Analysis of risk factors for the development of cognitive dysfunction in patients with cerebral small vessel disease and the construction of a predictive model

Establishment and evaluation of a clinical prediction model for cognitive impairment in patients with cerebral small vessel disease

BACKGROUND

There are currently no effective prediction methods for evaluating the occurrence of cognitive impairment in patients with cerebral small vessel disease (CSVD).

AIMS

To investigate the risk factors for cognitive dysfunction in patients with CSVD and to construct a risk prediction model.

METHODS

A retrospective study was conducted on 227 patients with CSVD. All patients were assessed by brain magnetic resonance imaging (MRI), and the Montreal Cognitive Assessment (MoCA) was used to assess cognitive status. In addition, the patient's medical records were also recorded. The clinical data were divided into a normal cognitive function group and a cognitive impairment group. A MoCA score < 26 (an additional 1 point for education < 12 years) is defined as cognitive dysfunction.

RESULTS

A total of 227 patients (mean age 66.7 ± 6.99 years) with CSVD were included in this study, of whom 68.7% were male and 100 patients (44.1%) developed cognitive impairment. Age (OR = 1.070; 95% CI = 1.015 ~ 1.128, p < 0.05), hypertension (OR = 2.863; 95% CI = 1.438 ~ 5.699, p < 0.05), homocysteine(HCY) (OR = 1.065; 95% CI = 1.005 ~ 1.127, p < 0.05), lacunar infarct score(Lac_score) (OR = 2.732; 95% CI = 1.094 ~ 6.825, P < 0.05), and CSVD total burden (CSVD_score) (OR = 3.823; 95% CI = 1.496 ~ 9.768, P < 0.05) were found to be independent risk factors for cognitive decline in the present study. The above 5 variables were used to construct a nomogram, and the model was internally validated by using bootstrapping with a C-index of 0.839. The external model validation C-index was 0.867.

CONCLUSIONS

The nomogram model based on brain MR images and clinical data helps in individualizing the probability of cognitive impairment progression in patients with CSVD.

A novel rat model of cerebral small vessel disease based on vascular risk factors of hypertension, aging, and cerebral hypoperfusion

Cerebral small vessel disease (CSVD) is a major cause of vascular cognitive impairment and functional loss in elderly patients. Progressive remodeling of cerebral microvessels due to arterial hypertension or other vascular risk factors, such as aging, can cause dementia or stroke. Typical imaging characteristics of CSVD include cerebral microbleeds (CMB), brain atrophy, small subcortical infarctions, white matter hyperintensities (WMH), and enlarged perivascular spaces (EPVS). Nevertheless, no animal models that reflect all the different aspects of CSVD have been identified. Here, we generated a new CSVD animal model using D-galactose (D-gal) combined with cerebral hypoperfusion in spontaneously hypertensive rats (SHR), which showed all the hallmark pathological features of CSVD and was based on vascular risk factors. SHR were hypodermically injected with D-gal (400 mg/kg/d) and underwent modified microcoil bilateral common carotid artery stenosis surgery. Subsequently, neurological assessments and behavioral tests were performed, followed by vascular ultrasonography, electron microscopy, flow cytometry, and histological analyses. Our rat model showed multiple cerebrovascular pathologies, such as CMB, brain atrophy, subcortical small infarction, WMH, and EPVS, as well as the underlying causes of CSVD pathology, including oxidative stress injury, decreased cerebral blood flow, structural and functional damage to endothelial cells, increased blood-brain barrier permeability, and inflammation. The use of this animal model will help identify new therapeutic targets and subsequently aid the development and testing of novel therapeutic interventions. Main process of the study: Firstly, we screened for optimal conditions for mimicking aging by injecting D-gal into rats for 4 and 8 weeks. Subsequently, we performed modified microcoil BCAS intervention for 4 and 8 weeks in rats to screen for optimal hypoperfusion conditions. Finally, based on these results, we combined D-gal for 8 weeks and modified microcoil BCAS for 4 weeks to explore the changes in SHR.

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.

A nomogram for predicting mild cognitive impairment in older adults with hypertension

BACKGROUND

Hyper- and hypotension increase the risk of cognitive dysfunction. As effective control of blood pressure can reduce the risk of mild cognitive impairment (MCI), early risk assessment is necessary to identify MCI in senile hypertension as soon as possible and reduce the risk of developing dementia. No perfect risk-prediction model or nomogram has been developed to evaluate the risk of MCI in older adults with hypertension. We aimed to develop a nomogram model for predicting MCI in older patients with hypertension.

