RIPK1, MLKL are Potential Biomarkers for Large Artery Atherosclerotic Acute Ischemic Stroke

RIPK1, MLKL are Potential Biomarkers for Large Artery Atherosclerotic Acute 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 RIPK1, MLKL are Potential Biomarkers for Large Artery Atherosclerotic Acute Ischemic Stroke Zhidi He, Wenqiang Qiu, Yuyan Bao, Feng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8178833/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 Background: This research aimed to investigate the connection between large artery atherosclerosis-associated acute ischemic stroke (LAA-AIS) and the key protein receptor-interacting serine-threonine kinase 1 (RIPK1), mixed lineage kinase domain-like protein (MLKL) of necroptosis. Methods: We enrolled 111 patients with LAA-AIS along with 111 age and gender-matched healthy controls. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum RIPK1 and MLKL levels. Spearman correlation analysis was used to explore the correlation between RIPK1, MLKL, and the severity of LAA-AIS. Plotted receiver operating characteristic (ROC) curves to evaluate the diagnostic valueof RIPK1 and MLKL in predicting LAA-AIS. The independent risk variables of LAA-AIS were investigated using binary logistic regression analysis. Results: The serum levels of RIPK1 [0.25 ng/ml (IQR, 0.20, 0.34) vs 0.19 ng/ml (IQR, 0.16, 0.21)] and MLKL [3.16 ng/ml (IQR, 1.93- 5.22) vs 2.16 (IQR, 1.56-3.27)] in LAA-AIS patients were significantly higher than controls. On admission, the correlation coefficients between MLKL and NIHSS, MKLK and mRS score were 0.467 ( P <0.001) and 0.430 ( P =0.001), respectively. There was a weak but positive correlation between MLKL and the volume of cerebral infarction under MRI ( Rs =0.286, P =0.002) and unstable carotid atherosclerotic plaque ( Rs =0.482, P =0.001). Adding RIPK1 and MLKL to the basic model including traditional risk factors related to LAA-AIS improved the area under curve （AUC） from 0.799 to 0.882. Regression analysis suggested MLKL (OR=4.858, 95% CI: 1.256-18.786, P =0.022) is an independent risk factor for LAA-AIS. Conclusion: Our study demonstrated thatserum levels of RIPK1 and MLKL may be associated with LAA -AIS. necroptosis RIPK1 MLKL atherosclerosis acute ischemic stroke Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ischemic stroke, precipitated by the occlusion of cerebral arteries, stands as a predominant global contributor to mortality and disability [ 1 ] . As ischemia persists in both duration and severity, the neurological integrity of the ischemic penumbra, encircling the central necrotic zone, undergoes progressive compromise [ 2 ] . Presently, clinical options for acute ischemic stroke remain confined to mechanical thrombectomy or thrombolytic drugs. Regrettably, both interventions exhibit suboptimal success rates and a narrow therapeutic window, underscoring the imperative for innovative therapeutic strategies [ 3 ] . To ameliorate the deleterious effects associated with acute ischemic stroke, a critical imperative exists to pioneer novel treatment approaches. Furthermore, drawbacks include limited availability of brain imaging technology, lengthier imaging times, increased costs, metal implantation or pacemaker contraindications, and claustrophobia, which all contribute to decreased use. As a result, new practical and quick adjunctive diagnostic techniques for acute ischemic stroke such as biomarkers are desperately needed in clinical practice. Relevant studies indicate that the necroptosis signaling pathway plays a significant role in the occurrence and development of acute ischemic stroke [ 4 ] . Necroptosis is a cell death mechanism mediated by the activation and interaction of receptor-interacting serine-threonine kinase 1 (RIPK1), receptor-interacting serine-threonine kinase 3 (RIPK3), and mixed lineage kinase domain-like protein (MLKL) through the activation of inflammatory receptors [ 5 ] . When cerebral ischemia occurs, activated microglial cells go to the site of the ischemia and release pertinent cytokines such as TNF-α, TRAIL, and FasL. The recruitment of RIPK1 and other proteins to form Complex I is triggered by the interaction of these death signals with their cell surface membrane receptors [ 4 ] . However, the lowered levels of ATP during the acute phase of cerebral ischemia are insufficient to maintain the activity of Caspase-8 because it partially depends on ATP levels [ 6 ] . As a result, a complex structure known as a necrosome is formed by RIPK1, RIPK3, and MLKL. Additionally, MLKL is phosphorylated by RIPK3 within the necrosome, causing its oligomerization and translocation to the cell membrane, which leads to membrane rupture and cell death [ 7 ] . In addition, due to large artery atherosclerosis (LAA) being the primary pathological factor of acute ischemic stroke (AIS), its pathogenic mechanism has been extensively studied [ 8 ] . KARUNAKARAN et al. proposed that the activation of necroptosis is linked to the lipid necrotic core sensitivity and inflammation in advanced human atherosclerosis [ 9 ] . Subsequently, their team further demonstrated that RIPK1 acts as a central driving factor of atherosclerotic inflammation through its ability to activate the NF-κB pathway and facilitate the release of inflammatory cytokines [ 10 ] . Although several animal experiments have suggested that the necroptosis-related factors RIPK1 and MLKL promote the occurrence and progression of cerebral infarction, and the necroptosis inhibitor Necrostatin-1 (NEC-1) has been shown to improve outcomes in ischemic stroke mice [ 11 – 13 ] , there is currently no clinical study on the relationship between RIPK1, MLKL, and large artery atherosclerotic acute ischemic stroke (LAA-AIS). In order to determine if proteins linked to necroptosis are potential biomarkers in the early stages of AIS, we set out to examine the relationship between necroptosis and LAA stroke in patients. Materials and Methods Study participants From January 2022 to December 2022, we consecutively enrolled 111 patients, who experienced large artery atherosclerotic acute ischemic stroke for the first time admitted within 24 h to the Department of Neurology, Taizhou hospital of Zhejiang Province affiliated to Wenzhou Medical University according to the World Health Organization standards [ 14 ] . The cases with LAA stroke were categorized by at least two neurologists using the TOAST (Trial of Org 10172 in Acute Stroke Treatment) criteria [ 15 ] . The exclusion criteria for patients with AIS were as follows: (1) Brain tumor or cerebral hemorrhage. (2) Severe systemic infectious diseases, autoimmune or connective tissue diseases, metabolic disorders such as endocrine disorders or toxicity. (3) Patients who were intolerant to magnetic resonance imaging (MRI) scans or there were contraindications to MRI scans. (4) Individuals with established risk factors for increased necroptosis levels include those with Alzheimer's disease, myocardial infarction, cirrhosis, chronic hepatitis, hematological cancers, and amyotrophic lateral sclerosis. The healthy control group consisted of 111 age- and gender-matched (in blocks of 5) healthy cases who were enrolled in our hospital's Medical Examination Center. The study was performed in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of Taizhou Hospital (ethical number: K20181204). All participants were informed of the study protocol and their written informed consents were obtained prior to enrollment. Clinical Varies and Imaging indicators On admission, data were gathered from the Clinical Electronic Case System regarding age, sex, smoking status, alcohol use, and past medical history of risk factors (hypertension, diabetes mellitus, hyperlipidemia, etc.). Two experienced neurologists assessed the patient's severity of AIS used the National Institutes of Health Stroke Scale (NIHSS; score ranges from 0 to 42, with higher scores indicating greater deficits) [ 16 ] , as well as the modified Rankin scale (mRS; score ranges from 0 to 5, with higher scores indicating greater deficits) [ 17 ] . All 111 LAA-AIS patients were routinely given a magnetic resonance imaging (MRI, 1.5 T) with