Correlation between white matter lesions and cognitive impairment after stroke

Correlation between white matter lesions and cognitive impairment after stroke | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation between white matter lesions and cognitive impairment after stroke Yuanhe Zhang, Xin Li, Yu Jing, Xianwen Zhang, Yufeng Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6898293/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 17 You are reading this latest preprint version Abstract Introduction :To investigate the correlation between white matter lesions (WML) and vascular cognitive impairment in patients with acute ischemic stroke. Materials and Methods :120 patients (79 males, 41 females, average age 65.63±10.12 years) with acute ischemic stroke hospitalized in the Department of Neurology of Mianyang City Central Hospital from February to November 2019 were collected,and collect general information such as patient age, hypertension, and diabetes.All patients underwent a complete cranial MRI, and the white matter lesions were scored on the Fazekas Scale, which were divided into mild WML group (1 - 3 points)(67 cases) and severe WML group (4 - 6 points)(53 cases). The WML volume of the enrolled patients was calculated by semi-automatic measurement software 3Dslicer.All enrolled patients completed the clinical dementia assessment (CDR) scale endpoint scores at 90 days and 1 year after onset; the difference in CDR endpoint scores between the two groups was compared using a chi-square test. Spearman correlation analysis was used to test the correlation between CDR endpoint scores and white matter lesion volume and ischemic stroke site. Results :The probabilities of suspected dementia and dementia in acute ischemic stroke patients with severe WML group 1 year after onset were (58.49%(31/53) and 35.85%(19/53)) were higher than those in mild WML group, respectively (52.24%(35/67), 25.37%(17/67)), while the proportion of patients without cognitive impairment in the mild WML group (22.39%(15/67)) was significantly higher than that of severe WML group (5.66%(3/53)), with statistical differences (P<0.05);WML volume was positively correlated with the CDR endpoint scores at 90 days after onset and 1 year in patients with ischemic stroke (R= 0.20, P =0.027;R= 0.21, P =0.022). Conclusion : The severity of WML in patients with acute cerebral infarction is an important factor affecting cognitive impairment after stroke. White matter disease Small vessel disease Stroke functional prognosis Dementia Figures Figure 1 Introduction In China,ischemic stroke is still the main cause of disability. With the development of treatment methods, the prognosis of patients with acute ischemic stroke has improved compared with the past [ 1 ] . However, about 70% of survivors have cognitive dysfunction [ 2 ] , which seriously increases the burden on society and families. White matter lesions (WML), as an important risk factor affecting stroke prognosis [ 3 ] , is prevalent in patients with acute ischemic stroke. With the development of imaging technology, the detection rate of WML increases with age, and the detection rate among ordinary people over 65 years old reaches 60–90% [ 4 ] .At present, a large number of studies have confirmed that WML is related to cognitive function, but few researchers have paid attention to the impact of acute ischemic stroke combined with WML of different severity on long-term cognitive function in patients.Moreover, the potential pathological process and biomarkers of WML and its development in cognitive impairment are still unclear. Further study is needed on whether the severity of WML in patients with acute ischemic stroke and whether their long-term cognitive function follows a dose-response model.Therefore, this study mainly explored the correlation between different degrees of white matter lesions in patients with acute ischemic stroke and cognitive function 90 days after onset and one year after onset, in order to find biomarkers that affect cognitive function in patients with acute ischemic stroke after one year. Materials and Methods (1) Objects 148 patients diagnosed with ischemic stroke who were hospitalized in the Department of Neurology of Mianyang City Central Hospital from February to November 2019 were collected, and 120 patients were finally enrolled after acceptance and exclusion criteria.All patients had cranial MRI within 7 days after admission; inclusion criteria: (1) The patient or family members signed the informed consent form and registered the information simultaneously;(2) Age 45 to 85 years old;(3) Patients with acute ischemic stroke diagnosed by cranial MRI diffusion-weighted imaging (DWI) within 72 hours of onset;(4) Patients with vessel-related white matter disease;(5) Patients with reserved follow-up phone calls. Exclusion criteria: (1) Other intracranial white matter hypersignal caused by clear causes (such as inflammatory, infection, degenerative, immunological and poisoning diseases);(2) Patients with incomplete general clinical and imaging data;(3) Those who refuse or do not cooperate with follow-up after discharge;(4) The ischemic stroke area is larger than 1/2 of the anterior circulation supply area;(5) Those with cognitive dysfunction before the disease. (2）Method 1.General data collection: The patient's age, gender, Body Mass Index (BMI), smoking history, drinking history, hypertension, diabetes history, NIHSS score on admission and maximum/low systolic blood pressure, maximum/low diastolic blood pressure, and systolic/diastolic blood pressure difference within one week of admission; laboratory indicators include: glycated hemoglobin (HbAlc), total cholesterol (TC), triglyceride (TG) and homocysteine (HCY). 2. Image data collection: All enrolled patients had complete cranial MRI (T1, T2 weighted, DWI and Flair sequences), head and neck vascular examination and dynamic electrocardiogram, and two neurologist attending physicians further classified ischemic stroke into stroke and TOAST classification [5], which are divided into: large artery atherosclerosis (largeearteryatherosclerosis (LAA)), arteriole occlusive (lacunar infarction)(small-arterial occlusion (SAO), cardiogenic embolism (CE), stroke-of-other determined cause (SOC) and stroke-of-undetermined cause (SUC), and the number of lacunar infarcts was calculated. At the same time, the patient's acute ischemic stroke was divided into frontal lobe, temporal parietal lobe, basal ganglia area (including thalamus) and posterior circulation cerebral infarction according to the location. 3. Fazekas score and grouping criteria for white matter lesions: Two neurologists were blinded and divided into two parts: periventricular white matter lesions (PWML) and deep white matter lesions (DWML) according to the Fazekas scale scoring criteria, with each part scoring 0-3 points. The total score of the two was 6 points, including 1 - 3 points in the mild WML group (67 cases) and 4 - 6 points in the severe WML group (53 cases). 4. Volume measurement of white matter lesions: Export each sequence of patient's skull MRI images in DICOM format to a test computer in the cranial MRI (GE1.5T Magnetic Resonance, United States, SignaHDxt) equipment repository, and import the saved images into the semi-automatic measurement software 3Dslicer (http://download.slicer.org/). Through this software, manual segmentation of Region of Interest (ROI) can be realized and 3D reconstruction of segmented areas can be performed. As the only free software for non-invasive analysis of brain white matter fibers, 3Dslicer automatically divides the outline of lesions is clearer and more accurate [6]. In this study, under the supervision of a deputy chief physician of neurology and an attending physician of imaging, the same neurologist selected MRIT2Flair sequence pictures of the patient's head on the equipment, and based on the diagnostic standard of white matter lesions, the areas showing white matter lesions on imaging were thresholded and delineated layer-by-layer, and reconstructed into a 3D model in the software. The software automatically quantitatively calculated the volume of white matter lesions, as shown in Figure 1. 