), Capital Medical University. Author Contribution YL and YW designed the study. YL and JH collected and processed the clinical data. YL performed the statistical analyses. YL drafted the manuscript. YL and YW critically revised the manuscript for important intellectual content. All authors contributed to the interpretation of the results and approved the final version of the manuscript. Acknowledgement We would like to thank Zeping Jin, Yixin Lin, and other colleagues from the Department of Neurosurgery, Beijing Chao-Yang Hospital, for their support and contributions to this study. References Ferrari F, Moretti A, Villa RF. Hyperglycemia in acute ischemic stroke: physiopathological and therapeutic complexity. Neural regeneration research. 2022;17(2):292-9. Yao M, Hao Y, Wang T, Xie M, Li H, Feng J, et al. 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Chen X, Liu Z, Miao J, Zheng W, Yang Q, Ye X, et al. High stress hyperglycemia ratio predicts poor outcome after mechanical thrombectomy for ischemic stroke. Journal of Stroke and Cerebrovascular Diseases. 2019;28(6):1668-73. Zhu B, Pan Y, Jing J, Meng X, Zhao X, Liu L, et al. Stress hyperglycemia and outcome of non-diabetic patients after acute ischemic stroke. Frontiers in Neurology. 2019;10:1003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6875883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471996508,"identity":"b372e7dc-93ce-480b-9de7-1767f31ebc56","order_by":0,"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":471996509,"identity":"c9f17ef5-5cab-44ff-a4df-903f772d9f61","order_by":1,"name":"Jumei Huang","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jumei","middleName":"","lastName":"Huang","suffix":""},{"id":471996511,"identity":"d867bf85-da8e-4d47-b50f-4d618c1a947b","order_by":2,"name":"Yang Wang","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-12 02:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6875883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6875883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85363491,"identity":"60f5b680-de59-4e4f-8ff8-43942bac2ed4","added_by":"auto","created_at":"2025-06-25 06:24:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21285,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of 90-day modified Rankin Scale (mRS) scores between the Glucose-Decreasing Group and the Glucose-Increasing Group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6875883/v1/420895b4d97402f778363a18.png"},{"id":85361962,"identity":"87db9318-f465-4a90-987d-2f074870aa65","added_by":"auto","created_at":"2025-06-25 06:16:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49685,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of OR and 95% CI predictors of poor outcome (mRS ≥ 3) at 90 days.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6875883/v1/8d109157f8540147b9c1f8e8.png"},{"id":85361966,"identity":"67d22a46-a1d0-4189-97de-70eab1f06c54","added_by":"auto","created_at":"2025-06-25 06:16:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36413,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of postoperative day 1 C-reactive protein (CRP) levels between the Glucose-Decreasing Group and the Glucose-Increasing Group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6875883/v1/d0772e002561708c6721f519.png"},{"id":85361967,"identity":"14c4f5a6-7c01-4796-a69d-9a09ce1890c1","added_by":"auto","created_at":"2025-06-25 06:16:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eComparison of postoperative day 1 IL-6 levels between the Glucose-Decreasing Group and the Glucose-Increasing Group. \u003cstrong\u003e(b)\u003c/strong\u003e Comparison of postoperative day 1 IL-10 levels between the Glucose-Decreasing Group and the Glucose-Increasing Group.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6875883/v1/31c9d415b65699175d97dc95.png"},{"id":92619120,"identity":"ecd87601-ff37-44b4-8dcc-e3bc6db61e43","added_by":"auto","created_at":"2025-10-01 18:31:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":823504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6875883/v1/06b3a436-f42f-4707-a5ff-37e591d3b6d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Postoperative Blood Glucose Trajectories and Inflammatory Markers: Prognostic Implications in Acute Ischemic Stroke Treated by Thrombectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute ischemic stroke (AIS) frequently triggers stress hyperglycemia, even in patients without pre-existing diabetes. Elevated blood glucose in the acute phase of stroke is consistently associated with worse neurological outcomes and higher mortality(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Hyperglycemia can exacerbate ischemic brain injury by disrupting the blood\u0026ndash;brain barrier, amplifying thrombo-inflammatory cascades, and increasing oxidative stress(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). As a result, glycemic control is considered an important aspect of acute stroke care, although clinical trials of intensive insulin therapy, such as the SHINE trial, have not demonstrated improved functional outcomes and have raised concerns about hypoglycemia(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMechanical