Association between total cerebral small vessel disease score and cognitive function in patients with vascular risk factors

Correlation analysis and predictive model construction of metabolic syndrome, complete blood count-derived inflammatory markers, and overall burden of cerebral small vessel disease

Background

The high burden of cerebral small vessel disease (CSVD) on neuroimaging is a significant risk factor for stroke, cognitive dysfunction, and emotional disorders. Currently, there is a lack of studies investigating the correlation between metabolic syndrome (MetS), complete blood count-derived inflammatory markers, and total CSVD burden. This study aims to evaluate the total CSVD imaging load using machine learning (ML) algorithms and to explore further the relationship between MetS, complete blood count-derived inflammatory markers, and CSVD load.

Methods

We included CSVD patients from Xijing Hospital (2012-2022). Univariate and lasso regression analyses identified variables linked to CSVD neuroimaging burden. Six ML models predicted CSVD burden based on MetS and inflammatory markers. Model performance was evaluated using ROCauc, PRauc, DCA, and calibration curves. The SHAP method validated model interpretability. The best-performing model was selected to develop a web-based calculator using the Shiny package.

Results

The Logistic regression model outperformed others in predicting CSVD burden. The model incorporated MetS, neutrophil-to-lymphocyte ratio (NLR), homocysteine (Hcy), age, smoking status, cystatin C (CysC), uric acid (UA), and prognostic nutritional index (PNI).

Conclusion

MetS, NLR, Hcy and CSVD high load were positively correlated, and the Logistic regression model could accurately predict the total CSVD load degree.

Recurrent endothelin-1 mediated vascular insult leads to cognitive impairment protected by trophic factor pleiotrophin

Vascular dementia (VaD) is a complex neurodegenerative condition, with cerebral small vessel dysfunctions as the central role in its pathogenesis. Given the lack of suitable animal models to study the disease pathogenesis, we developed a mouse model to closely emulate the clinical scenarios of recurrent transient ischemic attacks (TIAs) leading to VaD using vasoconstricting peptide Endothelin-1(ET-1). We observed that administration of ET-1 led to blood-brain barrier (BBB) disruption and detrimental changes in its components, such as endothelial cells and pericytes, along with neuronal loss and synaptic dysfunction, resulting in irreversible memory loss. Further, in our pursuit of understanding potential interventions, we co-administered pleiotrophin (PTN) alongside ET-1 injections. PTN exhibited remarkable efficacy in preserving vital components of the BBB, including endothelial cells and pericytes, thereby restoring BBB integrity, preventing neuronal loss, and enhancing memory function. Our findings give a valuable framework for understanding the detrimental effects of multiple TIAs on brain health and provide a useful animal model to explore VaD's underlying mechanisms further and pave the way for promising therapies.

Slow Gait Speed Predicts Incident Dementia, Mortality, and Long-Term Functional Outcome in Cerebral Small-Vessel Disease

Background

Gait impairment is observed in patients with small vessel disease (SVD); however, the association between gait function and long-term outcome remains unclear.

Objectives

This study aimed to clarify the predictive value of gait function on incident dementia, survival and functional outcome.

Methods

Data were derived from a Japanese cohort of patients with SVD. This study included 522 participants who underwent 3-m timed up and go test (TUG), and gait speed, TUG time, was divided into tertiles. Magnetic resonance imaging was used to evaluate severity of white matter hyperintensities, lacunes, and medial temporal atrophy. Primary outcome was dementia. All-cause death and functional outcome by modified Rankin scale at the last visit was also evaluated.

Results

The median age was 71 years, and median TUG time was 9.91 s. During follow-up period of 4.8 years, 32 cases of dementia occurred. Cox proportional hazard analysis revealed that slow gait speed (TUG time >  10.88 s) was associated with a significantly higher risk of incident dementia than fast (TUG time <  9.03) and middle (TUG time, 9.04-10.87 s) speeds after adjusting risk factors, Mini-Mental State Examination, SVD severity and brain atrophy (adjusted hazard ratio, 2.73; 95% confidence interval, 1.16-6.42, p = 0.022). Slow speed was also associated with mortality and poor functional outcome compared with other speeds (adjusted odds ratio, 4.19; 95% confidence interval 1.92-9.18, p <  0.001).

Conclusions

Gait function was associated with incident dementia, mortality and poor functional outcome independently of cognitive function, brain atrophy, and SVD severity.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis

Background

Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes.

Objective

The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD.

Methods

The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA).

Results

A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set.

Conclusion

This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Spatiotemporal consistency analysis of cerebral small vessel disease: an rs-fMRI study

Introduction

Cerebral small vessel disease (SVD) affects older adults, but traditional approaches have limited the understanding of the neural mechanisms of SVD. This study aimed to explore the effects of SVD on brain regions and its association with cognitive decline using the four-dimensional (spatiotemporal) consistency of local neural activity (FOCA) method.

Methods

Magnetic resonance imaging data from 42 patients with SVD and 38 healthy controls (HCs) were analyzed using the FOCA values. A two-sample t test was performed to compare the differences in FOCA values in the brain between the HCs and SVD groups. Pearson correlation analysis was conducted to analyze the association of various brain regions with SVD scores.

Results

The results revealed that the FOCA values in the right frontal_inf_oper, right temporal_pole_sup, and default mode network decreased, whereas those in the temporal_inf, hippocampus, basal ganglia, and cerebellum increased, in patients with SVD. Most of these varying brain regions were negatively correlated with SVD scores.

Discussion

This study suggested that the FOCA approach might have the potential to provide useful insights into the understanding of the neurophysiologic mechanisms of patients with SVD.

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

Background

The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.

Method

Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.

Results

Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.

Conclusion

These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning.