Clinical contribution of lipid profiles to the stroke progression in Chinese population: a single-centered cohort study

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Objectives: To explore lipid parameters and their impact on stroke outcomes in patients with and without thrombolysis. Methods: We prospectively enrolled acute ischemic stroke (AIS) patients, both with and without thrombolysis in our single center, and divided them into improvement (good outcome at 2 weeks) and non-improvement groups. Comparisons of demographic, laboratory, imaging, and clinical scaling data were conducted between the two groups. The performance and importance of each lipid (triglyceride, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein B100 (ApoB100), and lipoprotein a) were assessed using logistic regression and random forest models. Results: A total of 262 stroke patients were enrolled, with 165 receiving thrombolysis. It was found that ApoB100 levels were lower in patients who received thrombolysis (p < 0.001), and there were no significant differences in lipids between the improvement and non-improvement groups. The random forest model generated barplots showing the importance of lipids and risk factors in patients with AIS, indicating that HDL and ApoB100 from lipids (both over 15%) were more important for predicting favorable stroke outcomes. Conclusions: This study demonstrates that HDL and ApoB100 are key predictors of favorable stroke outcomes, as evaluated using a machine learning model. These findings highlight the potential value of incorporating HDL and ApoB100 into clinical risk assessment tools for stroke patients, warranting further investigation in larger, diverse cohorts. Trial registration : ChiCTR1800018315, 11/09/2018 Lipid machine learning stroke thrombolysis ApoB100 Figures Figure 1 Figure 2 Figure 3 Introduction Dyslipidemia is a prevalent risk factor for atherosclerotic cardiovascular disease (ASCVD), which encompasses ischemic stroke[ 1 , 2 ]. A lipid profile, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), lipoprotein (a) [Lp(a)], total cholesterol (TC), etc., is widely utilized in both clinical research and practice[ 3 , 4 ]. These lipid parameters are closely associated with the risk, diagnosis, and prognosis of ASCVD[ 5 – 7 ], as well as with cognitive decline in the elderly population[ 8 ]. When compared to a single biomarker within the lipid profile, a combined model demonstrates good efficacy in disease monitoring and prediction[ 9 ]. Clinical and genetic evidence suggested that low-density lipoprotein (LDL) can cause ASCVD[ 10 ]. The effect of LDL on ASCVD depends on its levels, magnitude and exposure duration[ 11 ]. A study in China revealed a J-shaped relationship between LDL-C and mortality, which can be affected by diabetes[ 12 ]. Guidelines have emphasized the significance of LDL levels in managing blood cholesterol[ 13 ]. Targeting LDL-C lowering by statins or proprotein convertase subtilisin/kexin type 9 inhibitors[ 14 ] in patients with stroke or transient ischemic attack alone can decrease the overall incidence of stroke recurrence and ASCVD, although there may be a slight increase in the risk of hemorrhagic stroke[ 15 ]. Lowering LDL-C levels to 70 mg/dL has been found to reduce the risk of recurrent ischemic events by 50%[ 16 ]. There are controversial debates about the risk of hemorrhage or dementia after aggressive LDL-C lowering[ 17 , 18 ]. Triglycerides (TG), though accounting for merely 7% of LDL content, are highly atherogenic. TG-rich lipoproteins and remnants are four times more atherogenic than LDL and are proven to cause coronary heart disease[ 19 , 20 ]. The TG/glucose ratio is linked to stroke recurrence and mortality, indicating its potential in risk stratification[ 21 , 22 ]. Remnant cholesterol (RC) levels are independently associated with ASCVD, even after adjusting for traditional risk factors, LDL-C, and ApoB, underscoring the importance of RC-lowering in primary prevention[ 23 ]. A recent study has found that oxidized phospholipid (oxPL) from apolipoprotein B (ApoB) containing lipoprotein predicts cardiovascular events more accurately than Lpa[ 24 ]. Lp(a), a glycoprotein attached to an LDL-like particle, is associated with coronary heart disease but not ischemic stroke[ 25 ], though it can independently promote atrial fibrillation[ 26 ]. Clinical and laboratory studies on the oxPL-ApoB have linked the components of lipids to thrombosis/thrombogenesis in ASCVD[ 27 ]. ApoB, a key component of LDL, exists in two forms: ApoB48 and ApoB100. Among these, ApoB100 is the primary structural and functional component of LDL and serves as a ligand for the LDL receptor[ 28 , 29 ]. Due to these properties, it is considered to play a central role in the development of atherosclerosis[ 30 , 31 ]. Mendelian randomization studies have shown associations between clinical lipid parameters (LDL, HDL, TC, TG, and Lp(a)) and stroke subtypes[ 32 ], small vessel disease[ 33 ], and even Alzheimer’s disease[ 27 ]. Lipid-lowering therapies targeting LDL and stabilizing plaques can reduce stroke incidence. Novel therapies targeting Lpa and TG show promise outcomes[ 34 ]. While targeted therapies have demonstrated significant value in disease prevention and treatment, many patients still require precision personalized therapy, especially when facing side effects of traditional treatments such as statin resistance and an increased risk of diabetes. Traditional and non-traditional lipid parameters should be considered together to understand their roles in disease, as their contributions to the same population need to be addressed. Our goal is to investigate the significance of the clinical lipid profile in stroke characteristics, including disease progression and improvement. Methods Patient enrollment and study design This is a prospective, clinical and single-centered study in Shanghai, China. We screened patients with mild ischemic stroke admitted to the Stroke Center of Shanghai Fifth People's Hospital (The Fifth People's Hospital of Shanghai, Fudan University) between Jan 1, 2018 and Sep 1, 2023, with the National Stroke Scale scores (NIHSS). The patient selection followed the Declaration of Helsinki, diagnostic and thrombolytic inclusion and exclusion criteria (Supplementary Materials). Figure 1 shows the selected patients' flowchart, the study design, and the sample size was calculated based on previous studies, with a power of 80% and a significance level of 0.05 (see Supplemental Materials). Written informed consent was obtained from all patients and their families. The Ethical Review Board of the Shanghai Fifth People's Hospital approved the study protocol before patient enrollment. Baseline data collection Baseline data were gathered from the medical records, covering age, sex, NIHSS score at admission, systolic blood pressure (SBP), and medical history of hypertension, diabetes, atrial fibrillation, dyslipidemia, smoking, and alcohol use. Blood samples were obtained upon admission, stored in a refrigerator at -80℃, or analyzed by clinical laboratory technicians within two hours. On the second day of admission, the concentrations of fasting plasma glucose, homocysteine, HDL, LDL, TC, TG, lipoprotein a (Lpa), fasting insulin, hemoglobin A1C (HbA1C), alanine transaminase (ALT), fibrinogen (FIB), international normalized ratio (INR), homocysteine (HCY), and C-reactive protein (CRP) were measured. Remnant cholesterol (RC) was calculated by subtracting HDL-C and LDL-C from TC. ApoB 100 levels were determined using an ELISA kit following its manufacture manual. Study outcomes In our study, variables were defined and measured to ensure accuracy and reliability of data. The primary independent variable was the lipid profiles such as HDL, LDL, TG, TC, and Lpa. The dependent variable or outcome was the change in NIHSS scores from baseline to the 2-week follow-up, which reflects the symptomatic changes of the patients with or without thrombolysis. Other covariates included age, gender, and comorbid conditions, which were extracted from electronic medical records. All measurements were conducted by trained healthcare professionals following standardized protocols to minimize measurement bias and ensure consistency across assessments. Clinical assessments On the first day of admission to the general ward and on the 14th day after admission, certified attending physicians assessed the patients' NIHSS scores. Symptomatic progression or early neurological deterioration (END) was defined as the emergence of new neurological symptoms/signs or any neurological worsening within 72 hours of stroke onset. In our stroke center, symptomatic improvement cases (the opposite of END) were identified using the following criteria: a decrease of ≥ 2 points in the total NIHSS score, or ≥ 1 point in the consciousness score (1a–1c), or ≥ 1 point in the motor score (5a–6b), or no new neurological deficit (even