thrombectomy (MT) has become the standard of care for large vessel occlusion (LVO) strokes, dramatically improving recanalization rates and outcomes(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, up to half of patients with successful recanalization still experience poor functional recovery, highlighting the need to identify modifiable predictors of outcome. While admission hyperglycemia has been linked to poor prognosis in several studies, its predictive value in thrombectomy patients remains debated. More recently, researchers have turned their attention to postoperative blood glucose trends, recognizing that a single admission glucose measurement may not adequately capture patient risk(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Limited evidence suggests that postoperative glucose increases could independently predict worse functional outcomes even in patients with successful recanalization, yet comprehensive studies specifically investigating postoperative glycemic trajectories in LVO stroke patients remain scarce.\u003c/p\u003e \u003cp\u003eEmerging data also indicate that hyperglycemia after stroke may amplify systemic inflammatory responses, further compromising neurological recovery. Elevated blood glucose can promote cytokine release, oxidative stress, and endothelial dysfunction, creating a pro-inflammatory environment that exacerbates secondary brain injury. In turn, inflammation itself can worsen glucose dysregulation, establishing a vicious cycle between metabolic and immune stress(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This underscores the need to explore postoperative glucose trajectories in the context of concurrent inflammatory markers to better understand their collective impact on functional outcomes. Therefore, this study aims to investigate the prognostic significance of postoperative blood glucose trends in patients with LVO stroke treated with MT, while also assessing whether an enhanced early postoperative inflammatory response (e.g., elevated CRP, IL-6, and IL-10 levels) may mediate these associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis was a retrospective cohort study conducted at one stroke center in Beijing, China, between March 2023 and September 2024. Consecutive patients who underwent mechanical thrombectomy for acute ischemic stroke were enrolled. Inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) acute ischemic stroke due to large vessel occlusion confirmed by imaging; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) successful mechanical thrombectomy within 24 hours of symptom onset; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) available blood glucose and inflammatory marker data within the first week post-procedure. Patients with incomplete medical records or early in-hospital mortality were excluded. This study was approved by the Ethics Committee of Beijing Chao-Yang Hospital, Capital Medical University (approval number: 2022-Ke-522).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical data, including age, sex, vascular risk factors (hypertension, diabetes mellitus, coronary artery disease, prior stroke), baseline National Institutes of Health Stroke Scale (NIHSS) scores, admission blood glucose levels, and body mass index (BMI), were collected from electronic medical records. Postoperative laboratory tests, including daily blood glucose levels for the first seven days, C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10) levels measured on postoperative day 1, were also recorded.\u003c/p\u003e\n\u003ch3\u003eBlood Glucose Trend Analysis\u003c/h3\u003e\n\u003cp\u003ePostoperative blood glucose levels were monitored at least once daily for the first seven days following mechanical thrombectomy, typically measured as fasting capillary blood glucose in the morning and 2-hour postprandial capillary blood glucose levels after each meal. The daily mean blood glucose level was calculated as the arithmetic mean of these measurements.\u003c/p\u003e \u003cp\u003eTo quantify postoperative glycemic trends, linear regression analysis was performed for each patient, treating the postoperative day number as the independent variable and the daily mean blood glucose level as the dependent variable. The slope of this regression line represented the rate of change in postoperative blood glucose over the first week. Patients were then stratified into two groups based on the median slope value within the cohort: the Glucose-Increasing Group (slope\u0026thinsp;\u0026ge;\u0026thinsp;median, indicating a stable or rising trend) and the Glucose-Decreasing Group (slope\u0026thinsp;\u0026lt;\u0026thinsp;median, indicating a declining trend). This approach enabled a standardized classification of postoperative glycemic trajectories to assess their impact on inflammatory markers and functional outcomes.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the 90-day functional outcome, assessed using the modified Rankin Scale (mRS) via outpatient clinic visits or structured telephone interviews. Poor functional outcome was defined as mRS\u0026thinsp;\u0026ge;\u0026thinsp;3. Secondary outcomes included postoperative inflammatory marker levels (CRP, IL-6, IL-10) measured on postoperative day 1.