Development and Validation of a Nomogram for Predicting Cognitive Impairment in Patients with Leukoaraiosis

Development and Validation of a Nomogram for Predicting Cognitive Impairment in Patients with Leukoaraiosis | 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 Development and Validation of a Nomogram for Predicting Cognitive Impairment in Patients with Leukoaraiosis Guoxin Zhang, Lijun Meng, Chunfang Huang, Yi Lu, Liping Yin, Wenwen Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7100701/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2025 Read the published version in BMC Neurology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Leukoaraiosis (LA) is a common cerebral small vessel disease in elderly populations that frequently leads to cognitive impairment and may progress to vascular dementia. Early identification of cognitive dysfunction risk remains challenging due to the subtle onset and lack of specific biomarkers. Objective To identify key risk factors for cognitive impairment in LA patients and develop a logistic regression-based prediction model to facilitate early clinical identification and intervention. Methods This retrospective study included 390 LA patients admitted to the Department of Neurology between June 2020 and April 2023. Patients were classified into cognitive impairment (CI) and non-cognitive impairment (NCI) groups based on Montreal Cognitive Assessment (MoCA) scores.Data collected included demographics, medical history, biochemical markers, and neuroimaging features. The dataset was randomly split 7:3 into training (n = 273) and validation (n = 117) sets. Univariate analysis identified significant variables (p < 0.05), which were then incorporated into multivariate logistic regression analysis. A nomogram was constructed based on the final model, and performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots for both training and validation sets. Results In the training set of 273 patients, 137 had cognitive impairment and 136 did not. Univariate analysis revealed that age, Fazekas score, intracranial arterial stenosis assessment (IASA), serum creatinine, and total bilirubin were significantly associated with cognitive impairment ( p < 0.05). Multivariate logistic regression identified age (OR = 1.17, 95%CI: 1.11–1.24), IASA (OR = 2.52, 95%CI: 1.64–3.68), and Fazekas score (OR = 2.58, 95%CI: 1.74–3.60) as independent risk factors. The logistic regression model demonstrated excellent discrimination with AUC values of 0.873 for both training and validation sets. Calibration curves showed good agreement between predicted and observed probabilities, confirming model reliability. Conclusions Age, intracranial arterial stenosis assessment, and Fazekas score are independent risk factors for cognitive impairment in LA patients. The logistic regression model with nomogram provides a clinically practical tool for early identification and risk stratification of high-risk patients, enabling timely intervention to improve outcomes. Leukoaraiosis Cognitive impairment Logistic regression Prediction model Nomogram Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Leukoaraiosis (LA), synonymous with white matter hyperintensities (WMH), represents a cardinal manifestation of cerebral small vessel disease that has emerged as one of the most significant neuroimaging markers of brain aging and cognitive vulnerability in elderly populations [ 1 ] . This pathological entity, characterized by bilateral periventricular and deep white matter lesions appearing as hyperintense signals on T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging sequences, demonstrates exponential prevalence increases with advancing age, affecting nearly all individuals beyond the eighth decade [ 2 ] . The clinical significance of LA transcends its radiological appearance, as mounting evidence implicates these white matter changes as harbingers of cognitive decline, functional disability, and progression toward vascular dementia [ 3 ] . The pathophysiological underpinnings of LA encompass a complex cascade involving chronic cerebral hypoperfusion, blood-brain barrier disruption, endothelial dysfunction, oxidative stress, and neuroinflammatory processes. These interconnected mechanisms culminate in demyelination, axonal loss, and disruption of critical white matter tracts that serve as neural highways connecting distributed brain networks essential for higher-order cognitive functions [ 4 ] . Consequently, LA-associated cognitive impairment predominantly manifests as deficits in executive function, processing speed, attention, and working memory—domains particularly vulnerable to white matter integrity loss. Epidemiological studies consistently demonstrate that cognitive dysfunction attributable to LA accounts for approximately 70% of vascular dementia cases, positioning this condition as a major contributor to the global burden of cognitive disorders [ 5 ] . Traditional statistical approaches to LA risk prediction have typically employed univariate analyses or simple multivariable models that may inadequately capture complex, potentially non-linear relationships among risk factors [ 6 ] . Logistic regression modeling, when appropriately implemented with rigorous variable selection and validation procedures, offers a robust framework for developing clinically applicable prediction tools [ 7 ] . The integration of logistic regression models with nomogram visualization represents a particularly promising approach, as nomograms translate complex statistical relationships into intuitive graphical interfaces that facilitate clinical decision-making and risk communication [ 8 ] . Nomograms have gained widespread acceptance across medical specialties due to their ability to provide individualized risk estimates while maintaining clinical interpretability [ 9 ] . In neurology, nomogram-based prediction models have demonstrated utility in predicting outcomes following stroke, traumatic brain injury, and neurodegenerative diseases. However, despite the clinical importance of LA-related cognitive impairment, comprehensive prediction models specifically designed for this population remain notably absent from the literature [ 10 ] . The development of an accurate, validated prediction model for cognitive impairment in LA patients holds substantial clinical implications. Such a tool could enable identification of high-risk individuals before symptom onset, facilitate risk stratification for clinical trials, guide monitoring intensity, and optimize resource allocation in healthcare systems increasingly strained by cognitive disorder burden [ 11 ] . Moreover, evidence-based prediction models could serve as foundations for developing personalized prevention strategies and therapeutic interventions tailored to individual risk profiles. Given the substantial clinical need for improved risk stratification in LA patients and the current gap in evidence-based prediction tools, this study aimed to develop and internally validate a comprehensive nomogram for predicting cognitive impairment in patients with leukoaraiosis [ 12 , 13 ] . Specifically, our objectives were: first, to systematically identify and quantify independent associations between demographic, clinical, biochemical, and neuroimaging variables and cognitive impairment in a well-characterized LA cohort; second, to develop a logistic regression-based prediction model and translate it into a clinically practical nomogram; and third, to validate the model's discriminative ability and calibration using training and validation datasets with comprehensive performance assessment including receiver operating characteristic curve analysis and calibration plots. We hypothesized that a multi-dimensional approach integrating readily available clinical variables would yield a robust prediction model with excellent discriminative performance and clinical utility for early identification of LA patients at high risk for cognitive decline, ultimately providing clinicians with an evidence-based tool to enhance decision-making and improve patient outcomes. 2. Methods 2.1 Study Design and Participants This retrospective cohort study was conducted at Qixia District Hospital, Nanjing, China, between June 2020 and April 2023. The study protocol was approved by the institutional review board (approval number: QXH-2020-015), and written informed consent was obtained from all participants or their legal guardians. Of 456 screened patients, 390 met eligibility criteria and were randomly allocated to training (n = 273) and validation (n = 117) cohorts using computer-generated randomization in a 7:3 ratio. Consecutive patients with radiologically confirmed leukoaraiosis were screened for eligibility. Inclusion criteria comprised: (1) bilateral white matter hyperintensities on fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging; (2) age 50–85 years; (3) availability of complete clinical, laboratory, and neuroimaging data. Exclusion criteria included: (1) history of stroke with National Institutes of Health Stroke Scale score ≥ 5; (2) known genetic leukoencephalopathies; (3) white matter lesions secondary to inflammatory or infectious etiologies; (4) concurrent neurodegenerative disorders; (5) severe systemic comorbidities precluding assessment; (6) incomplete data exceeding 20% of study variables. 2.2 Data Collection and Variable Definitions 2.2.1 Clinical Variables Demographic and clinical data were extracted from electronic medical records using standardized case report forms. Variables included age, sex, years of education, body mass index, history of hypertension (systolic blood pressure ≥ 140 mmHg or current antihypertensive therapy), type 2 diabetes mellitus (according to American Diabetes Association criteria or glucose-lowering medication use), smoking status, and alcohol consumption. 2.2.2 Laboratory Measurements Venous blood samples were collected after 12-hour overnight fasting within 24 hours of admission. Biochemical analyses were performed using certified laboratory protocols and included: serum creatinine, total bilirubin, lipid profile (total cholesterol, triglycerides, low-density lipoprotein), high-sensitivity C-reactive protein, fasting glucose, thyroid-stimulating hormone, lipoprotein-associated phospholipase A2, homocysteine, uric acid, and urinary albumin-to-creatinine ratio. 3.2.3 Cognitive Assessment Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) administered by certified neurologists. Educational bias was corrected by adding 1 point for participants with ≤ 12 years of education, as recommended by the original MoCA guidelines. Cognitive impairment was defined as adjusted MoCA score ≤ 26 points, consistent with established diagnostic criteria for mild cognitive impairment in Chinese populations. 2.2.4 Neuroimaging Protocol Brain magnetic resonance imaging was performed using a 1.5-Tesla scanner (Neusoft NSM-S15P) with standardized sequences including T1-weighted, T2-weighted, and FLAIR images. White matter hyperintensity burden was quantified using the Fazekas scale [ 14 ] , rating periventricular (0–3) and deep white matter (0–3) lesions separately, with total scores ranging 0–6. Intracranial arterial stenosis was assessed using time-of-flight magnetic resonance angiography and graded as: Grade 1 (no stenosis), Grade 2 (< 20% single-vessel stenosis), Grade 3 (20–40% stenosis), or Grade 4 (≥ 40% stenosis in ≥ 2 vessels). All imaging assessments were performed by two independent neuroradiologists blinded to clinical data, with inter-rater reliability κ > 0.85. 