Lower 25-hydroxyvitamin D is associated with severer white matter hyperintensity and cognitive function in patients with non-disabling ischemic cerebrovascular events.

OBJECTIVES: This study aimed to investigate the potential correlations among serum 25-hydroxyvitamin D [25(OH)D] levels, white matter hyperintensity (WMH) and cognitive function in patients with non-disabling ischemic cerebrovascular events (NICE). METHODS: This was a prospective investigation of 160 NICE patients with age of 40 years or older. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCA). White matter lesions were evaluated by WMH using Fazekas scores. Spearman correlation analysis and linear regression models were used to identify the associations between serum 25(OH)D levels and cognitive function. Binary logistic regression analysis models were used to evaluate the predictable value of serum 25(OH)D levels and WMH for cognitive impairment. RESULTS: Patients with inadequate 25(OH)D levels had lower MoCA score (P=0.008), and a higher proportion of severe WMH (P=0.043). Spearman correlation analysis demonstrated that serum 25(OH)D concentrations were positively associated with MoCA score (rs=0.185, P=0.019) while negatively related to the proportion of severe WMH (sWMH) (rs=-0.166, P=0.036).The association between 25(OH)D concentrations and MoCA score remained significant in linear regression (adjusted ß=0.012, 95%CI:0.001-0.203).Adjusted binary logistic regression analysis showed that the odds ratio of cognitive impairment with insufficient 25(OH)D concentration was 5.038 (95%CI:1.154-21.988) compared with the sufficient group and the sWMH (OR=2.728, 95%CI:1.230-6.051) was identified as an independent risk factor for cognitive decline in NICE patients. CONCLUSION: Serum 25(OH)D levels and white matter lesions were independently and significantly associated with cognitive impairment in NICE patients.

Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.

Background and purpose: Corpus callosum (CC) infarction is an extremely rare subtype of cerebral ischemic stroke, however, the symptoms of cognitive impairment often fail to attract early attention of patients, which seriously affects the long-term prognosis, such as high mortality, personality changes, mood disorders, psychotic reactions, financial burden and so on. This study seeks to develop and validate models for early predicting the risk of subjective cognitive decline (SCD) after CC infarction by machine learning (ML) algorithms. Methods: This is a prospective study that enrolled 213 (only 3.7%) CC infarction patients from a nine-year cohort comprising 8,555 patients with acute ischemic stroke. Telephone follow-up surveys were carried out for the patients with definite diagnosis of CC infarction one-year after disease onset, and SCD was identified by Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. Based on the significant features selected by the least absolute shrinkage and selection operator (LASSO), seven ML models including Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naïve Bayes (GNB), Complement Naïve Bayes (CNB), and Support vector machine (SVM) were established and their predictive performances were compared by different metrics. Importantly, the SHapley Additive exPlanations (SHAP) was also utilized to examine internal behavior of the highest-performance ML classifier. Results: The Logistic Regression (LR)-model performed better than other six ML-models in SCD predictability after the CC infarction, with the area under the receiver characteristic operator curve (AUC) of 77.1% in the validation set. Using LASSO and SHAP analysis, we found that infarction subregions of CC infarction, female, 3-month modified Rankin Scale (mRS) score, age, homocysteine, location of angiostenosis, neutrophil to lymphocyte ratio, pure CC infarction, and number of angiostenosis were the top-nine significant predictors in the order of importance for the output of LR-model. Meanwhile, we identified that infarction subregion of CC, female, 3-month mRS score and pure CC infarction were the factors which independently associated with the cognitive outcome. Conclusion: Our study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.

Association of neutrophil to lymphocyte ratio and D-dimer with functional outcome in patients with cerebral venous sinus thrombosis.