5. Vascular cognitive impairment score: Due to the impact of the epidemic, we chose a clinical dementia rating scale that was easy to follow up Clinical Dementia Rating (CDR), which can effectively reflect the progress of dementia [7], wherein 0 is no dementia, 0.5 is suspicious dementia, 1 is mild dementia, 2 is moderate dementia, and 3 is severe dementia. All selected patients had pre-disease CDR scores of 0. A neurologist followed up the CDR scores of the selected patients for 90 days (± 3 days) and 1 year (± 7 days) after onset (in this study, the CDR scores were assigned to 0 = normal, 0.5 = suspected dementia, 1 - 3 = dementia). (3) Statistical analysis The statistical analysis software is SPSS22.0. Measurement data that match or approximate normal distribution are expressed as mean ± standard deviation (x±s). The comparison of measurement data between groups uses independent sample t-test, the chi-square test is used for both count data, and the correlation test uses Pearson correlation analysis. Comparisons between groups with non-normal distribution were performed using Mann-Whitney non-parametric rank-sum test, expressed as median (25% digits, 75% digits), and the Spearman correlation test was used for correlation analysis. P<0.05 indicates statistical differences. Results 1.Comparison of general data between mild and severe WML groups The severe WML group was older than the mild WML group, and the statistical results were significantly different between the two groups (P<0.05); the highest systolic blood pressure and systolic pressure gradient levels in the severe WML group were higher than those in the mild WML group, and the statistical results were different (P<0.05).The prevalence of hypertension and diabetes in severe WML group was higher than that in mild WML group, and the statistical results were different (P<0.05).there were no differences in HbAlc, BMI, TC, TG, admission NIHSS score, minimum systolic blood pressure, maximum/low diastolic blood pressure, diastolic blood pressure difference, number of lacunar infarcts, HCY, gender, smoking history, drinking history, TOAST classification of cerebral infarction and cerebral infarction site statistical results between the mild and severe WML groups (P>0.05). The statistical results are shown in Tables 1-1 and 1-2. Table 1-1. Comparison of routine clinical data and imaging data between the two groups project Mild WML group（n=67） Severe WML variant group（n=53） test statistics P Age (years,x ± s) 66.94±6.74 70.38±7.08 -2.16 0.033 a maximum systolic blood pressure（mmHg，x ± s） 158.03±15.89 165.17±16.70 -2.39 0.018 a minimum systolic blood pressure（mmHg，x ± s） 118.06±16.37 119.32±14.16 -0.44 0.658 a Maximum diastolic blood pressure （mmHg，x ± s） 93.02±9.02 95.17±12.29 -1.11 0.270 a minimum diastolic blood pressure（mmHg，x ± s） 66.45±8.77 66.15±8.25 0.19 0.850 a systolic pressure gradient（mmHg，x ± s） 39.97±12.59 45.85±16.55 -2.21 0.029 a diastolic pressure difference（mmHg，x ± s） 26.58±8.11 29.02±10.66 -1.43 0.155 a BMI 24.42±3.31 23.91±4.75 0.69 0.490 a Gender Male [case（%）] Female [case（%）] 41（61.19） 26（38.81） 38（71.70） 15（28.30） 1.45 0.228 b Smoking history [case（%）] 22（32.84） 20（37.74） 0.31 0.576 b Drinking history [case（%）] 22（32.84） 18（33.96） 0.02 0.897 b History of hypertension [case（%）] 37（55.22） 40（75.47） 5.23 0.022 b History of diabetes [case（%）] 19（28.36） 25（47.17） 4.51 0.034 b TOAST Classification of Cerebral Infarction LAA[case（%）] SAO[case（%）] CE[case（%）] Other reasons [case（%）] 35(52.24) 20(29.85) 7(10.48) 5(7.46) 33(62.26) 19(35.85) 0(0) 2(3.77) 6.73 0.081 b Cerebral infarction site 2.32 0.509 b frontal lobe [case（%）] 9（13.43） 11（20.75） temporo-parietal [case（%）] 10（14.93） 9（16.98） basal ganglia [case（%）] 30（44.78） 24（45.28） posterior circulation [case（%）] 18（26.87） 9（16.98） lacunar infarcts [case,Median（25%，75%）] 1(1,2) 1(1,2) -0.96 0.335 c Note: "a" represents independent sample t-test,"b" represents chi-square test, and "c" represents Mann-Whitney rank-sum test. Table 1-2. Comparison of routine biochemical indicators between the two groups project Mild WML group（n=67） Severe WML variant group（n=53） test statistics P Admission to NIHSS (points) 3.00（2.00，6.00） 2.00（1.00,4.00） -1.218 0.223 b TC(mmol/L, x ± s) 4.35±0.99 4.21±0.97 0.78 0.439 a TG(mmol/L, x ± s) 1.91±1.07 1.74±0.98 0.86 0.393 a HbAlc(%, x ± s) 6.97±2.16 7.29±1.76 -0.88 0.382 a HCY[umol/L, Median（25%，75%）] 12.40(10.30,16.60) 13.40(10.60,16.70) -0.72 0.472 b Note: “a” represents independent sample t test, and “b” represents Mann-Whitney rank sum test. 2.WML and risk factors regression analysis Binary logistic regression analysis was performed with the two groups of mild and severe cerebral white matter lesions as the dependent variable and age, hypertension, maximum systolic blood pressure, systolic blood pressure difference, and diabetes mellitus as the independent variables.As age increases, the degree of cerebral white matter lesions becomes more severe; and WML is more severe in patients with long-term hypertension and diabetes mellitus (P<0.05). See Table 2 for details. Table 2.Binary Logistic Regression Analysis of Risk Factors Between Mild and Severe WML Groups Project B Wald P OR 95%CI Age 0.08 6.33 0.012 1.08 1.02~1.15 hypertensive disease -1.05 4.82 0.028 0.35 0.14~0.89 diabetes -0.98 4.93 0.026 0.38 0.16~0.89 systolic blood pressure 0.02 0.80 0.372 1.02 0.98~1.05 systolic differential pressure 0.01 0.42 0.516 1.01 0.98~1.05 Note: WML was used as the dependent variable, where the setting method was to set mild WML as 1 and severe WML as 2. Age, hypertension, maximum systolic blood pressure, systolic blood pressure difference and diabetes mellitus were used as independent variables to analyze the correlation between each variable and the two groups. The results of hypertensive disease, maximum systolic blood pressure, systolic blood pressure difference and diabetes mellitus were excluded from the age factor. 3.Comparison of cognitive scales at various time points between the two groups The chi-square test was used to compare the difference in CDR scores (90 d of onset and 1 year) between the two groups, yielding a higher probability of suspected dementia and dementia at 1 year in the severe WML group than in the mild WML group (p<0.05). See Table 3. Table 3.Comparison of stroke function scales at various time points of stroke between the two groups project Mild WML group（n=67） Severe WML variant group（n=53） test statistics P 90d CDR score normalcy [case（%）] suspected dementia [case（%）] dementia [case（%）] 24（35.82） 33（49.25） 10（14.93） 5（7.43） 30（44.78） 18（26.87） 4.43 0.109 One year CDR score normalcy [case（%）] suspected dementia [case（%）] dementia [case（%）] 15（22.39） 35（52.24） 17（25.37） 3（5.66） 31（58.49） 19（35.85） 6.81 0.033 4 .Analysis of WML Volume and Infarct Site Correlating with Cognitive Function at 90 Days and 1 Year Afterward Spearman correlation test was used to analyze the relationship between CDR score at 90 days and 1 year after onset and the volume of WML and the location of acute cerebral infarction. CDR scores of ischemic stroke patients at 90 days and 1 year after onset were positively correlated with the volume of white matter lesions (P<0.05). See Table 4 for details. Table 4. Relationship between 4.WML volume and stroke prognosis and cognitive function Project Volume of WML Acute cerebral infarction area R P R P 90d CDR score 0.20 0.027 -0.49 0.598 