2.3 Statistical Analysis Continuous variables were assessed for normality and expressed as mean ± standard deviation or median (interquartile range) as appropriate, while categorical variables were presented as frequencies and percentages, with baseline characteristics compared between training and validation cohorts using appropriate statistical tests. Missing data (< 20% for any variable) were handled using multiple imputation with random forest algorithms, followed by univariate analysis in the training cohort to identify variables associated with cognitive impairment (p < 0.05), which were then entered into multivariable logistic regression using forward stepwise selection with model assumptions verified and multicollinearity assessed. Model performance was evaluated using area under the receiver operating characteristic curve for discrimination and calibration plots with Hosmer-Lemeshow test for calibration, with internal validation performed using bootstrap resampling (1,000 iterations) to obtain bias-corrected estimates, and a nomogram constructed based on the final model for clinical application. All analyses were conducted using R version 4.2.0 and SPSS version 26.0, with statistical significance set at p < 0.05, and the study adhered to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Table 1 Baseline Characteristics of Training and Validation Sets Variable Total Dataset (n = 390) Training Set (n = 273) Validation Set (n = 117) p-value Age, median (IQR) 74 (69, 80) 75 (70, 80) 74 (68, 79) 0.302 Hypertension 0.845 No 111 (28) 79 (29) 32 (27) Yes 279 (72) 194 (71) 85 (73) Type 2 Diabetes 0.267 No 264 (68) 190 (70) 74 (63) Yes 126 (32) 83 (30) 43 (37) Smoking History 0.504 No 269 (69) 185 (68) 84 (72) Yes 121 (31) 88 (32) 33 (28) Alcohol History 0.549 No 309 (79) 219 (80) 90 (77) Yes 81 (21) 54 (20) 27 (23) Triglycerides (mmol/L) 1.54 (1.03, 2.92) 1.54 (1.02, 2.8) 1.55 (1.05, 3.24) 0.816 Total Cholesterol (mmol/L) 3.82 (2.73, 4.85) 3.85 (2.82, 4.84) 3.78 (2.59, 4.86) 0.574 Low-Density Lipoprotein (mmol/L) 2.07 (1.48, 2.79) 2.12 (1.46, 2.82) 2.05 (1.53, 2.57) 0.231 Uric Acid (µmol/L) 309 (238, 375) 311 (245, 375) 297 (233, 375) 0.265 Lp-PLA2 (ng/mL) 180.67 (146.69, 223.32) 180.67 (146.65, 220.19) 186.03 (146.8, 226.61) 0.648 Fasting Glucose (mmol/L) 5.67 (5.06, 6.61) 5.61 (5.04, 6.5) 5.77 (5.17, 6.87) 0.199 Albumin-to-Creatinine Ratio 1.8 (1.2, 3.1) 1.8 (1.3, 3.3) 1.8 (1.2, 2.7) 0.063 TSH (mIU/L) 2.32 (1.58, 3.79) 2.41 (1.61, 4.03) 2.22 (1.54, 3.21) 0.189 Creatinine (µmol/L) 70 (60, 84) 69 (60, 83) 73 (62, 85.1) 0.273 High-sensitivity CRP (mg/L) 1.5 (0.71, 5.4) 1.61 (0.8, 6.9) 1.2 (0.67, 4) 0.104 Total Bilirubin (µmol/L) 12.6 (9.7, 17.1) 12.5 (9.7, 17.1) 12.8 (9.3, 17.5) 0.379 Urea (mmol/L) 5.3 (4.5, 6.47) 5.4 (4.5, 6.5) 5.3 (4.3, 6.35) 0.695 Homocysteine (µmol/L) 12.92 (11.23, 16.45) 12.92 (11.3, 16.5) 12.92 (10.45, 16.3) 0.355 Intracranial Arterial Stenosis Assessment 0.52 Grade 1 187 (48) 127 (47) 60 (51) Grade 2 132 (34) 93 (34) 39 (33) Grade 3 52 (13) 37 (14) 15 (13) Grade 4 19 (5) 16 (6) 3 (3) Fazekas Score 0.996 Grade 1 141 (36) 98 (36) 43 (37) Grade 2 138 (35) 96 (35) 42 (36) Grade 3 67 (17) 48 (18) 19 (16) Grade 4 25 (6) 18 (7) 7 (6) Grade 5 19 (5) 13 (5) 6 (5) Cognitive Impairment 0.309 No 203 (52) 137 (50) 66 (56) Yes 187 (48) 136 (50) 51 (44) Table 2 Univariate Analysis of Factors Associated with Cognitive Impairment in Training Set Variable Cognitive Impairment (n = 137) Non-Cognitive Impairment (n = 136) p-value Demographics Age, median (IQR) 79.0 (78.0, 80.0) 71.0 (70.0, 73.0) < 0.001 Male sex, n/total 78/59 70/66 0.433 Hypertension 46/91 103/33 0.118 Type 2 Diabetes 97/40 93/43 0.762 Smoking History 96/41 47/89 0.491 Alcohol History 112/25 107/29 0.627 Triglycerides (mmol/L) 1.67 (1.46, 1.86) 1.45 (1.21, 1.69) 0.1 Total Cholesterol (mmol/L) 3.83 (3.6, 4.16) 3.86 (3.32, 4.2) 0.797 Low-Density Lipoprotein (mmol/L) 2.12 (1.92, 2.3) 2.12 (1.84, 2.38) 0.899 Uric Acid (µmol/L) 307.0 (287.0, 320.62) 317.0 (296.98, 332.0) 0.187 Lp-PLA2 (ng/mL) 117.83 (161.05, 185.49) 182.83 (171.13, 203.12) 0.159 Fasting Glucose (mmol/L) 5.7 (5.44, 5.96) 5.51 (5.23, 5.78) 0.155 Albumin-to-Creatinine Ratio 1.8 (1.72, 2.23) 1.76 (1.62, 2.24) 0.294 TSH (mIU/L) 2.42 (2.21, 2.78) 2.41 (2.13, 2.88) 0.817 Creatinine (µmol/L) 65.0 (62.0, 69.0) 74.5 (70.0, 80.0) 0.001 High-sensitivity CRP (mg/L) 1.53 (1.2, 1.82) 1.85 (1.25, 2.4) 0.501 Total Bilirubin (µmol/L) 11.8 (11.1, 12.8) 13.35 (12.4, 15.2) 0.028 Urea (mmol/L) 5.3 (5.0, 5.4) 5.46 (5.1, 6.0) 0.2 Homocysteine (µmol/L) 12.87 (12.75, 12.92) 13.32 (12.51, 13.44) 0.145 Intracranial Arterial Stenosis Assessment(Grade 1/2/3/4) 40/9/86/2 28/53/41/14 < 0.001 Fazekas Score(Grade 1/2/3/4/5) 48/74/1/3/11 24/48/15/12/37 < 0.001 Table 3 Multivariable Logistic Regression Analysis for Cognitive Impairment Variable β Coefficient Standard Error Wald χ² p-value Odds Ratio 95% CI Lower 95% CI Upper Age 0.161 0.027 6.01 < 0.001 1.17 1.11 1.24 Creatinine 0.011 0.006 1.7 0.088 1.01 0.998 1.02 Intracranial Arterial Stenosis Assessment 0.898 0.207 4.35 < 0.001 2.52 1.64 3.68 Fazekas Score 0.918 0.186 4.95 < 0.001 2.58 1.74 3.6 Total Bilirubin 0.0294 0.0233 1.26 0.207 1.03 -0.0163 0.0751 Constant -16.258 2.2 -7.39 < 0.001 8.69×10⁻⁸ 1.17×10⁻⁹ 6.00×10⁻⁶ 3. Results Study Population and Baseline Characteristics A total of 456 patients with suspected cerebral small vessel disease were initially screened for enrollment. After applying inclusion and exclusion criteria, 390 patients with radiologically confirmed leukoaraiosis were included in the final analysis. The study flowchart illustrating patient selection and randomization is presented in Fig. 1 . Participants were randomly allocated to training (n = 273, 70%) and validation (n = 117, 30%) cohorts using computer-generated randomization. Baseline demographic, clinical, laboratory, and neuroimaging characteristics showed no statistically significant differences between training and validation cohorts (all p > 0.05), confirming successful randomization (Table 1 ). In the training cohort, 137 patients (50.2%) were classified as having cognitive impairment based on adjusted MoCA scores while 136 patients (49.8%) had preserved cognitive function. The median age of the overall cohort was 74 years (interquartile range: 69–80 years), with a slight male predominance (51%). Cardiovascular risk factors were prevalent, with hypertension present in 72% of patients and type 2 diabetes in 32%. Regarding lifestyle factors, 31% had a smoking history and 21% reported alcohol consumption. Univariate Analysis of Risk Factors Univariate analysis in the training cohort identified five variables significantly associated with cognitive impairment (Table 2 ). Age demonstrated the strongest association, with patients in the cognitive impairment group being significantly older than those with preserved cognition (median age: 79.0 vs 71.0 years, p < 0.001). Among laboratory parameters, patients with cognitive impairment exhibited significantly lower serum creatinine levels (65.0 vs 74.5 µmol/L, p = 0.001) and reduced total bilirubin concentrations (11.8 vs 13.35 µmol/L, p = 0.028) compared to cognitively intact patients. Neuroimaging assessments revealed robust associations with cognitive status. The intracranial arterial stenosis assessment showed significant between-group differences (p < 0.001), with cognitive impairment patients demonstrating higher proportions of severe stenosis grades. Similarly, Fazekas scores differed significantly between groups (p < 0.001), with higher scores reflecting greater white matter hyperintensity burden in the cognitive impairment cohort. No significant associations were observed between cognitive impairment and sex, comorbidities (hypertension, diabetes), lifestyle factors (smoking, alcohol consumption), or other laboratory parameters including lipid profile, inflammatory markers, thyroid function, or homocysteine levels (all p > 0.05). Multivariable Logistic Regression Analysis Forward stepwise multivariable logistic regression analysis was performed incorporating the five variables that achieved statistical significance in univariate analysis. The final model identified three independent risk factors for cognitive impairment in leukoaraiosis patients (Table 3 ). Age emerged as the most significant independent predictor, with each one-year increase associated with a 17% increase in cognitive impairment odds (OR = 1.17, 95% CI: 1.11–1.24, p < 0.001). Intracranial arterial stenosis assessment demonstrated a strong independent association, with each grade increase corresponding to a 2.52-fold increase in cognitive impairment odds (OR = 2.52, 95% CI: 1.64–3.68, p < 0.001). The Fazekas score also showed a robust independent association, with each point increase conferring a 2.58-fold increase in cognitive impairment odds (OR = 2.58, 95% CI: 1.74–3.60, p < 0.001). Notably, while serum creatinine and total bilirubin demonstrated significant associations in univariate analysis, neither retained statistical significance in the multivariable model (p = 0.088 and p = 0.207, respectively), suggesting their effects may be mediated through other factors or represent confounding relationships rather than independent causal pathways. Model Performance and Validation Discriminative Performance The logistic regression model demonstrated excellent discriminative ability across both training and validation cohorts. In the training set, receiver operating characteristic curve analysis yielded an area under the curve (AUC) of 0.873 (95% CI: 0.831–0.915), indicating superior ability to distinguish between patients with and without cognitive impairment (Fig. 2 ). Internal validation using the independent validation cohort confirmed robust model performance, with an AUC of 0.872 (95% CI: 0.816–0.928) (Fig. 4 ). The minimal difference between training and validation AUC values (0.873 vs 0.872) suggested negligible overfitting and good model generalizability. Bootstrap internal validation with 1,000 iterations yielded an optimism-corrected AUC of 0.869, providing a realistic estimate of expected performance in new patient populations. Model Calibration Calibration assessment demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. The calibration plot for the training set showed close adherence to the ideal 45-degree line (Fig. 3), with the Hosmer-Lemeshow goodness-of-fit test confirming adequate calibration (p = 0.421). Similarly, the validation set calibration plot demonstrated maintained calibration performance (Fig. 5), with a non-significant Hosmer-Lemeshow test (p = 0.387). Classification Performance Metrics Using the optimal cutoff point determined by the Youden index (0.52), the model achieved balanced classification performance in the validation set: sensitivity 78.4%, specificity 81.6%, positive predictive value 79.1%, negative predictive value 81.0%, and overall accuracy 80.1%. These metrics indicate robust performance in correctly identifying both patients with and without cognitive impairment. Nomogram Development and Clinical Application Based on the final multivariable logistic regression model, a nomogram was constructed to facilitate clinical risk assessment (Fig. 6 ). The nomogram integrates the three independent risk factors—age, intracranial arterial stenosis assessment, and Fazekas score—with point scales proportional to their respective regression coefficients. The nomogram provides an intuitive tool for calculating individualized cognitive impairment risk. To use the nomogram, clinicians first locate the patient's age on the age axis and draw a vertical line to the points axis to determine the age-related score. This process is repeated for intracranial arterial stenosis assessment grade and Fazekas score. The sum of all three scores corresponds to the total points, which can be converted to predicted probability by drawing a vertical line from the total points axis to the probability axis. For example, a 78-year-old patient with Grade 3 intracranial arterial stenosis and Fazekas score of 4 would receive approximately 65 points for age, 75 points for stenosis grade, and 80 points for Fazekas score, totaling 220 points, corresponding to approximately 85% probability of cognitive impairment. The nomogram demonstrated excellent clinical utility during development and validation phases, providing reliable individualized risk estimates that could inform clinical decision-making, guide monitoring intensity, and facilitate patient counseling regarding cognitive decline risk. Model Validation Summary The comprehensive validation process confirmed the nomogram's robust performance characteristics. The consistency of discriminative performance across training and validation sets, combined with excellent calibration, supports the model's reliability and potential for clinical implementation. The nomogram successfully translates complex statistical relationships into an accessible clinical tool that maintains high accuracy while providing interpretable individualized risk assessments for cognitive impairment in leukoaraiosis patients. 