if not measurable via NIHSS scores). Imaging evaluation Each enrolled patient received 3-T magnetic resonance imaging (MRI) of the head, magnetic resonance angiography (MRA), or 64-layer CT (Siemens, Forchheim, Germany) and CT angiography (CTA) (Canon, Tokyo, Japan) within 48 hours of stroke onset. Two clinical neurologists (D.H. and Y.W.) evaluated the MRI images and clarified the ischemic stroke diagnosis, white matter hyperintensity and microbleeds. Intracranial artery stenosis or atherosclerosis severity in MRA was measured by the radiologist (a certain diagnostic doctor of the Radiology department) and neurologist (D.H.) by calculating the ratio of the long diameter of the plaque and the artery diameter as < 50%. Statistical analysis Statistical analysis was performed using SPSS 26.0 (IBM Corp, Armonk, New York), Python 3.13.1 (Python Software Foundation, https://www.python.org ) and GraphPad Prism 10.3.1 (GraphPad Software for macOS, Boston, Massachusetts, USA, https://www.graphpad.com ) (see Supplemental Material). Patients were divided into two groups based on the presence of thrombolysis (recombinant tissue plasminogen activator, or rtPA usage), and of improvement (NIHSS scores changes). The Mann-Whitney U test or t-test was used to compare these groups. Logistic analysis was conducted to identify independent factors for early stroke progression. The random forest model was applied to assess the importance of each lipid profiles in relation to stroke improvement. Statistical significance was set at p < 0.05 (two-tailed). Results Characteristics of the sample population Of 700 patients with acute ischemic stroke, 262 were included in the final analysis (Table 1 ). These patients were divided into two groups: those treated with thrombolysis (n = 165) and those without thrombolysis (n = 97). Comparisons between the two groups revealed significant differences in atrial fibrillation (p = 0.017), admission NIHSS (p < 0.001), and ApoB 100 levels (p < 0.001). Further subgroup analysis was conducted within each thrombolysis group. Table 1. Comparisons between ischemic stroke patients with or without thrombolysis With thrombolysis(n=165) Without thrombolysis(n=97) p Demographic data Age, years † 67(11.5) 65(11.2) 0.29 Male 95(58%) 70(42%) 0.018 Hypertension 132(80%) 78(80%) 0.94 Diabetes mellitus Atrial fibrillation Coronary artery disease 57(35%) 27(16%) 13(8%) 38(39%) 6(6%) 9(9%) 0.45 .017 * 0.69 Smoking 63(38%) 38(39%) 0.87 Drinking 27(16%) 26(27%) 0.04 * Fatty liver 72(44%) 47(51%) 0.29 Admission NIHSS † 6(3.9) 3(2.9) < .001 * 7-day NIHSS 3(4) 3(4) 0.65 Laboratory data † ApoB 100, ng/ml 30.4(14.2) 59.7(16.2) < .001 * TC, mmol/L 4.5(2.4) 4.5(2) 0.24 TG, mmol/L 1.4(0.9) 1.5(0.8) 0.40 HDL, mmol/L 1.1(0.3) 1.1(0.3) 0.28 LDL, mmol/L 2.8(0.9) 3.0(1.0) 0.21 HCY, μmol/L 15.6(10.1) 12.3(7.5) 0.78 Lp(a), nmol/L 47.9(70.3) 47.7(52.6) 0.98 HbA1C, mmol/L 6.5(1.4) 6.7(1.8) 0.30 ALT, mmol/L 17.4(14.9) 18.5(12.7) 0.55 INR 1(0.1) 1(0.1) 0.30 Imaging White matter lesion 163(98%) 96(99%) 0.61 Microbleeds 92(56%) 54(56%) 0.51 MCA stenosis 41(27%) 21(17%) 0.09 ACA stenosis 14(7%) 9(8%) 0.68 PCA stenosis 24(16%) 18(20%) 0.44 BA stenosis 11(7%) 11(12%) 0.21 Abbreviations: NIHSS = National Institute of Health stroke scale; TC = total cholesterol; TG = total triglyceride; HDL = high density lipoprotein; LDL = low density lipoprotein; HCY = homocysteine; ALT = alanine transaminase; INR = international normalized ratio; MCA = middle cerebral artery; ACA = anterior cerebral artery; PCA = posterior cerebral artery; BA = basilar artery. Unless specified, values are numbers of patients (%). † Mean (standard deviation). * Statistically significant. For patients who received thrombolysis, they were categorized into an improvement group and a non-improvement group (Supplementary Table 1). Significant differences were found in the proportion of females (p = 0.004), patients with atrial fibrillation (p = 0.045), and admission NIHSS scores (p < 0.001). In contrast, for patients who did not receive thrombolysis (Supplementary Table 2), the only significant difference between the improvement and non-improvement groups was in admission NIHSS scores. Our analysis revealed no significant differences in neuroimaging changes between patients who underwent thrombolysis and those who did not. This includes the presence of white matter lesions, microbleeds, and stenosis in the middle, anterior, or posterior cerebral arteries. However, it is worth noting that middle cerebral artery stenosis were observed more frequently in patients who received thrombolysis (Table 1 ). Our analysis also detected no significant differences in the neuroimaging markers between the improvement and non -improvement groups, regardless of whether patients had undergone thrombolysis (Supplementary Table 1–2). Logistic analysis of lipids in the discrimination between symptom improvement and non-improvement To identify independent predictors of stroke improvement in patients who underwent thrombolysis, logistic analysis was performed on potential risk factors (Table 2 ). The results indicated that sex (p = 0.003, OR: 0.27, 95% CI: 0.12–0.63) and admission NIHSS scores (p < 0.001, OR: 0.72, 95% CI: 0.63–0.82) were independently associated with symptomatic improvement. However, conventional lipid parameters were not found to be independent predictors of stroke progression in this group. Additionally, ApoB 100 levels were independently associated with stroke improvement in thrombolysis patients (p < 0.001, OR = 0.87, 95% CI: 0.84–0.91, Supplementary Table 3). Table 2. Multivariable regression analysis of factors for symptomatic improvement in patients with thrombolysis Beta OR p 95%CI Admission NIHSS -0.33 0.72 <.001 * 0.63-0.82 age 0.04 1.0 .048 * 1.00-1.01 ApoB 100 -0.18 0.98 0.22 0.95-1.01 sex -1.13 0.27 0.003 * 0.12-0.63 After adjusting TG, TC, LDL, HDL, Lpa, and glucose levels. Abbreviations: OR, odd ratio; NIHSS, National Institute of Health stroke scale; TC = total cholesterol; TG = total triglyceride; HDL = high density lipoprotein; LDL = low density lipoprotein. * indicates statistically significant. Importance of clinical factors in the discrimination between symptomatic improvement and non-improvement To further assess the predictive value of clinical lipid parameters for symptomatic improvement, a random forest model was employed (Fig. 2 ). After incorporating baseline risk factors, admission NIHSS scores emerged as the most important feature in distinguishing improvement in thrombolysis patients, followed by ApoB 100 levels (Fig. 2 A), with a model accuracy of 70%. In non-thrombolysis patients, ApoB 100 levels were not identified as an important feature, with the model accuracy being 75% (Fig. 2 B). Subsequent analysis of lipid profiles in the random forest model revealed that ApoB 100 and HDL levels were significant contributors to the improvement of stroke patients who received thrombolysis (Fig. 3 A, with a model accuracy of 70%) and those who did not (Fig. 3 B, with a model accuracy of 70%). Discussion In our single-centered and prospective cohort study on Chinese in Shanghai, we investigated the associations between lipid profile and good functional outcome in stroke and contributions of lipids to the stroke improvements, and we found no significant difference between the improvement group and non-improvement group. Our study attempted to independently evaluate the clinical performance of the lipid profile, including HDL, LDL, TC, TG, Lpa, and ApoB100, using a machine learning model in a population-based setting. Firstly, our cohort results showed that ApoB100 levels decreased after rtPA administration and were independently associated with a good outcome in patients who received rtPA. However, none of the lipid parameters were significantly associated with symptomatic improvement (i.e., a decrease in NIHSS scores over 2 weeks) in patients who did or did not receive thrombolysis. Zhang W. et al also found ApoB 100 levels were associated with good outcome at the 90 day in stroke patients[ 35 ]. And LDL-C was not associated with 3-month good outcome in stroke but pre-stroke statin usage could modify the influence of LDL-C[ 36 ]. However, some studies also found that HDL[ 37 ], TG, Lpa[ 38 ] and TG/glucose ratio[ 39 ] could predict the post-thrombolysis poor outcome, hemorrhagic transformation[ 40 ] or mortality in stroke patients[ 41 , 42 ]. These results differ from ours, possibly due to differences in study populations, lipid measurement methods, or follow-up durations. Emerging evidence has highlighted the intimate connection between fibrinolysis and lipoprotein metabolism, suggesting that interactions between these systems may influence stroke outcomes. Different kind of lipids has different effect on fibrinolysis: HDL can increase tPA synthesis and secretion, LDL can increase tPA-PAI-1 interaction and Lpa decreases tPA activity and plasmin activity[ 43 – 45 ]. Atherosclerotic