One year CDR score 0.21 0.022 -0.04 0.630 Discussion At present, most researches focus on the risk factors related to the recovery of motor function in stroke patients, while less attention is paid to the cognitive impairment in patients with acute ischemic stroke complicated with different degrees of WML.Cognitive impairment after stroke has seriously affected the quality of life of surviving patients and greatly increased the family burden of patients.With the in-depth study of cerebral microvascular disease, some studies have found that WML affects cognitive execution ability and information processing speed by damaging the integrity of white matter [8] .The integrity of white matter is related to the connectivity of the brain network. The more serious the vascular injury, the greater the damage to the connectivity of the brain network and the greater the impact on cognition [9] .According to Fazakes visual classification, the larger volume of WML is related to the decline of the whole brain function or cognitive impairment in specific areas, but the relative evidence effect is weak [10] .The possible mechanism is that WML passes through the injured white matter bundle through the long-distance axon of the brain, resulting in the atrophy of the distal cerebral cortex [11] .WML is particularly related to the decline of information processing speed and executive function [12,13] .In this study, the probability of suspected dementia and dementia in patients with cerebral infarction in moderate and severe WML group was significantly higher than that in mild WML group, while the rate of patients with normal cognition in mild WML group was much higher than that in severe WML group, and the results between groups were statistically significant (P<0.05).The results suggest that the more severe the white matter lesion is, the greater the probability of dementia in stroke patients within one year.After quantifying that volume of white matt lesions,We found that the volume of white matter lesions was positively correlated with CDR endpoint scores of patients with cerebral infarction at 90 days and 1 year (P<0.05), although the correlation is not that high.There is no obvious correlation between the location of acute cerebral infarction and TOAST classification and cognitive prognosis of patients with cerebral infarction, which further shows that the severity of WML is one of the influencing factors of cognitive prognosis of patients with acute cerebral infarction.Through the analysis of the factors affecting the severity of white matter, it is found that controllable factors such as hypertension, diabetes, the highest systolic blood pressure at 7 days after admission and poor systolic blood pressure are related to the severity of WML, so strict control of these factors may play a positive role in the cognitive decline of patients with acute cerebral infarction one year later.In addition, in this study, we have obtained that it is reliable to quantify the white matter volume using 3Dslicer.This study also has some limitations: Firstly,The sample size of the studied population is relatively small.Secondly, this study was conducted during the epidemic in COVID-19, and the emotional rating scale and MRI results of the selected patients after 90 days and 1 year were not collected, so it is impossible to further explore the dynamic relationship between the development of cognitive impairment and WML after 90 days and 1 year.However, according to the analysis results of the correlation between the severity of WML and the cognition of patients with cerebral infarction 90 days and 1 year later, it provides clues for the follow-up study on the correlation between the dynamic changes of WML and cognitive function. Conclusion The severity of WML in patients with acute cerebral infarction is an important factor affecting cognitive impairment after stroke. Declarations Statement of Ethics: Study approval statement: This study has been reviewed by the Ethics Committee of Mianyang City Central Hospital (Approval No: S-2020-035). Consent to participate statement:All participants signed an informed consent form by themselves or their relatives. Conflict of Interest Statement: The authors have no conflicts of interest to declare. Funding Sources ： Fund project: Key project of Sichuan Province Health Commission (20ZD020) Author Contributions: Yuanhe Zhang:Writing – original draft 、Data curation、Formal analysis、Methodology、Project administration XinLi：Formal analysis、Supervision、Validation and Data curation YuJing：Investigation、Data curation and Supervision Xianwen Zhang:Supervision、Validation Yufeng Tang：Supervision、Funding acquisition、Methodology、Validation and Writing – review & editing References Toyoda K , Yoshimura S , Nakai M , et al. Twenty-Year Change in Severity and Outcome of Ischemic and Hemorrhagic Strokes[J]. JAMA Neurology, 2022, 79(1):61-69. DOI: 10.1001/jamaneurol.2021.4346 Koton S , Pike J R , Johansen M , et al. Association of Ischemic Stroke Incidence, Severity, and Recurrence With Dementia in the Atherosclerosis Risk in Communities Cohort Study[J]. JAMA neurology, 2022, 79(3):271-280. DOI: 10.1177/10738584221090208 Georgakis M K , Duering M , Wardlaw J M , et al. WMH and long-term outcomes in ischemic stroke: A systematic review and meta-analysis[J]. Neurology, 2019, 92(12):e1298–e1308. DOI: 10.1212/WNL.0000000000007142 Wardlaw JM , Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications[J]. The Lancet Neurology, 2019, 18(7).684–696. DOI: 10.1016/S1474-4422(19)30079-1 Chinese Medical Association, Journal of Chinese Medical Association, General Practice Branch of Chinese Medical Association, et al. Guidelines for Primary Diagnosis and Treatment of Ischemic Stroke (2021) [J]. Journal of Chinese General Practitioners,2021,20(09):927-946. DOI:10.3760/cma.j.cn114798-20210804-00590. Norton I, Essayed W, Zhang F, et al. SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research [published correction appears in Cancer Res. 2018 May 1;78(9):2445. doi: 10.1158/0008-5472.CAN-18-0560]. Cancer Res. 2017;77(21):e101-e103. doi:10.1158/0008-5472.CAN-17-0332 Mioshi E, Flanagan E, Knopman D. Detecting clinical change with the CDR-FTLD: differences between FTLD and AD dementia. Int J Geriatr Psychiatry. 2017;32(9):977-982. doi:10.1002/gps.4556 Chen Jing,Ge Anyan,Zhou Ying et al. White matter integrity mediates the associations between white matter hyperintensities and cognitive function in patients with silent cerebrovascular diseases.[J] .CNS Neurosci Ther, 2023, 29: 412-428.DOI: 10.1111/cns.14015 DeJong Nathan R,Jansen Jacobus F A,van Boxtel Martin P J et al. Cognitive resilience depends on white matter connectivity: The Maastricht Study.[J] .Alzheimers Dement, 2023, 19: 1164-1174. DOI: 10.1002/alz.12758 Sivakumar Leka,Riaz Parnian,Kate Mahesh et al. White matter hyperintensity volume predicts persistent cognitive impairment in transient ischemic attack and minor stroke.[J] .Int J Stroke, 2017, 12(3): 264-272. DOI: 10.1177/1747493016676612 Ter Telgte Annemieke,van Leijsen Esther M C,Wiegertjes Kim et al. Cerebral small vessel disease: from a focal to a global perspective.[J] .Nat Rev Neurol, 2018, 14(7): 387-398. DOI: 10.1038/s41582-018-0014-y Alber Jessica,Alladi Suvarna,Bae Hee-Joon et al. White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities.[J] .Alzheimers Dement (N Y), 2019, 5(undefined): 107-117. DOI: 10.1016/j.trci.2019.02.001 Vergoossen L , Jansen J , Sloten T , et al. Interplay of White Matter Hyperintensities, Cerebral Networks, and Cognitive Function in an Adult Population: Diffusion-Tensor Imaging in the Maastricht Study[J]. Radiology, 2020, 298(2):202634. DOI: 10.1148/radiol.2021202634 Additional Declarations No competing interests reported. 