Early Prediction Model of Post-Stroke Cognitive Impairment Based on Routine Clinical Blood Biomarkers

Early Prediction Model of Post-Stroke Cognitive Impairment Based on Routine Clinical Blood Biomarkers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Prediction Model of Post-Stroke Cognitive Impairment Based on Routine Clinical Blood Biomarkers Yanyang Li, Huaqiang Li, Xinyu Zhang, Xiangwen Hao, Jingqi Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9196398/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective: To evaluate the predictive value of routine clinical blood biomarkers obtained early after admission for post-stroke cognitive impairment (PSCI) and to develop and validate an early prediction model. Methods: Consecutive patients with first-ever acute ischemic stroke admitted to Shanghai Fourth People’s Hospital between March and December 2024 were enrolled. Ten routine biomarkers measured within 24 h of admission were collected: C-reactive protein (CRP), thyroid-stimulating hormone (TSH), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), lymphocyte count (LYC), mean corpuscular hemoglobin concentration (MCHC), glycated hemoglobin (HbA1c), homocysteine (HCY), β-hydroxybutyrate (β-HB), and total cholesterol (TC). Cognitive function was assessed at 3 months using the Telephone Montreal Cognitive Assessment (T-MoCA); PSCI was defined as T-MoCA <19. Patients were randomly split (7:3) into a training set (n=92) and a validation set (n=39). Predictors were selected using LASSO regression, and a Cox proportional hazards model was built and visualized with a nomogram. Model performance was evaluated by ROC/AUC, C-index, decision curve analysis (DCA), and calibration. The study protocol complied with the Declaration of Helsinki was registered in the Chinese Clinical Trial Registry (ChiCTR2400082449). Results: A total of 131 patients were included (PSCI, n=65; non-PSCI, n=66). LASSO selected four candidate predictors: TSH, LYC, HCY, and TC. The model achieved an AUC of 0.69 (95% CI, 0.632–0.742) and a C-index of 0.687 in the training set, and an AUC of 0.58 (95% CI, 0.485–0.673) and a C-index of 0.579 in the validation set. DCA suggested a net clinical benefit across threshold probabilities of 0%–75% in the training set and 0%–56.25% in the validation set. The Hosmer–Lemeshow test indicated good calibration (P>0.05). Conclusion: TSH, LYC, HCY, and TC showed potential value for early PSCI prediction; however, the model based solely on routine blood biomarkers demonstrated limited discrimination. Larger multicenter cohorts and more specific biomarkers and/or multimodal features are warranted to improve early PSCI risk stratification. ischemic stroke post-stroke cognitive impairment blood biomarkers prediction model LASSO regression Figures Figure 1 Figure 2 Figure 3 1 Introduction Ischemic stroke (IS) remains a leading cause of death and disability worldwide. The global burden of IS increased substantially from 1990 to 2021 and is projected to continue rising in the coming decades( 1 , 2 ). Survivors of IS frequently develop post-stroke cognitive impairment (PSCI), which adversely affects functional recovery and quality of life. Previous studies reported that more than 70% of IS survivors may experience cognitive deficits across follow-up periods( 3 ). PSCI is associated with higher mortality and disability and poorer activities of daily living and mental health( 4 , 5 ). Because early PSCI can be clinically subtle yet potentially modifiable, accessible tools for early risk identification are needed. Risk prediction models based on neuropsychological scales and neuroimaging features have shown promise. For example, the Montreal Cognitive Assessment (MoCA) has been widely used in cognitive impairment research( 6 , 7 ) and has shown that early performance can predict later PSCI risk( 8 ). Several risk scores integrating clinical and imaging variables achieved good discrimination( 9 – 11 ). However, scale-based assessment is limited in patients with aphasia or sensory deficits and may be subject to rater variability. Neuroimaging is more costly and time-consuming, may carry radiation or contrast-related risks in specific settings, and imaging features are not fully standardized for routine decision-making, which may limit scalability in broad screening. Blood-based biomarkers are convenient and cost-effective, offering good translational potential. Meta-analyses and clinical studies have linked homocysteine (HCY), C-reactive protein (CRP), total cholesterol (TC), LDL-C, thyroid-stimulating hormone (TSH), and HbA1c with post-stroke cognitive outcomes( 12 – 14 ). Inflammatory markers such as CRP and IL-6 in the acute phase have also been summarized as independent predictors of subsequent PSCI( 15 ). Nevertheless, most studies focus on single or limited biomarkers, and evidence is still insufficient for a practical model integrating multiple routine laboratory tests. Therefore, we focused on routine clinical blood biomarkers. Using LASSO for feature selection, we developed a Cox regression model and a nomogram to estimate the risk of PSCI within 90 days after IS, aiming to provide an accessible and economical tool for early risk stratification. 2 Materials and Methods This was a prospective observational cohort study. Consecutive patients with first-ever acute ischemic stroke admitted to Shanghai Fourth People’s Hospital from March to December 2024 were screened. The study protocol complied with the Declaration of Helsinki, was approved by the Ethics Committee of Shanghai Fourth People’s Hospital (No. 2024056-001), and was registered in the Chinese Clinical Trial Registry (ChiCTR2400082449) on March 29, 2024. Inclusion criteria were: ( 1 ) age ≥ 18 years; ( 2 ) first-ever acute ischemic stroke confirmed by CT or MRI; ( 3 ) time from symptom onset to admission < 72 h; ( 4 ) clear consciousness and ability to cooperate with examinations and follow-up; and ( 5 ) complete clinical data and written informed consent. Exclusion criteria were: ( 1 ) severe hearing, speech, visual, or writing impairment precluding cognitive testing; ( 2 ) ICU admission or critical illness; ( 3 ) previous stroke; ( 4 ) pre-stroke Alzheimer’s disease or other dementias; ( 5 ) refusal or inability to complete MoCA/T-MoCA or the 3-month follow-up; ( 6 ) lipemic blood samples affecting test accuracy; ( 7 ) loss to follow-up or death during follow-up; ( 8 ) severe hepatic, renal, or cardiac dysfunction, malignant tumors; or ( 9 ) severe psychiatric disorders or medications with substantial cognitive effects. The process is shown in Fig. 1 . Baseline data included demographics (age, sex), medical history (hypertension, coronary heart disease, atrial fibrillation, surgery history), lifestyle (smoking, alcohol), clinical information (National Institutes of Health Stroke Scale score at admission, thrombolysis, medication use), and laboratory results. National Institutes of Health Stroke Scale (NIHSS) ( 16 – 18 )was assessed by experienced clinicians. Fasting venous blood (2 mL, EDTA) was collected the morning after admission and immediately tested for TSH, TG, LDL-C, HCY, TC, β-HB, CRP, LYC, MCHC, and HbA1c under standardized quality-control procedures. Cognitive assessment: At 3 months after stroke, trained therapists administered the Telephone Montreal Cognitive Assessment (T-MoCA; total score 22)( 19 , 20 ), covering six domains( 21 , 22 ). PSCI was defined as T-MoCA < 19, and non-PSCI as T-MoCA ≥ 19. Statistical analysis: Analyses were performed using R (v4.2.0). Normally distributed continuous variables were described as mean ± SD and compared using the independent-samples t-test. Non-normally distributed variables were described as median (IQR) and compared with the Mann–Whitney U test. Categorical variables were compared with the chi-square test or Fisher’s exact test. Patients were randomly split (7:3) into a training set (n = 92) and a validation set (n = 39), and balance was assessed by comparing variable distributions. Predictors were selected using LASSO regression (10-fold cross-validation; lambda.min). A Cox proportional hazards model was then built and visualized with a nomogram. Discrimination was evaluated by ROC/AUC and the C-index; clinical utility by decision curve analysis (DCA); and calibration by the Hosmer–Lemeshow test and calibration plots. A two-sided P < 0.05 was considered statistically significant. 