vaccines based on peptides from ApoB 100 have demonstrated promising results, and novel therapies modulating immune responses are emerging in the treatment of atherosclerosis[ 46 , 47 ]. It has been found that PCSK7 binding to ApoB 100 can increase its secretion, whereas the loss of PCSK7 leads to ApoB 100 degradation and reduces lipid accumulation in non-alcoholic fatty liver disease[ 48 ]. However, limited research has focused on the association between ApoB 100 and stroke outcomes. Wu et al discovered that ApoB 100 from extracellular vesicles in patients with spinal cord injury promotes the occurrence of coronary heart disease[ 49 ]. Our study found the associations between plasma ApoB 100 levels and stroke outcomes in patients received thrombolysis. Our random forest modeling revealed distinct predictive contributions of clinical lipid parameters in acute ischemic stroke (AIS) patients stratified by thrombolysis status, with ApoB-100 and HDL levels emerging as superior predictors of favorable functional outcomes. This systematic evaluation of lipid profile impacts enhances clinical understanding of lipid pathophysiology and clarifies the relative prognostic weight of individual lipid metrics within homogeneous patient cohorts. Zhang A. et al. established significant correlations between specific lipid components (TG, HDL, LDL) and neurological deficit severity (NIHSS scores), particularly in patients with comorbid chronic kidney disease[ 50 ]. Complementing this, Pikija et al. identified LDL-C as an independent predictor of favorable 3-month outcomes (modified Rankin Scale [mRS] ≤ 2) in anterior circulation occlusion patients undergoing endovascular thrombectomy[ 51 ]. While these studies underscore lipid-stroke interactions, analyses of individual lipid parameter contributions remain underexplored. The field has advanced significantly since Quehenberger et al.'s seminal work defining the human plasma lipidome through quantification of > 500 lipid species[ 52 ]. Modern lipidomics now enables systematic identification and validation of lipid biomarkers in ischemic stroke, offering unprecedented resolution for understanding lipid-mediated mechanisms[ 53 ]. Throughout ischemic stroke progression, lipids dynamically contribute to both injury and recovery, highlighting their complex roles in stroke pathophysiology[ 54 , 55 ]. In the initial minutes to hours of an ischemic stroke, the brain rapidly releases free fatty acids[ 56 ] from cell membranes via phospholipase A2 (PLA2), breaking down membrane phospholipids and releasing arachidonic acid (AA) and other polyunsaturated fatty acids (PUFAs)[ 57 ]. These free fatty acids serve as substrates for lipid mediators. During the subsequent hours to days, eicosanoids derived from AA become active. Prostaglandins like PGE2 and PGI2 can either promote inflammation or exert neuroprotective effects, depending on context[ 58 , 59 ]. Lipoxins such as LXA4 begin to resolve inflammation and promote tissue repair[ 57 ]. The balance between these mediators significantly influences brain damage and recovery. In the chronic phase (days to weeks), neuroprotective lipid mediators like DHA-derived neuroprotectin D1 enhance neuronal survival and promote neurogenesis[ 57 ]. The brain's lipid composition normalizes as damaged cells are cleared and new connections form. Lipids also support myelin sheath regeneration, crucial for restoring neural communication. Random forest algorithm is a nonparametric approach accommodating categorical, quantitative outcomes and survival times and is also suitable for analysing complex data such as omics data[ 60 ]. This method can test the relevance of each predictor by variable importance measures[ 61 ]. In our study, we employed it to assess how each lipid factor impacts the favorable outcome of stroke in patients who did and did not undergo thrombolysis. This study has certain limitations. Firstly, as a cohort study executed in a single stroke center, it only enrolled a Chinese population with a limited sample size. For more comprehensive results in the future, it is necessary to incorporate a wider range of population samples from different clinical centers, covering different ethnicities, geographical regions, and age groups, rather than being restricted to patients with mild to moderate strokes. Secondly, the study lacks long-term follow-up data, such as the NIHSS, Barthel index, and cognitive assessments at 12 months or beyond. Finally, the scope of the lipid profile could be expanded by incorporating additional parameters like apoA and ox-LDL. Declarations Acknowledgements We express our sincere gratitude to the enrolled patients and their family for their valuable contributions. Conflict of interest None Dual publication None Author contributions Conceptualization: D.H., Y.T. and D.W.; Data curation: Y. W., and S.Z.; Formal analysis: D.H. and X.Y; Funding acquisition: D.H. and D.W.; Investigation: D.H. and Y.W.; Methodology: D.H.; Project administration: D.W.; Resources: D.W.; Software: D.H.; Supervision: D.W.; Validation: D.H.; Visualization: D.H.; Roles/Writing - original draft: D.H.; and Writing - review & editing: D.W. and Y.T. Funding This study was supported by the grant from the Healthcare Commission of Minhang District, Shanghai (No. 2022MW01; 2024MWDXK04). Human ethics and consent to participate declarations The data collection and participant enrollment were approved by the Ethical Review Board of Shanghai Fifth People’s Hospital, and written informed consent was obtained from all patients or their families. 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Zhang W, Wang R, Shi F: Impact of serum apolipoproteins on the prognosis of acute ischemic stroke after thrombolysis. J Stroke Cerebrovasc Dis 2024, 33: 107944. Kang YR, Kim JT, Lee JS, Kim BJ, Kang K, Lee SJ, Kim JG, Cha JK, Kim DH, Park TH, et al: Differential influences of LDL cholesterol on functional outcomes after intravenous thrombolysis according to prestroke statin use. Sci Rep 2022, 12: 15478. Nardi K, Engelter S, Strbian D, Sarikaya H, Arnold M, Casoni F, Ford GA, Cordonnier C, Lyrer P, Bordet R, et al: Lipid profiles and outcome in patients treated by intravenous thrombolysis for cerebral ischemia. Neurology 2012, 79: 1101-1108. Wang R, Kong W, Zhang W: Serum Lipoprotein(a) as Predictive Factor for Early Neurological Deterioration of Acute Ischemic Stroke After Thrombolysis. Int J Gen Med 2024, 17: 3791-3798. Deng M, Song K, Xu W, He G, Hu J, Xiao H, Zhou N, Chen S, Xu G, Tong Y, et al: Association of higher triglyceride-glucose index and triglyceride-to-high-density lipoprotein cholesterol ratio with early neurological deterioration after thrombolysis in acute ischemic stroke patients. Front Neurol 2024, 15: 1421655. Zhang W, Li W, Tian R, Cao L: High-density lipoprotein level is associated with hemorrhage transformation after ischemic stroke treatment with intravenous thrombolysis: A systematic review and meta-analysis. J Clin Neurosci 2022, 106: 122-127. Mutch NJ: Fibrinolytic pathophysiologies: still the poor cousin of hemostasis? J Thromb Haemost 2023, 21: 2645-2647. Dai W, Castleberry M, Zheng Z: Tale of two systems: the intertwining duality of fibrinolysis and lipoprotein metabolism. J Thromb Haemost 2023, 21: 2679-2696. Zheng Z, Nakamura K, Gershbaum S, Wang X, Thomas S, Bessler M, Schrope B, Krikhely A, Liu RM, Ozcan L, et al: Interacting hepatic PAI-1/tPA gene regulatory pathways influence impaired fibrinolysis severity in obesity. J Clin Invest 2020, 130: 4348-4359. Glueck CJ, Glueck HI, Tracy T, Speirs J, McCray C, Stroop D: Relationships between lipoprotein(a), lipids, apolipoproteins, basal and stimulated fibrinolytic regulators, and D-dimer. Metabolism 1993, 42: 236-246. Collen D, Lijnen HR: The tissue-type plasminogen activator story. Arterioscler Thromb Vasc Biol 2009, 29: 1151-1155. Nilsson J, Björkbacka H, Fredrikson GN: Apolipoprotein B100 autoimmunity and atherosclerosis - disease mechanisms and therapeutic potential. Curr Opin Lipidol 2012, 23: 422-428. Nilsson J, Wigren M, Shah PK: Vaccines against atherosclerosis. Expert Rev Vaccines 2013, 12: 311-321. Sachan V, Le Dévéhat M, Roubtsova A, Essalmani R, Laurendeau JF, Garçon D, Susan-Resiga D, Duval S, Mikaeeli S, Hamelin J, et al: PCSK7: A novel regulator of apolipoprotein B and a potential target against non-alcoholic fatty liver disease. Metabolism 2024, 150: 155736. Wu C, Chen J, Zhang J, Hong H, Jiang J, Ji C, Li C, Xia M, Xu G, Cui Z: Extracellular vesicles loaded with ApoB-100 protein affect the occurrence of coronary heart disease in patients after injury of spinal cord. Int J Biol Macromol 2024, 277: 134330. Zhang A, Deng W, Zhang B, Ren M, Tian L, Ge J, Bai J, Hu H, Cui L: Association of lipid profiles with severity and outcome of acute ischemic stroke in patients with and without chronic kidney disease. Neurol Sci 2021, 42: 2371-2378. Pikija S, Sztriha LK, Killer-Oberpfalzer M, Weymayr F, Hecker C, Ramesmayer C, Hauer L, Sellner J: Contribution of Serum Lipid Profiles to Outcome After Endovascular Thrombectomy for Anterior Circulation Ischemic Stroke. Mol Neurobiol 2019, 56: 4582-4588. Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, Bandyopadhyay S, Jones KN, Kelly S, Shaner RL, et al: Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 2010, 51: 3299-3305. Huang XX, Li L, Jiang RH, Yu JB, Sun YQ, Shan J, Yang J, Ji J, Cheng SQ, Dong YF, et al: Lipidomic analysis identifies long-chain acylcarnitine as a target for ischemic stroke. J Adv Res 2024, 61: 133-149. Segatto M, Pallottini V: Facts about Fats: New Insights into the Role of Lipids in Metabolism, Disease and Therapy. Int J Mol Sci 2020, 21 . Kloska A, Malinowska M, Gabig-Ciminska M, Jakobkiewicz-Banecka J: Lipids and Lipid Mediators Associated with the Risk and Pathology of Ischemic Stroke. Int J Mol Sci 2020, 21 . Hamilton JA, Hillard CJ, Spector AA, Watkins PA: Brain uptake and utilization of fatty acids, lipids and lipoproteins: application to neurological disorders. J Mol Neurosci 2007, 33: 2-11. Wu H, Liu H, Zuo F, Zhang L: Adenoviruses-mediated RNA interference targeting cytosolic phospholipase A2α attenuates focal ischemic brain damage in mice. Mol Med Rep 2018, 17: 5601-5610. Ling QL, Mohite AJ, Murdoch E, Akasaka H, Li QY, So SP, Ruan KH: Creating a mouse model resistant to induced ischemic stroke and cardiovascular damage. Sci Rep 2018, 8: 1653. Ruan KH, Deng H, So SP: Engineering of a protein with cyclooxygenase and prostacyclin synthase activities that converts arachidonic acid to prostacyclin. Biochemistry 2006, 45: 14003-14011. Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, McClure LA, Howard G, Simon N: OBLIQUE RANDOM SURVIVAL FORESTS. Ann Appl Stat 2019, 13: 1847-1883. Hu J, Szymczak S: A review on longitudinal data analysis with random forest. Brief Bioinform 2023, 24 . Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterials.docx Cite Share Download PDF Status: Published Journal Publication published 17 Oct, 2025 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 04 Jul, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor invited by journal 15 May, 2025 Editor assigned by journal 13 May, 2025 Submission checks completed at journal 13 May, 2025 First submitted to journal 09 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6625180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467028823,"identity":"4707726d-1712-483f-a817-b460f19faab5","order_by":0,"name":"Duanlu Hou","email":"","orcid":"","institution":"Shanghai Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Duanlu","middleName":"","lastName":"Hou","suffix":""},{"id":467028824,"identity":"fb05d2ad-1d24-476e-9d0d-332d7445054b","order_by":1,"name":"Yuanyuan Wang","email":"","orcid":"","institution":"Shanghai Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Wang","suffix":""},{"id":467028825,"identity":"303fbcdf-3a90-47a6-9a3c-d73e638043fc","order_by":2,"name":"Shuang Zhai","email":"","orcid":"","institution":"Shanghai Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Zhai","suffix":""},{"id":467028826,"identity":"99b16412-c829-4b14-98fe-a9dd9da1eee1","order_by":3,"name":"Xiaoli Yang","email":"","orcid":"","institution":"Shanghai Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Yang","suffix":""},{"id":467028827,"identity":"0788fa5f-0fba-47e5-8b2f-c29d40ef5423","order_by":4,"name":"Yuping Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBADHgYG9oMPPhjY2BGjFEbzJBvOKEhLJloLCJhJ83w4xNhASIs9+9nDr3nb7siY8y9Ik7YxOMDMwH746Aa8tvDkpVnztj3jsZzx8LB1jsEdPgaetLQb+B2WY2bM23aYx+DGgcTbOQbPmBkkeMzwa+F/A9diIG1hcJixgaAWiRzjx2At5xuMpBmI0nLjjRnjnHPPgLYAA7nHIC2ZjZBf2PtzjD+8Kbtjb3D++MEHP/7Y2PGzHz6GVwsQsEnxMBxgYJBIgHIJKAcB5o8/QFr4DxChdhSMglEwCkYkAAD98Ex797P1wgAAAABJRU5ErkJggg==","orcid":"","institution":"Huashan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuping","middleName":"","lastName":"Tang","suffix":""},{"id":467028828,"identity":"cd59d316-4569-4063-9b29-8d0f1e14dbcb","order_by":5,"name":"Danhong Wu","email":"","orcid":"","institution":"Shanghai Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Danhong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-05-09 05:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6625180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6625180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12883-025-04444-6","type":"published","date":"2025-10-17T15:58:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84215197,"identity":"796499b2-cb17-4dea-8f98-d0c7f64bf8af","added_by":"auto","created_at":"2025-06-09 10:37:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94493,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patients’ enrollment\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6625180/v1/6c57ee50f1b9943275d969b6.png"},{"id":84216895,"identity":"2fed5b4e-a6d8-4abe-a79e-7cfdfc2ec2d2","added_by":"auto","created_at":"2025-06-09 10:53:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20332,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of features importance for 2-week favorable outcome. The length of the bars (x-axis) shows the importance of the factors in models, model A shows an increased efficacy of 0.7 in patients with thrombolysis, and model B shows an increased efficacy of 0.75 by random forest estimation in patients without thrombolysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6625180/v1/826d985c9f6a73852c1b27be.png"},{"id":84216532,"identity":"471f9abe-8e9c-461e-8810-dd0843654bb5","added_by":"auto","created_at":"2025-06-09 10:45:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19767,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of features importance for 2-week favorable outcome. The length of the bars (x-axis) shows the importance of the factors in models, model A shows an increased efficacy of 0.7 in patients with thrombolysis, and model B shows an increased efficacy of 0.7 by random forest estimation in patients without thrombolysis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6625180/v1/4983e697329949c8b970fc2c.png"},{"id":93956679,"identity":"49cd7b1e-0364-4d0c-b642-9264c7c75d47","added_by":"auto","created_at":"2025-10-20 16:11:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3697368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6625180/v1/5d08cf5d-b392-4148-abe2-54094adfcd35.pdf"},{"id":84215201,"identity":"c9e56b2c-47a7-49ff-a030-6796e10169b7","added_by":"auto","created_at":"2025-06-09 10:37:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":577138,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6625180/v1/23edd0c9577ffd451f2bf6c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical contribution of lipid profiles to the stroke progression in Chinese population: a single-centered cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDyslipidemia is a prevalent risk factor for atherosclerotic cardiovascular disease (ASCVD), which encompasses ischemic stroke[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A lipid profile, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), lipoprotein (a) [Lp(a)], total cholesterol (TC), etc., is widely utilized in both clinical research and practice[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These lipid parameters are closely associated with the risk, diagnosis, and prognosis of ASCVD[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], as well as with cognitive decline in the elderly population[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. When compared to a single biomarker within the lipid profile, a combined model demonstrates good efficacy in disease monitoring and prediction[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinical and genetic evidence suggested that low-density lipoprotein (LDL) can cause ASCVD[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The effect of LDL on ASCVD depends on its levels, magnitude and exposure duration[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study in China revealed a J-shaped relationship between LDL-C and mortality, which can be affected by diabetes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Guidelines have emphasized the significance of LDL levels in managing blood cholesterol[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Targeting LDL-C lowering by statins or proprotein convertase subtilisin/kexin type 9 inhibitors[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] in patients with stroke or transient ischemic attack alone can decrease the overall incidence of stroke recurrence and ASCVD, although there may be a slight increase in the risk of hemorrhagic stroke[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lowering LDL-C levels to 70 mg/dL has been found to reduce the risk of recurrent ischemic events by 50%[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. There are controversial debates about the risk of hemorrhage or dementia after aggressive LDL-C