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临床研究
基于MRI 3D-FLAIR序列对终末期肾病患者脑白质高信号病变特征与临床指标相关性研究
邹帆 王海宝 方杰 齐向明 周宇 李晓舒

Cite this article as: ZOU F, WANG H B, FANG J, et al. A study investigating the correlation between white matter hyperintensities lesion characteristics in end-stage renal disease patients and clinical indicators using MRI 3D-FLAIR[J]. Chin J Magn Reson Imaging, 2025, 16(6): 55-59, 65.本文引用格式:邹帆, 王海宝, 方杰, 等. 基于MRI 3D-FLAIR序列对终末期肾病患者脑白质高信号病变特征与临床指标相关性研究[J]. 磁共振成像, 2025, 16(6): 55-59, 65. DOI:10.12015/issn.1674-8034.2025.06.008.


[摘要] 目的 通过MRI三维液体抑制反转恢复(three dimensional-fluid attenuated inversion recovery, 3D-FLAIR)序列检测终末期肾病(end-stage renal disease, ESRD)患者脑白质高信号(white matter hyperintensities, WMH)的体积及分布特点,探讨病变发生是否存在特征性脑区以及WMH体积与临床生化指标和认知功能的相关性。材料与方法 共纳入81例ESRD患者及77例健康对照组MRI 3D-T1WI、3D-FLAIR图像数据及临床生化指标,利用CAT12软件分析3D-T1WI图像得到各个被试者全脑体积,利用病灶分割软件(lesion segmentation tool, LST)中的病灶预测算法(lesion prediction algorithm, LPA)分析3D-FLAIR图像得到被试者全脑WMH体积和分布图。使用两样本t检验和Mann-Whitney U检验分析两组间认知功能评分和WMH严重程度(全脑WMH体积与全脑体积比)的差异性,使用非参数映射(non-parametric mapping, NPM)软件中Liebermeister检验分析两组WMH分布图的差异性,采用偏相关分析控制年龄和性别的影响后,评估WMH严重程度与临床生化指标及认知功能之间的相关性,进一步根据WMH严重程度对两组进行判别,并绘制ROC曲线。结果 ESRD组认知功能评分低于对照组[蒙特利尔认知评估量表评分(Montreal Cognitive Assessment, MoCA):22.44±5.23 vs. 26.06±3.20,P<0.001;简易智能状态检查量表评分(Mini-Mental State Examination, MMSE):25.96±3.81 vs. 28.61±1.85,P<0.001];ESRD组WMH严重程度高于对照组[1.40(2.60)vs. 0.36(0.40),P<0.001];ESRD组侧脑室旁出现WMH的比例更高(Z值:1.914~6.483,P<0.05);WMH严重程度与认知功能无关(P>0.05),与血清白蛋白和肾小球滤过率呈负相关(r=-0.337,P=0.002;r=-0.231,P=0.041)。ROC曲线下面积为0.817(95% CI:0.751~0.884)。结论 ESRD患者脑室周围白质易于受到损伤,且与肾功能和血清白蛋白下降密切相关,WMH可作为有效判别ESRD脑白质损害的重要影像学指标。
[Abstract] Objective To investigate the volume and distribution characteristics of white matter hyperintensities (WMH) in patients with end-stage renal disease (ESRD) using MRI three dimensional-fluid attenuated inversion recovery (3D-FLAIR), and to determine if specific brain regions are more susceptible to lesion development and to assess the relationship between WMH volume and clinical biochemical markers and cognitive function.Materials and Methods MRI image data and clinical biochemical indices from 81 ESRD patients and 77 healthy controls were collected. CAT12 software was used to analyze 3D-T1WI images to obtain the whole brain volume of each subject. The 3D-FLAIR images were analyzed by Lesion Prediction Algorithm (LPA) in Lesion Segmentation Tool (LST) to obtain the volume and distribution map of WMH. Two-sample t-test and Mann-Whitney U test were used to analyze the differences in cognitive function scores and the severity of WMH (the ratio of WMH to total brain volume) between the two groups. The Liebermeister test in non-parametric mapping (NPM) software was employed to compare the distribution maps between the two groups. After controlling for the effects of age and gender using partial correlation analysis, the correlations between the severity of WMH and both clinical biochemical indicators as well as cognitive function were assessed. Furthermore, the two groups were categorized according to the severity of WMH, and the ROC curve was constructed.Results The cognitive function scores of the ESRD group were significantly lower compared to the control group [Montreal Cognitive Assessment (MoCA): 22.44 ± 5.23 vs. 26.06 ± 3.20, P < 0.001; Mini-Mental State Examination (MMSE): 25.96 ± 3.81 vs. 28.61 ± 1.85, P < 0.001]. The severity of WMH in the ESRD group was significantly higher than in the control group [1.40 (2.60) vs. 0.36 (0.40), P < 0.001]. The proportion of WMH in the ESRD group was also higher (Z: 1.914 to 6.483, P < 0.05). Although WMH severity was not associated with cognitive function (P > 0.05), it was negatively correlated with serum albumin and glomerular filtration rate (r = -0.337, P = 0.002; r = -0.231, P = 0.041). The area under the ROC curve was 0.817 (95% CI: 0.751 to 0.884).Conclusions Periventricular white matter is particularly vulnerable to damage in ESRD patients, which is closely linked to the decline in renal function and serum albumin levels. WMH serves as a significant imaging marker for effectively distinguishing white matter damage in ESRD.
[关键词] 终末期肾脏病;磁共振成像;脑白质高信号
[Keywords] end-stage renal disease;white matter hyperintensities;magnetic resonance imaging

