Risk Factor Analysis in Patients Exhibiting Cerebral Microbleeds and the Correlation with Cognitive Impairment

Objective: To identify the risk factors contributing to cerebral microbleeds (CMBs), analyze the correlation between the quantity and distribution of CMBs and overall cognitive performance, including specific cognitive domains in patients, and investigate the underlying mechanisms by which CMBs impact cognitive function. Methods: Patients diagnosed with cerebral small vessel disease were recruited between September 2022 and September 2023. Clinical baseline data were systematically gathered. The Montreal Cognitive Assessment (MoCA) was employed to evaluate patients' cognitive status. CMBs were identified via susceptibility-weighted imaging (SWI), noting their locations and quantities. Patients were categorized into two cohorts: those without CMBs and those with CMBs. This division facilitated the comparison of basic clinical data and laboratory indicators, aiming to elucidate the risk factors associated with CMBs. Within the CMBs cohort, patients were further classified based on the number of CMBs into mild, moderate, and severe groups, and according to CMBs' locations into deep, cortical-subcortical, and mixed groups. Spearman correlation analysis and ANOVA were utilized to compare the total MoCA scores, as well as scores in specific cognitive domains, across these groups. This approach enabled the analysis of the relationship between the quantity and location of CMBs and cognitive impairment. Results: Statistically significant differences were noted between patients with and without cerebral microbleeds (CMBs) regarding gender, age, hypertension, diabetes, history of cerebral infarction, history of alcohol consumption, glycosylated hemoglobin levels, low-density lipoprotein cholesterol, and homocysteine levels (p < .05). Multifactorial logistic regression analysis identified age, hypertension, diabetes, history of alcohol consumption, and elevated homocysteine as independent risk factors for the development of CMBs. Spearman correlation analysis revealed a linear correlation between the presence of CMBs and the total score of the MoCA (r = -.837, p < .001). The group with CMBs demonstrated a significant decline in visuospatial execution function and delayed recall abilities compared to the group without CMBs (p < .05). Specifically, deep CMBs were linked to impairments in visuospatial execution function, naming, attention, computational ability, language, delayed recall, and orientation (p < .05). Cortical-subcortical CMBs affected visuospatial execution function, attention, computational ability, and delayed recall ability(p < .05). Mixed CMBs impacted visuospatial execution function and naming (p < .05). Conclusion: Age, hypertension, diabetes, history of alcohol consumption, and elevated homocysteine levels are key independent risk factors for CMBs. There exists a linear relationship between the severity of CMBs and the extent of cognitive impairment. Patients with CMBs show notable deterioration in visuospatial execution function and delayed recall abilities. Furthermore, the location of CMBs influences various specific cognitive domains.