lowering[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTriglycerides (TG), though accounting for merely 7% of LDL content, are highly atherogenic. TG-rich lipoproteins and remnants are four times more atherogenic than LDL and are proven to cause coronary heart disease[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The TG/glucose ratio is linked to stroke recurrence and mortality, indicating its potential in risk stratification[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Remnant cholesterol (RC) levels are independently associated with ASCVD, even after adjusting for traditional risk factors, LDL-C, and ApoB, underscoring the importance of RC-lowering in primary prevention[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A recent study has found that oxidized phospholipid (oxPL) from apolipoprotein B (ApoB) containing lipoprotein predicts cardiovascular events more accurately than Lpa[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Lp(a), a glycoprotein attached to an LDL-like particle, is associated with coronary heart disease but not ischemic stroke[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], though it can independently promote atrial fibrillation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Clinical and laboratory studies on the oxPL-ApoB have linked the components of lipids to thrombosis/thrombogenesis in ASCVD[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. ApoB, a key component of LDL, exists in two forms: ApoB48 and ApoB100. Among these, ApoB100 is the primary structural and functional component of LDL and serves as a ligand for the LDL receptor[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Due to these properties, it is considered to play a central role in the development of atherosclerosis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMendelian randomization studies have shown associations between clinical lipid parameters (LDL, HDL, TC, TG, and Lp(a)) and stroke subtypes[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], small vessel disease[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and even Alzheimer\u0026rsquo;s disease[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Lipid-lowering therapies targeting LDL and stabilizing plaques can reduce stroke incidence. Novel therapies targeting Lpa and TG show promise outcomes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile targeted therapies have demonstrated significant value in disease prevention and treatment, many patients still require precision personalized therapy, especially when facing side effects of traditional treatments such as statin resistance and an increased risk of diabetes. Traditional and non-traditional lipid parameters should be considered together to understand their roles in disease, as their contributions to the same population need to be addressed. Our goal is to investigate the significance of the clinical lipid profile in stroke characteristics, including disease progression and improvement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient enrollment and study design\u003c/h2\u003e \u003cp\u003eThis is a prospective, clinical and single-centered study in Shanghai, China. We screened patients with mild ischemic stroke admitted to the Stroke Center of Shanghai Fifth People's Hospital (The Fifth People's Hospital of Shanghai, Fudan University) between Jan 1, 2018 and Sep 1, 2023, with the National Stroke Scale scores (NIHSS). The patient selection followed the Declaration of Helsinki, diagnostic and thrombolytic inclusion and exclusion criteria (Supplementary Materials). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the selected patients' flowchart, the study design, and the sample size was calculated based on previous studies, with a power of 80% and a significance level of 0.05 (see Supplemental Materials). Written informed consent was obtained from all patients and their families. The Ethical Review Board of the Shanghai Fifth People's Hospital approved the study protocol before patient enrollment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline data collection\u003c/h3\u003e\n\u003cp\u003eBaseline data were gathered from the medical records, covering age, sex, NIHSS score at admission, systolic blood pressure (SBP), and medical history of hypertension, diabetes, atrial fibrillation, dyslipidemia, smoking, and alcohol use. Blood samples were obtained upon admission, stored in a refrigerator at -80℃, or analyzed by clinical laboratory technicians within two hours. On the second day of admission, the concentrations of fasting plasma glucose, homocysteine, HDL, LDL, TC, TG, lipoprotein a (Lpa), fasting insulin, hemoglobin A1C (HbA1C), alanine transaminase (ALT), fibrinogen (FIB), international normalized ratio (INR), homocysteine (HCY), and C-reactive protein (CRP) were measured. Remnant cholesterol (RC) was calculated by subtracting HDL-C and LDL-C from TC. ApoB 100 levels were determined using an ELISA kit following its manufacture manual.\u003c/p\u003e\n\u003ch3\u003eStudy outcomes\u003c/h3\u003e\n\u003cp\u003eIn our study, variables were defined and measured to ensure accuracy and reliability of data. The primary independent variable was the lipid profiles such as HDL, LDL, TG, TC, and Lpa. The dependent variable or outcome was the change in NIHSS scores from baseline to the 2-week follow-up, which reflects the symptomatic changes of the patients with or without thrombolysis. Other covariates included age, gender, and comorbid conditions, which were extracted from electronic medical records. All measurements were conducted by trained healthcare professionals following standardized protocols to minimize measurement bias and ensure consistency across assessments.\u003c/p\u003e\n\u003ch3\u003eClinical assessments\u003c/h3\u003e\n\u003cp\u003eOn the first day of admission to the general ward and on the 14th day after admission, certified attending physicians assessed the patients' NIHSS scores. Symptomatic progression or early neurological deterioration (END) was defined as the emergence of new neurological symptoms/signs or any neurological worsening within 72 hours of stroke onset. In our stroke center, symptomatic improvement cases (the opposite of END) were identified using the following criteria: a decrease of \u0026ge;\u0026thinsp;2 points in the total NIHSS score, or \u0026ge;\u0026thinsp;1 point in the consciousness score (1a\u0026ndash;1c), or \u0026ge;\u0026thinsp;1 point in the motor score (5a\u0026ndash;6b), or no new neurological deficit (even if not measurable via NIHSS scores).\u003c/p\u003e\n\u003ch3\u003eImaging evaluation\u003c/h3\u003e\n\u003cp\u003eEach enrolled patient received 3-T magnetic resonance imaging (MRI) of the head, magnetic resonance angiography (MRA), or 64-layer CT (Siemens, Forchheim, Germany) and CT angiography (CTA) (Canon, Tokyo, Japan) within 48 hours of stroke onset. Two clinical neurologists (D.H. and Y.W.) evaluated the MRI images and clarified the ischemic stroke diagnosis, white matter hyperintensity and microbleeds. Intracranial artery stenosis or atherosclerosis severity in MRA was measured by the radiologist (a certain diagnostic doctor of the Radiology department) and neurologist (D.H.) by calculating the ratio of the long diameter of the plaque and the artery diameter as \u0026lt;\u0026thinsp;50%.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 26.0 (IBM Corp, Armonk, New York), Python 3.13.1 (Python Software Foundation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org\u003c/span\u003e\u003cspan address=\"https://www.python.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism 10.3.1 (GraphPad Software for macOS, Boston, Massachusetts, USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (see Supplemental Material). Patients were divided into two groups based on the presence of thrombolysis (recombinant tissue plasminogen activator, or rtPA usage), and of improvement (NIHSS scores changes). The Mann-Whitney U test or t-test was used to compare these groups. Logistic analysis was conducted to identify independent factors for early stroke progression. The random forest model was applied to assess the importance of each lipid profiles in relation to stroke improvement. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of the sample population\u003c/h2\u003e\n \u003cp\u003eOf 700 patients with acute ischemic stroke, 262 were included in the final analysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). These patients were divided into two groups: those treated with thrombolysis (n\u0026thinsp;=\u0026thinsp;165) and those without thrombolysis (n\u0026thinsp;=\u0026thinsp;97). Comparisons between the two groups revealed significant differences in atrial fibrillation (p\u0026thinsp;=\u0026thinsp;0.017), admission NIHSS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ApoB 100 levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further subgroup analysis was conducted within each thrombolysis group.