Cui Yu, Zhao Tong, Zhang Weifu, Wang Rongguo, Hu Ming, He Xiying, Wang Ying, Xie Hongyan
The Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong, China.
Shandong First Medical University, Ji'nan, Shandong, China.
Gerontol Geriatr Med. 2024 Sep 4;10:23337214241278497. doi: 10.1177/23337214241278497. eCollection 2024 Jan-Dec.
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. 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. 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 ( < .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 ( = -.837, < .001). The group with CMBs demonstrated a significant decline in visuospatial execution function and delayed recall abilities compared to the group without CMBs ( < .05). Specifically, deep CMBs were linked to impairments in visuospatial execution function, naming, attention, computational ability, language, delayed recall, and orientation ( < .05). Cortical-subcortical CMBs affected visuospatial execution function, attention, computational ability, and delayed recall ability( < .05). Mixed CMBs impacted visuospatial execution function and naming ( < .05). 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.
为了确定导致脑微出血(CMBs)的危险因素,分析CMBs的数量和分布与整体认知表现(包括患者的特定认知领域)之间的相关性,并研究CMBs影响认知功能的潜在机制。在2022年9月至2023年9月期间招募了被诊断为脑小血管疾病的患者。系统收集临床基线数据。采用蒙特利尔认知评估量表(MoCA)评估患者的认知状态。通过磁敏感加权成像(SWI)识别CMBs,记录其位置和数量。患者被分为两组:无CMBs组和有CMBs组。这种分组便于比较基本临床数据和实验室指标,旨在阐明与CMBs相关的危险因素。在有CMBs组中,患者根据CMBs的数量进一步分为轻度、中度和重度组,并根据CMBs的位置分为深部、皮质 - 皮质下和混合组。利用Spearman相关性分析和方差分析比较这些组的MoCA总分以及特定认知领域的得分。这种方法能够分析CMBs的数量和位置与认知障碍之间的关系。有和无脑微出血(CMBs)的患者在性别、年龄、高血压、糖尿病、脑梗死病史、饮酒史、糖化血红蛋白水平、低密度脂蛋白胆固醇和同型半胱氨酸水平方面存在统计学显著差异(P < 0.05)。多因素逻辑回归分析确定年龄、高血压、糖尿病、饮酒史和同型半胱氨酸升高是CMBs发生的独立危险因素。Spearman相关性分析显示CMBs的存在与MoCA总分之间存在线性相关性(r = -0.837,P < 0.001)。与无CMBs组相比,有CMBs组在视觉空间执行功能和延迟回忆能力方面显著下降(P < 0.05)。具体而言,深部CMBs与视觉空间执行功能、命名、注意力、计算能力、语言、延迟回忆和定向方面的损害有关(P < 0.05)。皮质 - 皮质下CMBs影响视觉空间执行功能、注意力、计算能力和延迟回忆能力(P < 0.05)。混合性CMBs影响视觉空间执行功能和命名(P < 0.05)。年龄、高血压、糖尿病、饮酒史和同型半胱氨酸水平升高是CMBs的关键独立危险因素。CMBs的严重程度与认知障碍程度之间存在线性关系。有CMBs的患者在视觉空间执行功能和延迟回忆能力方面表现出明显恶化。此外,CMBs的位置影响各种特定认知领域。