4. Discussion This study successfully developed and validated a logistic regression-based nomogram for predicting cognitive impairment in patients with leukoaraiosis, identifying three independent risk factors: age, intracranial arterial stenosis assessment, and Fazekas score. The model demonstrated excellent discriminative performance with AUC values of 0.873 and 0.872 in training and validation sets, respectively, along with good calibration, suggesting robust clinical utility for early identification of high-risk patients. Key Risk Factors and Clinical Implications Age emerged as the strongest predictor of cognitive impairment, with each additional year conferring a 17% increase in odds. This finding aligns with extensive literature demonstrating age as a fundamental determinant of both leukoaraiosis progression and cognitive decline [ 15 , 16 ] . The age-related risk likely reflects cumulative effects of vascular aging, reduced cerebral blood flow, diminished cerebrovascular reserve, and increased susceptibility to ischemic injury. From a clinical perspective, this emphasizes the importance of heightened cognitive monitoring in elderly patients with leukoaraiosis, particularly those over 75 years of age [ 17 ] . A prospective cohort study by van Dinther et al., which included 181 participants, indicated that impaired baseline cerebral blood flow is associated with accelerated cognitive decline in patients with vascular cognitive impairment after two years. This study confirmed that chronic cerebral hypoperfusion is an important pathophysiological mechanism in the development of vascular cognitive impairment [ 18 ] . Fazekas score demonstrated a strong association with cognitive impairment (OR = 2.58), confirming the well-established relationship between white matter hyperintensity burden and cognitive dysfunction. The Fazekas scale provides a standardized, reproducible assessment of white matter lesion severity that directly correlates with underlying pathological changes including demyelination, axonal loss, and disruption of critical neural networks. Higher Fazekas scores indicate more extensive white matter damage, potentially affecting frontal-subcortical circuits essential for executive function, processing speed, and working memory. This finding supports the use of routine Fazekas scoring in clinical practice as both a diagnostic tool and prognostic indicator. A review study on MRI white matter aging by Gunning-Dixon and Raz indicated that age is the strongest predictor of the severity of white matter hyperintensities in normal aging populations. This study confirmed that white matter aging can lead to a state of disrupted connectivity associated with declines in episodic memory, executive function, and information processing speed [ 19 ] . Intracranial arterial stenosis assessment proved to be an independent predictor (OR = 2.52), highlighting the critical role of large vessel disease in leukoaraiosis-related cognitive impairment. Intracranial stenosis may contribute to cognitive decline through multiple mechanisms: chronic cerebral hypoperfusion leading to ischemic white matter damage, impaired cerebrovascular reactivity reducing the brain's ability to respond to metabolic demands, and increased risk of microembolic events causing cumulative brain injury. This finding suggests that comprehensive vascular assessment, including evaluation of intracranial arteries, should be integral to the clinical evaluation of leukoaraiosis patients.A study by Chen et al., which included 96 patients with chronic vertebrobasilar artery stenosis, indicated that patients in the CTP decompensated group had significantly lower MMSE and FAB scores compared to those in the CTP normal and compensated groups. This study confirmed that intracranial artery stenosis can lead to frontal lobe damage through chronic cerebral hypoperfusion, thereby reducing the patient's attention, verbal fluency, spatial structuring, short-term memory, and executive function [ 20 ] .A 2-year follow-up study by Liu et al., which included 173 asymptomatic patients with middle cerebral artery stenosis, pointed out that patients with poor collateral circulation experienced more frequent impairments in executive function, attention, and information processing speed. This study confirmed that intracranial atherosclerotic stenosis leads to brain atrophy, cognitive decline, and dementia through its pathophysiological mechanism by worsening cerebral hypoperfusion [ 21 ] . Model Performance and Clinical Utility The nomogram achieved excellent discrimination with AUC values exceeding 0.87 in both training and validation cohorts, indicating superior ability to distinguish between patients with and without cognitive impairment. The consistency of performance across different patient samples, combined with good calibration, suggests the model's reliability and potential generalizability. The excellent calibration, as evidenced by calibration plots closely following the ideal line and non-significant Hosmer-Lemeshow tests, indicates that predicted probabilities accurately reflect observed outcomes across the full range of risk. The nomogram's clinical utility lies in its ability to integrate multiple risk factors into a single, easily interpretable tool that provides individualized risk estimates. Unlike traditional approaches that consider risk factors in isolation, the nomogram captures the combined effect of age, vascular pathology, and white matter burden to generate personalized predictions. This comprehensive approach may enable clinicians to identify high-risk patients before overt cognitive symptoms develop, facilitating early intervention when therapeutic strategies may be most effective. While numerous studies have investigated individual risk factors for cognitive impairment in leukoaraiosis, this study represents one of the first comprehensive prediction models specifically designed for this population [ 22 ] . Previous research has consistently identified age and white matter burden as risk factors, but the inclusion of intracranial arterial stenosis assessment as an independent predictor provides novel insights into the vascular mechanisms underlying cognitive decline. The observed associations between lower serum creatinine and total bilirubin levels with cognitive impairment in univariate analysis, though not independent in multivariable modeling, warrant further investigation. These findings may reflect complex relationships between metabolic factors, vascular health, and brain function that could inform future research directions [ 23 ] . The nomogram has several potential clinical applications. First, it could serve as a screening tool in clinical practice, enabling systematic identification of leukoaraiosis patients at high risk for cognitive decline. Second, it may facilitate risk stratification for clinical trial enrollment, ensuring appropriate patient selection for intervention studies. Third, the tool could guide the intensity and frequency of cognitive monitoring, allowing for more personalized follow-up schedules based on individual risk profiles. Implementation of the nomogram in clinical practice would require integration into electronic health record systems and training of healthcare providers. The model's reliance on readily available clinical variables (age, standard neuroimaging assessments) enhances its practical feasibility and potential for widespread adoption. The identification of these three independent predictors provides insights into the pathophysiological mechanisms underlying leukoaraiosis-related cognitive impairment. The convergence of age-related vascular vulnerability, large vessel stenosis, and white matter damage suggests that cognitive decline results from the interaction of systemic vascular aging and focal cerebrovascular pathology. This supports a "multiple-hit" hypothesis where the combination of chronic hypoperfusion, impaired vascular reactivity, and cumulative white matter injury exceeds the brain's compensatory capacity, leading to clinically apparent cognitive dysfunction [ 24 , 25 ] . A retrospective analysis study by Sun et al. (involving 1,061 patients with ischemic stroke) developed a nomogram model based on quantitative white matter hyperintensity characteristics to predict the risk of ischemic stroke recurrence within one year. The model had a C-index of 0.709, which was superior to the model based on the Fazekas score (C-index 0.647). This study confirmed the value of quantitative white matter hyperintensity assessment in the prediction of cerebrovascular diseases [ 26 ] . Conclusions This study developed and validated a clinically practical nomogram for predicting cognitive impairment in leukoaraiosis patients, identifying age, intracranial arterial stenosis assessment, and Fazekas score as independent risk factors. The model demonstrated excellent discrimination and calibration, suggesting robust clinical utility for early identification of high-risk patients. These findings provide a foundation for personalized risk assessment and may facilitate timely interventions to prevent or delay cognitive decline in this vulnerable population. The nomogram represents a step toward precision medicine in cerebrovascular disease, offering clinicians an evidence-based tool to enhance decision-making and improve patient outcomes. Future research should focus on external validation of the nomogram in independent, multicenter cohorts to confirm generalizability. Prospective longitudinal studies are needed to validate the model's ability to predict cognitive decline over time and to assess the impact of interventions in high-risk patients identified by the nomogram. Investigation of additional biomarkers, including cerebrospinal fluid markers, advanced MRI metrics, and genetic variants, may further enhance predictive accuracy. The development of digital health applications incorporating the nomogram could facilitate widespread clinical implementation and enable real-time risk assessment. Additionally, studies evaluating the cost-effectiveness of nomogram-guided care compared to standard practice would inform healthcare policy decisions. Limitations Several limitations should be acknowledged. First, the single-center retrospective design may limit generalizability to other populations and healthcare settings, and external validation in multicenter cohorts is needed to confirm the nomogram's broader applicability. Second, while MoCA demonstrates superior sensitivity for detecting mild cognitive impairment compared to MMSE and is particularly well-suited for assessing executive function and visuospatial domains commonly affected in leukoaraiosis, it may not capture all subtle domain-specific deficits that comprehensive neuropsychological batteries might detect. Third, the cross-sectional design precludes assessment of temporal relationships and disease progression over time, limiting our ability to establish causal relationships and predict longitudinal cognitive trajectories. Fourth, the relatively modest sample size, though adequate for model development according to statistical guidelines, may limit the detection of additional predictors, subgroup effects, or rare but clinically relevant associations. Additionally, the study did not incorporate potentially relevant biomarkers that might enhance predictive accuracy, including cerebrospinal fluid markers (such as amyloid-β, tau proteins), serum inflammatory markers (interleukin-6, tumor necrosis factor-α), genetic factors (APOE genotype, cerebrovascular disease-related polymorphisms), or advanced neuroimaging metrics (diffusion tensor imaging parameters, cerebral blood flow measurements, brain volumetric analyses). Furthermore, the lack of detailed medication history, particularly the use of neuroprotective agents or cognitive enhancers, represents another limitation that could influence cognitive outcomes. The binary classification of cognitive status, while clinically practical, may not capture the full spectrum of cognitive changes and subtle gradations of impairment. Future longitudinal studies incorporating these additional variables and utilizing more comprehensive cognitive assessments may further improve model performance and clinical utility. Abbreviations AUC Area Under the Curve BMI Body Mass Index CI Cognitive Impairment CRP C-Reactive Protein FLAIR Fluid-Attenuated Inversion Recovery IASA Intracranial Arterial Stenosis Assessment IQR Interquartile Range LA Leukoaraiosis LDL Low-Density Lipoprotein Lp-PLA2 Lipoprotein-associated Phospholipase A2 MoCA Montreal Cognitive Assessment MRI Magnetic Resonance Imaging NCI Non-Cognitive Impairment OR Odds Ratio ROC Receiver Operating Characteristic TSH Thyroid-Stimulating Hormone WMH White Matter Hyperintensities Declarations Human Ethics and Consent to Participate This retrospective study conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Qixia District Hospital, Nanjing (approval number: NQH-CSVD-2020-06). All participants provided written informed consent before enrollment. The study protocol, including data collection procedures and imaging assessments, was reviewed and approved by the institutional review board. All research procedures complied with institutional guidelines for the protection of human research subjects. All participants or their legal guardians provided written informed consent before enrollment. Consent to Publish Written informed consent for publication was obtained from all participants or their legal guardians. All participants consented to the publication of their anonymized data in this research article. Funding This research was supported by the Nanjing City Health Science and Technology Development Special Fund Project (Grant Numbers: YKK23218). Clinical Trial Number Not applicable. Competing Interests The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors' Contributions G.Z. and L.M. contributed equally as first authors to study design, data collection, statistical analysis, and manuscript preparation. C.H., Y.L., and L.Y. participated in data collection and clinical assessments. W.X. and J.L. contributed equally as corresponding authors to study conception, supervision, and manuscript review. All authors read and approved the final manuscript. Data Availability The datasets used and analyzed during the current study are available from the corresponding authors on reasonable request. The raw data supporting the conclusions of this article include patient demographic information, clinical variables, laboratory measurements, neuroimaging data, and cognitive assessment scores. Due to the sensitive nature of patient health information and privacy protection requirements under Chinese healthcare regulations and institutional policies, the datasets cannot be made publicly available. However, de-identified data may be shared with qualified researchers for legitimate academic purposes following approval by the institutional review board of