diffusion-weighted imaging (DWI) within 24 hours of being admitted. One skilled neuroradiologist who was blinded with the clinical or laboratory findings calculated the DWI lesion volumes. Contrary to hyperechoic homogeneous plaques, which are mostly fibrous and less likely to develop cerebrovascular ischemia, hypoechoic, heterogeneous plaques are associated with intraplaque hemorrhage as well as increased lipid concentration and a necrotic core of the plaque [ 18 ] . As a result, carotid plaques can be classified into three groups based on ultrasound results: no plaque group, stable plaque group, and unstable plaque group, as determined by one ultra sonographer of intermediate rank or above. Blood Collection and Laboratory Within 24 h stroke onset, blood was drawn after fasting overnight for the following tests: the fasting blood glucose (FBG), Glycated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), homocysteine (HCY), white blood cell (WBC), high-sensitivity C-reactive protein (Hs-CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine, uric acid concentrations, were measured at the hospital's clinical laboratory. In addition, aliquots of the serum sample were promptly stored at -80°C following centrifugation to test the level of RIPK1 and MLKL in serum. An enzyme-linked immunosorbent assay (ELISA) kits (CLOUD-CLONE CORP, Wuhan, China) was used to measure their level. According to the manufacturer, the lowest detectable level of human RIPK1 was found to be 0.059 ng/ml, and MLKL was 0.112 ng/ml. The kits had 10% and 12% intra- and inter-assay CV%s, respectively. Statistics SPSS 26.0 (Statistical Product and Service Solutions version 26.0) and GraphPad Prism 8.0 were used for statistical analysis. The mean ± standard deviation (SD) was used to express normally distributed continuous data, whereas the median and interquartile range (IQR) were used to express skewed distributions. Frequency and percentage (N, %) were used to express categorical variables. Continuous values between the two groups were compared using the t test, corrected t test, or Mann-Whitney U test; categorical data were compared using Pearson's Chi-Squared test, continuity correction, or Fisher's exact test. To determine cutoff criteria and assess the accuracy of RIPK1, MLKL predictions, the area under the receiver operating characteristic curve (ROC) was utilized. The basic model and the basic model with RIPK1, MLKL were compared using the ROC curve for prediction analysis. In order to estimate multivariate adjusted odds ratios (ORs) and 95% confidence intervals (CIs), binary logistic regression analysis was employed to examine potential risk variables for LAA-AIS. For the prediction analysis of the comparison of independent risk factors, the ROC curve was utilized. A statistically significant P value was defined as ≤ 0.05. Results from January 2022 to December 2022, there were 187 consecutive patients who had with LAA-AIS diagnosed within 24 hours after the beginning of symptoms. 22 patients were excluded due to severe infection, 14 because of malignant tumor, 12 of other systems’ critical disease, 10 patients did not consent, while 15 individuals were omitted due to lacking of relevant data. Shown in Fig. 1 . Basic characteristics of the study participants. A total of 111 patients with LAA-AIS and 111 healthy controls were included in the final analysis. The basic characteristics of the study subjects were shown in Table 1 . Within the research sample, the mean age of the AIS patients was 65.22 ± 11.21 years, with 54.95% of them being male. Meanwhile, the mean age of the controls was 63.98 ± 10.07 years. The median NIHSS score on admission was 3 points (IQR, 2–7). As a result of our findings, patients with LAA-AIS had considerably greater median serum RIPK1 levels than controls [0.25 ng/ml (IQR, 0.20,0.34) vs 0.19 ng/ml (0.16,0.21), respectively, P < 0.001]. MLKL levels were 3.16(1.93,5.22) and 2.16(1.56,3.27), in the AIS and control groups respectively ( P = 0.018). Table 1 displays the basic attributes of both groups. Besides, we found that the proportion of WBC, SBP, HbA1 C , Hs-CRP, LDL-C and HCY were higher in the patients with AIS than in the healthy controls ( P < 0.05). Table 1 Baseline characteristics of patients with acute ischemic stroke and normal cases Characteristic Patients (n = 111) Controls (n = 111) P -value Age (years) 65.22 ± 11.21 63.98 ± 10.07 0.398 Male (%) 61(54.95%) 61(54.95%) 1 Smokers (%) 37(33.30%) 31(30.10%) 0.611 Alcohol consumers 34(30.60%) 29(28.20%) 0.691 BMI (kg/m2) 24.56 ± 3.20 24.43 ± 4.17 0.866 Systolic blood pressure (mmHg) 148.71 ± 20.58 141.19 ± 20.50 0.008 Diastolic blood pressure, (mmHg) 82.01 ± 13.90 79.90 ± 12.15 0.241 Hypertension (%) 72(64.86%) 64(57.66%) 0.270 Diabetes mellitus (%) 32(28.83%) 21(18.92%) 0.083 Hypercholesterolemia (%) 30(27.03%) 23(20.72%) 0.270 Infarct volume (cm 3 , IQR; n = 111) 3.99(2.29–7.17) N/A N/A Time from onset to inclusion (h, IQR) 13.5(12–18) N/A N/A mRS scores 3(1–4) N/A N/A Admission median NIHSS scores (IQR) 3(2–7) N/A N/A Laboratory findings Glucose level (mmol/L; mean ± SD) 5.91 ± 2.13 5.49 ± 1.27 0.079 Glycated hemoglobin(%) 6.10(5.90–7.30) 5.80(5.40–6.20) < 0.001 Total cholesterol (mmol/L; mean ± SD) 4.82 ± 0.75 4.55 ± 1.10 0.054 Triglyceride (mmol/L; median, IQR) 1.20(0.93–1.60) 1.16(0.87–1.65) 0.676 HDL-C (mmol/L; median, IQR) 1.15(0.99–1.37) 1.39(1.19–1.71) < 0.001 LDL -C (mmol/L; median, IQR) 2.63(2.28–3.47) 2.34(2.06–2.64) < 0.001 Hs-CRP (mg/L; median, IQR) 2.30(1.10–4.80) 1.60(1.00-2.70) 0.017 WBC (× 10 9 /L, median, IQR) 6.30(5.20–7.70) 5.70(4.80-7.00) 0.031 homocysteine (µmol/L; median, IQR) 12.10(9.80–15.20) 9.20(6.80-11.2-) < 0.001 Serum creatinine(µmol/L, mean ± SD) 70(61–83) 68(59–81) 0.310 Uric acid µmol/L, median, IQR) 311.78 ± 98.14 325.58 ± 91.95 0.291 ALT (U/L, mean ± SD) 17(12,23) 17(12,25.5) 0.626 AST (U/L, mean ± SD) 21(18,26) 21(17,26) 0.946 RIPK1(ng/ml; median, IQR) 0.25(0.20–0.34) 0.19(0.16–0.21) < 0.001 MLKL (ng/ml; median, IQR) 3.16(1.93–5.22) 2.16(1.56–3.27) 0.018 The continuous variables are expressed as the mean ± standard deviation (SD) or the median (interquartile range). The categorical values are presented as the frequencies (percentages). Abbreviations: BMI, body mass index; NIHSS, National Institutes of Health Stroke Scale; mRS, modified Rankin Scale. HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Hs-CRP: high-sensitivity C-reactive protein; WBC: white blood cell; ALT, alanine aminotransferase; AST, aspartate aminotransferase; RIPK1, receptor-interacting serine-threonine kinase 1; MLKL, mixed lineage kinase domain-like protein. N/A, not applicable Statistically significant values are identified in boldface. Correlations of serum RIPK1, MLKL levels with stroke lesion volume, carotid plaque, severity and disability The serum levels of RIPK1 were correlated with the MLKL in both the control and stroke groups (LAA-AIS, Rs = 0.466, P < 0.001; Controls, Rs = 0.536, P < 0.001) (data not shown in table); The serum MLKL levels were found to have positive but weak correlation with core ischemic volume of LAA-AIS ( Rs = 0.286, P = 0.002), and mild correlation with unstable carotid plaque ( Rs = 0.482, P = 0.001). Besides, on the day of onset the level of serum MLKL is associated with the severity of acute ischemic stroke as measured by NIHSS and mRS assessments. The relationship between serum MLKL levels and the NIHSS score was significantly positive ( Rs = 0.467, P < 0.001), as well as the mRS score ( Rs = 0.430, P = 0.001) on admission. Although there was a positive but weak link between RIPK1 levels and carotid plaque ( Rs = 0.376, P = 0.001), the RIPK1 levels were not associated with stroke lesion volume or mRS scores either on the day of the stroke or on the seventh day, as Shown in Table 2 . Table 2 correlations of serum RIPK1, MLKL levels with stroke lesion volume, carotid plaque, severity and disability in LAA-AIS patients RIPK1 MLKL R S P R S P MLKL 0.466 < 0.001 / / MRI lesion volume, cm 3 0.027 0.778 0.286 0.002 Unstable carotid plaque 0.376 0.001 0.482 0.001 NIHSS(Day 1) 0.208 0.028 0.467 < 0.001 NIHSS(Day 7) 0.045 0.636 0.106 0.061 mRS (Day 1) 0.010 0.919 0.430 0.001 mRS (Day 7) 0.078 0.417 0.063 0.516 NIHSS, National Institutes of Health Stroke Scale; mRS, modified Rankin Scale; RIPK1, receptor-interacting serine-threonine kinase 1; MLKL, mixed lineage kinase domain-like protein. Statistically significant values are identified in boldface. Diagnostic efficacy of RIPK1, MLKL in LAA-AIS Using the ROC curve, the cut-off values for serum RIPK1 and MLKL levels in predicting LAA-AIS were determined (Fig. 2 (A)). It was recommended that RIPK1 have a cut-off value of more than 0.24 ng/ml in order to diagnose LAA-AIS, with an AUC of 0.769 (95% CI: 0.707–0.831; P < 0.001). with this cutoff, the sensitivity was 88.35%, the specificity was 52.25%, and the Yuden Index was 0.406. The ROC curve indicated that 2.45 ng/ml could be the ideal cut-off value for MLKL level as an indicator for auxiliary diagnosis of AIS. This resulted in a sensitivity of 86.36% and a specificity of 66.33%, with an AUC of 0.835(95% CI: 0.767–0.905; P 0.24 ng/ml and MLKL > 2.45 ng/ml that were different between the patients and controls were 54.95% and 11.71%, respectively, and 69.37% and 15.32% respectively ( P < 0.001), shown in Fig. 2 (B), (C). According to the analysis of differences shown in Table 1 , the AUC for the risk ratio model (Basic model) containing other established risk factors, such as SBP, HbA1c, HDL-C, LDL-C, WBC, Hs-CRP, and HCY, was 0.799 (95% CI: 0.716–0.881; P < 0.001), as shown in Fig. 3 . The addition of RIPK1 and MLKL to the Basic model resulted in a considerable improvement of the prediction value of LAA-AIS, with the AUC reaching 0.882 (95% CI: 0.813–0.951; P < 0.001). Shown in Table 4 . Independent risk predictors and model analysis in LAA-AIS Factors that exhibited significant correlations ( P < 0.05) in the univariate analysis were incorporated in a multivariate regression analysis, as illustrated in Table 3 , where the dependent variable was the occurrence of LAA-AIS. The results suggested that HbA1c(OR = 2.063, 95% CI:1.076–3.957, P = 0.029), LDL-C (OR = 3.820, 95% CI:1.386–10.528, P = 0.010), and MLKL (OR = 4.858, 95% CI: 1.256–18.786, P = 0.022) level may be independent risk factors for LAA-AIS. We used the AUC to analyze the predicted values of the HbA1c, LDL-C, and the two indictors with MLKL for LAA-AIS. We found that the AUC value of HbA1c was 0.618 (95% CI: 0.500-0.735, P = 0.055), LDL-C was 0.712 (95% CI: 0.608–0.816, P = 0.001), and the two indicators with MLKL (Combined factors) was 0.881(95% CI: 0.813–0.950, P < 0.001), as shown in Fig. 4 . Table 3 Binary logistic regression analysis to identify LAA-AIS Univariate analysis Multivariate analysis P OR (95% CI) P OR (95% CI) Systolic blood pressure 0.009 1.018(1.004–1.032) 0.624 0.992(0.962–1.024) HbA1c 0.002 1.606(1.186–2.175) 0.029 2.063(1.076–3.957) LDL-C < 0.001 2.535(1.641–3.915) 0.010 3.820(1.386–10.528) HDL-C < 0.001 0.072(0.025–0.21) 0.534 1.975(0.231–16.914) HCY 0.036 1.048(1.003–1.095) 0.567 0.906(0.646–1.27) WBC 0.017 1.205(1.034–1.406) 0.096 1.347(0.948–1.913) Hs-CRP 0.002 1.202(1.069–1.351) 0.053 1.365(0.996–1.872) RIPK1 0.014 1.007(1.001–1.013) 0.463 1.235(0.745–2.043) MLKL < 0.001 1.661(1.27–2.172) 0.022 4.858(1.256–18.786) HbA1c, Glycated hemoglobin; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; HCY, homocysteine; WBC, white blood cell; Hs-CRP, high-sensitivity C-reactive protein; RIPK1, receptor-interacting serine-threonine kinase 1; MLKL, mixed lineage kinase domain-like protein. Statistically significant values are identified in boldface. Table 4 The accuracy of independent risk factors to diagnose LAA-AIS AUC 95% CI P MLKL 0.835 0.767–0.905 < 0.001 RIPK1 0.769 0.707–0.831 < 0.001 HbA1C 0.618 0.500-0.735 0.055 LDL-C 0.712 0.608–0.816 0.001 Basic model 0.799 0.716–0.881 < 0.001 Basic model + RIPK1 + MLKL 0.882 0.813–0.951 < 0.001 Combined factors 0.881 0.813–0.950 < 0.001 Statistically significant values are identified in boldface. Discussion In this study, we found a significant increase in RIPK1 and MLKL levels in patients with acute ischemic stroke due to large artery atherosclerosis compared to normal controls. As markers of necroptosis, MLKL showed a stronger association than RIPK1 with the severity of LAA-AIS, infarct volume, and carotid atherosclerotic plaque burden. Both RIPK1 and MLKL demonstrated good predictive ability for LAA-AIS, with elevated serum levels of RIPK1 and MLKL being associated with a higher risk of LAA-AIS. The positive correlation between MLKL and LAA-AIS remained independent of established risk factors, including glycated hemoglobin, systolic blood pressure, white blood cell count, high-sensitivity C-reactive protein, homocysteine, low-density lipoprotein and high-density lipoprotein. To the best of our knowledge, this study is the first case-control study to investigate the association between serum RIPK1 and MLKL levels and the risk in LAA-AIS patients. Previous studies have indicated a close association between the occurrence of necroptosis and the development of various diseases, such as cancer, inflammatory diseases, autoimmune diseases, and degenerative diseases [ 19 , 20 ] . Researchers Karunakara et al. discovered that patients with unstable carotid artery atherosclerosis have higher levels of RIPK3 and MLKL expression, and that progressed atherosclerosis is associated with MLKL phosphorylation. They also proposed that necroptosis can act as a sign of the susceptibility of atherosclerotic plaques, which is similar to our results [ 9 ] . In addition to directly leading to cellular lytic death, necroptosis can also exacerbate LAA-AIS through the perspective of inflammatory response. The evidence provided by Zhang et al. suggested that RIPK1 may promote early AS development by facilitating monocyte infiltration and inflammation [ 11 ] . Similarly, Karunakara et al. also found that the RIPK1 gene is upregulated in early atherosclerotic lesions in mice and human, suggesting that RIPK1 plays a central role in the inflammatory signaling pathways during the development of atherosclerosis [ 10 ] . Nec-1 can significantly reduce the mRNA expression of IL-1α, IL-1β, and TNF-α in D-Galactose-induced aged mice after surgery, reducing neuroinflammation and alleviating postoperative cognitive impairment [ 21 ] . In our study, we also found elevated levels of WBC and Hs-CRP, among patients in the case group compared to the control group, indicating increased inflammation. Previous studies have unequivocally shown that patients with elevated levels of WBC in the context of acute ischemic stroke facing significantly heightened risks of mortality, stroke recurrence, and novel vascular events [ 22 , 23 ] . Additionally, increased Hs-CRP levels were closely linked to the risk of mortality or recurrent stroke [ 24 ] . Our study shows that HbA1c is the independent risk factor for LAA-AIS and KLAPPROTH S found that worsened early edema and worse functional outcome was linked with higher shortterm serum blood gloucose level, but not with HbA1c levels [ 25 ] . Besides, it has been shown that HbA1c is an independent risk factor for recurrent cerebral infarction [ 26 ] cause high blood glucose level does lead to macrophage proliferation and white blood cell count increase [ 27 ] . Necroptosis may have an impact on LAA-AIS through lipid metabolism in addition to the inflammation. The pathogenic mechanisms that lead to the development of atherosclerosis include endothelial cell dysfunction, intramural lipid accumulation, aberrant smooth muscle cell proliferation and migration, macrophage engulfment of lipids to generate foam cells, plaque formation, and rupture [ 28 ] . Rasheed et al. found greater lipid retention in the plaque along with reductions in circulating cholesterol after MlKL knockdown. They therefore concluded that MLKL might interact with cholesterol efflux proteins to change their function [ 29 ] . In our study, we found a mild association between RIPK1 and MLKL with the occurrence of carotid artery plaques. This may be due to the overexpression of MLKL which exacerbates the increase of inflammatory mediators such as caspase-1, IL-1β, mediated by ox-LDL, thereby intensifying the inflammatory response [ 30 ] . There are somoe limitations