3 Results A total of 131 patients were included after screening 385 admissions, comprising 65 patients with PSCI and 66 without PSCI at 3 months. Baseline characteristics: The two groups were comparable in age, sex, NIHSS score, and major comorbidities (all P > 0.05). Acute-phase MoCA scores were lower in the PSCI group than in the non-PSCI group (P 0.05) (Table 2 ). Training/validation split: The 7:3 random split yielded balanced distributions of baseline variables and biomarkers between the training and validation sets (all P > 0.05) (Table 3 ). Model development: LASSO identified four non-zero coefficients at lambda.min (TSH, LYC, HCY, and TC), which were used to build the Cox model and a nomogram for 90-day PSCI risk estimation (Fig. 2 ). Model validation: In the training set, the model achieved an AUC of 0.69 and a C-index of 0.687; in the validation set, the AUC was 0.58 and the C-index was 0.579. Decision curve analysis indicated potential net benefit across threshold probabilities of 0%–75% (training) and 0%–56.25% (validation). Calibration was acceptable by the Hosmer–Lemeshow test (P > 0.05) and calibration plots (Fig. 3 ). Table 1 Baseline characteristics Item PSCI (n = 65) Non-PSCI (n = 66) P Age, years 71(64–74) 66.5(59-72.5) > 0.05 NIHSS score 1(0–3) 1(0–3) > 0.05 Acute-phase MoCA score 22(16.5–24) 27( 26 – 29 ) 0.05 Female, n 22 20 > 0.05 Hypertension 35 44 > 0.05 No hypertension 30 22 > 0.05 Coronary heart disease 4 6 > 0.05 No coronary heart disease 61 60 > 0.05 Atrial fibrillation 1 3 > 0.05 No atrial fibrillation 63 63 > 0.05 History of surgery 8 11 > 0.05 No history of surgery 57 55 > 0.05 Thrombolysis 11 13 > 0.05 Medication therapy 54 53 > 0.05 Smoking 9 10 > 0.05 Alcohol use 6 6 > 0.05 Antihypertensive drugs (yes) 27 32 > 0.05 Antihypertensive drugs (no) 29 30 > 0.05 Antihypertensive drugs (unknown) 9 4 > 0.05 Lipid-lowering drugs (yes) 9 9 > 0.05 Lipid-lowering drugs (no) 46 53 > 0.05 Lipid-lowering drugs (unknown) 10 4 > 0.05 Antiplatelet therapy (yes) 8 10 > 0.05 Antiplatelet therapy (no) 47 52 > 0.05 Antiplatelet therapy (unknown) 10 4 > 0.05 Anticoagulant therapy (yes) 3 5 > 0.05 Anticoagulant therapy (no) 58 56 > 0.05 Anticoagulant therapy (unknown) 4 5 > 0.05 Table 2 Routine blood biomarkers Item PSCI (n = 65) Non-PSCI (n = 66) P CRP (mg/L) 1.87(0.8–5.92) 1.52(0.8–4.40) > 0.05 TSH (mIU/L) 1.44(0.94–2.40) 1.55(1.08–2.03) > 0.05 TG (mmol/L) 1.39(0.96–2.09) 1.45(1.14–1.84) > 0.05 LDL-C (mmol/L) 2.80 ± 0.81 2.94 ± 0.96 > 0.05 LYC (×10^9/L) 1.58(1.18–2.12) 1.97(1.28–2.33) > 0.05 MCHC (g/L) 335(328.5–340) 336(325.75–342) > 0.05 HbA1c (%) 6.1(5.7–7.25) 6.2(5.9–7.73) > 0.05 HCY (µmol/L) 14.9(11.7-20.45) 14.65(11.98–16.88) > 0.05 β-HB (mmol/L) 0.1(0.1–0.2) 0.1(0.1–0.3) > 0.05 TC (mmol/L) 4.13(3.26–4.93) 3.9(2.7–4.89) > 0.05 Table 3 Balance between training and validation sets Item Training (n = 92) Validation (n = 39) P Age, years 68.27 ± 8.88 66.28 ± 10.54 > 0.05 MoCA score 26( 21 – 28 ) 26( 22 – 27 ) > 0.05 NIHSS score 1(0–3) 1(0–4) > 0.05 Cognitive impairment, n 47 18 > 0.05 Male, n 58 31 > 0.05 Female, n 34 8 > 0.05 Hypertension 57 22 > 0.05 No hypertension 35 17 > 0.05 Coronary heart disease 8 2 > 0.05 No coronary heart disease 84 37 > 0.05 Atrial fibrillation 3 1 > 0.05 No atrial fibrillation 89 38 > 0.05 History of surgery 13 6 > 0.05 No history of surgery 79 33 > 0.05 Thrombolysis 15 9 > 0.05 Medication therapy 77 30 > 0.05 Smoking 15 4 > 0.05 Alcohol use 8 4 > 0.05 Antihypertensive drugs (yes) 40 19 > 0.05 Antihypertensive drugs (no) 42 17 > 0.05 Antihypertensive drugs (unknown) 10 3 > 0.05 Lipid-lowering drugs (yes) 23 8 > 0.05 Lipid-lowering drugs (no) 59 27 > 0.05 Lipid-lowering drugs (unknown) 10 4 > 0.05 Antiplatelet therapy (yes) 10 8 > 0.05 Antiplatelet therapy (no) 72 27 > 0.05 Antiplatelet therapy (unknown) 10 4 > 0.05 Anticoagulant therapy (yes) 3 2 > 0.05 Anticoagulant therapy (no) 79 33 > 0.05 Anticoagulant therapy (unknown) 10 4 > 0.05 CRP (mg/L) 2.27(0.8–5.73) 1.49(0.8–4.28) > 0.05 TSH (mIU/L) 1.56(1.0-2.11) 1.30(0.94–1.90) > 0.05 TG (mmol/L) 1.40(1.08–2.11) 1.48(1.19–1.88) > 0.05 LDL-C (mmol/L) 2.85 ± 0.83 2.92 ± 1.02 > 0.05 LYC (×10^9/L) 1.71(1.16–2.24) 1.72(1.26–2.29) > 0.05 MCHC (g/L) 336(328–341) 333(323–340) > 0.05 HbA1c (%) 6.2(5.9–7.7) 6(5.8–7.3) > 0.05 HCY (µmol/L) 14.85(11.73–18.93) 14.70(12-16.3) > 0.05 β-HB (mmol/L) 0.1(0.1–0.2) 0.1(0.1–0.3) > 0.05 TC (mmol/L) 4.03(2.81–4.85) 4.06(3.10–5.14) > 0.05 4 Discussion In this prospective cohort study, we explored the feasibility of predicting PSCI using routine blood biomarkers obtained within 24 h of admission. LASSO selected TSH, LYC, HCY, and TC as candidate predictors. Although the model showed modest discrimination in the training set, performance declined in the independent validation set, suggesting that routine biomarkers alone may not be sufficient for accurate early PSCI prediction. TSH may influence cognitive outcomes through pathways related to energy metabolism, synaptic function, and neuroinflammation ( 23 , 24 ). Low TSH has been associated with cognitive impairment in older populations ( 25 ). While TSH did not differ significantly between groups in our univariable comparisons, its selection by LASSO suggests potential incremental information when combined with other markers. TC is a core driver of atherosclerosis and is linked to ischemic stroke risk( 26 , 27 ). Dyslipidemia may contribute to chronic hypoperfusion, white matter injury, and altered neuronal membrane stability, thereby affecting cognition ( 28 , 29 ). The observed trend toward higher TC in PSCI warrants further validation. LYC reflects peripheral immune status. Post-stroke immunosuppression and neuro-immune interactions may influence synaptic plasticity and recovery, potentially affecting cognition ( 30 , 31 ). However, LYC is susceptible to infections and medications( 32 ), highlighting the need for dynamic measurements or integrative modeling. HCY is implicated in endothelial dysfunction, oxidative stress, and neuroinflammation and has been linked to post-stroke cognitive deficits( 33 – 35 ). Its predictive value may vary by age, sex, and follow-up window( 36 , 37 ), which may partly explain the limited external performance in a predominantly male cohort with 3-month assessment. Limitations include the single-center design, relatively small sample size (especially the validation set), a restricted feature space limited to routine laboratory tests, and cognitive assessment at a single follow-up time point. Future multicenter studies with larger samples, longitudinal cognitive assessments, and multimodal features (neuroimaging and multi-omics biomarkers) are needed. Modeling approaches tailored to binary outcomes and rigorous external validation may further improve early risk stratification. 