\u003c/p\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Comparisons between ischemic stroke patients with or without thrombolysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWith thrombolysis(n=165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithout thrombolysis(n=97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eDemographic data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65(11.2)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95(58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70(42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e132(80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78(80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57(35%)\u003c/p\u003e\n \u003cp\u003e27(16%)\u003c/p\u003e\n \u003cp\u003e13(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38(39%)\u003c/p\u003e\n \u003cp\u003e6(6%)\u003c/p\u003e\n \u003cp\u003e9(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63(38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38(39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27(16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26(27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFatty liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72(44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47(51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdmission NIHSS\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6(3.9)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3(2.9)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7-day NIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eLaboratory data\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eApoB 100, ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.4(14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.7(16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5(2.4)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5(2)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4(0.9)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5(0.8)\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL,\u0026nbsp;mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHCY, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.6(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.3(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLp(a), nmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.9(70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.7(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALT,\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.4(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.5(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eImaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite matter lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163(98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96(99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMicrobleeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92(56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54(56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCA stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41(27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACA stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCA stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24(16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBA stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAbbreviations: NIHSS = National Institute of Health stroke scale; TC = total cholesterol; TG = total triglyceride; HDL = high density lipoprotein; LDL = low density lipoprotein; HCY = homocysteine; ALT = alanine transaminase; INR = international normalized ratio; MCA = middle cerebral artery; ACA = anterior cerebral artery; PCA = posterior cerebral artery; BA = basilar artery.\u003c/p\u003e\n \u003cp\u003eUnless specified, values are numbers of patients (%).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003eMean (standard deviation).\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003eStatistically significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eFor patients who received thrombolysis, they were categorized into an improvement group and a non-improvement group (Supplementary Table 1). Significant differences were found in the proportion of females (p\u0026thinsp;=\u0026thinsp;0.004), patients with atrial fibrillation (p\u0026thinsp;=\u0026thinsp;0.045), and admission NIHSS scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, for patients who did not receive thrombolysis (Supplementary Table 2), the only significant difference between the improvement and non-improvement groups was in admission NIHSS scores.\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eOur analysis revealed no significant differences in neuroimaging changes between patients who underwent thrombolysis and those who did not. This includes the presence of white matter lesions, microbleeds, and stenosis in the middle, anterior, or posterior cerebral arteries. However, it is worth noting that middle cerebral artery stenosis were observed more frequently in patients who received thrombolysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Our analysis also detected no significant differences in the neuroimaging markers between the improvement and non -improvement groups, regardless of whether patients had undergone thrombolysis (Supplementary Table\u0026nbsp;1\u0026ndash;2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLogistic analysis of lipids in the discrimination between symptom improvement and non-improvement\u003c/h2\u003e\n \u003cp\u003eTo identify independent predictors of stroke improvement in patients who underwent thrombolysis, logistic analysis was performed on potential risk factors (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results indicated that sex (p\u0026thinsp;=\u0026thinsp;0.003, OR: 0.27, 95% CI: 0.12\u0026ndash;0.63) and admission NIHSS scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR: 0.72, 95% CI: 0.63\u0026ndash;0.82) were independently associated with symptomatic improvement. However, conventional lipid parameters were not found to be independent predictors of stroke progression in this group. Additionally, ApoB 100 levels were independently associated with stroke improvement in thrombolysis patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.84\u0026ndash;0.91, Supplementary Table\u0026nbsp;3).\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eTable 2. Multivariable regression analysis of factors for symptomatic improvement in patients with thrombolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAdmission NIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.63-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e.048\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.00-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eApoB 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.95-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003esex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.12-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eAfter adjusting TG, TC, LDL, HDL, Lpa, and glucose levels.\u003c/p\u003e\n \u003cp\u003eAbbreviations: OR, odd ratio; NIHSS, National Institute of Health stroke scale; TC = total cholesterol; TG = total triglyceride; HDL = high density lipoprotein; LDL = low density lipoprotein. \u003csup\u003e*\u003c/sup\u003eindicates statistically significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eImportance of clinical factors in the discrimination between symptomatic improvement and non-improvement\u003c/h2\u003e\n \u003cp\u003eTo further assess the predictive value of clinical lipid parameters for symptomatic improvement, a random forest model was employed (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). After incorporating baseline risk factors, admission NIHSS scores emerged as the most important feature in distinguishing improvement in thrombolysis patients, followed by ApoB 100 levels (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), with a model accuracy of 70%. In non-thrombolysis patients, ApoB 100 levels were not identified as an important feature, with the model accuracy being 75% (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eSubsequent analysis of lipid profiles in the random forest model revealed that ApoB 100 and HDL levels were significant contributors to the improvement of stroke patients who received thrombolysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, with a model accuracy of 70%) and those who did not (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, with a model accuracy of 70%).