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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-6898293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481245667,"identity":"390a4485-6b2d-40e4-a928-7ec1e3cbe47a","order_by":0,"name":"Yuanhe Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China·Mianyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuanhe","middleName":"","lastName":"Zhang","suffix":""},{"id":481245668,"identity":"a84542e7-5674-4cab-9480-0d746ed958e4","order_by":1,"name":"Xin Li","email":"","orcid":"","institution":"Sichuan Science City Hospotal","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":481245669,"identity":"077b2967-1bfe-4770-af98-fa120fc32bbc","order_by":2,"name":"Yu Jing","email":"","orcid":"","institution":"University of Electronic Science and Technology of China·Mianyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jing","suffix":""},{"id":481245670,"identity":"f7c66ae6-2f6f-4a9a-a55a-63e3fe5d82ce","order_by":3,"name":"Xianwen Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China·Mianyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianwen","middleName":"","lastName":"Zhang","suffix":""},{"id":481245671,"identity":"2c5fd24a-370d-4b48-8ccd-ec8392d8b4aa","order_by":4,"name":"Yufeng Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDCCA0BcAcT8zMwHHxCv5QwQS7azJRuQpsXgPI+ZAFE6+G6fMZM4UHHHbvNhBjMGhhqbaIJaJM/lALWceZa87TBD2gOGY2m5DYS0GJzhMZP+2HY42ewww3EDxobDxGmROPjvcLJxM2ObBAlaGg7bGTAzsxGnRfIMW7HFgWOHEyQOszEbJBDjF74zzBtvHKg5bM/ff/7jgw81NoS1MDBwgCMwEawygbByEGB/ACLtiVM8CkbBKBgFIxIAACPdQ8qkjjkkAAAAAElFTkSuQmCC","orcid":"","institution":"University of Electronic Science and Technology of China·Mianyang Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-06-15 12:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6898293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6898293/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-03565-5","type":"published","date":"2025-11-25T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86420089,"identity":"d3b21ce7-3301-4ae2-9f14-cdb3f6eabd3d","added_by":"auto","created_at":"2025-07-10 12:41:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":434591,"visible":true,"origin":"","legend":"\u003cp\u003e3Dslicer operating interface and 3D model reconstruction diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6898293/v1/e4771f523d6089adeff7d8bf.png"},{"id":97178402,"identity":"27cd7788-3d25-44d8-ae42-ee985dc65352","added_by":"auto","created_at":"2025-12-01 16:09:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1010533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6898293/v1/3fca624d-acdd-47fc-bce8-9bb571a00a4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation between white matter lesions and cognitive impairment after stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn China,ischemic stroke is still the main cause of disability. With the development of treatment methods, the prognosis of patients with acute ischemic stroke has improved compared with the past\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, about 70% of survivors have cognitive dysfunction \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, which seriously increases the burden on society and families. White matter lesions (WML), as an important risk factor affecting stroke prognosis \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, is prevalent in patients with acute ischemic stroke. With the development of imaging technology, the detection rate of WML increases with age, and the detection rate among ordinary people over 65 years old reaches 60\u0026ndash;90%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.At present, a large number of studies have confirmed that WML is related to cognitive function, but few researchers have paid attention to the impact of acute ischemic stroke combined with WML of different severity on long-term cognitive function in patients.Moreover, the potential pathological process and biomarkers of WML and its development in cognitive impairment are still unclear. Further study is needed on whether the severity of WML in patients with acute ischemic stroke and whether their long-term cognitive function follows a dose-response model.Therefore, this study mainly explored the correlation between different degrees of white matter lesions in patients with acute ischemic stroke and cognitive function 90 days after onset and one year after onset, in order to find biomarkers that affect cognitive function in patients with acute ischemic stroke after one year.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e(1) Objects\u003c/p\u003e\n\u003cp\u003e148 patients diagnosed with ischemic stroke who were hospitalized in the Department of Neurology of Mianyang City Central Hospital from February to November 2019 were collected, and 120 patients were finally enrolled after acceptance and exclusion criteria.All patients had cranial MRI within 7 days after admission; inclusion criteria: (1) The patient or family members signed the informed consent form and registered the information simultaneously;(2) Age 45 to 85 years old;(3) Patients with acute ischemic stroke diagnosed by cranial MRI diffusion-weighted imaging (DWI) within 72 hours of onset;(4) Patients with vessel-related white matter disease;(5) Patients with reserved follow-up phone calls. Exclusion criteria: (1) Other intracranial white matter hypersignal caused by clear causes (such as inflammatory, infection, degenerative, immunological and poisoning diseases);(2) Patients with incomplete general clinical and imaging data;(3) Those who refuse or do not cooperate with follow-up after discharge;(4) The ischemic stroke area is larger than 1/2 of the anterior circulation supply area;(5) Those with cognitive dysfunction before the disease.\u003c/p\u003e\n\u003cp\u003e(2）Method\u003c/p\u003e\n\u003cp\u003e1.General data collection: The patient\u0026apos;s age, gender, Body Mass Index (BMI), smoking history, drinking history, hypertension, diabetes history, NIHSS score on admission and maximum/low systolic blood pressure, maximum/low diastolic blood pressure, and systolic/diastolic blood pressure difference within one week of admission; laboratory indicators include: glycated hemoglobin (HbAlc), total cholesterol (TC), triglyceride (TG) and homocysteine (HCY).\u003c/p\u003e\n\u003cp\u003e2. Image data collection: All enrolled patients had complete cranial MRI (T1, T2 weighted, DWI and Flair sequences), head and neck vascular examination and dynamic electrocardiogram, and two neurologist attending physicians further classified ischemic stroke into stroke and TOAST classification [5], which are divided into: large artery atherosclerosis (largeearteryatherosclerosis (LAA)), arteriole occlusive (lacunar infarction)(small-arterial occlusion (SAO), cardiogenic embolism (CE), stroke-of-other determined cause (SOC) and stroke-of-undetermined cause (SUC), and the number of lacunar infarcts was calculated. At the same time, the patient\u0026apos;s acute ischemic stroke was divided into frontal lobe, temporal parietal lobe, basal ganglia area (including thalamus) and posterior circulation cerebral infarction according to the location.\u003c/p\u003e\n\u003cp\u003e3. Fazekas score and grouping criteria for white matter lesions: Two neurologists were blinded and divided into two parts: periventricular white matter lesions (PWML) and deep white matter lesions (DWML) according to the Fazekas scale scoring criteria, with each part scoring 0-3 points. The total score of the two was 6 points, including 1 - 3 points in the mild WML group (67 cases) and 4 - 6 points in the severe WML group (53 cases).