for this study which is worth future investigaiton. First, it is crucial to validate these results in a bigger investigation because this one-site study had a limited sample size. Second, we haven’t gotten chance to investgate the correlation of serum MLKL and RIPK1 with other subtypes of ischemic stroke.Third, we did not follow up the patients over time or evaluate the changes in blood MLKL and RIPK1 levels more than 24 hours after stroke onset. Conclusion Our research showed that both MLKL and serum RIPK1 levels on admission were linked to acute ischaemic stroke in major arteries with atherosclerosis, with MLKL being more significantly increase. Elevated levels in a Chinese sample may represent a new, independent LAA-AIS diagnostic maker. Conclusions on the relationship of the necroptosis markers RIPK1, MLKL with AIS, however, will require a great deal more study. Declarations Acknowledgments We appreciate all the patients who participated in our study and thereby made this work possible. Authors’ contributions F.W. conceived and designed the research. Z.D.H analyzed the data and drafted the manuscript. W.Q.W and Y.Y.B.collected the data and prepared all the figures. All authors reviewed the manuscript. Funding The study was supported by grants from the Zhejiang Provincial Basic and Public Welfare Research Program (Grant Number: LGF21H020005) and the Zhejiang Provincial Medicine and Health Research Foundation (Grant Number: 2021RC141). Data availability The data supporting this study’s findings are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The studies involving humans were approved by Ethics Committee of Taizhou Hospital, Zhejiang Province, China. 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1","display":"","copyAsset":false,"role":"figure","size":679884,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for recruiting LAA-AIS Patients LAA stroke: large artery atherosclerotic stroke\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8178833/v1/50d1f8bbee0b59af0fbabba4.jpg"},{"id":97673928,"identity":"1aa7204e-3bdc-4c35-8226-2337fce39edf","added_by":"auto","created_at":"2025-12-08 09:41:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":524241,"visible":true,"origin":"","legend":"\u003cp\u003eA receiver operational characteristic curves of serum RIPK1, MLKL to forecast LAA-AIS. B the difference between the patients and controls in serum RIPK1 \u0026gt; 0.24 ng/ml. C the difference in serum MLKL concentration between the patients and controls (\u0026gt; 2.45 ng/ml).\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8178833/v1/6d0dacc09467ca7355baafe1.jpg"},{"id":97656473,"identity":"5c5a5737-ee43-43c3-917d-b51d2a3d6135","added_by":"auto","created_at":"2025-12-08 07:12:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109564,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves (ROC) of the Basic model and the Basic model plus RIPK1, MLKL. Basic model: systolic blood pressure, glycated hemoglobin, white blood cell, homocysteine, low-density lipoprotein cholesterol, and high-sensitivity C-reactive protein. AUC, area under curve\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8178833/v1/27000d1b34672ba558f536b2.png"},{"id":97674917,"identity":"ddfef93b-142e-4893-81f6-394dec45ff4a","added_by":"auto","created_at":"2025-12-08 09:44:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40230,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves (ROC) of the independent risk factors including Glycated hemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL) and the two indicators with MLKL. AUC, area under curve\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8178833/v1/1d6c362c91c41abdff4d6e82.png"},{"id":98438757,"identity":"f0e7afba-08ec-4ad1-b563-1ce0dbd4f257","added_by":"auto","created_at":"2025-12-17 17:00:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2240090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8178833/v1/b6ac3d36-4a28-4b04-8878-f829801af95e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"RIPK1, MLKL are Potential Biomarkers for Large Artery Atherosclerotic Acute Ischemic Stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke, precipitated by the occlusion of cerebral arteries, stands as a predominant global contributor to mortality and disability\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As ischemia persists in both duration and severity, the neurological integrity of the ischemic penumbra, encircling the central necrotic zone, undergoes progressive compromise\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Presently, clinical options for acute ischemic stroke remain confined to mechanical thrombectomy or thrombolytic drugs. Regrettably, both interventions exhibit suboptimal success rates and a narrow therapeutic window, underscoring the imperative for innovative therapeutic strategies\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. To ameliorate the deleterious effects associated with acute ischemic stroke, a critical imperative exists to pioneer novel treatment approaches. Furthermore, drawbacks include limited availability of brain imaging technology, lengthier imaging times, increased costs, metal implantation or pacemaker contraindications, and claustrophobia, which all contribute to decreased use. As a result, new practical and quick adjunctive diagnostic techniques for acute ischemic stroke such as biomarkers are desperately needed in clinical practice.\u003c/p\u003e\u003cp\u003eRelevant studies indicate that the necroptosis signaling pathway plays a significant role in the occurrence and development of acute ischemic stroke\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Necroptosis is a cell death mechanism mediated by the activation and interaction of receptor-interacting serine-threonine kinase 1 (RIPK1), receptor-interacting serine-threonine kinase 3 (RIPK3), and mixed lineage kinase domain-like protein (MLKL) through the activation of inflammatory receptors\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. When cerebral ischemia occurs, activated microglial cells go to the site of the ischemia and release pertinent cytokines such as TNF-α, TRAIL, and FasL. The recruitment of RIPK1 and other proteins to form Complex I is triggered by the interaction of these death signals with their cell surface membrane receptors\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, the lowered levels of ATP during the acute phase of cerebral ischemia are insufficient to maintain the activity of Caspase-8 because it partially depends on ATP levels\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. As a result, a complex structure known as a necrosome is formed by RIPK1, RIPK3, and MLKL. Additionally, MLKL is phosphorylated by RIPK3 within the necrosome, causing its oligomerization and translocation to the cell membrane, which leads to membrane rupture and cell death\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, due to large artery atherosclerosis (LAA) being the primary pathological factor of acute ischemic stroke (AIS), its pathogenic mechanism has been extensively studied\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. KARUNAKARAN et al. proposed that the activation of necroptosis is linked to the lipid necrotic core sensitivity and inflammation in advanced human atherosclerosis\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Subsequently, their team further demonstrated that RIPK1 acts as a central driving factor of atherosclerotic inflammation through its ability to activate the NF-κB pathway and facilitate the release of inflammatory cytokines\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Although several animal experiments have suggested that the necroptosis-related factors RIPK1 and MLKL promote the occurrence and progression of cerebral infarction, and the necroptosis inhibitor Necrostatin-1 (NEC-1) has been shown to improve outcomes in ischemic stroke mice\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, there is currently no clinical study on the relationship between RIPK1, MLKL, and large artery atherosclerotic acute ischemic stroke (LAA-AIS). In order to determine if proteins linked to necroptosis are potential