Qixia District Hospital and execution of appropriate data sharing agreements. Researchers interested in accessing the data should contact the corresponding authors Wenwen Xu ( [email protected] ) and Jianning Li ( [email protected] ) with a detailed research proposal outlining the intended use of the data. The nomogram calculator and associated statistical code used for model development and validation are available upon request to facilitate replication and validation studies. References Brito AC, Levy DF, Schneck SM, et al. Leukoaraiosis Is Not Associated With Recovery From Aphasia in the First Year After Stroke[J]. Neurobiol Lang (Camb), 2023,4(4):536-549. Chen W, Lin H, Lyu M, et al. The potential role of leukoaraiosis in remodeling the brain network to buffer cognitive decline: a Leukoaraiosis And Disability study from Alzheimer's Disease Neuroimaging Initiative[J]. Quant Imaging Med Surg, 2021,11(1):183-203. Gu Z, Sun X, Wu C, et al. Lower 25-hydroxyvitamin D is associated with severer white matter hyperintensity and cognitive function in patients with non-disabling ischemic cerebrovascular events[J]. J Stroke Cerebrovasc Dis, 2023,32(10):107311. Ihara M, Okamoto Y, Takahashi R. Suitability of the Montreal cognitive assessment versus the mini-mental state examination in detecting vascular cognitive impairment[J]. J Stroke Cerebrovasc Dis, 2013,22(6):737-741. Jokinen H, Kalska H, Ylikoski R, et al. Longitudinal cognitive decline in subcortical ischemic vascular disease--the LADIS Study[J]. Cerebrovasc Dis, 2009,27(4):384-391. Jokinen H, Koikkalainen J, Laakso HM, et al. Global Burden of Small Vessel Disease-Related Brain Changes on MRI Predicts Cognitive and Functional Decline[J]. Stroke, 2020,51(1):170-178. Kumral E, Güllüoğlu H, Alakbarova N, et al. Cognitive Decline in Patients with Leukoaraiosis Within 5 Years after Initial Stroke[J]. J Stroke Cerebrovasc Dis, 2015,24(10):2338-2347. Lamar M, Dannhauser TM, Walker Z, et al. Memory complaints with and without memory impairment: the impact of leukoaraiosis on cognition[J]. J Int Neuropsychol Soc, 2011,17(6):1104-1112. Lin CJ, Tu PC, Chern CM, et al. Connectivity features for identifying cognitive impairment in presymptomatic carotid stenosis[J]. PLoS One, 2014,9(1):e85441. Marzi C, Scheda R, Salvadori E, et al. Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis[J]. Front Hum Neurosci, 2023,17:1231513. Peng Y, Li Q, Qin L, et al. Combination of Serum Neurofilament Light Chain Levels and MRI Markers to Predict Cognitive Function in Ischemic Stroke[J]. Neurorehabil Neural Repair, 2021,35(3):247-255. Podemski R, Pokryszko-Dragan A, Zagrajek M, et al. Mild cognitive impairment and event-related potentials in patients with cerebral atrophy and leukoaraiosis[J]. Neurol Sci, 2008,29(6):411-416. Ryberg C, Rostrup E, Paulson OB, et al. Corpus callosum atrophy as a predictor of age-related cognitive and motor impairment: a 3-year follow-up of the LADIS study cohort[J]. J Neurol Sci, 2011,307(1-2):100-105. Pradeep A, Raghavan S, Przybelski SA, et al. Can white matter hyperintensities based Fazekas visual assessment scales inform about Alzheimer's disease pathology in the population?[J]. Alzheimers Res Ther, 2024,16(1):157. Salvadori E, Pasi M, Poggesi A, et al. Predictive value of MoCA in the acute phase of stroke on the diagnosis of mid-term cognitive impairment[J]. J Neurol, 2013,260(9):2220-2227. Sivakumar L, Riaz P, Kate M, 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. Tan C, Peng W, Deng Y. [Risk factors and predictive factors of cognitive deterioration in patients of vascular cognitive impairment no dementia with subcortical ischemic vascular disease][J]. Zhonghua Yi Xue Za Zhi, 2014,94(5):352-355. Tarumi T, Zhang R. Cerebral blood flow in normal aging adults: cardiovascular determinants, clinical implications, and aerobic fitness[J]. J Neurochem, 2018,144(5):595-608. Gunning-Dixon FM, Brickman AM, Cheng JC, et al. Aging of cerebral white matter: a review of MRI findings[J]. Int J Geriatr Psychiatry, 2009,24(2):109-117. Deng Y, Wang L, Sun X, et al. Association Between Cerebral Hypoperfusion and Cognitive Impairment in Patients With Chronic Vertebra-Basilar Stenosis[J]. Front Psychiatry, 2018,9:455. Meng Y, Yu K, Zhang L, et al. Cognitive Decline in Asymptomatic Middle Cerebral Artery Stenosis Patients with Moderate and Poor Collaterals: A 2-Year Follow-Up Study[J]. Med Sci Monit, 2019,25:4051-4058. Verdelho A, Madureira S, Moleiro C, et al. Depressive symptoms predict cognitive decline and dementia in older people independently of cerebral white matter changes: the LADIS study[J]. J Neurol Neurosurg Psychiatry, 2013,84(11):1250-1254. Wang Z, Bai L, Liu Q, et al. Corpus callosum integrity loss predicts cognitive impairment in Leukoaraiosis[J]. Ann Clin Transl Neurol, 2020,7(12):2409-2420. Yamauchi H, Fukuyama H, Shio H. Corpus callosum atrophy in patients with leukoaraiosis may indicate global cognitive impairment[J]. Stroke, 2000,31(7):1515-1520. Yuan CL, Yi R, Dong Q, et al. The relationship between diabetes-related cognitive dysfunction and leukoaraiosis[J]. Acta Neurol Belg, 2021,121(5):1101-1110. Sun Y, Xia W, Wei R, et al. Quantitative Analysis of White Matter Hyperintensities as a Predictor of 1-Year Risk for Ischemic Stroke Recurrence[J]. Neurol Ther, 2024,13(5):1467-1482. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2025 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 04 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 12 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Editor invited by journal 22 Jul, 2025 Submission checks completed at journal 20 Jul, 2025 First submitted to journal 20 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7100701","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501533759,"identity":"da430d9e-1cc1-4dc3-9f26-f5b1dbbae7a2","order_by":0,"name":"Guoxin Zhang","email":"","orcid":"","institution":"Qixia District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guoxin","middleName":"","lastName":"Zhang","suffix":""},{"id":501533760,"identity":"1be43312-8f89-4f35-908e-3d89b52c0e06","order_by":1,"name":"Lijun Meng","email":"","orcid":"","institution":"The Affiliated Hospital of Yangzhou University, Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Meng","suffix":""},{"id":501533761,"identity":"621eef7f-1ccb-4774-b883-4bea7e0fd1c0","order_by":2,"name":"Chunfang Huang","email":"","orcid":"","institution":"Qixia District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunfang","middleName":"","lastName":"Huang","suffix":""},{"id":501533762,"identity":"226bcb07-8761-4199-9c3b-edcfbaee3f07","order_by":3,"name":"Yi Lu","email":"","orcid":"","institution":"Qixia District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Lu","suffix":""},{"id":501533763,"identity":"735d548b-32c5-4d26-ba29-ae8b407bf16a","order_by":4,"name":"Liping Yin","email":"","orcid":"","institution":"Qixia District 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianning","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-11 10:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7100701/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7100701/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12883-025-04464-2","type":"published","date":"2025-11-20T15:58:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89469658,"identity":"bf23c3a7-48a4-4178-b2d8-87996a5d8200","added_by":"auto","created_at":"2025-08-20 09:17:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopment and Validation of Nomogram for Predicting Cognitive Impairment in Leukoaraiosis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/5e33b3c869ac7a9718e69c8a.png"},{"id":89469659,"identity":"3e5f0162-63be-43d9-a3cf-72f0394488f0","added_by":"auto","created_at":"2025-08-20 09:17:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166347,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for Training Set\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/485a89b32b330a621f48f421.png"},{"id":89469660,"identity":"1b617368-c91d-4c3d-9a93-e91cf6db123c","added_by":"auto","created_at":"2025-08-20 09:17:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109945,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration Plot for Training Set\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/b0f025f347b8ad34bbbfd915.png"},{"id":89471440,"identity":"f22cdae0-9612-498d-90b8-65545aeaa2a6","added_by":"auto","created_at":"2025-08-20 09:33:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182893,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for Validation Set\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/a77d2716490267db1dfbe840.png"},{"id":89471038,"identity":"225b6ea6-a81e-4bc4-bb6d-2fb79834fa4b","added_by":"auto","created_at":"2025-08-20 09:25:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106957,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration Plot for Validation Set\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/9dddaeca6edad63985410ba1.png"},{"id":89469662,"identity":"383c4b70-d71a-4237-a9e6-a1a3d0ef4a56","added_by":"auto","created_at":"2025-08-20 09:17:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97257,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for Predicting Cognitive Impairment in Leukoaraiosis Patients\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/b2becb0b38b5fa7215323f46.png"},{"id":96650386,"identity":"8757b7f8-af8b-4c78-a6b7-03101f483f4e","added_by":"auto","created_at":"2025-11-24 16:11:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1843501,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7100701/v1/6c09d1cd-b829-414b-9427-eba9f6305449.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram for Predicting Cognitive Impairment in Patients with Leukoaraiosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLeukoaraiosis (LA), synonymous with white matter hyperintensities (WMH), represents a cardinal manifestation of cerebral small vessel disease that has emerged as one of the most significant neuroimaging markers of brain aging and cognitive vulnerability in elderly populations\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This pathological entity, characterized by bilateral periventricular and deep white matter lesions appearing as hyperintense signals on T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging sequences, demonstrates exponential prevalence increases with advancing age, affecting nearly all individuals beyond the eighth decade\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The clinical significance of LA transcends its radiological appearance, as mounting evidence implicates these white matter changes as harbingers of cognitive decline, functional disability, and progression toward vascular dementia\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe pathophysiological underpinnings of LA encompass a complex cascade involving chronic cerebral hypoperfusion, blood-brain barrier disruption, endothelial dysfunction, oxidative stress, and neuroinflammatory processes. These interconnected mechanisms culminate in demyelination, axonal loss, and disruption of critical white matter tracts that serve as neural highways connecting distributed brain networks essential for higher-order cognitive functions\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Consequently, LA-associated cognitive impairment predominantly manifests as deficits in executive function, processing speed, attention, and working memory\u0026mdash;domains particularly vulnerable to white matter integrity loss. Epidemiological studies consistently demonstrate that cognitive dysfunction attributable to LA accounts for approximately 70% of vascular dementia cases, positioning this condition as a major contributor to the global burden of cognitive disorders\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTraditional statistical approaches to LA risk prediction have typically employed univariate analyses or simple multivariable models that may inadequately capture complex, potentially non-linear relationships among risk factors\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Logistic regression modeling, when appropriately implemented with rigorous variable selection and validation procedures, offers a robust framework for developing clinically applicable prediction tools\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The integration of logistic regression models with nomogram visualization represents a particularly promising approach, as nomograms translate complex statistical relationships into intuitive graphical interfaces that facilitate clinical decision-making and risk communication\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNomograms have gained widespread acceptance across medical specialties due to their ability to provide individualized risk estimates while maintaining clinical interpretability\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In neurology, nomogram-based prediction models have demonstrated utility in predicting outcomes following stroke, traumatic brain injury, and neurodegenerative diseases. However, despite the clinical importance of LA-related cognitive impairment, comprehensive prediction models specifically designed for this population remain notably absent from the literature\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe development of an accurate, validated prediction model for cognitive impairment in LA patients holds substantial clinical implications. Such a tool could enable identification of high-risk individuals before symptom onset, facilitate risk stratification for clinical trials, guide monitoring intensity, and optimize resource allocation in healthcare systems increasingly strained by cognitive disorder burden\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Moreover, evidence-based prediction models could serve as foundations for developing personalized prevention strategies and therapeutic interventions tailored to individual risk profiles.