\u003c/p\u003e\n\u003cp\u003e4. Volume measurement of white matter lesions: Export each sequence of patient\u0026apos;s skull MRI images in DICOM format to a test computer in the cranial MRI (GE1.5T Magnetic Resonance, United States, SignaHDxt) equipment repository, and import the saved images into the semi-automatic measurement software 3Dslicer (http://download.slicer.org/). Through this software, manual segmentation of Region of Interest (ROI) can be realized and 3D reconstruction of segmented areas can be performed. As the only free software for non-invasive analysis of brain white matter fibers, 3Dslicer automatically divides the outline of lesions is clearer and more accurate [6]. In this study, under the supervision of a deputy chief physician of neurology and an attending physician of imaging, the same neurologist selected MRIT2Flair sequence pictures of the patient\u0026apos;s head on the equipment, and based on the diagnostic standard of white matter lesions, the areas showing white matter lesions on imaging were thresholded and delineated layer-by-layer, and reconstructed into a 3D model in the software. The software automatically quantitatively calculated the volume of white matter lesions, as shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e5. Vascular cognitive impairment score: Due to the impact of the epidemic, we chose a clinical dementia rating scale that was easy to follow up Clinical Dementia Rating (CDR), which can effectively reflect the progress of dementia [7], wherein 0 is no dementia, 0.5 is suspicious dementia, 1 is mild dementia, 2 is moderate dementia, and 3 is severe dementia. All selected patients had pre-disease CDR scores of 0. A neurologist followed up the CDR scores of the selected patients for 90 days (\u0026plusmn; 3 days) and 1 year (\u0026plusmn; 7 days) after onset (in this study, the CDR scores were assigned to 0 = normal, 0.5 = suspected dementia, 1 - 3 = dementia).\u003c/p\u003e\n\u003cp\u003e(3) Statistical analysis\u003c/p\u003e\n\u003cp\u003eThe statistical analysis software is SPSS22.0. Measurement data that match or approximate normal distribution are expressed as mean \u0026plusmn; standard deviation (x\u0026plusmn;s). The comparison of measurement data between groups uses independent sample t-test, the chi-square test is used for both count data, and the correlation test uses Pearson correlation analysis. Comparisons between groups with non-normal distribution were performed using Mann-Whitney non-parametric rank-sum test, expressed as median (25% digits, 75% digits), and the Spearman correlation test was used for correlation analysis. P\u0026lt;0.05 indicates statistical differences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1.Comparison of general data between mild and severe WML groups\u003c/p\u003e\n\u003cp\u003eThe severe WML group was older than the mild WML group, and the statistical results were significantly different between the two groups (P\u0026lt;0.05); the highest systolic blood pressure and systolic pressure gradient levels in the severe WML group were higher than those in the mild WML group, and the statistical results were different (P\u0026lt;0.05).The prevalence of hypertension and diabetes in severe WML group was higher than that in mild WML group, and the statistical results were different (P\u0026lt;0.05).there were no differences in HbAlc, BMI, TC, TG, admission NIHSS score, minimum systolic blood pressure, maximum/low diastolic blood pressure, diastolic blood pressure difference, number of lacunar infarcts, HCY, gender, smoking history, drinking history, TOAST classification of cerebral infarction and cerebral infarction site statistical results between the mild and severe WML groups (P\u0026gt;0.05). The statistical results are shown in Tables 1-1 and 1-2.\u003c/p\u003e\n\u003cp\u003eTable 1-1. Comparison of routine clinical data and imaging data between the two groups\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"120%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eproject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003eMild WML group（n=67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003eSevere WML variant group（n=53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003etest statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eAge (years,x \u0026plusmn; s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e66.94\u0026plusmn;6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e70.38\u0026plusmn;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.033\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003emaximum systolic blood pressure（mmHg，x \u0026plusmn; s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e158.03\u0026plusmn;15.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e165.17\u0026plusmn;16.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.018\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eminimum systolic blood pressure（mmHg，x \u0026plusmn; s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e118.06\u0026plusmn;16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e119.32\u0026plusmn;14.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.658\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eMaximum diastolic blood pressure\u0026nbsp;（mmHg，x \u0026plusmn; s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e93.02\u0026plusmn;9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e95.17\u0026plusmn;12.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.270\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eminimum diastolic blood pressure（mmHg，x \u0026plusmn; s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e66.45\u0026plusmn;8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e66.15\u0026plusmn;8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.850\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003esystolic pressure gradient（mmHg，x \u0026plusmn; s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e39.97\u0026plusmn;12.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e45.85\u0026plusmn;16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.029\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003ediastolic pressure difference（mmHg，x \u0026plusmn; s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e26.58\u0026plusmn;8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e29.02\u0026plusmn;10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.155\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e24.42\u0026plusmn;3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e23.91\u0026plusmn;4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.490\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMale [case（%）]\u003c/p\u003e\n \u003cp\u003eFemale [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41（61.19）\u003c/p\u003e\n \u003cp\u003e26（38.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38（71.70）\u003c/p\u003e\n \u003cp\u003e15（28.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.228\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eSmoking history [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e22（32.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e20（37.74）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.576\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eDrinking history [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e22（32.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e18（33.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.897\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eHistory of hypertension [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e37（55.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e40（75.