5 Conclusion TSH, LYC, HCY, and TC showed potential value for early prediction of PSCI at 3 months after ischemic stroke, but the model based solely on routine blood biomarkers demonstrated limited discrimination. More specific biomarkers and multimodal features, validated in larger multicenter cohorts, are required to enable accurate early screening and targeted interventions for PSCI. Abbreviations PSCI post-stroke cognitive impairment CRP C-reactive protein TSH thyroid-stimulating hormone TG triglycerides LDL-C low-density lipoprotein cholesterol LYC lymphocyte count MCHC mean corpuscular hemoglobin concentration HbA1c glycated hemoglobin HCY homocysteine β-HB β-hydroxybutyrate TC total cholesterol T-MoCA Telephone Montreal Cognitive Assessment MoCA Montreal Cognitive Assessment IS ischemic stroke DCA decision curve analysis Declarations Ethics approval and consent to participate The study protocol complied with the Declaration of Helsinki, was approved by the Ethics Committee of Shanghai Fourth People’s Hospital (No. 2024056-001), and was registered in the Chinese Clinical Trial Registry (ChiCTR2400082449) on March 29, 2024. Informed consent was obtained from the participants and their legal guardians or appropriate representatives before the commencement of data collection. Consent for publication Not applicable. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no conflict of interest. Funding This work was supported by the National Natural Science Foundation of China (No. 82222019), the National Key Research and Development Program of China 2023YFC2505900, and the “Medicine + X” Interdisciplinary Research Program of Tongji University (No. 2025-0674-ZD-01). Authors' contributions HL and QM contributed to the conception and design of the study. Data were acquired by HL, YL, and XH. Data were analyzed and interpreted by YL and HL. The manuscript was drafted by YL. LT, QM, and JC critically revised the manuscript for important intellectual content. Statistical analyses were performed by HL and XZ. All authors read and approved the final manuscript. Acknowledgements We thank all patients who participated in the study and the staff members at participating hospitals. This work was supported by the National Natural Science Foundation of China (No. 82222019), the National Key Research and Development Program of China 2023YFC2505900, and the “Medicine + X” Interdisciplinary Research Program of Tongji University (No. 2025-0674-ZD-01). References Fan J, Li X, Yu X, Liu Z, Jiang Y, Fang Y, et al. Global Burden, Risk Factor Analysis, and Prediction Study of Ischemic Stroke, 1990-2030. Neurology. 2023;101(2):e137–e50. Wang YJ, Li ZX, Gu HQ, Zhai Y, Jiang Y, Zhao XQ, et al. China Stroke Statistics 2019: A Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke Vasc Neurol. 2020;5(3):211–39. Filler J, Georgakis MK, Dichgans M. Risk factors for cognitive impairment and dementia after stroke: a systematic review and meta-analysis. Lancet Healthy Longev. 2024;5(1):e31–e44. Oksala NK, Jokinen H, Melkas S, Oksala A, Pohjasvaara T, Hietanen M, et al. Cognitive impairment predicts poststroke death in long-term follow-up. J Neurol Neurosurg Psychiatry. 2009;80(11):1230–5. Tatemichi TK, Paik M, Bagiella E, Desmond DW, Pirro M, Hanzawa LK. Dementia after stroke is a predictor of long-term survival. Stroke. 1994;25(10):1915–9. Jia X, Wang Z, Huang F, Su C, Du W, Jiang H, et al. A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatry. 2021;21(1):485. Wei X, Liu Y, Li J, Zhu Y, Li W, Zhu Y, et al. MoCA and MMSE for the detection of post-stroke cognitive impairment: a comparative diagnostic test accuracy systematic review and meta‑analysis. J Neurol. 2025;272(6):407. Salvadori E, Cova I, Mele F, Pomati S, Pantoni L. Prediction of post-stroke cognitive impairment by Montreal Cognitive Assessment (MoCA) performances in acute stroke: comparison of three normative datasets. Aging Clin Exp Res. 2022;34(8):1855–63. Lim JS, Oh MS, Lee JH, Jung S, Kim C, Jang MU, et al. Prediction of post-stroke dementia using NINDS-CSN 5-minute neuropsychology protocol in acute stroke. Int Psychogeriatr. 2017;29(5):777–84. Kandiah N, Chander RJ, Lin X, Ng A, Poh YY, Cheong CY, et al. Cognitive Impairment after Mild Stroke: Development and Validation of the SIGNAL2 Risk Score. J Alzheimers Dis. 2016;49(4):1169–77. Chander RJ, Lam BYK, Lin X, Ng AYT, Wong APL, Mok VCT, et al. Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke. Sci Rep. 2017;7(1):12441. Kim KY, Shin KY, Chang KA. Potential Biomarkers for Post-Stroke Cognitive Impairment: A Systematic Review and Meta-Analysis. Int J Mol Sci. 2022;23(2). Chen Z, Liang X, Zhang C, Wang J, Chen G, Zhang H, et al. Correlation of thyroid dysfunction and cognitive impairments induced by subcortical ischemic vascular disease. Brain Behav. 2016;6(4):e00452. Xu L, Xiong Q, Du Y, Huang LW, Yu M. Nonlinear relationship between glycated hemoglobin and cognitive impairment after acute mild ischemic stroke. BMC Neurol. 2023;23(1):116. Zhang MS, Liang JH, Yang MJ, Ren YR, Cheng DH, Wu QH, et al. Low Serum Superoxide Dismutase Is Associated With a High Risk of Cognitive Impairment After Mild Acute Ischemic Stroke. Front Aging Neurosci. 2022;14:834114. Fischer U, Arnold M, Nedeltchev K, Brekenfeld C, Ballinari P, Remonda L, et al. NIHSS score and arteriographic findings in acute ischemic stroke. Stroke. 2005;36(10):2121–5. Mak A, Matouk C, Avery EW, Behland J, Frey D, Madai VI, et al. Similar admission NIHSS may represent larger tissue-at-risk in patients with right-sided versus left-sided large vessel occlusion. J Neurointerv Surg. 2022;14(10):985–91. Hong XC, Shu MC, Bao SR, Chen SY, Weng YX, Lin SL. Analysis of factors influencing poor neurological outcomes in patients with acute ischemic stroke. Ann Med. 2025;57(1):2458209. Sujanthan S, Rajkumar G, Dainty KN, Barense M, Lanctot KL, Owen AM, et al. Faster Thrombolysis Is Associated With Improved Cognitive Outcomes in Patients With Acute Ischemic Stroke Treated With Alteplase and Tenecteplase: A Substudy of the AcT Trial. Stroke. 2025;56(10):2858–65. Chappelle SD, Gigliotti C, Leger GC, Peavy GM, Jacobs DM, Banks SJ, et al. Comparison of the telephone-Montreal Cognitive Assessment (T-MoCA) and Telephone Interview for Cognitive Status (TICS) as screening tests for early Alzheimer's disease. Alzheimers Dement. 2023;19(10):4599–608. Pendlebury ST, Welch SJ, Cuthbertson FC, Mariz J, Mehta Z, Rothwell PM. Telephone assessment of cognition after transient ischemic attack and stroke: modified telephone interview of cognitive status and telephone Montreal Cognitive Assessment versus face-to-face Montreal Cognitive Assessment and neuropsychological battery. Stroke. 2013;44(1):227–9. Zietemann V, Kopczak A, Muller C, Wollenweber FA, Dichgans M. Validation of the Telephone Interview of Cognitive Status and Telephone Montreal Cognitive Assessment Against Detailed Cognitive Testing and Clinical Diagnosis of Mild Cognitive Impairment After Stroke. Stroke. 2017;48(11):2952–7. Faust B, Billesbolle CB, Suomivuori CM, Singh I, Zhang K, Hoppe N, et al. Autoantibody mimicry of hormone action at the thyrotropin receptor. Nature. 2022;609(7928):846–53. Kim DK, Choi H, Lee W, Choi H, Hong SB, Jeong JH, et al. Brain hypothyroidism silences the immune response of microglia in Alzheimer's disease animal model. Sci Adv. 2024;10(11):eadi1863. Adams R, Oh ES, Yasar S, Lyketsos CG, Mammen JS. Endogenous and Exogenous Thyrotoxicosis and Risk of Incident Cognitive Disorders in Older Adults. JAMA Intern Med. 2023;183(12):1324–31. Gu X, Li Y, Chen S, Yang X, Liu F, Li Y, et al. Association of Lipids With Ischemic and Hemorrhagic Stroke: A Prospective Cohort Study Among 267 500 Chinese. Stroke. 2019;50(12):3376–84. Ciplak S, Adiguzel A, Deniz YZ, Aba M, Ozturk U. The Role of the Low-Density Lipoprotein/High-Density Lipoprotein Cholesterol Ratio as an Atherogenic Risk Factor in Young Adults with Ischemic Stroke: A Case-Control Study. Brain Sci. 2023;13(8). Xue LL, Cheng J, Du RL, Luo BY, Chen L, Xiao QX, et al. Bone marrow mesenchymal stem cells alleviate neurological dysfunction by reducing autophagy damage via downregulation of SYNPO2 in neonatal hypoxic-ischemic encephalopathy rats. Cell Death Dis. 2025;16(1):131. Valenza M, Birolini G, Cattaneo E. The translational potential of cholesterol-based therapies for neurological disease. Nat Rev Neurol. 