biomarkers in the early stages of AIS, we set out to examine the relationship between necroptosis and LAA stroke in patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy participants\u003c/h2\u003e\u003cp\u003eFrom January 2022 to December 2022, we consecutively enrolled 111 patients, who experienced large artery atherosclerotic acute ischemic stroke for the first time admitted within 24 h to the Department of Neurology, Taizhou hospital of Zhejiang Province affiliated to Wenzhou Medical University according to the World Health Organization standards \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The cases with LAA stroke were categorized by at least two neurologists using the TOAST (Trial of Org 10172 in Acute Stroke Treatment) criteria\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The exclusion criteria for patients with AIS were as follows: (1) Brain tumor or cerebral hemorrhage. (2) Severe systemic infectious diseases, autoimmune or connective tissue diseases, metabolic disorders such as endocrine disorders or toxicity. (3) Patients who were intolerant to magnetic resonance imaging (MRI) scans or there were contraindications to MRI scans. (4) Individuals with established risk factors for increased necroptosis levels include those with Alzheimer's disease, myocardial infarction, cirrhosis, chronic hepatitis, hematological cancers, and amyotrophic lateral sclerosis. The healthy control group consisted of 111 age- and gender-matched (in blocks of 5) healthy cases who were enrolled in our hospital's Medical Examination Center.\u003c/p\u003e\u003cp\u003e The study was performed in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of Taizhou Hospital (ethical number: K20181204). All participants were informed of the study protocol and their written informed consents were obtained prior to enrollment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical Varies and Imaging indicators\u003c/h3\u003e\n\u003cp\u003eOn admission, data were gathered from the Clinical Electronic Case System regarding age, sex, smoking status, alcohol use, and past medical history of risk factors (hypertension, diabetes mellitus, hyperlipidemia, etc.). Two experienced neurologists assessed the patient's severity of AIS used the National Institutes of Health Stroke Scale (NIHSS; score ranges from 0 to 42, with higher scores indicating greater deficits)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, as well as the modified Rankin scale (mRS; score ranges from 0 to 5, with higher scores indicating greater deficits)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. All 111 LAA-AIS patients were routinely given a magnetic resonance imaging (MRI, 1.5 T) with diffusion-weighted imaging (DWI) within 24 hours of being admitted. One skilled neuroradiologist who was blinded with the clinical or laboratory findings calculated the DWI lesion volumes. Contrary to hyperechoic homogeneous plaques, which are mostly fibrous and less likely to develop cerebrovascular ischemia, hypoechoic, heterogeneous plaques are associated with intraplaque hemorrhage as well as increased lipid concentration and a necrotic core of the plaque\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. As a result, carotid plaques can be classified into three groups based on ultrasound results: no plaque group, stable plaque group, and unstable plaque group, as determined by one ultra sonographer of intermediate rank or above.\u003c/p\u003e\n\u003ch3\u003eBlood Collection and Laboratory\u003c/h3\u003e\n\u003cp\u003eWithin 24 h stroke onset, blood was drawn after fasting overnight for the following tests: the fasting blood glucose (FBG), Glycated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), homocysteine (HCY), white blood cell (WBC), high-sensitivity C-reactive protein (Hs-CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine, uric acid concentrations, were measured at the hospital's clinical laboratory. In addition, aliquots of the serum sample were promptly stored at -80\u0026deg;C following centrifugation to test the level of RIPK1 and MLKL in serum. An enzyme-linked immunosorbent assay (ELISA) kits (CLOUD-CLONE CORP, Wuhan, China) was used to measure their level. According to the manufacturer, the lowest detectable level of human RIPK1 was found to be 0.059 ng/ml, and MLKL was 0.112 ng/ml. The kits had 10% and 12% intra- and inter-assay CV%s, respectively.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eSPSS 26.0 (Statistical Product and Service Solutions version 26.0) and GraphPad Prism 8.0 were used for statistical analysis. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) was used to express normally distributed continuous data, whereas the median and interquartile range (IQR) were used to express skewed distributions. Frequency and percentage (N, %) were used to express categorical variables. Continuous values between the two groups were compared using the t test, corrected t test, or Mann-Whitney U test; categorical data were compared using Pearson's Chi-Squared test, continuity correction, or Fisher's exact test. To determine cutoff criteria and assess the accuracy of RIPK1, MLKL predictions, the area under the receiver operating characteristic curve (ROC) was utilized. The basic model and the basic model with RIPK1, MLKL were compared using the ROC curve for prediction analysis. In order to estimate multivariate adjusted odds ratios (ORs) and 95% confidence intervals (CIs), binary logistic regression analysis was employed to examine potential risk variables for LAA-AIS. For the prediction analysis of the comparison of independent risk factors, the ROC curve was utilized. A statistically significant \u003cem\u003eP\u003c/em\u003e value was defined as \u0026le;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003efrom January 2022 to December 2022, there were 187 consecutive patients who had with LAA-AIS diagnosed within 24 hours after the beginning of symptoms. 22 patients were excluded due to severe infection, 14 because of malignant tumor, 12 of other systems\u0026rsquo; critical disease, 10 patients did not consent, while 15 individuals were omitted due to lacking of relevant data. Shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e .\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBasic characteristics of the study participants.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 111 patients with LAA-AIS and 111 healthy controls were included in the final analysis. The basic characteristics of the study subjects were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Within the research sample, the mean age of the AIS patients was 65.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21 years, with 54.95% of them being male. Meanwhile, the mean age of the controls was 63.98\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07 years. The median NIHSS score on admission was 3 points (IQR, 2\u0026ndash;7). As a result of our findings, patients with LAA-AIS had considerably greater median serum RIPK1 levels than controls [0.25 ng/ml (IQR, 0.20,0.34) vs 0.19 ng/ml (0.16,0.21), respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. MLKL levels were 3.16(1.93,5.22) and 2.16(1.56,3.27), in the AIS and control groups respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the basic attributes of both groups. Besides, we found that the proportion of WBC, SBP, HbA1\u003csub\u003eC\u003c/sub\u003e, Hs-CRP, LDL-C and HCY were higher in the patients with AIS than in the healthy controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\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 patients with acute ischemic stroke and normal cases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-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\u003e65.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.98\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61(54.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61(54.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmokers (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37(33.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(30.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(30.