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using GraphPad Prism version 10.0 (GraphPad Software, San Diego, CA, USA). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range [IQR]) as appropriate, and compared using the independent samples t-test or Mann-Whitney U test. Categorical variables were expressed as counts and percentages, and compared using the chi-square test or Fisher's exact test. Multivariate logistic regression was performed to identify independent predictors of poor functional outcome (mRS\u0026thinsp;\u0026ge;\u0026thinsp;3), adjusting for potential confounders (age, baseline NIHSS score, diabetes, hypertension, coronary artery disease, and prior stroke). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of the Patients\u003c/h2\u003e \u003cp\u003eA total of 150 patients who underwent mechanical thrombectomy for acute ischemic stroke were included in this study. Patients were stratified into two groups based on the median slope of their 7-day postoperative blood glucose levels: the Glucose-Increasing Group (n\u0026thinsp;=\u0026thinsp;75) and the Glucose-Decreasing Group (n\u0026thinsp;=\u0026thinsp;75).\u003c/p\u003e \u003cp\u003eThe baseline characteristics of the two groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no statistically significant differences in age, gender distribution, baseline NIHSS scores, admission blood glucose levels, body mass index (BMI), or the prevalence of hypertension, diabetes, coronary artery disease (CAS), and prior stroke history between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study patients according to the blood glucose trend group (Glucose-Decreasing Group vs. Glucose-Increasing Group).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlucose-Decreasing Group (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose-Increasing Group (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of 90-Day Functional Outcomes According to Blood Glucose Trend\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of this study was the 90-day functional status, assessed using the mRS. The Glucose-Increasing Group had a higher median 90-day mRS score of 4.0 (IQR, 2.0\u0026ndash;5.0) compared to 3.0 (IQR, 2.0\u0026ndash;4.0) in the Glucose-Decreasing Group. This difference was statistically significant (P\u0026thinsp;=\u0026thinsp;0.030), indicating that a increasing postoperative blood glucose trend was associated with poorer functional outcomes at 90 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Moreover, when analyzing the distribution of mRS categories (0\u0026ndash;2 vs. 3\u0026ndash;6), the proportion of patients with good functional outcomes (mRS \u0026le; 2) was higher in the Glucose-Decreasing Group (40%) compared to the Glucose-Increasing Group (32%). However, this difference was not statistically significant (P = 0.395).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Analysis of Blood Glucose Trend Group and Functional Outcomes\u003c/h2\u003e \u003cp\u003eTo further assess whether the blood glucose trend group independently predicted poor functional outcomes (defined as mRS\u0026thinsp;\u0026ge;\u0026thinsp;3 at 90 days), we performed a multivariate logistic regression analysis adjusting for potential confounders, including age, baseline NIHSS score, diabetes, hypertension, coronary artery disease (CAD), and prior stroke.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and illustrated in the forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the blood glucose trend group (Glucose-Increasing vs. Glucose-Decreasing) was not an independent predictor of poor outcome (odds ratio [OR], 0.67; 95% CI, 0.34\u0026ndash;1.37; P\u0026thinsp;=\u0026thinsp;0.272). Other baseline factors, including age (OR, 0.99; P\u0026thinsp;=\u0026thinsp;0.709), baseline NIHSS (OR, 0.98; P\u0026thinsp;=\u0026thinsp;0.368), diabetes (OR, 1.00; P\u0026thinsp;=\u0026thinsp;0.998), hypertension (OR, 1.21; P\u0026thinsp;=\u0026thinsp;0.595), CAD (OR, 1.03; P\u0026thinsp;=\u0026thinsp;0.951), and prior stroke (OR, 0.75; P\u0026thinsp;=\u0026thinsp;0.529), were also not significantly associated with poor functional outcomes in this cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multivariate logistic regression analysis for predicting poor functional outcome (mRS\u0026thinsp;\u0026ge;\u0026thinsp;3). OR are presented with 95% confidence intervals (CI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Glucose Trend Group (Increasing vs. Decreasing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior Stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Postoperative Day 1 C-Reactive Protein (CRP) Levels Between Blood Glucose Trend Groups\u003c/h2\u003e \u003cp\u003eTo evaluate the inflammatory response in the early postoperative period, CRP levels were measured on postoperative day 1 and compared between the two blood glucose trend groups.\u003c/p\u003e \u003cp\u003eThe Glucose-Increasing Group demonstrated a slightly higher median CRP level of 5.2 mg/L (IQR, 3.2\u0026ndash;7.0 mg/L), compared to a median of 4.1 mg/L (IQR, 2.8\u0026ndash;5.3 mg/L) in the Glucose-Decreasing Group. This difference was statistically significant (P\u0026thinsp;=\u0026thinsp;0.022), suggesting a modestly greater early postoperative inflammatory response in patients whose blood glucose tended to increase over the first week. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the distribution of CRP levels in the two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Postoperative Day 1 IL-6 and IL-10 Levels Between Blood Glucose Trend Groups\u003c/h2\u003e \u003cp\u003eTo further characterize the postoperative inflammatory and anti-inflammatory responses, we measured interleukin-6 (IL-6) and interleukin-10 (IL-10) levels on postoperative day 1. The Glucose-Increasing Group demonstrated a moderately higher median IL-6 level of 6.9 pg/mL (IQR, 5.3\u0026ndash;8.2 pg/mL), compared to 5.7 pg/mL (IQR, 4.6\u0026ndash;7.4 pg/mL) in the Glucose-Decreasing Group. This difference was statistically significant (P\u0026thinsp;=\u0026thinsp;0.015), indicating a modestly enhanced early postoperative inflammatory response in patients whose blood glucose tended to increase over the first week (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eSimilarly, the Glucose-Increasing Group exhibited a higher median IL-10 concentration of 134 pg/mL (IQR, 96\u0026ndash;177 pg/mL), compared to 104 pg/mL (IQR, 84\u0026ndash;135 pg/mL) in the Glucose-Decreasing Group. This difference was also statistically significant (P\u0026thinsp;=\u0026thinsp;0.0017), indicating a moderately enhanced early postoperative anti-inflammatory response in patients with a postoperative increase in blood glucose (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMaintaining optimal glycemic levels in the acute period after ischemic stroke has important clinical implications, particularly in patients with large vessel occlusion (LVO). Hyperglycemia is a common stress response in acute stroke and has long been associated with worse neurological outcomes(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In the context of mechanical thrombectomy for LVO, our findings reinforce that elevated postoperative blood glucose correlates with poorer functional recovery. This aligns with prior study showing that even when successful recanalization is achieved, patients who exhibit high glucose levels in the hours to days following thrombectomy have higher rates of \u0026ldquo;futile recanalization\u0026rdquo; \u0026ndash; meaning they fail to attain good functional outcomes despite restored blood flow(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The clinical relevance is underscored by evidence that post-thrombectomy hyperglycemia is linked to increased 3-month mortality and disability(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Taken together, these observations highlight that glycemic trends after stroke are more than a benign epiphenomenon; rather, they likely reflect the severity of the injury and may actively contribute to secondary brain damage.\u003c/p\u003e \u003cp\u003e Current stroke guidelines acknowledge the detrimental association of hyperglycemia with outcome and recommend avoiding extreme hyperglycemia, although the optimal target range remains debated. Notably, while aggressive glucose control might intuitively seem beneficial, randomized trials in acute ischemic stroke have not shown improved outcomes with intensive insulin therapy compared to standard management. The SHINE trial, for example, demonstrated that tightly controlling blood glucose (80\u0026ndash;130 mg/dL via IV insulin) conferred no functional benefit over a more moderate insulin regimen targeting\u0026thinsp;\u0026lt;\u0026thinsp;180 mg/dL, while carrying added risk of hypoglycemia(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These findings suggest that uncontrolled hyperglycemia is harmful, but simply lowering glucose without addressing the underlying ischemic injury or stress response may not reverse the damage. Our study reinforces the importance of avoiding sustained postoperative hyperglycemia in LVO stroke patients, yet it also prompts careful consideration of how and when to intervene. Achieving a euglycemic state through safe methods is still an essential supportive goal, but overly aggressive correction can be counterproductive. Clinicians should aim for prudent glycemic control (e.g. keeping glucose\u0026thinsp;~\u0026thinsp;140\u0026ndash;180 mg/dL) in the post-thrombectomy period while monitoring closely for hypoglycemia(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The robust association between post-stroke hyperglycemia and poor outcomes in our cohort emphasizes that these patients are a high-risk population where vigilant metabolic management and supportive care are warranted.