METHODS

We selected 345 older patients with hypertension in Xixiangtang District, Nanning City, as the modeling group and divided into the MCI (n = 197) and non-MCI groups (n = 148). Comparing the general conditions, lifestyle, disease factors, psychosocial and other indicators. Logistic regression was used to analyze risk factors for MCI in older hypertensive patients, and R Programming Language was used to draw the nomogram. We selected 146 older patients with hypertension in Qingxiu District, Nanning City, as the verification group. The effectiveness and discrimination ability of the nomogram was evaluated through internal and external verification.

RESULTS

Multivariate logistic regression analysis identified 11 factors, including hypertension grade, education level, complicated diabetes, hypertension years, stress history, smoking, physical exercise, reading, social support, sleep disorders, and medication compliance, as risk factors for MCI in older patients with hypertension. To develop a nomogram model, the validity of the prediction model was evaluated by fitting the curve, which revealed a good fit for both the modeling (P = 0.98) and verification groups (P = 0.96). The discrimination of the nomogram model was evaluated in the modeling group using a receiver operating characteristic curve. The area under the curve was 0.795, and the Hosmer-Lemeshow test yielded P = 0.703. In the validation group, the area under the curve was 0.765, and the Hosmer-Lemeshow test yielded P = 0.234.

CONCLUSIONS

We developed a nomogram to help clinicians identify high-risk groups for MCI among older patients with hypertension. This model demonstrated good discrimination and validity, providing a scientific basis for community medical staff to evaluate and identify the risk of MCI in these patients at an early stage.

Cognitive dysfunction associated with COVID-19: Prognostic role of circulating biomarkers and microRNAs

COVID-19 is renowned as a multi-organ disease having subacute and long-term effects with a broad spectrum of clinical manifestations. The evolving scientific and clinical evidence demonstrates that the frequency of cognitive impairment after COVID-19 is high and it is crucial to explore more clinical research and implement proper diagnostic and treatment strategies. Several central nervous system complications have been reported as comorbidities of COVID-19. The changes in cognitive function associated with neurodegenerative diseases develop slowly over time and are only diagnosed at an already advanced stage of molecular pathology. Hence, understanding the common links between COVID-19 and neurodegenerative diseases will broaden our knowledge and help in strategizing prognostic and therapeutic approaches. The present review focuses on the diverse neurodegenerative changes associated with COVID-19 and will highlight the importance of major circulating biomarkers and microRNAs (miRNAs) associated with the disease progression and severity. The literature analysis showed that major proteins associated with central nervous system function, such as Glial fibrillary acidic protein, neurofilament light chain, p-tau 181, Ubiquitin C-terminal hydrolase L1, S100 calcium-binding protein B, Neuron-specific enolase and various inflammatory cytokines, were significantly altered in COVID-19 patients. Furthermore, among various miRNAs that are having pivotal roles in various neurodegenerative diseases, miR-146a, miR-155, Let-7b, miR-31, miR-16 and miR-21 have shown significant dysregulation in COVID-19 patients. Thus the review consolidates the important findings from the numerous studies to unravel the underlying mechanism of neurological sequelae in COVID-19 and the possible association of circulatory biomarkers, which may serve as prognostic predictors and therapeutic targets in future research.

Biomarkers involved in the pathogenesis of cerebral small-vessel disease

Cerebral small-vessel disease (CSVD) has been found to have a strong association with vascular cognitive impairment (VCI) and functional loss in elderly patients. At present, the diagnosis of CSVD mainly relies on brain neuroimaging markers, but they cannot fully reflect the overall picture of the disease. Currently, some biomarkers were found to be related to CSVD, but the underlying mechanisms remain unclear. We aimed to systematically review and summarize studies on the progress of biomarkers related to the pathogenesis of CSVD, which is mainly the relationship between these indicators and neuroimaging markers of CSVD. Concerning the pathophysiological mechanism of CSVD, the biomarkers of CSVD have been described as several categories related to sporadic and genetic factors. Monitoring of biomarkers might contribute to the early diagnosis and progression prediction of CSVD, thus providing ideas for better diagnosis and treatment of CSVD.