2023;19(10):583–98. Liu C, Shi D, Ni X, You S, Wu X, Zhuang S, et al. Correlations among lymphocyte count, white matter hyperintensity and brain atrophy in patients with ischemic stroke. Front Aging Neurosci. 2024;16:1492078. Liu Y, Zhong Z, Chen J, Kuo H, Chen X, Wang P, et al. Brain activation patterns in patients with post-stroke cognitive impairment during working memory task: a functional near-infrared spectroscopy study. Front Neurol. 2024;15:1419128. Tsalta-Mladenov ME, Andonova SP. Peripheral blood cell count ratios as a predictor of poor functional outcome in patients with acute ischemic stroke. Neurol Res. 2024;46(3):213–9. Li Y, Chen X, Zhou R, Xu W, Wang X, Chao W, et al. Correlation Between Cognitive Impairment and Homocysteine and S100B Protein in Patients with Progressive Ischemic Stroke. Neuropsychiatr Dis Treat. 2023;19:209–17. Cui L, Lu P, Li S, Pan Y, Wang M, Li Z, et al. Relationship Among Homocysteine, Inflammation and Cognitive Impairment in Patients with Acute Ischemic Stroke and Transient Ischemic Attack. Neuropsychiatr Dis Treat. 2021;17:3607–16. Wu JX, Xue J, Zhuang L, Liu CF. Plasma parameters and risk factors of patients with post-stroke cognitive impairment. Ann Palliat Med. 2020;9(1):45–52. Zhou S, Chen J, Cheng L, Fan K, Xu M, Ren W, et al. Age-Dependent Association Between Elevated Homocysteine and Cognitive Impairment in a Post-stroke Population: A Prospective Study. Front Nutr. 2021;8:691837. Li R, Weng H, Pan Y, Meng X, Liao X, Wang M, et al. Relationship between homocysteine levels and post-stroke cognitive impairment in female and male population: from a prospective multicenter study. J Transl Int Med. 2021;9(4):264–72. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 30 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9196398","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620619117,"identity":"d9f1d18a-6602-4491-9889-0e7eeee05df0","order_by":0,"name":"Yanyang Li","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yanyang","middleName":"","lastName":"Li","suffix":""},{"id":620619118,"identity":"2acab5a0-9c57-419a-a51e-7c53f53183b0","order_by":1,"name":"Huaqiang Li","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huaqiang","middleName":"","lastName":"Li","suffix":""},{"id":620619120,"identity":"dde40eaf-ac4f-43be-b640-e80430269f77","order_by":2,"name":"Xinyu Zhang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Zhang","suffix":""},{"id":620619122,"identity":"ea7e3db5-cbc5-4fa4-997f-3649facb211f","order_by":3,"name":"Xiangwen Hao","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Xiangwen","middleName":"","lastName":"Hao","suffix":""},{"id":620619124,"identity":"965563a5-bf77-408a-95b3-f037a5a42cc8","order_by":4,"name":"Jingqi Chen","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Jingqi","middleName":"","lastName":"Chen","suffix":""},{"id":620619126,"identity":"f7cf2c07-03ea-49e1-87fb-e722f493dbb8","order_by":5,"name":"Qiuhong Man","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Qiuhong","middleName":"","lastName":"Man","suffix":""},{"id":620619127,"identity":"0ab54e0f-2b34-43a7-bb36-52cd03bac294","order_by":6,"name":"Li Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPgbGBgaGCjA74QADAzNhLWxsIC1nDEjSAiQY2wxgfGK0yDc3fi6c90fOnH/BwwMMFdaJDexnDxByWLP0zG0GxpYzHgAddiY9sYEnL4GQlgZp3m0GiRtuHEg4wNh2OLFBgseAkJbm37xzYFr+EaelTZq3AajlfANQSwNRWhLbrHmOGRsb3AAGcsKxdOM2nhz8WviZjz++zVMjJ2dw/kzyhw811rL97Gfwa0EAiZwEhgSQvUSqB9l3/ADxikfBKBgFo2BEAQDq/ERDeF6ALQAAAABJRU5ErkJggg==","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2026-03-23 06:39:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9196398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9196398/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106975868,"identity":"9bdd89a2-7dad-4cac-99a8-48c9b27882c8","added_by":"auto","created_at":"2026-04-15 10:42:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205038,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9196398/v1/46e0dbbc634baff03c0ee369.png"},{"id":106975929,"identity":"cd4dae12-6ff8-4bc1-aa6f-9b2b35b58bdc","added_by":"auto","created_at":"2026-04-15 10:42:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135580,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO feature selection and nomogram. (a) LASSO regression coefficient profiles. Each curve represents a predictor. The Y-axis denotes the coefficient value; the lower X-axis shows log(λ), and the upper X-axis indicates the number of non-zero coefficients. (b) Cross-validation curve for the LASSO regression. The X-axis represents log(λ), and the Y-axis represents the partial likelihood deviance (lower values indicate better fit). The numbers at the top denote the number of variables retained at each λ. The left and right dashed lines represent λ_min and λ_se, respectively. (c) Nomogram derived from the Cox regression model for predicting PSCI. The vertical axis lists the predictors, with line length reflecting the contribution of each factor to PSCI. The “Points” row indicates the score for each variable value, and “Total Points” represents the sum of these scores. The total points correspond to the linear predictor and the probability of PSCI within 90 days after IS.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9196398/v1/fc6fea2b8054e1185fb70ff8.png"},{"id":106975823,"identity":"f25e1102-9dd0-4559-a165-5a1c4b4514d2","added_by":"auto","created_at":"2026-04-15 10:42:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250378,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves, decision curve analysis, and calibration plots. (a) ROC curves for the training and validation sets. The X-axis represents False Positives (FP), denoting the number of samples incorrectly predicted as positive, while the Y-axis represents True Positives (TP), denoting the number of samples correctly predicted as positive. (b) Decision Curve Analysis (DCA) for the training and validation sets. The X-axis indicates the threshold probability for intervention, and the Y-axis represents the Net Benefit. (c) Calibration curves for the training and validation sets. The X-axis denotes the predicted probability of the event, and the Y-axis denotes the observed probability. The diagonal dashed line indicates the ideal prediction (predicted = observed), and the solid black line represents the fitted model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9196398/v1/187d3a4a38b30aaf3811576d.png"},{"id":106976008,"identity":"8c8db1aa-b803-4267-96e8-1d8696139e70","added_by":"auto","created_at":"2026-04-15 10:43:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1188335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9196398/v1/ac736da2-a216-4699-a985-9f089c38151f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Prediction Model of Post-Stroke Cognitive Impairment Based on Routine Clinical Blood Biomarkers","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIschemic stroke (IS) remains a leading cause of death and disability worldwide. The global burden of IS increased substantially from 1990 to 2021 and is projected to continue rising in the coming decades(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Survivors of IS frequently develop post-stroke cognitive impairment (PSCI), which adversely affects functional recovery and quality of life. Previous studies reported that more than 70% of IS survivors may experience cognitive deficits across follow-up periods(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). PSCI is associated with higher mortality and disability and poorer activities of daily living and mental health(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Because early PSCI can be clinically subtle yet potentially modifiable, accessible tools for early risk identification are needed.