\u003c/p\u003e\u003cp\u003eGiven the substantial clinical need for improved risk stratification in LA patients and the current gap in evidence-based prediction tools, this study aimed to develop and internally validate a comprehensive nomogram for predicting cognitive impairment in patients with leukoaraiosis\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Specifically, our objectives were: first, to systematically identify and quantify independent associations between demographic, clinical, biochemical, and neuroimaging variables and cognitive impairment in a well-characterized LA cohort; second, to develop a logistic regression-based prediction model and translate it into a clinically practical nomogram; and third, to validate the model's discriminative ability and calibration using training and validation datasets with comprehensive performance assessment including receiver operating characteristic curve analysis and calibration plots. We hypothesized that a multi-dimensional approach integrating readily available clinical variables would yield a robust prediction model with excellent discriminative performance and clinical utility for early identification of LA patients at high risk for cognitive decline, ultimately providing clinicians with an evidence-based tool to enhance decision-making and improve patient outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study was conducted at Qixia District Hospital, Nanjing, China, between June 2020 and April 2023. The study protocol was approved by the institutional review board (approval number: QXH-2020-015), and written informed consent was obtained from all participants or their legal guardians. Of 456 screened patients, 390 met eligibility criteria and were randomly allocated to training (n\u0026thinsp;=\u0026thinsp;273) and validation (n\u0026thinsp;=\u0026thinsp;117) cohorts using computer-generated randomization in a 7:3 ratio.\u003c/p\u003e\u003cp\u003eConsecutive patients with radiologically confirmed leukoaraiosis were screened for eligibility. Inclusion criteria comprised: (1) bilateral white matter hyperintensities on fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging; (2) age 50\u0026ndash;85 years; (3) availability of complete clinical, laboratory, and neuroimaging data. Exclusion criteria included: (1) history of stroke with National Institutes of Health Stroke Scale score\u0026thinsp;\u0026ge;\u0026thinsp;5; (2) known genetic leukoencephalopathies; (3) white matter lesions secondary to inflammatory or infectious etiologies; (4) concurrent neurodegenerative disorders; (5) severe systemic comorbidities precluding assessment; (6) incomplete data exceeding 20% of study variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection and Variable Definitions\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Clinical Variables\u003c/h2\u003e\u003cp\u003eDemographic and clinical data were extracted from electronic medical records using standardized case report forms. Variables included age, sex, years of education, body mass index, history of hypertension (systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or current antihypertensive therapy), type 2 diabetes mellitus (according to American Diabetes Association criteria or glucose-lowering medication use), smoking status, and alcohol consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Laboratory Measurements\u003c/h2\u003e\u003cp\u003eVenous blood samples were collected after 12-hour overnight fasting within 24 hours of admission. Biochemical analyses were performed using certified laboratory protocols and included: serum creatinine, total bilirubin, lipid profile (total cholesterol, triglycerides, low-density lipoprotein), high-sensitivity C-reactive protein, fasting glucose, thyroid-stimulating hormone, lipoprotein-associated phospholipase A2, homocysteine, uric acid, and urinary albumin-to-creatinine ratio.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Cognitive Assessment\u003c/h2\u003e\u003cp\u003eCognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) administered by certified neurologists. Educational bias was corrected by adding 1 point for participants with \u0026le;\u0026thinsp;12 years of education, as recommended by the original MoCA guidelines. Cognitive impairment was defined as adjusted MoCA score\u0026thinsp;\u0026le;\u0026thinsp;26 points, consistent with established diagnostic criteria for mild cognitive impairment in Chinese populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Neuroimaging Protocol\u003c/h2\u003e\u003cp\u003eBrain magnetic resonance imaging was performed using a 1.5-Tesla scanner (Neusoft NSM-S15P) with standardized sequences including T1-weighted, T2-weighted, and FLAIR images. White matter hyperintensity burden was quantified using the Fazekas scale\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, rating periventricular (0\u0026ndash;3) and deep white matter (0\u0026ndash;3) lesions separately, with total scores ranging 0\u0026ndash;6. Intracranial arterial stenosis was assessed using time-of-flight magnetic resonance angiography and graded as: Grade 1 (no stenosis), Grade 2 (\u0026lt;\u0026thinsp;20% single-vessel stenosis), Grade 3 (20\u0026ndash;40% stenosis), or Grade 4 (\u0026ge;\u0026thinsp;40% stenosis in \u0026ge;\u0026thinsp;2 vessels). All imaging assessments were performed by two independent neuroradiologists blinded to clinical data, with inter-rater reliability κ\u0026thinsp;\u0026gt;\u0026thinsp;0.85.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were assessed for normality and expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) as appropriate, while categorical variables were presented as frequencies and percentages, with baseline characteristics compared between training and validation cohorts using appropriate statistical tests. Missing data (\u0026lt;\u0026thinsp;20% for any variable) were handled using multiple imputation with random forest algorithms, followed by univariate analysis in the training cohort to identify variables associated with cognitive impairment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which were then entered into multivariable logistic regression using forward stepwise selection with model assumptions verified and multicollinearity assessed. Model performance was evaluated using area under the receiver operating characteristic curve for discrimination and calibration plots with Hosmer-Lemeshow test for calibration, with internal validation performed using bootstrap resampling (1,000 iterations) to obtain bias-corrected estimates, and a nomogram constructed based on the final model for clinical application. All analyses were conducted using R version 4.2.0 and SPSS version 26.0, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the study adhered to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines.\u003c/p\u003e\u003cp\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 of Training and Validation Sets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Dataset (n\u0026thinsp;=\u0026thinsp;390)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining Set (n\u0026thinsp;=\u0026thinsp;273)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValidation Set (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (69, 80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (70, 80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (68, 79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.302\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e279 (72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194 (71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType 2 Diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264 (68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190 (70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185 (68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e309 (79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90 (77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54 (1.03, 2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.54 (1.02, 2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55 (1.05, 3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.82 (2.73, 4.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.85 (2.82, 4.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.78 (2.59, 4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-Density Lipoprotein (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.07 (1.48, 2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12 (1.46, 2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.05 (1.53, 2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric Acid (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e309 (238, 375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 (245, 375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e297 (233, 375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLp-PLA2 (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180.67 (146.69, 223.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180.67 (146.65, 220.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186.03 (146.8, 226.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting Glucose (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.67 (5.06, 6.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.61 (5.04, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.77 (5.17, 6.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin-to-Creatinine Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8 (1.2, 3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8 (1.3, 3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8 (1.2, 2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.063\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\u003e2.32 (1.58, 3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.41 (1.61, 4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.22 (1.54, 3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (60, 84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (60, 83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (62, 85.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-sensitivity CRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5 (0.71, 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.61 (0.8, 6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2 (0.67, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.6 (9.7, 17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5 (9.7, 17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.8 (9.3, 17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.3 (4.5, 6.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4 (4.5, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3 (4.3, 6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomocysteine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.92 (11.23, 16.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.92 (11.3, 16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.92 (10.45, 16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntracranial Arterial Stenosis Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFazekas Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eUnivariate Analysis of Factors Associated with Cognitive Impairment in Training Set\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCognitive Impairment (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Cognitive Impairment (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.0 (78.0, 80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.0 (70.0, 73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex, n/total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78/59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70/66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.433\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\u003e46/91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103/33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType 2 Diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97/40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96/41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47/89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112/25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107/29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.67 (1.46, 1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.21, 1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.83 (3.6, 4.