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our single-centered and prospective cohort study on Chinese in Shanghai, we investigated the associations between lipid profile and good functional outcome in stroke and contributions of lipids to the stroke improvements, and we found no significant difference between the improvement group and non-improvement group. Our study attempted to independently evaluate the clinical performance of the lipid profile, including HDL, LDL, TC, TG, Lpa, and ApoB100, using a machine learning model in a population-based setting.\u003c/p\u003e \u003cp\u003eFirstly, our cohort results showed that ApoB100 levels decreased after rtPA administration and were independently associated with a good outcome in patients who received rtPA. However, none of the lipid parameters were significantly associated with symptomatic improvement (i.e., a decrease in NIHSS scores over 2 weeks) in patients who did or did not receive thrombolysis.\u003c/p\u003e \u003cp\u003eZhang W. et al also found ApoB 100 levels were associated with good outcome at the 90 day in stroke patients[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. And LDL-C was not associated with 3-month good outcome in stroke but pre-stroke statin usage could modify the influence of LDL-C[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, some studies also found that HDL[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], TG, Lpa[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and TG/glucose ratio[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] could predict the post-thrombolysis poor outcome, hemorrhagic transformation[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] or mortality in stroke patients[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These results differ from ours, possibly due to differences in study populations, lipid measurement methods, or follow-up durations. Emerging evidence has highlighted the intimate connection between fibrinolysis and lipoprotein metabolism, suggesting that interactions between these systems may influence stroke outcomes. Different kind of lipids has different effect on fibrinolysis: HDL can increase tPA synthesis and secretion, LDL can increase tPA-PAI-1 interaction and Lpa decreases tPA activity and plasmin activity[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAtherosclerotic vaccines based on peptides from ApoB 100 have demonstrated promising results, and novel therapies modulating immune responses are emerging in the treatment of atherosclerosis[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. It has been found that PCSK7 binding to ApoB 100 can increase its secretion, whereas the loss of PCSK7 leads to ApoB 100 degradation and reduces lipid accumulation in non-alcoholic fatty liver disease[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, limited research has focused on the association between ApoB 100 and stroke outcomes. Wu et al discovered that ApoB 100 from extracellular vesicles in patients with spinal cord injury promotes the occurrence of coronary heart disease[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Our study found the associations between plasma ApoB 100 levels and stroke outcomes in patients received thrombolysis.\u003c/p\u003e \u003cp\u003eOur random forest modeling revealed distinct predictive contributions of clinical lipid parameters in acute ischemic stroke (AIS) patients stratified by thrombolysis status, with ApoB-100 and HDL levels emerging as superior predictors of favorable functional outcomes. This systematic evaluation of lipid profile impacts enhances clinical understanding of lipid pathophysiology and clarifies the relative prognostic weight of individual lipid metrics within homogeneous patient cohorts.\u003c/p\u003e \u003cp\u003eZhang A. et al. established significant correlations between specific lipid components (TG, HDL, LDL) and neurological deficit severity (NIHSS scores), particularly in patients with comorbid chronic kidney disease[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Complementing this, Pikija et al. identified LDL-C as an independent predictor of favorable 3-month outcomes (modified Rankin Scale [mRS]\u0026thinsp;\u0026le;\u0026thinsp;2) in anterior circulation occlusion patients undergoing endovascular thrombectomy[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. While these studies underscore lipid-stroke interactions, analyses of individual lipid parameter contributions remain underexplored. The field has advanced significantly since Quehenberger et al.'s seminal work defining the human plasma lipidome through quantification of \u0026gt;\u0026thinsp;500 lipid species[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Modern lipidomics now enables systematic identification and validation of lipid biomarkers in ischemic stroke, offering unprecedented resolution for understanding lipid-mediated mechanisms[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThroughout ischemic stroke progression, lipids dynamically contribute to both injury and recovery, highlighting their complex roles in stroke pathophysiology[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In the initial minutes to hours of an ischemic stroke, the brain rapidly releases free fatty acids[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] from cell membranes via phospholipase A2 (PLA2), breaking down membrane phospholipids and releasing arachidonic acid (AA) and other polyunsaturated fatty acids (PUFAs)[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These free fatty acids serve as substrates for lipid mediators. During the subsequent hours to days, eicosanoids derived from AA become active. Prostaglandins like PGE2 and PGI2 can either promote inflammation or exert neuroprotective effects, depending on context[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Lipoxins such as LXA4 begin to resolve inflammation and promote tissue repair[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The balance between these mediators significantly influences brain damage and recovery. In the chronic phase (days to weeks), neuroprotective lipid mediators like DHA-derived neuroprotectin D1 enhance neuronal survival and promote neurogenesis[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The brain's lipid composition normalizes as damaged cells are cleared and new connections form. Lipids also support myelin sheath regeneration, crucial for restoring neural communication.\u003c/p\u003e \u003cp\u003eRandom forest algorithm is a nonparametric approach accommodating categorical, quantitative outcomes and survival times and is also suitable for analysing complex data such as omics data[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This method can test the relevance of each predictor by variable importance measures[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In our study, we employed it to assess how each lipid factor impacts the favorable outcome of stroke in patients who did and did not undergo thrombolysis.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. Firstly, as a cohort study executed in a single stroke center, it only enrolled a Chinese population with a limited sample size. For more comprehensive results in the future, it is necessary to incorporate a wider range of population samples from different clinical centers, covering different ethnicities, geographical regions, and age groups, rather than being restricted to patients with mild to moderate strokes. Secondly, the study lacks long-term follow-up data, such as the NIHSS, Barthel index, and cognitive assessments at 12 months or beyond. Finally, the scope of the lipid profile could be expanded by incorporating additional parameters like apoA and ox-LDL.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the enrolled patients and their family for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: D.H., Y.T. and D.W.; Data curation: Y. W., and S.Z.; Formal analysis: D.H. and X.Y; Funding acquisition: D.H. and D.W.; Investigation: D.H. and Y.W.; Methodology: D.H.; Project administration: D.W.; Resources: D.W.; Software: D.H.; Supervision: D.W.; Validation: D.H.; Visualization: D.H.; Roles/Writing - original draft: D.H.; and Writing - review \u0026amp; editing: D.W. and Y.T.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the grant from the Healthcare Commission of Minhang District, Shanghai (No. 2022MW01; 2024MWDXK04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collection and participant enrollment were approved by the Ethical Review Board of Shanghai Fifth People’s Hospital, and written informed consent was obtained from all patients or their families. All methods were carried out in accordance with relevant guidelines and regulations in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study's findings are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKopin L, Lowenstein C: \u003cstrong\u003eDyslipidemia.\u003c/strong\u003e\u003cem\u003eAnn Intern Med \u003c/em\u003e2017, \u003cstrong\u003e167:\u003c/strong\u003eItc81-itc96.