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29(28.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148.71\u0026thinsp;\u0026plusmn;\u0026thinsp;20.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141.19\u0026thinsp;\u0026plusmn;\u0026thinsp;20.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.01\u0026thinsp;\u0026plusmn;\u0026thinsp;13.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.90\u0026thinsp;\u0026plusmn;\u0026thinsp;12.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.241\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\u003e72(64.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64(57.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(28.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(18.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercholesterolemia (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30(27.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(20.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfarct volume (cm\u003csup\u003e3\u003c/sup\u003e, IQR; n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.99(2.29\u0026ndash;7.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime from onset to inclusion (h, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.5(12\u0026ndash;18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(1\u0026ndash;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission median NIHSS scores (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(2\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaboratory findings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose level (mmol/L; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlycated hemoglobin(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.10(5.90\u0026ndash;7.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.80(5.40\u0026ndash;6.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mmol/L; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride (mmol/L; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20(0.93\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16(0.87\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15(0.99\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.39(1.19\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL -C (mmol/L; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.63(2.28\u0026ndash;3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.34(2.06\u0026ndash;2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP (mg/L; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30(1.10\u0026ndash;4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60(1.00-2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.30(5.20\u0026ndash;7.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.70(4.80-7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehomocysteine (\u0026micro;mol/L; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.10(9.80\u0026ndash;15.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.20(6.80-11.2-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine(\u0026micro;mol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70(61\u0026ndash;83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68(59\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid \u0026micro;mol/L, median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e311.78\u0026thinsp;\u0026plusmn;\u0026thinsp;98.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325.58\u0026thinsp;\u0026plusmn;\u0026thinsp;91.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(12,23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17(12,25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21(18,26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(17,26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIPK1(ng/ml; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25(0.20\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19(0.16\u0026ndash;0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLKL (ng/ml; median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.16(1.93\u0026ndash;5.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.16(1.56\u0026ndash;3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\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\u003eThe continuous variables are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or the median (interquartile range). The categorical values are presented as the frequencies (percentages). Abbreviations: BMI, body mass index; NIHSS, National Institutes of Health Stroke Scale; mRS, modified Rankin Scale. HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Hs-CRP: high-sensitivity C-reactive protein; WBC: white blood cell; ALT, alanine aminotransferase; AST, aspartate aminotransferase; RIPK1, receptor-interacting serine-threonine kinase 1; MLKL, mixed lineage kinase domain-like protein. N/A, not applicable Statistically significant values are identified in boldface.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations of serum RIPK1, MLKL levels with stroke lesion volume, carotid plaque, severity and disability\u003c/h2\u003e\u003cp\u003eThe serum levels of RIPK1 were correlated with the MLKL in both the control and stroke groups (LAA-AIS, \u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.466, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Controls, \u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.536, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (data not shown in table); The serum MLKL levels were found to have positive but weak correlation with core ischemic volume of LAA-AIS (\u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.286, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and mild correlation with unstable carotid plaque (\u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.482, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Besides, on the day of onset the level of serum MLKL is associated with the severity of acute ischemic stroke as measured by NIHSS and mRS assessments. The relationship between serum MLKL levels and the NIHSS score was significantly positive (\u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.467, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as the mRS score (\u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.430, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) on admission. Although there was a positive but weak link between RIPK1 levels and carotid plaque (\u003cem\u003eRs\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.376, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), the RIPK1 levels were not associated with stroke lesion volume or mRS scores either on the day of the stroke or on the seventh day, as Shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003ecorrelations of serum RIPK1, MLKL levels with stroke lesion volume, carotid plaque, severity and disability in LAA-AIS patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRIPK1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMLKL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csub\u003eS\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003csub\u003eS\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLKL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e/\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e/\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRI lesion volume, cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnstable carotid plaque\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIHSS(Day 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIHSS(Day 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS (Day 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS (Day 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.516\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\u003eNIHSS, National Institutes of Health Stroke Scale; mRS, modified Rankin Scale; RIPK1, receptor-interacting serine-threonine kinase 1; MLKL, mixed lineage kinase domain-like protein. Statistically significant values are identified in boldface.