\u003c/p\u003e \u003cp\u003eRisk prediction models based on neuropsychological scales and neuroimaging features have shown promise. For example, the Montreal Cognitive Assessment (MoCA) has been widely used in cognitive impairment research(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and has shown that early performance can predict later PSCI risk(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Several risk scores integrating clinical and imaging variables achieved good discrimination(\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, scale-based assessment is limited in patients with aphasia or sensory deficits and may be subject to rater variability. Neuroimaging is more costly and time-consuming, may carry radiation or contrast-related risks in specific settings, and imaging features are not fully standardized for routine decision-making, which may limit scalability in broad screening.\u003c/p\u003e \u003cp\u003eBlood-based biomarkers are convenient and cost-effective, offering good translational potential. Meta-analyses and clinical studies have linked homocysteine (HCY), C-reactive protein (CRP), total cholesterol (TC), LDL-C, thyroid-stimulating hormone (TSH), and HbA1c with post-stroke cognitive outcomes(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Inflammatory markers such as CRP and IL-6 in the acute phase have also been summarized as independent predictors of subsequent PSCI(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Nevertheless, most studies focus on single or limited biomarkers, and evidence is still insufficient for a practical model integrating multiple routine laboratory tests.\u003c/p\u003e \u003cp\u003eTherefore, we focused on routine clinical blood biomarkers. Using LASSO for feature selection, we developed a Cox regression model and a nomogram to estimate the risk of PSCI within 90 days after IS, aiming to provide an accessible and economical tool for early risk stratification.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003eThis was a prospective observational cohort study. Consecutive patients with first-ever acute ischemic stroke admitted to Shanghai Fourth People\u0026rsquo;s Hospital from March to December 2024 were screened. The study protocol complied with the Declaration of Helsinki, was approved by the Ethics Committee of Shanghai Fourth People\u0026rsquo;s Hospital (No. 2024056-001), and was registered in the Chinese Clinical Trial Registry (ChiCTR2400082449) on March 29, 2024.\u003c/p\u003e \u003cp\u003eInclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) first-ever acute ischemic stroke confirmed by CT or MRI; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) time from symptom onset to admission\u0026thinsp;\u0026lt;\u0026thinsp;72 h; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) clear consciousness and ability to cooperate with examinations and follow-up; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) complete clinical data and written informed consent. Exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) severe hearing, speech, visual, or writing impairment precluding cognitive testing; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ICU admission or critical illness; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) previous stroke; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) pre-stroke Alzheimer\u0026rsquo;s disease or other dementias; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) refusal or inability to complete MoCA/T-MoCA or the 3-month follow-up; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) lipemic blood samples affecting test accuracy; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) loss to follow-up or death during follow-up; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) severe hepatic, renal, or cardiac dysfunction, malignant tumors; or (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) severe psychiatric disorders or medications with substantial cognitive effects. The process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBaseline data included demographics (age, sex), medical history (hypertension, coronary heart disease, atrial fibrillation, surgery history), lifestyle (smoking, alcohol), clinical information (National Institutes of Health Stroke Scale score at admission, thrombolysis, medication use), and laboratory results. National Institutes of Health Stroke Scale (NIHSS) (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)was assessed by experienced clinicians.\u003c/p\u003e \u003cp\u003eFasting venous blood (2 mL, EDTA) was collected the morning after admission and immediately tested for TSH, TG, LDL-C, HCY, TC, β-HB, CRP, LYC, MCHC, and HbA1c under standardized quality-control procedures.\u003c/p\u003e \u003cp\u003eCognitive assessment: At 3 months after stroke, trained therapists administered the Telephone Montreal Cognitive Assessment (T-MoCA; total score 22)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), covering six domains(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). PSCI was defined as T-MoCA\u0026thinsp;\u0026lt;\u0026thinsp;19, and non-PSCI as T-MoCA\u0026thinsp;\u0026ge;\u0026thinsp;19.\u003c/p\u003e \u003cp\u003eStatistical analysis: Analyses were performed using R (v4.2.0). Normally distributed continuous variables were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared using the independent-samples t-test. Non-normally distributed variables were described as median (IQR) and compared with the Mann\u0026ndash;Whitney U test. Categorical variables were compared with the chi-square test or Fisher\u0026rsquo;s exact test. Patients were randomly split (7:3) into a training set (n\u0026thinsp;=\u0026thinsp;92) and a validation set (n\u0026thinsp;=\u0026thinsp;39), and balance was assessed by comparing variable distributions. Predictors were selected using LASSO regression (10-fold cross-validation; lambda.min). A Cox proportional hazards model was then built and visualized with a nomogram. Discrimination was evaluated by ROC/AUC and the C-index; clinical utility by decision curve analysis (DCA); and calibration by the Hosmer\u0026ndash;Lemeshow test and calibration plots. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eA total of 131 patients were included after screening 385 admissions, comprising 65 patients with PSCI and 66 without PSCI at 3 months.\u003c/p\u003e \u003cp\u003eBaseline characteristics: The two groups were comparable in age, sex, NIHSS score, and major comorbidities (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Acute-phase MoCA scores were lower in the PSCI group than in the non-PSCI group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoutine biomarkers: No significant between-group differences were observed for CRP, TSH, TG, LDL-C, LYC, MCHC, HbA1c, HCY, β-HB, or TC (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraining/validation split: The 7:3 random split yielded balanced distributions of baseline variables and biomarkers between the training and validation sets (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModel development: LASSO identified four non-zero coefficients at lambda.min (TSH, LYC, HCY, and TC), which were used to build the Cox model and a nomogram for 90-day PSCI risk estimation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModel validation: In the training set, the model achieved an AUC of 0.69 and a C-index of 0.687; in the validation set, the AUC was 0.58 and the C-index was 0.579. Decision curve analysis indicated potential net benefit across threshold probabilities of 0%\u0026ndash;75% (training) and 0%\u0026ndash;56.25% (validation). Calibration was