\u003c/li\u003e\n\u003cli\u003eVirani SS, Morris PB, Agarwala A, Ballantyne CM, Birtcher KK, Kris-Etherton PM, Ladden-Stirling AB, Miller M, Orringer CE, Stone NJ: \u003cstrong\u003e2021 ACC Expert Consensus Decision Pathway on the Management of ASCVD Risk Reduction in Patients With Persistent Hypertriglyceridemia: A Report of the American College of Cardiology Solution Set Oversight Committee.\u003c/strong\u003e\u003cem\u003eJ Am Coll Cardiol \u003c/em\u003e2021, \u003cstrong\u003e78:\u003c/strong\u003e960-993.\u003c/li\u003e\n\u003cli\u003eWan H, Wu H, Wei Y, Wang S, Ji Y: \u003cstrong\u003eNovel lipid profiles and atherosclerotic cardiovascular disease risk: insights from a latent profile analysis.\u003c/strong\u003e\u003cem\u003eLipids Health Dis \u003c/em\u003e2025, \u003cstrong\u003e24:\u003c/strong\u003e71.\u003c/li\u003e\n\u003cli\u003eNordestgaard BG, Langsted A, Mora S, Kolovou G, Baum H, Bruckert E, Watts GF, Sypniewska G, Wiklund O, Bor\u0026eacute;n J, et al: \u003cstrong\u003eFasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points-a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine.\u003c/strong\u003e\u003cem\u003eEur Heart J \u003c/em\u003e2016, \u003cstrong\u003e37:\u003c/strong\u003e1944-1958.\u003c/li\u003e\n\u003cli\u003eVitturi BK, Gagliardi RJ: \u003cstrong\u003eThe prognostic significance of the lipid profile after an ischemic stroke.\u003c/strong\u003e\u003cem\u003eNeurol Res \u003c/em\u003e2022, \u003cstrong\u003e44:\u003c/strong\u003e139-145.\u003c/li\u003e\n\u003cli\u003eLangsted A, Nordestgaard BG: \u003cstrong\u003eNonfasting versus fasting lipid profile for cardiovascular risk prediction.\u003c/strong\u003e\u003cem\u003ePathology \u003c/em\u003e2019, \u003cstrong\u003e51:\u003c/strong\u003e131-141.\u003c/li\u003e\n\u003cli\u003eRyu JC, Bae JH, Ha SH, Kwon B, Song Y, Lee DH, Kim BJ, Kang DW, Kwon SU, Kim JS, Chang JY: \u003cstrong\u003eAssociation between lipid profile changes and risk of in-stent restenosis in ischemic stroke patients with intracranial stenosis: A retrospective cohort study.\u003c/strong\u003e\u003cem\u003ePLoS One \u003c/em\u003e2023, \u003cstrong\u003e18:\u003c/strong\u003ee0284749.\u003c/li\u003e\n\u003cli\u003eLiu L, Huang X, Feng L, Wu Y: \u003cstrong\u003eInternal Lipid Profile and Body Lipid Profile in Relation to Cognition: A Cross-Sectional Study in Southern China.\u003c/strong\u003e\u003cem\u003eAm J Alzheimers Dis Other Demen \u003c/em\u003e2020, \u003cstrong\u003e35:\u003c/strong\u003e1533317520962660.\u003c/li\u003e\n\u003cli\u003eLiu Y, Jin X, Fu K, Li J, Xue W, Tian L, Teng W: \u003cstrong\u003eNon-traditional lipid profiles and the risk of stroke: A systematic review and meta-analysis.\u003c/strong\u003e\u003cem\u003eNutr Metab Cardiovasc Dis \u003c/em\u003e2023, \u003cstrong\u003e33:\u003c/strong\u003e698-714.\u003c/li\u003e\n\u003cli\u003eFerence BA, Ginsberg HN, Graham I, Ray KK, Packard CJ, Bruckert E, Hegele RA, Krauss RM, Raal FJ, Schunkert H, et al: \u003cstrong\u003eLow-density lipoproteins cause atherosclerotic cardiovascular disease. 1. 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\u003cstrong\u003e21\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eKloska A, Malinowska M, Gabig-Ciminska M, Jakobkiewicz-Banecka J: \u003cstrong\u003eLipids and Lipid Mediators Associated with the Risk and Pathology of Ischemic Stroke.\u003c/strong\u003e\u003cem\u003eInt J Mol Sci \u003c/em\u003e2020, \u003cstrong\u003e21\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eHamilton JA, Hillard CJ, Spector AA, Watkins PA: \u003cstrong\u003eBrain uptake and utilization of fatty acids, lipids and lipoproteins: application to neurological disorders.\u003c/strong\u003e\u003cem\u003eJ Mol Neurosci \u003c/em\u003e2007, \u003cstrong\u003e33:\u003c/strong\u003e2-11.\u003c/li\u003e\n\u003cli\u003eWu H, Liu H, Zuo F, Zhang L: \u003cstrong\u003eAdenoviruses-mediated RNA interference targeting cytosolic phospholipase A2\u0026alpha; attenuates focal ischemic brain damage in mice.\u003c/strong\u003e\u003cem\u003eMol Med Rep \u003c/em\u003e2018, \u003cstrong\u003e17:\u003c/strong\u003e5601-5610.\u003c/li\u003e\n\u003cli\u003eLing QL, Mohite AJ, Murdoch E, Akasaka H, Li QY, So SP, Ruan KH: \u003cstrong\u003eCreating a mouse model resistant to induced ischemic stroke and cardiovascular damage.\u003c/strong\u003e\u003cem\u003eSci Rep \u003c/em\u003e2018, \u003cstrong\u003e8:\u003c/strong\u003e1653.\u003c/li\u003e\n\u003cli\u003eRuan KH, Deng H, So SP: \u003cstrong\u003eEngineering of a protein with cyclooxygenase and prostacyclin synthase activities that converts arachidonic acid to prostacyclin.\u003c/strong\u003e\u003cem\u003eBiochemistry \u003c/em\u003e2006, \u003cstrong\u003e45:\u003c/strong\u003e14003-14011.\u003c/li\u003e\n\u003cli\u003eJaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, McClure LA, Howard G, Simon N: \u003cstrong\u003eOBLIQUE RANDOM SURVIVAL FORESTS.\u003c/strong\u003e\u003cem\u003eAnn Appl Stat \u003c/em\u003e2019, \u003cstrong\u003e13:\u003c/strong\u003e1847-1883.\u003c/li\u003e\n\u003cli\u003eHu J, Szymczak S: \u003cstrong\u003eA review on longitudinal data analysis with random forest.\u003c/strong\u003e\u003cem\u003eBrief Bioinform \u003c/em\u003e2023, \u003cstrong\u003e24\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lipid, machine learning, stroke, thrombolysis, ApoB100","lastPublishedDoi":"10.21203/rs.3.rs-6625180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6625180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eClinical performance and contribution of lipid profile in atherosclerosis is well established but require further investigation in stroke patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To explore lipid parameters and their impact on stroke outcomes in patients with and without thrombolysis.\u003c/p\u003e\n\u003cp\u003eMethods: We prospectively enrolled acute ischemic stroke (AIS) patients, both with and without thrombolysis in our single center, and divided them into improvement (good outcome at 2 weeks) and non-improvement groups. Comparisons of demographic, laboratory, imaging, and clinical scaling data were conducted between the two groups. The performance and importance of each lipid (triglyceride, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein B100 (ApoB100), and lipoprotein a) were assessed using logistic regression and random forest models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 262 stroke patients were enrolled, with 165 receiving thrombolysis. It was found that ApoB100 levels were lower in patients who received thrombolysis (p \u0026lt; 0.001), and there were no significant differences in lipids between the improvement and non-improvement groups. The random forest model generated barplots showing the importance of lipids and risk factors in patients with AIS, indicating that HDL and ApoB100 from lipids (both over 15%) were more important for predicting favorable stroke outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study demonstrates that HDL and ApoB100 are key predictors of favorable stroke outcomes, as evaluated using a machine learning model. These findings highlight the potential value of incorporating HDL and ApoB100 into clinical risk assessment tools for stroke patients, warranting further investigation in larger, diverse cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e: ChiCTR1800018315, 11/09/2018\u003c/p\u003e","manuscriptTitle":"Clinical contribution of lipid profiles to the stroke progression in Chinese population: a single-centered cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 10:37:08","doi":"10.21203/rs.3.rs-6625180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-04T20:11:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T13:09:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T12:03:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3843995929426477715685204729448723978","date":"2025-06-06T09:18:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34373866054577486128572128852207400975","date":"2025-06-05T07:42:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T07:11:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-15T15:13:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T11:03:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T11:01:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-05-09T05:17:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15e2bd15-e1e2-41f8-b150-ff70ff23cf12","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:08:21+00:00","versionOfRecord":{"articleIdentity":"rs-6625180","link":"https://doi.org/10.1186/s12883-025-04444-6","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2025-10-17 15:58:38","publishedOnDateReadable":"October 17th, 2025"},"versionCreatedAt":"2025-06-09 10:37:08","video":"","vorDoi":"10.1186/s12883-025-04444-6","vorDoiUrl":"https://doi.org/10.1186/s12883-025-04444-6","workflowStages":[]},"version":"v1","identity":"rs-6625180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6625180","identity":"rs-6625180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}