\u003c/p\u003e \u003cp\u003eThe observation that many patients developed significant hyperglycemia after mechanical thrombectomy is biologically plausible given the profound stress of a large stroke. Acute ischemic stroke triggers a cascade of neuroendocrine stress responses. Critical illness and tissue injury activate the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis and sympathetic nervous system, leading to surges in counterregulatory hormones (catecholamines, cortisol) that antagonize insulin and drive up blood glucose levels(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In parallel, severe stroke induces a systemic inflammatory reaction that further disrupts glucose homeostasis. Pro-inflammatory cytokines such as IL-6 and tumor necrosis factor (TNF-α) are released during the acute phase and can impair insulin signaling, exacerbating insulin resistance(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The net result is \u0026ldquo;stress hyperglycemia,\u0026rdquo; a state of transient hyperglycemia that occurs despite previously normal glycemic control. This mechanism explains why even non-diabetic patients with a large stroke can experience marked blood glucose elevations.\u003c/p\u003e \u003cp\u003eIn our study, the postoperative glucose rise likely reflects this neuroendocrine and inflammatory surge following reperfusion therapy. Reperfusion of ischemic brain tissue, while necessary for salvage, can itself augment metabolic stress through reperfusion injury and oxidative stress, potentially contributing to hyperglycemia. Moreover, mechanical thrombectomy often involves general anesthesia or sedatives and periprocedural dextrose-containing fluids, which might also influence perioperative glucose levels. It should be noted that stress hyperglycemia is usually defined as a transient phenomenon resolving as the acute illness abates(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). By contrast, chronic hyperglycemia due to diabetes is a long-standing condition that can worsen stroke outcomes through pre-existing vascular damage. Distinguishing stress-induced hyperglycemia from uncontrolled diabetes is challenging in retrospective data, but both are relevant. Importantly, the pathophysiological impact of acute hyperglycemia on the injured brain is thought to be detrimental: excess glucose in acute stroke can augment lactic acidosis in ischemic tissue, increase neuronal calcium influx, promote free radical formation, and compromise the blood\u0026ndash;brain barrier, thereby enlarging infarct size and increasing risk of hemorrhagic transformation(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These mechanisms offer a plausible explanation for the worse outcomes observed in patients with higher postoperative glucose levels.\u003c/p\u003e \u003cp\u003eOur findings also revealed a relationship between elevated postoperative blood glucose and heightened inflammation, as evidenced by higher CRP and cytokine levels (IL-6 and IL-10) in these patients. This intersection of hyperglycemia and inflammation is well-recognized in critical illness and stroke. IL-6, a prototypical pro-inflammatory cytokine, rises early after ischemic stroke and correlates with stroke severity and poor prognosis(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). CRP, an acute-phase reactant largely driven by IL-6, typically peaks within 2\u0026ndash;6 days after stroke and is also a marker of systemic inflammatory burden(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In our cohort, patients with pronounced hyperglycemia tended to have higher IL-6 and CRP levels, suggesting a more intense systemic inflammatory response accompanying the stress hyperglycemia. This is consistent with the concept that severe strokes (which often produce greater inflammatory responses) are also the ones most likely to precipitate significant stress hyperglycemia. Inflammatory cytokines like IL-6 can create a vicious cycle by worsening insulin resistance and contributing to further glucose elevation(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Conversely, hyperglycemia itself may amplify inflammation; acute spikes in glucose have been shown to increase circulating cytokine concentrations via oxidative stress mechanisms(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Thus, hyperglycemia and inflammation can be mutually reinforcing, each exacerbating the other\u0026rsquo;s deleterious effects on the injured brain.\u003c/p\u003e \u003cp\u003eIt is noteworthy that IL-10 was also elevated in patients with high glucose. IL-10 is an anti-inflammatory cytokine, often rising as a compensatory response to counteract excessive inflammation. Elevated IL-10 levels in the hyperglycemic group might reflect the body\u0026rsquo;s attempt to modulate the heightened inflammatory state. The exact role of IL-10 in stroke is complex; while it generally conveys neuroprotective and anti-inflammatory effects, higher IL-10 has been paradoxically associated with worse outcomes in some analyses, possibly because it signifies a reaction to severe initial injury(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In our study, the parallel rise of IL-10 with IL-6 and CRP in hyperglycemic patients likely indicates a more substantial immune activation overall in those individuals. These findings support a pathophysiological link between metabolic stress and inflammation after stroke: patients with greater stress (larger infarcts, more tissue injury) develop both higher glucose and a stronger inflammatory response. This milieu \u0026ndash; hyperglycemia coupled with inflammation \u0026ndash; can potentiate secondary brain damage by promoting endothelial dysfunction, edema, and hemorrhagic complications(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Our results therefore underscore the importance of considering inflammatory markers alongside glucose levels when assessing post-stroke patients. Future studies might explore whether anti-inflammatory strategies or insulin therapy modulate these intertwined pathways to improve outcomes.