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.022\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eHistory of diabetes [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e19（28.36）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e25（47.17）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.034\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eTOAST Classification of Cerebral Infarction LAA[case（%）]\u003c/p\u003e\n \u003cp\u003eSAO[case（%）]\u003c/p\u003e\n \u003cp\u003eCE[case（%）]\u003c/p\u003e\n \u003cp\u003eOther reasons [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35(52.24)\u003c/p\u003e\n \u003cp\u003e20(29.85)\u003c/p\u003e\n \u003cp\u003e7(10.48)\u003c/p\u003e\n \u003cp\u003e5(7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33(62.26)\u003c/p\u003e\n \u003cp\u003e19(35.85)\u003c/p\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003cp\u003e2(3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.081\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eCerebral infarction site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.509\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003efrontal lobe [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e9（13.43）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e11（20.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003etemporo-parietal [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e10（14.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e9（16.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003ebasal ganglia [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e30（44.78）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e24（45.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003eposterior circulation [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e18（26.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e9（16.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0281%;\"\u003e\n \u003cp\u003elacunar infarcts [case,Median（25%，75%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.844%;\"\u003e\n \u003cp\u003e1(1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5081%;\"\u003e\n \u003cp\u003e1(1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2116%;\"\u003e\n \u003cp\u003e0.335\u003csup\u003ec\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u0026quot;a\u0026quot; represents independent sample t-test,\u0026quot;b\u0026quot; represents chi-square test, and \u0026quot;c\u0026quot; represents Mann-Whitney rank-sum test.\u003c/p\u003e\n\u003cp\u003eTable 1-2. Comparison of routine biochemical indicators between the two groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"120%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.343%;\"\u003e\n \u003cp\u003eproject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5054%;\"\u003e\n \u003cp\u003eMild WML group（n=67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4368%;\"\u003e\n \u003cp\u003eSevere WML variant group（n=53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1625%;\"\u003e\n \u003cp\u003etest statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5523%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.343%;\"\u003e\n \u003cp\u003eAdmission to NIHSS (points)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5054%;\"\u003e\n \u003cp\u003e3.00（2.00，6.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4368%;\"\u003e\n \u003cp\u003e2.00（1.00,4.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1625%;\"\u003e\n \u003cp\u003e-1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5523%;\"\u003e\n \u003cp\u003e0.223\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.343%;\"\u003e\n \u003cp\u003eTC(mmol/L, x \u0026plusmn; s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5054%;\"\u003e\n \u003cp\u003e4.35\u0026plusmn;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4368%;\"\u003e\n \u003cp\u003e4.21\u0026plusmn;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1625%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5523%;\"\u003e\n \u003cp\u003e0.439\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.343%;\"\u003e\n \u003cp\u003eTG(mmol/L, x \u0026plusmn; s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5054%;\"\u003e\n \u003cp\u003e1.91\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4368%;\"\u003e\n \u003cp\u003e1.74\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1625%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5523%;\"\u003e\n \u003cp\u003e0.393\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.343%;\"\u003e\n \u003cp\u003eHbAlc(%, x \u0026plusmn; s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5054%;\"\u003e\n \u003cp\u003e6.97\u0026plusmn;2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4368%;\"\u003e\n \u003cp\u003e7.29\u0026plusmn;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1625%;\"\u003e\n \u003cp\u003e-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5523%;\"\u003e\n \u003cp\u003e0.382\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.343%;\"\u003e\n \u003cp\u003eHCY[umol/L, Median（25%，75%）]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5054%;\"\u003e\n \u003cp\u003e12.40(10.30,16.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4368%;\"\u003e\n \u003cp\u003e13.40(10.60,16.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1625%;\"\u003e\n \u003cp\u003e-0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5523%;\"\u003e\n \u003cp\u003e0.472\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u0026ldquo;a\u0026rdquo; represents independent sample t test, and \u0026ldquo;b\u0026rdquo; represents Mann-Whitney rank sum test.\u003c/p\u003e\n\u003cp\u003e2.WML and risk factors regression analysis\u003c/p\u003e\n\u003cp\u003eBinary logistic regression analysis was performed with the two groups of mild and severe cerebral white matter lesions as the dependent variable and age, hypertension, maximum systolic blood pressure, systolic blood pressure difference, and diabetes mellitus as the independent variables.As age increases, the degree of cerebral white matter lesions becomes more severe; and WML is more severe in patients with long-term hypertension and diabetes mellitus (P\u0026lt;0.05). See Table 2 for details.\u003c/p\u003e\n\u003cp\u003eTable 2.Binary Logistic Regression Analysis of Risk Factors Between Mild and Severe WML Groups\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"120%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6606%;\"\u003e\n \u003cp\u003eProject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6354%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5235%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4116%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6606%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6354%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5235%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4116%;\"\u003e\n \u003cp\u003e1.02~1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6606%;\"\u003e\n \u003cp\u003ehypertensive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6354%;\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5235%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4116%;\"\u003e\n \u003cp\u003e0.14~0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6606%;\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6354%;\"\u003e\n \u003cp\u003e-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5235%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4116%;\"\u003e\n \u003cp\u003e0.16~0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6606%;\"\u003e\n \u003cp\u003esystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6354%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5235%;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4116%;\"\u003e\n \u003cp\u003e0.98~1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6606%;\"\u003e\n \u003cp\u003esystolic differential pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6354%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8845%;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5235%;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4116%;\"\u003e\n \u003cp\u003e0.98~1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: WML was used as the dependent variable, where the setting method was to set mild WML as 1 and severe WML as 2. Age, hypertension, maximum systolic blood pressure, systolic blood pressure difference and diabetes mellitus were used as independent variables to analyze the correlation between each variable and the two groups. The results of hypertensive disease, maximum systolic blood pressure, systolic blood pressure difference and diabetes mellitus were excluded from the age factor.