acceptable by the Hosmer\u0026ndash;Lemeshow test (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and calibration plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSCI (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PSCI (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(64\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5(59-72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute-phase MoCA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(16.5\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo coronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo atrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo history of surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombolysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drugs (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drugs (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drugs (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant therapy (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant therapy (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant therapy (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRoutine blood biomarkers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSCI (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PSCI (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.87(0.8\u0026ndash;5.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52(0.8\u0026ndash;4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44(0.94\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55(1.08\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39(0.96\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45(1.14\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYC (\u0026times;10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58(1.18\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97(1.28\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e335(328.5\u0026ndash;340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e336(325.75\u0026ndash;342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.1(5.7\u0026ndash;7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2(5.9\u0026ndash;7.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCY (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.9(11.7-20.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.65(11.98\u0026ndash;16.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-HB (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1(0.1\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1(0.1\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.13(3.26\u0026ndash;4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9(2.7\u0026ndash;4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBalance between training and validation sets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.28\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive impairment, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo coronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo atrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo history of surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombolysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drugs (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drugs (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drugs (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet therapy (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant therapy (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant therapy (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant therapy (unknown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27(0.8\u0026ndash;5.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49(0.8\u0026ndash;4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56(1.0-2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30(0.94\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40(1.08\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48(1.19\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYC (\u0026times;10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71(1.16\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72(1.26\u0026ndash;2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336(328\u0026ndash;341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333(323\u0026ndash;340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2(5.9\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(5.8\u0026ndash;7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCY (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.85(11.73\u0026ndash;18.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.70(12-16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-HB (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1(0.1\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1(0.1\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.03(2.81\u0026ndash;4.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06(3.10\u0026ndash;5.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this prospective cohort study, we explored the feasibility of predicting PSCI using routine blood biomarkers obtained within 24 h of admission. LASSO selected TSH, LYC, HCY, and TC as candidate predictors. Although the model showed modest discrimination in the training set, performance declined in the independent validation set, suggesting that routine biomarkers alone may not be sufficient for accurate early PSCI prediction.\u003c/p\u003e \u003cp\u003eTSH may influence cognitive outcomes through pathways related to energy metabolism, synaptic function, and neuroinflammation (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Low TSH has been associated with cognitive impairment in older populations (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). While TSH did not differ significantly between groups in our univariable comparisons, its selection by LASSO suggests potential incremental information when combined with other markers.\u003c/p\u003e \u003cp\u003eTC is a core driver of atherosclerosis and is linked to ischemic stroke risk(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Dyslipidemia may contribute to chronic hypoperfusion, white matter injury, and altered neuronal membrane stability, thereby affecting cognition (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The observed trend toward higher TC in PSCI warrants further validation.\u003c/p\u003e \u003cp\u003eLYC reflects peripheral immune status. Post-stroke immunosuppression and neuro-immune interactions may influence synaptic plasticity and recovery, potentially affecting cognition (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, LYC is susceptible to infections and medications(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), highlighting the need for dynamic measurements or integrative modeling.\u003c/p\u003e \u003cp\u003eHCY is implicated in endothelial dysfunction, oxidative stress, and neuroinflammation and has been linked to post-stroke cognitive deficits(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Its predictive value may vary by age, sex, and follow-up window(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), which may partly explain the limited external performance in a predominantly male cohort with 3-month assessment.\u003c/p\u003e \u003cp\u003eLimitations include the single-center design, relatively small sample size (especially the validation set), a restricted feature space limited to routine laboratory tests, and cognitive assessment at a single follow-up time point. Future multicenter studies with larger samples, longitudinal cognitive assessments, and multimodal features (neuroimaging and multi-omics biomarkers) are needed. Modeling approaches tailored to binary outcomes and rigorous external validation may further improve early risk stratification.