BACKGROUND: Investigations on the risk factors for the prognosis of cerebral venous sinus thrombosis (CVST) are limited. This study aimed to explore whether specific inflammatory factors and coagulation indictors are associated with functional outcome in patients treated for CVST. METHODS: This retrospective study included 137 patients admitted to our hospital between January 2010 and October 2021. The functional outcome was assessed with the modified Rankin Scale (mRS) score at discharge. Patients were divided into two groups, 102 patients with favorable outcomes (mRS 0-1) and 35 patients with poor outcomes (mRS 2-6). The clinical indexes were compared between two groups. Multivariable logistic regression was performed to identify the independent influencing factors for poor outcomes of CVST patients. The prognostic indicators were analyzed using the receiver operating characteristic (ROC) curve. RESULTS: Compared with the favorable outcome group, the incidence of impaired consciousness and brain lesion, the levels of D-dimer, RDW, neutrophil count, neutrophil to lymphocyte ratio (NLR) and red blood cell distribution width to platelet ratio (%) on admission were significantly higher in the poor outcome group, while the level of lymphocyte count was significantly lower. After multivariable logistic regression analysis, baseline D-dimer level (odds ratio (OR), 1.180; 95% confidence interval (CI), 1.019-1.366, P = 0.027) and NLR (OR, 1.903; 95%CI, 1.232-2.938, P = 0.004) were significantly associated with unfavorable outcome at discharge. The ROC curve analysis showed that the areas under the curve of D-dimer, NLR and their combined detection for predicting worse outcome were 0.719, 0.707 and 0.786, respectively. CONCLUSIONS: Elevated D-dimer level and NLR on admission were associated with an increased risk of poor functional outcome in patients with CVST.

Correlations among peripheral blood markers, white matter hyperintensity, and cognitive function in patients with non-disabling ischemic cerebrovascular events.

Background: Both inflammation and cerebral white matter injury are closely associated with vascular cognitive impairment (VCI). The aim of this study was to analyze the correlation between peripheral serological markers, white matter injury, and cognitive function in patients with non-disabling ischemic cerebrovascular events (NICE); to identify potential biological markers for the diagnosis and prediction of VCI; and to provide a basis for the early diagnosis and intervention of VCI. Methods: We collected clinical data, along with demographic and medical history data, from 151 NICE patients. Fasting venous blood samples were collected. Based on the Montreal Cognitive Assessment (MoCA) after admission, we divided the patients into normal cognitive function (NCF) and VCI groups, and then classified them into mild white matter hyperintensity (mWMH) and severe white matter hyperintensity (sWMH) based on Fazekas scores. The differences in serological marker levels were compared between the cognitive function groups and the white matter hyperintensity groups. Binary logistic regression models and receiver operating characteristic curves were used to analyze the diagnostic predictive value of serological markers for VCI in patients with NICE and in the white matter hyperintensity subgroups. Results: Among 151 patients with NICE, 95 were male and 56 were female. Lymphocyte count (OR = 0.405, p = 0.010, 95% CI [0.201, 0.806]), red blood cell count (OR = 0.433, p = 0.010, 95% CI [0.228, 0.821]), and hemoglobin level (OR = 0.979, p = 0.046, 95% CI [0.958, 0.999]) were protective factors for cognitive function in patients with NICE. The sWMH group had a higher age, granulocyte/lymphoid ratio (NLR), and neutrophil percentage but a lower MoCA score, hemoglobin level, and lymphocyte count than the mWMH group. In the mWMH group, lymphocyte count (AUC = 0.713, p = 0.003, 95% CI [0.593, 0.833]) had an acceptable predictive value for the diagnosis of VCI, whereas white blood cell count (AUC = 0.672, p = 0.011, 95% CI [0.545, 0.799]), red blood cell count (AUC = 0.665, p = 0.014, 95% CI [0.545, 0.784]), and hemoglobin level (AUC = 0.634, p = 0.047, 95% CI [0.502, 0.765]) had marginal predictive value for the diagnosis of VCI. In the sWMH group, no significant differences were found in serological markers between the NCF and VCI groups. Conclusion: Lymphocyte count, red blood cell count, and hemoglobin level were independent protective factors for cognitive function in patients with NICE; they can be used as potential biological markers to distinguish VCI in patients with NICE and are applicable to subgroups of patients with mWMH.