\u003c/p\u003e\n\u003cp\u003e3.Comparison of cognitive scales at various time points between the two groups\u003c/p\u003e\n\u003cp\u003eThe chi-square test was used to compare the difference in CDR scores (90 d of onset and 1 year) between the two groups, yielding a higher probability of suspected dementia and dementia at 1 year in the severe WML group than in the mild WML group (p\u0026lt;0.05). See Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3.Comparison of stroke function scales at various time points of stroke between the two groups\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"120%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7631%;\"\u003e\n \u003cp\u003eproject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3309%;\"\u003e\n \u003cp\u003eMild WML group（n=67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8038%;\"\u003e\n \u003cp\u003eSevere WML variant group（n=53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8267%;\"\u003e\n \u003cp\u003etest statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2656%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7631%;\"\u003e\n \u003cp\u003e90d CDR score\u003c/p\u003e\n \u003cp\u003enormalcy [case（%）]\u003c/p\u003e\n \u003cp\u003esuspected dementia [case（%）]\u003c/p\u003e\n \u003cp\u003edementia [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24（35.82）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;33（49.25）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;10（14.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8038%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5（7.43）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;30（44.78）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;18（26.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8267%;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2656%;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7631%;\"\u003e\n \u003cp\u003eOne year\u0026nbsp;CDR score\u003c/p\u003e\n \u003cp\u003enormalcy [case（%）]\u003c/p\u003e\n \u003cp\u003esuspected dementia [case（%）]\u003c/p\u003e\n \u003cp\u003edementia [case（%）]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3309%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;15（22.39）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;35（52.24）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;17（25.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8038%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3（5.66）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;31（58.49）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;19（35.85）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8267%;\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2656%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e4 .Analysis of WML Volume and Infarct Site Correlating with Cognitive Function at 90 Days and 1 Year Afterward\u003c/p\u003e\n\u003cp\u003eSpearman correlation test was used to analyze the relationship between CDR score at 90 days and 1 year after onset and the volume of WML and the location of acute cerebral infarction.\u0026nbsp;CDR scores of ischemic stroke patients at 90 days and 1 year after onset were positively correlated with the volume of white matter lesions (P\u0026lt;0.05).\u0026nbsp;See Table 4 for details.\u003c/p\u003e\n\u003cp\u003eTable 4. Relationship between 4.WML volume and stroke prognosis and cognitive function\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"120%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eProject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eVolume of WML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eAcute cerebral infarction area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e90d CDR score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOne year CDR score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present, most researches focus on the risk factors related to the recovery of motor function in stroke patients, while less attention is paid to the cognitive impairment in patients with acute ischemic stroke complicated with different degrees of WML.Cognitive impairment after stroke has seriously affected the quality of life of surviving patients and greatly increased the family burden of patients.With the in-depth study of cerebral microvascular disease, some studies have found that WML affects cognitive execution ability and information processing speed by damaging the integrity of white matter\u003csup\u003e\u0026nbsp;[8]\u003c/sup\u003e.The integrity of white matter is related to the connectivity of the brain network. The more serious the vascular injury, the greater the damage to the connectivity of the brain network and the greater the impact on cognition \u003csup\u003e[9]\u003c/sup\u003e.According to Fazakes visual classification, the larger volume of WML is related to the decline of the whole brain function or cognitive impairment in specific areas, but the relative evidence effect is weak\u003csup\u003e\u0026nbsp;[10]\u003c/sup\u003e.The possible mechanism is that WML passes through the injured white matter bundle through the long-distance axon of the brain, resulting in the atrophy of the distal cerebral cortex \u003csup\u003e[11]\u003c/sup\u003e.WML is particularly related to the decline of information processing speed and executive function\u003csup\u003e\u0026nbsp;[12,13]\u003c/sup\u003e.In this study, the probability of suspected dementia and dementia in patients with cerebral infarction in moderate and severe WML group was significantly higher than that in mild WML group, while the rate of patients with normal cognition in mild WML group was much higher than that in severe WML group, and the results between groups were statistically significant (P\u0026lt;0.05).The results suggest that the more severe the white matter lesion is, the greater the probability of dementia in stroke patients within one year.After quantifying that volume of white matt lesions,We found that the volume of white matter lesions was positively correlated with CDR endpoint scores of patients with cerebral infarction at 90 days and 1 year (P\u0026lt;0.05), although the correlation is not that high.There is no obvious correlation between the location of acute cerebral infarction and TOAST classification and cognitive prognosis of patients with cerebral infarction, which further shows that the severity of WML is one of the influencing factors of cognitive prognosis of patients with acute cerebral infarction.Through the analysis of the factors affecting the severity of white matter, it is found that controllable factors such as hypertension, diabetes, the highest systolic blood pressure at 7 days after admission and poor systolic blood pressure are related to the severity of WML, so strict control of these factors may play a positive role in the cognitive decline of patients with acute cerebral infarction one year later.In addition, in this study, we have obtained that it is reliable to quantify the white matter volume using 3Dslicer.This study also has some limitations: Firstly,The sample size of the studied population is relatively small.Secondly, this study was conducted during the epidemic in COVID-19, and the emotional rating scale and MRI results of the selected patients after 90 days and 1 year were not collected, so it is impossible to further explore the dynamic relationship between the development of cognitive impairment and WML after 90 days and 1 year.However, according to the analysis results of the correlation between the severity of WML and the cognition of patients with cerebral infarction 90 days and 1 year later, it provides clues for the follow-up study on the correlation between the dynamic changes of WML and cognitive function.