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.86 (3.32, 4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-Density Lipoprotein (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.12 (1.92, 2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12 (1.84, 2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric Acid (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e307.0 (287.0, 320.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e317.0 (296.98, 332.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLp-PLA2 (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117.83 (161.05, 185.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182.83 (171.13, 203.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting Glucose (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.7 (5.44, 5.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.51 (5.23, 5.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin-to-Creatinine Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8 (1.72, 2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.76 (1.62, 2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.294\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\u003e2.42 (2.21, 2.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.41 (2.13, 2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.0 (62.0, 69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.5 (70.0, 80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-sensitivity CRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53 (1.2, 1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85 (1.25, 2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.8 (11.1, 12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.35 (12.4, 15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.3 (5.0, 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.46 (5.1, 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomocysteine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.87 (12.75, 12.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.32 (12.51, 13.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntracranial Arterial Stenosis Assessment(Grade 1/2/3/4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40/9/86/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28/53/41/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFazekas Score(Grade 1/2/3/4/5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48/74/1/3/11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24/48/15/12/37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eMultivariable Logistic Regression Analysis for Cognitive Impairment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI Lower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95% CI Upper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntracranial Arterial Stenosis Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFazekas Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.0163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-16.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.69\u0026times;10⁻⁸\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.17\u0026times;10⁻⁹\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.00\u0026times;10⁻⁶\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\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003eStudy Population and Baseline Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 456 patients with suspected cerebral small vessel disease were initially screened for enrollment. After applying inclusion and exclusion criteria, 390 patients with radiologically confirmed leukoaraiosis were included in the final analysis. The study flowchart illustrating patient selection and randomization is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were randomly allocated to training (n\u0026thinsp;=\u0026thinsp;273, 70%) and validation (n\u0026thinsp;=\u0026thinsp;117, 30%) cohorts using computer-generated randomization.\u003c/p\u003e\u003cp\u003eBaseline demographic, clinical, laboratory, and neuroimaging characteristics showed no statistically significant differences between training and validation cohorts (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming successful randomization (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the training cohort, 137 patients (50.2%) were classified as having cognitive impairment based on adjusted MoCA scores while 136 patients (49.8%) had preserved cognitive function. The median age of the overall cohort was 74 years (interquartile range: 69\u0026ndash;80 years), with a slight male predominance (51%). Cardiovascular risk factors were prevalent, with hypertension present in 72% of patients and type 2 diabetes in 32%. Regarding lifestyle factors, 31% had a smoking history and 21% reported alcohol consumption.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate Analysis of Risk Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnivariate analysis in the training cohort identified five variables significantly associated with cognitive impairment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Age demonstrated the strongest association, with patients in the cognitive impairment group being significantly older than those with preserved cognition (median age: 79.0 vs 71.0 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eAmong laboratory parameters, patients with cognitive impairment exhibited significantly lower serum creatinine levels (65.0 vs 74.5 \u0026micro;mol/L, p\u0026thinsp;=\u0026thinsp;0.001) and reduced total bilirubin concentrations (11.8 vs 13.35 \u0026micro;mol/L, p\u0026thinsp;=\u0026thinsp;0.028) compared to cognitively intact patients.\u003c/p\u003e\u003cp\u003eNeuroimaging assessments revealed robust associations with cognitive status. The intracranial arterial stenosis assessment showed significant between-group differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with cognitive impairment patients demonstrating higher proportions of severe stenosis grades. Similarly, Fazekas scores differed significantly between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher scores reflecting greater white matter hyperintensity burden in the cognitive impairment cohort.\u003c/p\u003e\u003cp\u003eNo significant associations were observed between cognitive impairment and sex, comorbidities (hypertension, diabetes), lifestyle factors (smoking, alcohol consumption), or other laboratory parameters including lipid profile, inflammatory markers, thyroid function, or homocysteine levels (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariable Logistic Regression Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eForward stepwise multivariable logistic regression analysis was performed incorporating the five variables that achieved statistical significance in univariate analysis. The final model identified three independent risk factors for cognitive impairment in leukoaraiosis patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAge emerged as the most significant independent predictor, with each one-year increase associated with a 17% increase in cognitive impairment odds (OR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.11\u0026ndash;1.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Intracranial arterial stenosis assessment demonstrated a strong independent association, with each grade increase corresponding to a 2.52-fold increase in cognitive impairment odds (OR\u0026thinsp;=\u0026thinsp;2.52, 95% CI: 1.64\u0026ndash;3.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Fazekas score also showed a robust independent association, with each point increase conferring a 2.58-fold increase in cognitive impairment odds (OR\u0026thinsp;=\u0026thinsp;2.58, 95% CI: 1.74\u0026ndash;3.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eNotably, while serum creatinine and total bilirubin demonstrated significant associations in univariate analysis, neither retained statistical significance in the multivariable model (p\u0026thinsp;=\u0026thinsp;0.088 and p\u0026thinsp;=\u0026thinsp;0.207, respectively), suggesting their effects may be mediated through other factors or represent confounding relationships rather than independent causal pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Performance and Validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscriminative Performance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe logistic regression model demonstrated excellent discriminative ability across both training and validation cohorts. In the training set, receiver operating characteristic curve analysis yielded an area under the curve (AUC) of 0.873 (95% CI: 0.831\u0026ndash;0.915), indicating superior ability to distinguish between patients with and without cognitive impairment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Internal validation using the independent validation cohort confirmed robust model performance, with an AUC of 0.872 (95% CI: 0.816\u0026ndash;0.928) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe minimal difference between training and validation AUC values (0.873 vs 0.872) suggested negligible overfitting and good model generalizability. Bootstrap internal validation with 1,000 iterations yielded an optimism-corrected AUC of 0.869, providing a realistic estimate of expected performance in new patient populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Calibration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCalibration assessment demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. The calibration plot for the training set showed close adherence to the ideal 45-degree line (Fig.\u0026nbsp;3), with the Hosmer-Lemeshow goodness-of-fit test confirming adequate calibration (p\u0026thinsp;=\u0026thinsp;0.421). Similarly, the validation set calibration plot demonstrated maintained calibration performance (Fig.\u0026nbsp;5), with a non-significant Hosmer-Lemeshow test (p\u0026thinsp;=\u0026thinsp;0.387).\u003c/p\u003e\u003cp\u003e\u003cb\u003eClassification Performance Metrics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the optimal cutoff point determined by the Youden index (0.52), the model achieved balanced classification performance in the validation set: sensitivity 78.4%, specificity 81.6%, positive predictive value 79.1%, negative predictive value 81.0%, and overall accuracy 80.1%. These metrics indicate robust performance in correctly identifying both patients with and without cognitive impairment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNomogram Development and Clinical Application\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the final multivariable logistic regression model, a nomogram was constructed to facilitate clinical risk assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The nomogram integrates the three independent risk factors\u0026mdash;age, intracranial arterial stenosis assessment, and Fazekas score\u0026mdash;with point scales proportional to their respective regression coefficients.\u003c/p\u003e\u003cp\u003eThe nomogram provides an intuitive tool for calculating individualized cognitive impairment risk. To use the nomogram, clinicians first locate the patient's age on the age axis and draw a vertical line to the points axis to determine the age-related score. This process is repeated for intracranial arterial stenosis assessment grade and Fazekas score. The sum of all three scores corresponds to the total points, which can be converted to predicted probability by drawing a vertical line from the total points axis to the probability axis.\u003c/p\u003e\u003cp\u003eFor example, a 78-year-old patient with Grade 3 intracranial arterial stenosis and Fazekas score of 4 would receive approximately 65 points for age, 75 points for stenosis grade, and 80 points for Fazekas score, totaling 220 points, corresponding to approximately 85% probability of cognitive impairment.\u003c/p\u003e\u003cp\u003eThe nomogram demonstrated excellent clinical utility during development and validation phases, providing reliable individualized risk estimates that could inform clinical decision-making, guide monitoring intensity, and facilitate patient counseling regarding cognitive decline risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Validation Summary\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe comprehensive validation process confirmed the nomogram's robust performance characteristics. The consistency of discriminative performance across training and validation sets, combined with excellent calibration, supports the model's reliability and potential for clinical implementation. The nomogram successfully translates complex statistical relationships into an accessible clinical tool that maintains high accuracy while providing interpretable individualized risk assessments for cognitive impairment in leukoaraiosis patients.