\u003c/p\u003e \u003cp\u003eOur results are in line with a growing body of literature highlighting post-stroke hyperglycemia as a predictor of poor outcome, especially in LVO stroke treated with thrombectomy. Numerous prior studies have documented that an elevated admission glucose impairs recovery after endovascular therapy. For instance, Kim et al. reported that patients presenting with blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;140 mg/dL had significantly lower chances of functional independence at 3 months(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). More recent analyses have shifted focus from a single glucose value to the trajectory and dynamics of glucose post-stroke. Merlino et al. introduced the concept of \u0026ldquo;persistent hyperglycemia,\u0026rdquo; finding that patients who remained hyperglycemic from admission through 24 hours had higher mortality and hemorrhagic transformation rates despite successful thrombectomy(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, a systematic review and meta-analysis by Perez-Vega et al. confirmed that higher peri-procedural glucose levels are associated with worse functional outcomes after mechanical thrombectomy across multiple studies(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Our study\u0026rsquo;s finding of a strong association between postoperative glucose rise and poor 90-day outcomes echoes these reports, and further supports that stress hyperglycemia indices\u0026mdash;such as the glucose-to-A1c ratio (GAR) or stress hyperglycemia ratio (SHR)\u0026mdash;which adjust for chronic glycemic status, have been shown to be robust predictors of stroke outcomes(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These results reinforce and expand upon existing observations in a retrospective clinical cohort, underscoring the significant prognostic value of postoperative glycemic trends.\u003c/p\u003e \u003cp\u003eThis study has several limitations that warrant consideration and also highlight opportunities for future research. First, as a single-center, retrospective analysis, the ability to infer causality is limited, and unmeasured confounding factors may have influenced both glycemic trends and outcomes. Our reliance on intermittent fingerstick capillary blood glucose measurements\u0026mdash;rather than continuous glucose monitoring\u0026mdash;could have missed significant glycemic fluctuations, suggesting the need for future studies to incorporate continuous or more frequent glucose assessments. Furthermore, the inflammatory markers (CRP, IL-6, and IL-10) were measured only at specific postoperative time points, which might not fully capture the dynamic inflammatory response. Future investigations could employ serial measurements to better map the trajectory of inflammation and its interplay with stress hyperglycemia. We also focused on early postoperative glucose trends within the first week after thrombectomy, without tracking longer-term glucose control or delayed metabolic effects. Prospective, multicenter studies with larger sample sizes and standardized glucose monitoring protocols are needed to confirm our findings and to explore whether active interventions\u0026mdash;such as targeted insulin therapy or anti-inflammatory treatments\u0026mdash;can improve functional outcomes in patients exhibiting stress hyperglycemia after LVO stroke. Finally, incorporating newer metrics like the stress hyperglycemia ratio or glucose variability indices may provide more nuanced risk stratification, guiding tailored therapeutic approaches in this vulnerable population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings suggest that an increasing trend in postoperative blood glucose during the first week after mechanical thrombectomy for large vessel occlusion stroke is associated with higher inflammatory marker levels and poorer 90-day functional outcomes. These results underscore the clinical importance of monitoring postoperative glucose trajectories and highlight the complex interplay between stress hyperglycemia, inflammation, and stroke recovery. While causality cannot be definitively established in this retrospective analysis, our study reinforces the need for vigilant glycemic management and suggests that future prospective investigations incorporating continuous glucose monitoring and targeted metabolic interventions may help improve outcomes in this high-risk population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNo potential conflicts of interest were reported by the authors.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Multidisciplinary Clinical Research Innovation Team Program of Beijing Chao-Yang Hospital (Project Number: CYDXK202204), Capital Medical University.