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDiagnostic efficacy of RIPK1, MLKL in LAA-AIS\u003c/h3\u003e\n\u003cp\u003eUsing the ROC curve, the cut-off values for serum RIPK1 and MLKL levels in predicting LAA-AIS were determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A)). It was recommended that RIPK1 have a cut-off value of more than 0.24 ng/ml in order to diagnose LAA-AIS, with an AUC of 0.769 (95% CI: 0.707\u0026ndash;0.831; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). with this cutoff, the sensitivity was 88.35%, the specificity was 52.25%, and the Yuden Index was 0.406. The ROC curve indicated that 2.45 ng/ml could be the ideal cut-off value for MLKL level as an indicator for auxiliary diagnosis of AIS. This resulted in a sensitivity of 86.36% and a specificity of 66.33%, with an AUC of 0.835(95% CI: 0.767\u0026ndash;0.905; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a Yuden Index of 0.547, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The percentages of serum RIPK1\u0026thinsp;\u0026gt;\u0026thinsp;0.24 ng/ml and MLKL\u0026thinsp;\u0026gt;\u0026thinsp;2.45 ng/ml that were different between the patients and controls were 54.95% and 11.71%, respectively, and 69.37% and 15.32% respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (B), (C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the analysis of differences shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the AUC for the risk ratio model (Basic model) containing other established risk factors, such as SBP, HbA1c, HDL-C, LDL-C, WBC, Hs-CRP, and HCY, was 0.799 (95% CI: 0.716\u0026ndash;0.881; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The addition of RIPK1 and MLKL to the Basic model resulted in a considerable improvement of the prediction value of LAA-AIS, with the AUC reaching 0.882 (95% CI: 0.813\u0026ndash;0.951; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eIndependent risk predictors and model analysis in LAA-AIS\u003c/h3\u003e\n\u003cp\u003eFactors that exhibited significant correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the univariate analysis were incorporated in a multivariate regression analysis, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where the dependent variable was the occurrence of LAA-AIS. The results suggested that HbA1c(OR\u0026thinsp;=\u0026thinsp;2.063, 95% CI:1.076\u0026ndash;3.957, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), LDL-C (OR\u0026thinsp;=\u0026thinsp;3.820, 95% CI:1.386\u0026ndash;10.528, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), and MLKL (OR\u0026thinsp;=\u0026thinsp;4.858, 95% CI: 1.256\u0026ndash;18.786, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) level may be independent risk factors for LAA-AIS. We used the AUC to analyze the predicted values of the HbA1c, LDL-C, and the two indictors with MLKL for LAA-AIS. We found that the AUC value of HbA1c was 0.618 (95% CI: 0.500-0.735, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055), LDL-C was 0.712 (95% CI: 0.608\u0026ndash;0.816, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and the two indicators with MLKL (Combined factors) was 0.881(95% CI: 0.813\u0026ndash;0.950, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary logistic regression analysis to identify LAA-AIS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.018(1.004\u0026ndash;1.032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.992(0.962\u0026ndash;1.024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.606(1.186\u0026ndash;2.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.063(1.076\u0026ndash;3.957)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.535(1.641\u0026ndash;3.915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.820(1.386\u0026ndash;10.528)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.072(0.025\u0026ndash;0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.975(0.231\u0026ndash;16.914)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.048(1.003\u0026ndash;1.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.906(0.646\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.205(1.034\u0026ndash;1.406)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.347(0.948\u0026ndash;1.913)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.202(1.069\u0026ndash;1.351)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.365(0.996\u0026ndash;1.872)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIPK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.007(1.001\u0026ndash;1.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.235(0.745\u0026ndash;2.043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLKL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.661(1.27\u0026ndash;2.172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.858(1.256\u0026ndash;18.786)\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\u003eHbA1c, Glycated hemoglobin; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; HCY, homocysteine; WBC, white blood cell; Hs-CRP, high-sensitivity C-reactive protein; RIPK1, receptor-interacting serine-threonine kinase 1; MLKL, mixed lineage kinase domain-like protein. Statistically significant values are identified in boldface.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe accuracy of independent risk factors to diagnose LAA-AIS\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLKL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.767\u0026ndash;0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIPK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.707\u0026ndash;0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.500-0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.608\u0026ndash;0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.716\u0026ndash;0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic model\u0026thinsp;+\u0026thinsp;RIPK1\u0026thinsp;+\u0026thinsp;MLKL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.813\u0026ndash;0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.813\u0026ndash;0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eStatistically significant values are identified in boldface.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found a significant increase in RIPK1 and MLKL levels in patients with acute ischemic stroke due to large artery atherosclerosis compared to normal controls. As markers of necroptosis, MLKL showed a stronger association than RIPK1 with the severity of LAA-AIS, infarct volume, and carotid atherosclerotic plaque burden. Both RIPK1 and MLKL demonstrated good predictive ability for LAA-AIS, with elevated serum levels of RIPK1 and MLKL being associated with a higher risk of LAA-AIS. The positive correlation between MLKL and LAA-AIS remained independent of established risk factors, including glycated hemoglobin, systolic blood pressure, white blood cell count, high-sensitivity C-reactive protein, homocysteine, low-density lipoprotein and high-density lipoprotein. To the best of our knowledge, this study is the first case-control study to investigate the association between serum RIPK1 and MLKL levels and the risk in LAA-AIS patients.