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study successfully developed and validated a logistic regression-based nomogram for predicting cognitive impairment in patients with leukoaraiosis, identifying three independent risk factors: age, intracranial arterial stenosis assessment, and Fazekas score. The model demonstrated excellent discriminative performance with AUC values of 0.873 and 0.872 in training and validation sets, respectively, along with good calibration, suggesting robust clinical utility for early identification of high-risk patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eKey Risk Factors and Clinical Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAge emerged as the strongest predictor of cognitive impairment, with each additional year conferring a 17% increase in odds. This finding aligns with extensive literature demonstrating age as a fundamental determinant of both leukoaraiosis progression and cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The age-related risk likely reflects cumulative effects of vascular aging, reduced cerebral blood flow, diminished cerebrovascular reserve, and increased susceptibility to ischemic injury. From a clinical perspective, this emphasizes the importance of heightened cognitive monitoring in elderly patients with leukoaraiosis, particularly those over 75 years of age\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. A prospective cohort study by van Dinther et al., which included 181 participants, indicated that impaired baseline cerebral blood flow is associated with accelerated cognitive decline in patients with vascular cognitive impairment after two years. This study confirmed that chronic cerebral hypoperfusion is an important pathophysiological mechanism in the development of vascular cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFazekas score demonstrated a strong association with cognitive impairment (OR\u0026thinsp;=\u0026thinsp;2.58), confirming the well-established relationship between white matter hyperintensity burden and cognitive dysfunction. The Fazekas scale provides a standardized, reproducible assessment of white matter lesion severity that directly correlates with underlying pathological changes including demyelination, axonal loss, and disruption of critical neural networks. Higher Fazekas scores indicate more extensive white matter damage, potentially affecting frontal-subcortical circuits essential for executive function, processing speed, and working memory. This finding supports the use of routine Fazekas scoring in clinical practice as both a diagnostic tool and prognostic indicator. A review study on MRI white matter aging by Gunning-Dixon and Raz indicated that age is the strongest predictor of the severity of white matter hyperintensities in normal aging populations. This study confirmed that white matter aging can lead to a state of disrupted connectivity associated with declines in episodic memory, executive function, and information processing speed\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIntracranial arterial stenosis assessment proved to be an independent predictor (OR\u0026thinsp;=\u0026thinsp;2.52), highlighting the critical role of large vessel disease in leukoaraiosis-related cognitive impairment. Intracranial stenosis may contribute to cognitive decline through multiple mechanisms: chronic cerebral hypoperfusion leading to ischemic white matter damage, impaired cerebrovascular reactivity reducing the brain's ability to respond to metabolic demands, and increased risk of microembolic events causing cumulative brain injury. This finding suggests that comprehensive vascular assessment, including evaluation of intracranial arteries, should be integral to the clinical evaluation of leukoaraiosis patients.A study by Chen et al., which included 96 patients with chronic vertebrobasilar artery stenosis, indicated that patients in the CTP decompensated group had significantly lower MMSE and FAB scores compared to those in the CTP normal and compensated groups. This study confirmed that intracranial artery stenosis can lead to frontal lobe damage through chronic cerebral hypoperfusion, thereby reducing the patient's attention, verbal fluency, spatial structuring, short-term memory, and executive function\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.A 2-year follow-up study by Liu et al., which included 173 asymptomatic patients with middle cerebral artery stenosis, pointed out that patients with poor collateral circulation experienced more frequent impairments in executive function, attention, and information processing speed. This study confirmed that intracranial atherosclerotic stenosis leads to brain atrophy, cognitive decline, and dementia through its pathophysiological mechanism by worsening cerebral hypoperfusion\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Performance and Clinical Utility\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe nomogram achieved excellent discrimination with AUC values exceeding 0.87 in both training and validation cohorts, indicating superior ability to distinguish between patients with and without cognitive impairment. The consistency of performance across different patient samples, combined with good calibration, suggests the model's reliability and potential generalizability. The excellent calibration, as evidenced by calibration plots closely following the ideal line and non-significant Hosmer-Lemeshow tests, indicates that predicted probabilities accurately reflect observed outcomes across the full range of risk. The nomogram's clinical utility lies in its ability to integrate multiple risk factors into a single, easily interpretable tool that provides individualized risk estimates. Unlike traditional approaches that consider risk factors in isolation, the nomogram captures the combined effect of age, vascular pathology, and white matter burden to generate personalized predictions. This comprehensive approach may enable clinicians to identify high-risk patients before overt cognitive symptoms develop, facilitating early intervention when therapeutic strategies may be most effective.\u003c/p\u003e\u003cp\u003eWhile numerous studies have investigated individual risk factors for cognitive impairment in leukoaraiosis, this study represents one of the first comprehensive prediction models specifically designed for this population\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Previous research has consistently identified age and white matter burden as risk factors, but the inclusion of intracranial arterial stenosis assessment as an independent predictor provides novel insights into the vascular mechanisms underlying cognitive decline.\u003c/p\u003e\u003cp\u003eThe observed associations between lower serum creatinine and total bilirubin levels with cognitive impairment in univariate analysis, though not independent in multivariable modeling, warrant further investigation. These findings may reflect complex relationships between metabolic factors, vascular health, and brain function that could inform future research directions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe nomogram has several potential clinical applications. First, it could serve as a screening tool in clinical practice, enabling systematic identification of leukoaraiosis patients at high risk for cognitive decline. Second, it may facilitate risk stratification for clinical trial enrollment, ensuring appropriate patient selection for intervention studies. Third, the tool could guide the intensity and frequency of cognitive monitoring, allowing for more personalized follow-up schedules based on individual risk profiles. Implementation of the nomogram in clinical practice would require integration into electronic health record systems and training of healthcare providers. The model's reliance on readily available clinical variables (age, standard neuroimaging assessments) enhances its practical feasibility and potential for widespread adoption.\u003c/p\u003e\u003cp\u003eThe identification of these three independent predictors provides insights into the pathophysiological mechanisms underlying leukoaraiosis-related cognitive impairment. The convergence of age-related vascular vulnerability, large vessel stenosis, and white matter damage suggests that cognitive decline results from the interaction of systemic vascular aging and focal cerebrovascular pathology. This supports a \"multiple-hit\" hypothesis where the combination of chronic hypoperfusion, impaired vascular reactivity, and cumulative white matter injury exceeds the brain's compensatory capacity, leading to clinically apparent cognitive dysfunction\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA retrospective analysis study by Sun et al. (involving 1,061 patients with ischemic stroke) developed a nomogram model based on quantitative white matter hyperintensity characteristics to predict the risk of ischemic stroke recurrence within one year. The model had a C-index of 0.709, which was superior to the model based on the Fazekas score (C-index 0.647). This study confirmed the value of quantitative white matter hyperintensity assessment in the prediction of cerebrovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study developed and validated a clinically practical nomogram for predicting cognitive impairment in leukoaraiosis patients, identifying age, intracranial arterial stenosis assessment, and Fazekas score as independent risk factors. The model demonstrated excellent discrimination and calibration, suggesting robust clinical utility for early identification of high-risk patients. These findings provide a foundation for personalized risk assessment and may facilitate timely interventions to prevent or delay cognitive decline in this vulnerable population. The nomogram represents a step toward precision medicine in cerebrovascular disease, offering clinicians an evidence-based tool to enhance decision-making and improve patient outcomes. Future research should focus on external validation of the nomogram in independent, multicenter cohorts to confirm generalizability. Prospective longitudinal studies are needed to validate the model's ability to predict cognitive decline over time and to assess the impact of interventions in high-risk patients identified by the nomogram. Investigation of additional biomarkers, including cerebrospinal fluid markers, advanced MRI metrics, and genetic variants, may further enhance predictive accuracy.\u003c/p\u003e\u003cp\u003eThe development of digital health applications incorporating the nomogram could facilitate widespread clinical implementation and enable real-time risk assessment. Additionally, studies evaluating the cost-effectiveness of nomogram-guided care compared to standard practice would inform healthcare policy decisions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the single-center retrospective design may limit generalizability to other populations and healthcare settings, and external validation in multicenter cohorts is needed to confirm the nomogram's broader applicability. Second, while MoCA demonstrates superior sensitivity for detecting mild cognitive impairment compared to MMSE and is particularly well-suited for assessing executive function and visuospatial domains commonly affected in leukoaraiosis, it may not capture all subtle domain-specific deficits that comprehensive neuropsychological batteries might detect. Third, the cross-sectional design precludes assessment of temporal relationships and disease progression over time, limiting our ability to establish causal relationships and predict longitudinal cognitive trajectories. Fourth, the relatively modest sample size, though adequate for model development according to statistical guidelines, may limit the detection of additional predictors, subgroup effects, or rare but clinically relevant associations.\u003c/p\u003e\u003cp\u003eAdditionally, the study did not incorporate potentially relevant biomarkers that might enhance predictive accuracy, including cerebrospinal fluid markers (such as amyloid-β, tau proteins), serum inflammatory markers (interleukin-6, tumor necrosis factor-α), genetic factors (APOE genotype, cerebrovascular disease-related polymorphisms), or advanced neuroimaging metrics (diffusion tensor imaging parameters, cerebral blood flow measurements, brain volumetric analyses). Furthermore, the lack of detailed medication history, particularly the use of neuroprotective agents or cognitive enhancers, represents another limitation that could influence cognitive outcomes. The binary classification of cognitive status, while clinically practical, may not capture the full spectrum of cognitive changes and subtle gradations of impairment. Future longitudinal studies incorporating these additional variables and utilizing more comprehensive cognitive assessments may further improve model performance and clinical utility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCognitive 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\"\u003eFLAIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFluid-Attenuated Inversion Recovery\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIASA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntracranial Arterial Stenosis Assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile Range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeukoaraiosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLow-Density Lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLp-PLA2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLipoprotein-associated Phospholipase A2\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\"\u003eMRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-Cognitive Impairment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\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\"\u003eWMH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite Matter Hyperintensities\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Qixia District Hospital, Nanjing (approval number: NQH-CSVD-2020-06). All participants provided written informed consent before enrollment. The study protocol, including data collection procedures and imaging assessments, was reviewed and approved by the institutional review board. All research procedures complied with institutional guidelines for the protection of human research subjects. All participants or their legal guardians provided written informed consent before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants or their legal guardians. All participants consented to the publication of their anonymized data in this research article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Nanjing City Health Science and Technology Development Special Fund Project (Grant Numbers: YKK23218).