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYL and YW designed the study. YL and JH collected and processed the clinical data. YL performed the statistical analyses. YL drafted the manuscript. YL and YW critically revised the manuscript for important intellectual content. All authors contributed to the interpretation of the results and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Zeping Jin, Yixin Lin, and other colleagues from the Department of Neurosurgery, Beijing Chao-Yang Hospital, for their support and contributions to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerrari F, Moretti A, Villa RF. Hyperglycemia in acute ischemic stroke: physiopathological and therapeutic complexity. Neural regeneration research. 2022;17(2):292-9.\u003c/li\u003e\n\u003cli\u003eYao M, Hao Y, Wang T, Xie M, Li H, Feng J, et al. A review of stress-induced hyperglycaemia in the context of acute ischaemic stroke: Definition, underlying mechanisms, and the status of insulin therapy. Frontiers in Neurology. 2023;14:1149671.\u003c/li\u003e\n\u003cli\u003eJohnston KC, Bruno A, Pauls Q, Hall CE, Barrett KM, Barsan W, et al. Intensive vs standard treatment of hyperglycemia and functional outcome in patients with acute ischemic stroke: the SHINE randomized clinical trial. Jama. 2019;322(4):326-35.\u003c/li\u003e\n\u003cli\u003eJadhav AP, Desai SM, Jovin TG. Indications for mechanical thrombectomy for acute ischemic stroke: current guidelines and beyond. Neurology. 2021;97(20_Supplement_2):S126-S36.\u003c/li\u003e\n\u003cli\u003eMerlino G, Smeralda C, Sponza M, Gigli GL, Lorenzut S, Marini A, et al. Dynamic hyperglycemic patterns predict adverse outcomes in patients with acute ischemic stroke undergoing mechanical thrombectomy. Journal of Clinical Medicine. 2020;9(6):1932.\u003c/li\u003e\n\u003cli\u003eRom S, Zuluaga-Ramirez V, Gajghate S, Seliga A, Winfield M, Heldt NA, et al. 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Frontiers in Neurology. 2019;10:1003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute ischemic stroke, Mechanical thrombectomy, Postoperative hyperglycemia, Inflammatory response, Functional outcome","lastPublishedDoi":"10.21203/rs.3.rs-6875883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6875883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperglycemia frequently occurs after acute ischemic stroke and is associated with worse neurological outcomes. However, the impact of postoperative blood glucose trends, especially in patients with large vessel occlusion (LVO) stroke undergoing mechanical thrombectomy (MT), remains unclear. In this retrospective cohort study of 150 patients with LVO stroke treated with MT between March 2023 and September 2024, we assessed the association between postoperative blood glucose trajectories and 90-day functional outcomes, as well as the potential inflammatory response underlying this association. Daily fingerstick capillary blood glucose levels (fasting and postprandial) were measured for the first seven days post-procedure, and linear regression was used to calculate the slope of the postoperative glucose trend for each patient. Patients were divided into Glucose-Increasing (n\u0026thinsp;=\u0026thinsp;75) and Glucose-Decreasing (n\u0026thinsp;=\u0026thinsp;75) groups based on the median slope. The primary outcome was 90-day functional status, assessed by the modified Rankin Scale (mRS) through outpatient clinic visits or structured telephone interviews; secondary outcomes included postoperative day 1 levels of C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10). Patients in the Glucose-Increasing Group had significantly higher median 90-day mRS scores (4.0 vs. 3.0; P\u0026thinsp;=\u0026thinsp;0.030) and higher postoperative CRP (5.2 vs. 4.1 mg/L; P\u0026thinsp;=\u0026thinsp;0.022), IL-6 (6.9 vs. 5.7 pg/mL; P\u0026thinsp;=\u0026thinsp;0.015), and IL-10 (134 vs. 104 pg/mL; P\u0026thinsp;=\u0026thinsp;0.0017) levels. Multivariate logistic regression adjusting for potential confounders did not identify glucose trend group as an independent predictor of poor outcome (mRS\u0026thinsp;\u0026ge;\u0026thinsp;3; odds ratio 0.67, 95% CI 0.34\u0026ndash;1.37; P\u0026thinsp;=\u0026thinsp;0.272). These findings suggest that an increasing postoperative blood glucose trend is associated with higher inflammatory markers and poorer functional outcomes in LVO stroke patients undergoing thrombectomy, although it may not be an independent predictor when adjusted for other factors, underscoring the need for future prospective studies.\u003c/p\u003e","manuscriptTitle":"Postoperative Blood Glucose Trajectories and Inflammatory Markers: Prognostic Implications in Acute Ischemic Stroke Treated by Thrombectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 06:16:09","doi":"10.21203/rs.3.rs-6875883/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9e2f21b-8c63-4180-bc2c-65c95784972a","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T18:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 06:16:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6875883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6875883","identity":"rs-6875883","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}