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eTSH, LYC, HCY, and TC showed potential value for early prediction of PSCI at 3 months after ischemic stroke, but the model based solely on routine blood biomarkers demonstrated limited discrimination. More specific biomarkers and multimodal features, validated in larger multicenter cohorts, are required to enable accurate early screening and targeted interventions for PSCI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epost-stroke cognitive impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethyroid-stimulating hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLYC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean corpuscular hemoglobin concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglycated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehomocysteine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eβ-HB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eβ-hydroxybutyrate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT-MoCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTelephone Montreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMontreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eischemic stroke\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol complied with the Declaration of Helsinki, was approved by the Ethics Committee of Shanghai Fourth People’s Hospital (No. 2024056-001), and was registered in the Chinese Clinical Trial Registry (ChiCTR2400082449) on March 29, 2024. Informed consent was obtained from the participants and their legal guardians or appropriate representatives\u0026nbsp;before the commencement of data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82222019), the National Key Research and Development Program of China 2023YFC2505900, and the “Medicine + X” Interdisciplinary Research Program of Tongji University (No. 2025-0674-ZD-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL and QM contributed to the conception and design of the study. Data were acquired by HL, YL, and XH. Data were analyzed and interpreted by YL and HL. The manuscript was drafted by YL. LT, QM, and JC critically revised the manuscript for important intellectual content. Statistical analyses were performed by HL and XZ. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all patients who participated in the study and the staff members at participating hospitals. This work was supported by the National Natural Science Foundation of China (No. 82222019), the National Key Research and Development Program of China 2023YFC2505900, and the “Medicine + X” Interdisciplinary Research Program of Tongji University (No. 2025-0674-ZD-01).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFan J, Li X, Yu X, Liu Z, Jiang Y, Fang Y, et al. Global Burden, Risk Factor Analysis, and Prediction Study of Ischemic Stroke, 1990-2030. Neurology. 2023;101(2):e137\u0026ndash;e50.\u003c/li\u003e\n\u003cli\u003eWang YJ, Li ZX, Gu HQ, Zhai Y, Jiang Y, Zhao XQ, et al. China Stroke Statistics 2019: A Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke Vasc Neurol. 2020;5(3):211\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eFiller J, Georgakis MK, Dichgans M. Risk factors for cognitive impairment and dementia after stroke: a systematic review and meta-analysis. Lancet Healthy Longev. 2024;5(1):e31\u0026ndash;e44.\u003c/li\u003e\n\u003cli\u003eOksala NK, Jokinen H, Melkas S, Oksala A, Pohjasvaara T, Hietanen M, et al. Cognitive impairment predicts poststroke death in long-term follow-up. J Neurol Neurosurg Psychiatry. 2009;80(11):1230\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eTatemichi TK, Paik M, Bagiella E, Desmond DW, Pirro M, Hanzawa LK. Dementia after stroke is a predictor of long-term survival. Stroke. 1994;25(10):1915\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eJia X, Wang Z, Huang F, Su C, Du W, Jiang H, et al. A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatry. 2021;21(1):485.\u003c/li\u003e\n\u003cli\u003eWei X, Liu Y, Li J, Zhu Y, Li W, Zhu Y, et al. MoCA and MMSE for the detection of post-stroke cognitive impairment: a comparative diagnostic test accuracy systematic review and meta‑analysis. J Neurol. 2025;272(6):407.\u003c/li\u003e\n\u003cli\u003eSalvadori E, Cova I, Mele F, Pomati S, Pantoni L. Prediction of post-stroke cognitive impairment by Montreal Cognitive Assessment (MoCA) performances in acute stroke: comparison of three normative datasets. Aging Clin Exp Res. 2022;34(8):1855\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eLim JS, Oh MS, Lee JH, Jung S, Kim C, Jang MU, et al. Prediction of post-stroke dementia using NINDS-CSN 5-minute neuropsychology protocol in acute stroke. Int Psychogeriatr. 2017;29(5):777\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eKandiah N, Chander RJ, Lin X, Ng A, Poh YY, Cheong CY, et al. Cognitive Impairment after Mild Stroke: Development and Validation of the SIGNAL2 Risk Score. J Alzheimers Dis. 2016;49(4):1169\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eChander RJ, Lam BYK, Lin X, Ng AYT, Wong APL, Mok VCT, et al. Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke. Sci Rep. 2017;7(1):12441.\u003c/li\u003e\n\u003cli\u003eKim KY, Shin KY, Chang KA. Potential Biomarkers for Post-Stroke Cognitive Impairment: A Systematic Review and Meta-Analysis. Int J Mol Sci. 2022;23(2).\u003c/li\u003e\n\u003cli\u003eChen Z, Liang X, Zhang C, Wang J, Chen G, Zhang H, et al. Correlation of thyroid dysfunction and cognitive impairments induced by subcortical ischemic vascular disease. Brain Behav. 2016;6(4):e00452.\u003c/li\u003e\n\u003cli\u003eXu L, Xiong Q, Du Y, Huang LW, Yu M. Nonlinear relationship between glycated hemoglobin and cognitive impairment after acute mild ischemic stroke. BMC Neurol. 2023;23(1):116.\u003c/li\u003e\n\u003cli\u003eZhang MS, Liang JH, Yang MJ, Ren YR, Cheng DH, Wu QH, et al. Low Serum Superoxide Dismutase Is Associated With a High Risk of Cognitive Impairment After Mild Acute Ischemic Stroke. Front Aging Neurosci. 2022;14:834114.\u003c/li\u003e\n\u003cli\u003eFischer U, Arnold M, Nedeltchev K, Brekenfeld C, Ballinari P, Remonda L, et al. NIHSS score and arteriographic findings in acute ischemic stroke. Stroke. 2005;36(10):2121\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eMak A, Matouk C, Avery EW, Behland J, Frey D, Madai VI, et al. Similar admission NIHSS may represent larger tissue-at-risk in patients with right-sided versus left-sided large vessel occlusion. J Neurointerv Surg. 2022;14(10):985\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eHong XC, Shu MC, Bao SR, Chen SY, Weng YX, Lin SL. Analysis of factors influencing poor neurological outcomes in patients with acute ischemic stroke. Ann Med. 2025;57(1):2458209.\u003c/li\u003e\n\u003cli\u003eSujanthan S, Rajkumar G, Dainty KN, Barense M, Lanctot KL, Owen AM, et al. Faster Thrombolysis Is Associated With Improved Cognitive Outcomes in Patients With Acute Ischemic Stroke Treated With Alteplase and Tenecteplase: A Substudy of the AcT Trial. Stroke. 2025;56(10):2858\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eChappelle SD, Gigliotti C, Leger GC, Peavy GM, Jacobs DM, Banks SJ, et al. Comparison of the telephone-Montreal Cognitive Assessment (T-MoCA) and Telephone Interview for Cognitive Status (TICS) as screening tests for early Alzheimer\u0026apos;s disease. Alzheimers Dement. 2023;19(10):4599\u0026ndash;608.\u003c/li\u003e\n\u003cli\u003ePendlebury ST, Welch SJ, Cuthbertson FC, Mariz J, Mehta Z, Rothwell PM. Telephone assessment of cognition after transient ischemic attack and stroke: modified telephone interview of cognitive status and telephone Montreal Cognitive Assessment versus face-to-face Montreal Cognitive Assessment and neuropsychological battery. Stroke. 