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.Z. and L.M. contributed equally as first authors to study design, data collection, statistical analysis, and manuscript preparation. C.H., Y.L., and L.Y. participated in data collection and clinical assessments. W.X. and J.L. contributed equally as corresponding authors to study conception, supervision, and manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding authors on reasonable request. The raw data supporting the conclusions of this article include patient demographic information, clinical variables, laboratory measurements, neuroimaging data, and cognitive assessment scores. Due to the sensitive nature of patient health information and privacy protection requirements under Chinese healthcare regulations and institutional policies, the datasets cannot be made publicly available. However, de-identified data may be shared with qualified researchers for legitimate academic purposes following approval by the institutional review board of Qixia District Hospital and execution of appropriate data sharing agreements. Researchers interested in accessing the data should contact the corresponding authors Wenwen Xu ([email protected]) and Jianning Li ([email protected]) with a detailed research proposal outlining the intended use of the data. The nomogram calculator and associated statistical code used for model development and validation are available upon request to facilitate replication and validation studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrito AC, Levy DF, Schneck SM, et al. Leukoaraiosis Is Not Associated With Recovery From Aphasia in the First Year After Stroke[J]. Neurobiol Lang (Camb), 2023,4(4):536-549.\u003c/li\u003e\n\u003cli\u003eChen W, Lin H, Lyu M, et al. The potential role of leukoaraiosis in remodeling the brain network to buffer cognitive decline: a Leukoaraiosis And Disability study from Alzheimer\u0026apos;s Disease Neuroimaging Initiative[J]. Quant Imaging Med Surg, 2021,11(1):183-203.\u003c/li\u003e\n\u003cli\u003eGu Z, Sun X, Wu C, et al. Lower 25-hydroxyvitamin D is associated with severer white matter hyperintensity and cognitive function in patients with non-disabling ischemic cerebrovascular events[J]. J Stroke Cerebrovasc Dis, 2023,32(10):107311.\u003c/li\u003e\n\u003cli\u003eIhara M, Okamoto Y, Takahashi R. Suitability of the Montreal cognitive assessment versus the mini-mental state examination in detecting vascular cognitive impairment[J]. J Stroke Cerebrovasc Dis, 2013,22(6):737-741.\u003c/li\u003e\n\u003cli\u003eJokinen H, Kalska H, Ylikoski R, et al. Longitudinal cognitive decline in subcortical ischemic vascular disease--the LADIS Study[J]. Cerebrovasc Dis, 2009,27(4):384-391.\u003c/li\u003e\n\u003cli\u003eJokinen H, Koikkalainen J, Laakso HM, et al. Global Burden of Small Vessel Disease-Related Brain Changes on MRI Predicts Cognitive and Functional Decline[J]. Stroke, 2020,51(1):170-178.\u003c/li\u003e\n\u003cli\u003eKumral E, G\u0026uuml;ll\u0026uuml;oğlu H, Alakbarova N, et al. Cognitive Decline in Patients with Leukoaraiosis Within 5 Years after Initial Stroke[J]. J Stroke Cerebrovasc Dis, 2015,24(10):2338-2347.\u003c/li\u003e\n\u003cli\u003eLamar M, Dannhauser TM, Walker Z, et al. Memory complaints with and without memory impairment: the impact of leukoaraiosis on cognition[J]. J Int Neuropsychol Soc, 2011,17(6):1104-1112.\u003c/li\u003e\n\u003cli\u003eLin CJ, Tu PC, Chern CM, et al. Connectivity features for identifying cognitive impairment in presymptomatic carotid stenosis[J]. PLoS One, 2014,9(1):e85441.\u003c/li\u003e\n\u003cli\u003eMarzi C, Scheda R, Salvadori E, et al. Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis[J]. Front Hum Neurosci, 2023,17:1231513.\u003c/li\u003e\n\u003cli\u003ePeng Y, Li Q, Qin L, et al. Combination of Serum Neurofilament Light Chain Levels and MRI Markers to Predict Cognitive Function in Ischemic Stroke[J]. Neurorehabil Neural Repair, 2021,35(3):247-255.\u003c/li\u003e\n\u003cli\u003ePodemski R, Pokryszko-Dragan A, Zagrajek M, et al. Mild cognitive impairment and event-related potentials in patients with cerebral atrophy and leukoaraiosis[J]. Neurol Sci, 2008,29(6):411-416.\u003c/li\u003e\n\u003cli\u003eRyberg C, Rostrup E, Paulson OB, et al. Corpus callosum atrophy as a predictor of age-related cognitive and motor impairment: a 3-year follow-up of the LADIS study cohort[J]. J Neurol Sci, 2011,307(1-2):100-105.\u003c/li\u003e\n\u003cli\u003ePradeep A, Raghavan S, Przybelski SA, et al. Can white matter hyperintensities based Fazekas visual assessment scales inform about Alzheimer\u0026apos;s disease pathology in the population?[J]. Alzheimers Res Ther, 2024,16(1):157.\u003c/li\u003e\n\u003cli\u003eSalvadori E, Pasi M, Poggesi A, et al. Predictive value of MoCA in the acute phase of stroke on the diagnosis of mid-term cognitive impairment[J]. J Neurol, 2013,260(9):2220-2227.\u003c/li\u003e\n\u003cli\u003eSivakumar L, Riaz P, Kate M, 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.\u003c/li\u003e\n\u003cli\u003eTan C, Peng W, Deng Y. [Risk factors and predictive factors of cognitive deterioration in patients of vascular cognitive impairment no dementia with subcortical ischemic vascular disease][J]. Zhonghua Yi Xue Za Zhi, 2014,94(5):352-355.\u003c/li\u003e\n\u003cli\u003eTarumi T, Zhang R. Cerebral blood flow in normal aging adults: cardiovascular determinants, clinical implications, and aerobic fitness[J]. J Neurochem, 2018,144(5):595-608.\u003c/li\u003e\n\u003cli\u003eGunning-Dixon FM, Brickman AM, Cheng JC, et al. Aging of cerebral white matter: a review of MRI findings[J]. Int J Geriatr Psychiatry, 2009,24(2):109-117.\u003c/li\u003e\n\u003cli\u003eDeng Y, Wang L, Sun X, et al. Association Between Cerebral Hypoperfusion and Cognitive Impairment in Patients With Chronic Vertebra-Basilar Stenosis[J]. Front Psychiatry, 2018,9:455.\u003c/li\u003e\n\u003cli\u003eMeng Y, Yu K, Zhang L, et al. Cognitive Decline in Asymptomatic Middle Cerebral Artery Stenosis Patients with Moderate and Poor Collaterals: A 2-Year Follow-Up Study[J]. Med Sci Monit, 2019,25:4051-4058.\u003c/li\u003e\n\u003cli\u003eVerdelho A, Madureira S, Moleiro C, et al. Depressive symptoms predict cognitive decline and dementia in older people independently of cerebral white matter changes: the LADIS study[J]. J Neurol Neurosurg Psychiatry, 2013,84(11):1250-1254.\u003c/li\u003e\n\u003cli\u003eWang Z, Bai L, Liu Q, et al. Corpus callosum integrity loss predicts cognitive impairment in Leukoaraiosis[J]. Ann Clin Transl Neurol, 2020,7(12):2409-2420.\u003c/li\u003e\n\u003cli\u003eYamauchi H, Fukuyama H, Shio H. Corpus callosum atrophy in patients with leukoaraiosis may indicate global cognitive impairment[J]. Stroke, 2000,31(7):1515-1520.\u003c/li\u003e\n\u003cli\u003eYuan CL, Yi R, Dong Q, et al. The relationship between diabetes-related cognitive dysfunction and leukoaraiosis[J]. Acta Neurol Belg, 2021,121(5):1101-1110.\u003c/li\u003e\n\u003cli\u003eSun Y, Xia W, Wei R, et al. Quantitative Analysis of White Matter Hyperintensities as a Predictor of 1-Year Risk for Ischemic Stroke Recurrence[J]. Neurol Ther, 2024,13(5):1467-1482.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Leukoaraiosis, Cognitive impairment, Logistic regression, Prediction model, Nomogram, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7100701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7100701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLeukoaraiosis (LA) is a common cerebral small vessel disease in elderly populations that frequently leads to cognitive impairment and may progress to vascular dementia. Early identification of cognitive dysfunction risk remains challenging due to the subtle onset and lack of specific biomarkers.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo identify key risk factors for cognitive impairment in LA patients and develop a logistic regression-based prediction model to facilitate early clinical identification and intervention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective study included 390 LA patients admitted to the Department of Neurology between June 2020 and April 2023. Patients were classified into cognitive impairment (CI) and non-cognitive impairment (NCI) groups based on Montreal Cognitive Assessment (MoCA) scores.Data collected included demographics, medical history, biochemical markers, and neuroimaging features. The dataset was randomly split 7:3 into training (n\u0026thinsp;=\u0026thinsp;273) and validation (n\u0026thinsp;=\u0026thinsp;117) sets. Univariate analysis identified significant variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which were then incorporated into multivariate logistic regression analysis. A nomogram was constructed based on the final model, and performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots for both training and validation sets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn the training set of 273 patients, 137 had cognitive impairment and 136 did not. Univariate analysis revealed that age, Fazekas score, intracranial arterial stenosis assessment (IASA), serum creatinine, and total bilirubin were significantly associated with cognitive impairment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic regression identified age (OR\u0026thinsp;=\u0026thinsp;1.17, 95%CI: 1.11\u0026ndash;1.24), IASA (OR\u0026thinsp;=\u0026thinsp;2.52, 95%CI: 1.64\u0026ndash;3.68), and Fazekas score (OR\u0026thinsp;=\u0026thinsp;2.58, 95%CI: 1.74\u0026ndash;3.60) as independent risk factors. The logistic regression model demonstrated excellent discrimination with AUC values of 0.873 for both training and validation sets. Calibration curves showed good agreement between predicted and observed probabilities, confirming model reliability.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAge, intracranial arterial stenosis assessment, and Fazekas score are independent risk factors for cognitive impairment in LA patients. The logistic regression model with nomogram provides a clinically practical tool for early identification and risk stratification of high-risk patients, enabling timely intervention to improve outcomes.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram for Predicting Cognitive Impairment in Patients with Leukoaraiosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 09:17:39","doi":"10.21203/rs.3.rs-7100701/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-04T05:25:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T22:18:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285158858529180355178103967212498157592","date":"2025-08-26T04:56:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72700333188561514008724022958687374947","date":"2025-08-24T12:58:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T20:27:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154939189874638645158906936646047906744","date":"2025-08-20T16:18:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T16:56:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T11:54:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T06:27:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-20T06:18:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-07-20T06:16:01+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":"93510e06-33cb-47b9-82af-ad6172d95e57","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:07:01+00:00","versionOfRecord":{"articleIdentity":"rs-7100701","link":"https://doi.org/10.1186/s12883-025-04464-2","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2025-11-20 15:58:29","publishedOnDateReadable":"November 20th, 2025"},"versionCreatedAt":"2025-08-20 09:17:39","video":"","vorDoi":"10.1186/s12883-025-04464-2","vorDoiUrl":"https://doi.org/10.1186/s12883-025-04464-2","workflowStages":[]},"version":"v1","identity":"rs-7100701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7100701","identity":"rs-7100701","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}