\u003c/p\u003e\u003cp\u003ePrevious studies have indicated a close association between the occurrence of necroptosis and the development of various diseases, such as cancer, inflammatory diseases, autoimmune diseases, and degenerative diseases\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Researchers Karunakara et al. discovered that patients with unstable carotid artery atherosclerosis have higher levels of RIPK3 and MLKL expression, and that progressed atherosclerosis is associated with MLKL phosphorylation. They also proposed that necroptosis can act as a sign of the susceptibility of atherosclerotic plaques, which is similar to our results \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In addition to directly leading to cellular lytic death, necroptosis can also exacerbate LAA-AIS through the perspective of inflammatory response. The evidence provided by Zhang et al. suggested that RIPK1 may promote early AS development by facilitating monocyte infiltration and inflammation\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Similarly, Karunakara et al. also found that the RIPK1 gene is upregulated in early atherosclerotic lesions in mice and human, suggesting that RIPK1 plays a central role in the inflammatory signaling pathways during the development of atherosclerosis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Nec-1 can significantly reduce the mRNA expression of IL-1α, IL-1β, and TNF-α in D-Galactose-induced aged mice after surgery, reducing neuroinflammation and alleviating postoperative cognitive impairment \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In our study, we also found elevated levels of WBC and Hs-CRP, among patients in the case group compared to the control group, indicating increased inflammation. Previous studies have unequivocally shown that patients with elevated levels of WBC in the context of acute ischemic stroke facing significantly heightened risks of mortality, stroke recurrence, and novel vascular events\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Additionally, increased Hs-CRP levels were closely linked to the risk of mortality or recurrent stroke\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Our study shows that HbA1c is the independent risk factor for LAA-AIS and KLAPPROTH S found that worsened early edema and worse functional outcome was linked with higher shortterm serum blood gloucose level, but not with HbA1c levels\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Besides, it has been shown that HbA1c is an independent risk factor for recurrent cerebral infarction\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e cause high blood glucose level does lead to macrophage proliferation and white blood cell count increase\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNecroptosis may have an impact on LAA-AIS through lipid metabolism in addition to the inflammation. The pathogenic mechanisms that lead to the development of atherosclerosis include endothelial cell dysfunction, intramural lipid accumulation, aberrant smooth muscle cell proliferation and migration, macrophage engulfment of lipids to generate foam cells, plaque formation, and rupture \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Rasheed et al. found greater lipid retention in the plaque along with reductions in circulating cholesterol after MlKL knockdown. They therefore concluded that MLKL might interact with cholesterol efflux proteins to change their function\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In our study, we found a mild association between RIPK1 and MLKL with the occurrence of carotid artery plaques. This may be due to the overexpression of MLKL which exacerbates the increase of inflammatory mediators such as caspase-1, IL-1β, mediated by ox-LDL, thereby intensifying the inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere are somoe limitations for this study which is worth future investigaiton. First, it is crucial to validate these results in a bigger investigation because this one-site study had a limited sample size. Second, we haven\u0026rsquo;t gotten chance to investgate the correlation of serum MLKL and RIPK1 with other subtypes of ischemic stroke.Third, we did not follow up the patients over time or evaluate the changes in blood MLKL and RIPK1 levels more than 24 hours after stroke onset.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research showed that both MLKL and serum RIPK1 levels on admission were linked to acute ischaemic stroke in major arteries with atherosclerosis, with MLKL being more significantly increase. Elevated levels in a Chinese sample may represent a new, independent LAA-AIS diagnostic maker. Conclusions on the relationship of the necroptosis markers RIPK1, MLKL with AIS, however, will require a great deal more study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate \u0026nbsp;all the patients who participated in our study and thereby made this work possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.W. conceived and designed the research. Z.D.H analyzed the data and drafted the manuscript. W.Q.W \u0026nbsp; and Y.Y.B.collected the data and \u0026nbsp; prepared all the figures. \u0026nbsp;All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by grants from the Zhejiang Provincial Basic and Public Welfare Research Program (Grant Number: LGF21H020005) and the Zhejiang Provincial Medicine and Health Research Foundation (Grant Number: 2021RC141).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026rsquo;s findings are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by Ethics Committee of Taizhou Hospital, Zhejiang Province, China. The studies were conducted in accordance with the relevant guidelines and regulations in the Declaration of Helsinki and written informed consent was obtained from all patients or their families. Clinical trial number: not applicable.\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\u003eConflict of interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaini V, Guada L, Yavagal D R. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions [J]. Neurology, 2021, 97(20 Suppl 2): S6-s16.http://dx.doi.org/10.1212/wnl.0000000000012781\u003c/li\u003e\n\u003cli\u003eBack T. Pathophysiology of the ischemic penumbra--revision of a concept [J]. 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Inflammation, 2020, 43(6): 2222-2231.http://dx.doi.org/10.1007/s10753-020-01289-8\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":"necroptosis, RIPK1, MLKL, atherosclerosis, acute ischemic stroke","lastPublishedDoi":"10.21203/rs.3.rs-8178833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8178833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This research aimed to investigate the connection between large artery atherosclerosis-associated acute ischemic stroke (LAA-AIS) and the key protein receptor-interacting serine-threonine kinase 1 (RIPK1), mixed lineage kinase domain-like protein (MLKL) of necroptosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We enrolled 111 patients with LAA-AIS along with 111 age and gender-matched healthy controls. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum RIPK1 and MLKL levels. Spearman correlation analysis was used to explore the correlation between RIPK1, MLKL, and the severity of LAA-AIS. Plotted receiver operating characteristic (ROC) curves to evaluate the diagnostic valueof RIPK1 and MLKL in predicting LAA-AIS. The independent risk variables of LAA-AIS were investigated using binary logistic regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The serum levels of RIPK1 [0.25 ng/ml (IQR, 0.20, 0.34) vs 0.19 ng/ml (IQR, 0.16, 0.21)] and MLKL [3.16 ng/ml (IQR, 1.93- 5.22) vs 2.16 (IQR, 1.56-3.27)] in LAA-AIS patients were significantly higher than controls. On admission, the correlation coefficients between MLKL and NIHSS, MKLK and mRS score were 0.467 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and 0.430 (\u003cem\u003eP\u003c/em\u003e=0.001), respectively. There was a weak but positive correlation between MLKL and the volume of cerebral infarction under MRI (\u003cem\u003eRs\u003c/em\u003e=0.286, \u003cem\u003eP\u003c/em\u003e=0.002) and unstable carotid atherosclerotic plaque (\u003cem\u003eRs\u003c/em\u003e=0.482, \u003cem\u003eP\u003c/em\u003e=0.001). Adding RIPK1 and MLKL to the basic model including traditional risk factors related to LAA-AIS improved the area under curve （AUC） from 0.799 to 0.882. Regression analysis suggested MLKL (OR=4.858, 95% CI: 1.256-18.786, \u003cem\u003eP\u003c/em\u003e=0.022) is an independent risk factor for LAA-AIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003e\u0026nbsp;Our study demonstrated thatserum levels of RIPK1 and MLKL may be associated with LAA -AIS.\u003c/p\u003e","manuscriptTitle":"RIPK1, MLKL are Potential Biomarkers for Large Artery Atherosclerotic Acute Ischemic Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 07:12:23","doi":"10.21203/rs.3.rs-8178833/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":"9ad20e10-d35d-43d7-b035-86820a77489a","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-16T17:38:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 07:12:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8178833","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8178833","identity":"rs-8178833","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}