2013;44(1):227\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eZietemann V, Kopczak A, Muller C, Wollenweber FA, Dichgans M. Validation of the Telephone Interview of Cognitive Status and Telephone Montreal Cognitive Assessment Against Detailed Cognitive Testing and Clinical Diagnosis of Mild Cognitive Impairment After Stroke. Stroke. 2017;48(11):2952\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eFaust B, Billesbolle CB, Suomivuori CM, Singh I, Zhang K, Hoppe N, et al. Autoantibody mimicry of hormone action at the thyrotropin receptor. Nature. 2022;609(7928):846\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eKim DK, Choi H, Lee W, Choi H, Hong SB, Jeong JH, et al. Brain hypothyroidism silences the immune response of microglia in Alzheimer\u0026apos;s disease animal model. Sci Adv. 2024;10(11):eadi1863.\u003c/li\u003e\n\u003cli\u003eAdams R, Oh ES, Yasar S, Lyketsos CG, Mammen JS. Endogenous and Exogenous Thyrotoxicosis and Risk of Incident Cognitive Disorders in Older Adults. JAMA Intern Med. 2023;183(12):1324\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eGu X, Li Y, Chen S, Yang X, Liu F, Li Y, et al. Association of Lipids With Ischemic and Hemorrhagic Stroke: A Prospective Cohort Study Among 267 500 Chinese. Stroke. 2019;50(12):3376\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eCiplak S, Adiguzel A, Deniz YZ, Aba M, Ozturk U. The Role of the Low-Density Lipoprotein/High-Density Lipoprotein Cholesterol Ratio as an Atherogenic Risk Factor in Young Adults with Ischemic Stroke: A Case-Control Study. Brain Sci. 2023;13(8).\u003c/li\u003e\n\u003cli\u003eXue LL, Cheng J, Du RL, Luo BY, Chen L, Xiao QX, et al. Bone marrow mesenchymal stem cells alleviate neurological dysfunction by reducing autophagy damage via downregulation of SYNPO2 in neonatal hypoxic-ischemic encephalopathy rats. Cell Death Dis. 2025;16(1):131.\u003c/li\u003e\n\u003cli\u003eValenza M, Birolini G, Cattaneo E. The translational potential of cholesterol-based therapies for neurological disease. Nat Rev Neurol. 2023;19(10):583\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eLiu C, Shi D, Ni X, You S, Wu X, Zhuang S, et al. Correlations among lymphocyte count, white matter hyperintensity and brain atrophy in patients with ischemic stroke. Front Aging Neurosci. 2024;16:1492078.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhong Z, Chen J, Kuo H, Chen X, Wang P, et al. Brain activation patterns in patients with post-stroke cognitive impairment during working memory task: a functional near-infrared spectroscopy study. Front Neurol. 2024;15:1419128.\u003c/li\u003e\n\u003cli\u003eTsalta-Mladenov ME, Andonova SP. Peripheral blood cell count ratios as a predictor of poor functional outcome in patients with acute ischemic stroke. Neurol Res. 2024;46(3):213\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLi Y, Chen X, Zhou R, Xu W, Wang X, Chao W, et al. Correlation Between Cognitive Impairment and Homocysteine and S100B Protein in Patients with Progressive Ischemic Stroke. Neuropsychiatr Dis Treat. 2023;19:209\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eCui L, Lu P, Li S, Pan Y, Wang M, Li Z, et al. Relationship Among Homocysteine, Inflammation and Cognitive Impairment in Patients with Acute Ischemic Stroke and Transient Ischemic Attack. Neuropsychiatr Dis Treat. 2021;17:3607\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eWu JX, Xue J, Zhuang L, Liu CF. Plasma parameters and risk factors of patients with post-stroke cognitive impairment. Ann Palliat Med. 2020;9(1):45\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eZhou S, Chen J, Cheng L, Fan K, Xu M, Ren W, et al. Age-Dependent Association Between Elevated Homocysteine and Cognitive Impairment in a Post-stroke Population: A Prospective Study. Front Nutr. 2021;8:691837.\u003c/li\u003e\n\u003cli\u003eLi R, Weng H, Pan Y, Meng X, Liao X, Wang M, et al. Relationship between homocysteine levels and post-stroke cognitive impairment in female and male population: from a prospective multicenter study. J Transl Int Med. 2021;9(4):264\u0026ndash;72.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ischemic stroke, post-stroke cognitive impairment, blood biomarkers, prediction model, LASSO regression","lastPublishedDoi":"10.21203/rs.3.rs-9196398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9196398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate the predictive value of routine clinical blood biomarkers obtained early after admission for post-stroke cognitive impairment (PSCI) and to develop and validate an early prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eConsecutive patients with first-ever acute ischemic stroke admitted to Shanghai Fourth People’s Hospital between March and December 2024 were enrolled. Ten routine biomarkers measured within 24 h of admission were collected: C-reactive protein (CRP), thyroid-stimulating hormone (TSH), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), lymphocyte count (LYC), mean corpuscular hemoglobin concentration (MCHC), glycated hemoglobin (HbA1c), homocysteine (HCY), β-hydroxybutyrate (β-HB), and total cholesterol (TC). Cognitive function was assessed at 3 months using the Telephone Montreal Cognitive Assessment (T-MoCA); PSCI was defined as T-MoCA \u0026lt;19. Patients were randomly split (7:3) into a training set (n=92) and a validation set (n=39). Predictors were selected using LASSO regression, and a Cox proportional hazards model was built and visualized with a nomogram. Model performance was evaluated by ROC/AUC, C-index, decision curve analysis (DCA), and calibration. The study protocol complied with the Declaration of Helsinki was registered in the Chinese Clinical Trial Registry (ChiCTR2400082449).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 131 patients were included (PSCI, n=65; non-PSCI, n=66). LASSO selected four candidate predictors: TSH, LYC, HCY, and TC. The model achieved an AUC of 0.69 (95% CI, 0.632–0.742) and a C-index of 0.687 in the training set, and an AUC of 0.58 (95% CI, 0.485–0.673) and a C-index of 0.579 in the validation set. DCA suggested a net clinical benefit across threshold probabilities of 0%–75% in the training set and 0%–56.25% in the validation set. The Hosmer–Lemeshow test indicated good calibration (P\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eTSH, LYC, HCY, and TC showed potential value for early PSCI prediction; however, the model based solely on routine blood biomarkers demonstrated limited discrimination. Larger multicenter cohorts and more specific biomarkers and/or multimodal features are warranted to improve early PSCI risk stratification.\u003c/p\u003e","manuscriptTitle":"Early Prediction Model of Post-Stroke Cognitive Impairment Based on Routine Clinical Blood Biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 10:40:17","doi":"10.21203/rs.3.rs-9196398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-20T15:35:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157706112446764003447279793608839885698","date":"2026-04-10T01:33:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T01:19:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T01:10:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T17:43:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T09:20:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-03-28T09:04:39+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":"09f013c3-03f2-44ed-a465-72e60eeffd20","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T10:40:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 10:40:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9196398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9196398","identity":"rs-9196398","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}