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe severity of WML in patients with acute cerebral infarction is an important factor affecting cognitive impairment after stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement of Ethics:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy approval statement: This study has been reviewed by the Ethics Committee of Mianyang City Central Hospital (Approval No: S-2020-035).\u003c/p\u003e\n\u003cp\u003eConsent to participate statement:All participants signed an informed consent form by themselves or their relatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u003c/strong\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eFund project: Key project of Sichuan Province Health Commission (20ZD020)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuanhe Zhang:Writing \u0026ndash; original draft\u0026nbsp;、Data curation、Formal analysis、Methodology、Project administration\u003c/p\u003e\n\u003cp\u003eXinLi：Formal analysis、Supervision、Validation and Data curation\u003c/p\u003e\n\u003cp\u003eYuJing：Investigation、Data curation and Supervision\u003c/p\u003e\n\u003cp\u003eXianwen Zhang:Supervision、Validation\u003c/p\u003e\n\u003cp\u003eYufeng Tang：Supervision、Funding acquisition、Methodology、Validation and Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eToyoda K , Yoshimura S , Nakai M , et al. Twenty-Year Change in Severity and Outcome of Ischemic and Hemorrhagic Strokes[J]. JAMA Neurology, 2022, 79(1):61-69. DOI: 10.1001/jamaneurol.2021.4346\u003c/li\u003e\n\u003cli\u003eKoton S , Pike J R , Johansen M , et al. Association of Ischemic Stroke Incidence, Severity, and Recurrence With Dementia in the Atherosclerosis Risk in Communities Cohort Study[J]. JAMA neurology, 2022, 79(3):271-280. DOI: 10.1177/10738584221090208\u003c/li\u003e\n\u003cli\u003eGeorgakis M K , Duering M , Wardlaw J M , et al. WMH and long-term outcomes in ischemic stroke: A systematic review and meta-analysis[J]. Neurology, 2019, 92(12):e1298\u0026ndash;e1308. DOI: 10.1212/WNL.0000000000007142\u003c/li\u003e\n\u003cli\u003eWardlaw JM , Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications[J]. The Lancet Neurology, 2019, 18(7).684\u0026ndash;696. DOI: 10.1016/S1474-4422(19)30079-1\u003c/li\u003e\n\u003cli\u003eChinese Medical Association, Journal of Chinese Medical Association, General Practice Branch of Chinese Medical Association, et al. Guidelines for Primary Diagnosis and Treatment of Ischemic Stroke (2021) [J]. Journal of Chinese General Practitioners,2021,20(09):927-946. DOI:10.3760/cma.j.cn114798-20210804-00590.\u003c/li\u003e\n\u003cli\u003eNorton I, Essayed W, Zhang F, et al. SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research [published correction appears in Cancer Res. 2018 May 1;78(9):2445. doi: 10.1158/0008-5472.CAN-18-0560]. Cancer Res. 2017;77(21):e101-e103. doi:10.1158/0008-5472.CAN-17-0332\u003c/li\u003e\n\u003cli\u003eMioshi E, Flanagan E, Knopman D. Detecting clinical change with the CDR-FTLD: differences between FTLD and AD dementia. Int J Geriatr Psychiatry. 2017;32(9):977-982. doi:10.1002/gps.4556\u003c/li\u003e\n\u003cli\u003eChen Jing,Ge Anyan,Zhou Ying et al. White matter integrity mediates the associations between white matter hyperintensities and cognitive function in patients with silent cerebrovascular diseases.[J] .CNS Neurosci Ther, 2023, 29: 412-428.DOI: 10.1111/cns.14015\u003c/li\u003e\n\u003cli\u003eDeJong Nathan R,Jansen Jacobus F A,van Boxtel Martin P J et al. Cognitive resilience depends on white matter connectivity: The Maastricht Study.[J] .Alzheimers Dement, 2023, 19: 1164-1174. DOI: 10.1002/alz.12758\u003c/li\u003e\n\u003cli\u003eSivakumar Leka,Riaz Parnian,Kate Mahesh et al. White matter hyperintensity volume predicts persistent cognitive impairment in transient ischemic attack and minor stroke.[J] .Int J Stroke, 2017, 12(3): 264-272. DOI: 10.1177/1747493016676612\u003c/li\u003e\n\u003cli\u003eTer Telgte Annemieke,van Leijsen Esther M C,Wiegertjes Kim et al. Cerebral small vessel disease: from a focal to a global perspective.[J] .Nat Rev Neurol, 2018, 14(7): 387-398. DOI: 10.1038/s41582-018-0014-y\u003c/li\u003e\n\u003cli\u003eAlber Jessica,Alladi Suvarna,Bae Hee-Joon et al. White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities.[J] .Alzheimers Dement (N Y), 2019, 5(undefined): 107-117. DOI: 10.1016/j.trci.2019.02.001\u003c/li\u003e\n\u003cli\u003eVergoossen L , Jansen J , Sloten T , et al. Interplay of White Matter Hyperintensities, Cerebral Networks, and Cognitive Function in an Adult Population: Diffusion-Tensor Imaging in the Maastricht Study[J]. Radiology, 2020, 298(2):202634. DOI: 10.1148/radiol.2021202634\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"White matter disease, Small vessel disease, Stroke, functional prognosis, Dementia","lastPublishedDoi":"10.21203/rs.3.rs-6898293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6898293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e:To investigate the correlation between white matter lesions (WML) and vascular cognitive impairment in patients with acute ischemic stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e:120 patients (79 males, 41 females, average age 65.63±10.12 years) with acute ischemic stroke hospitalized in the Department of Neurology of Mianyang City Central Hospital from February to November 2019 were collected,and collect general information such as patient age, hypertension, and diabetes.All patients underwent a complete cranial MRI, and the white matter lesions were scored on the Fazekas Scale, which were divided into mild WML group (1 - 3 points)(67 cases) and severe WML group (4 - 6 points)(53 cases). The WML volume of the enrolled patients was calculated by semi-automatic measurement software 3Dslicer.All enrolled patients completed the clinical dementia assessment (CDR) scale endpoint scores at 90 days and 1 year after onset; the difference in CDR endpoint scores between the two groups was compared using a chi-square test. Spearman correlation analysis was used to test the correlation between CDR endpoint scores and white matter lesion volume and ischemic stroke site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:The probabilities of suspected dementia and dementia in acute ischemic stroke patients with severe WML group 1 year after onset were (58.49%(31/53) and 35.85%(19/53)) were higher than those in mild WML group, respectively (52.24%(35/67), 25.37%(17/67)), while the proportion of patients without cognitive impairment in the mild WML group (22.39%(15/67)) was significantly higher than that of severe WML group (5.66%(3/53)), with statistical differences (P\u0026lt;0.05);WML volume was positively correlated with the CDR endpoint scores at 90 days after onset and 1 year in patients with ischemic stroke (R= 0.20, P =0.027;R= 0.21, P =0.022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eThe severity of WML in patients with acute cerebral infarction is an important factor affecting cognitive impairment after stroke.\u003c/p\u003e","manuscriptTitle":"Correlation between white matter lesions and cognitive impairment after stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 12:41:40","doi":"10.21203/rs.3.rs-6898293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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