邹帆 1   王海宝 1   方杰 1   齐向明 2   周宇 1   李晓舒 1*  

1 安徽医科大学第一附属医院医学影像科,合肥 230022

2 安徽医科大学第一附属医院肾脏内科,合肥 230022

通信作者:李晓舒,E-mail:lixiaoshu2016@163.com

作者贡献声明::李晓舒负责设计本研究的方案,对稿件的重要内容进行了修改;邹帆负责起草和撰写稿件,获取、分析和解释本研究的数据;齐向明负责获取本研究的数据,对稿件重要内容进行了修改,并获得了安徽省自然科学基金项目和安徽高校自然科学研究项目资助;王海宝、方杰、缪莹莹、周宇负责获取、分析和解释本研究的数据,对稿件重要内容进行了修改。全体作者都同意发表最后的修改稿,同意对本研究的所有方面负责,确保本研究的准确性和诚信。


基金项目: 安徽省自然科学基金项目 1908085MH245 安徽高校自然科学研究项目 KJ2018A0493
收稿日期:2025-03-18
接受日期:2025-06-05
中图分类号:R445.2  R692.5 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2025.06.008
本文引用格式:邹帆, 王海宝, 方杰, 等. 基于MRI 3D-FLAIR序列对终末期肾病患者脑白质高信号病变特征与临床指标相关性研究[J]. 磁共振成像, 2025, 16(6): 55-59, 65. DOI:10.12015/issn.1674-8034.2025.06.008.

0 引言

       终末期肾病(end-stage renal disease, ESRD)指各种慢性肾脏疾病的终末阶段,一般肾小球滤过率降至15 mL/(min•1.73 m2)即可诊断[1]。脑小血管病变常常合并脑白质高信号(white matter hyperintensities, WMH),在磁共振T2WI或液体抑制反转恢复(fluid attenuated inversion recovery, FLAIR)上表现为侧脑室旁或大脑深部白质斑点状或片状高信号区[2, 3, 4]。有研究指出,ESRD可引起脑小血管或微血管疾病,患者相较于正常人更容易出现脑血管病变和WMH[5, 6, 7]。近年来的研究常通过WMH视觉评分方式分析ESRD患者的2D-FLAIR图像,评估患者WMH严重程度[8]。既往研究在多发性硬化的患者中发现,3D-FLAIR相较于2D-FLAIR具有对连续切片进行多平面重构的优点,能检测出更多的病灶,且图像具有更高的信噪比和对比度噪声比[9]。结合目前已有的病灶分割工具(lesion segmentation tool, LST)自动化分割WMH区域,得到具体的WMH体积和空间分布,从而可以进一步对WMH进行定量分析研究,并且该工具操作便捷,具有一定的可靠性[10, 11]。相关文献证实,不同的WMH空间分布模式与不同的潜在WMH病因相关[12, 13, 14]。目前对于ESRD患者WMH的相关研究存在主观性强、精确度低等不足,且缺少有关ESRD患者WMH空间分布的研究,因此本研究将采用LST等工具检测ESRD患者3D-FLAIR图像上WMH病灶影像特征,进而定量分析ESRD患者与对照组WMH的体积和空间分布差异,提高对ESRD患者WMH严重程度评估的可靠性和可重复性,并结合ESRD患者临床生化指标及认知功能评分,分析其中的相关性,为ESRD患者脑损伤机制提供影像学依据及新方法。

1 材料与方法

1.1 研究对象

       本研究纳入2024年4月至2024年10月期间在安徽医科大学第一附属医院高新院区肾脏内科住院,且符合《肾脏病与透析患者生存质量指导指南》标准的ESRD患者。排除标准:(1)年龄70岁以上;(2)急性肾功能衰竭、肾移植手术患者;(3)既往患有急性心脑血管病史;(4)存在急性感染的患者;(5)既往病史存在颅内器质性病变;(6)存在神经精神心理疾病等;(7)存在严重糖尿病及冠心病、重大躯体疾病等;(8)存在药物、酒精、毒品等物质依赖或滥用史。同期招募与ESRD患者组年龄、性别相对匹配的志愿者作为对照组。除上述排除标准外,对照组排除患有高血压、糖尿病等身体疾病,且无肾病史。ESRD组所有受试者均控制血压在正常范围内,行头颅磁共振检查当天早上,空腹接受实验室生化检查,包括肾小球滤过率及血清白蛋白浓度。

       本研究遵守《赫尔辛基宣言》,得到安徽医科大学生物伦理委员会批准(批准文号:2024407),所有受试者均签署了知情同意书。

1.2 MRI检查

       ESRD患者组及对照组使用Philips Ingenia 3.0 T磁共振机器(16通道头线圈)和Siemens MAGNETOM Vida 3.0T磁共振机器(64通道头线圈)进行头颅MRI扫描及磁共振数据采集。研究对象头部用套垫固定,保持整个扫描期间头不动。扫描前告知患者保持清醒、全身放松状态。扫描时患者佩戴耳塞,减少机器噪声的影响。全部研究对象进行3D-T1WI及3D-FLAIR矢状位扫描。Philips设备扫描序列及参数:(1)3D-T1WI序列,TR 6.5 ms,TE 2.9 ms,层厚1 mm,层数211,层间距0 mm,矩阵256×256,视野(field of view, FOV)256 mm×256 mm,扫描时间6 min 12 s;(2)3D-FLAIR序列,TR 4800 ms,TE 281 ms,TI 1650 ms,层厚1 mm,层数160,层间距0 mm,矩阵256×256,FOV 256 mm×256 mm,扫描时间5 min 45 s。Siemens设备扫描序列及参数:(1)3D-T1WI序列,TR 2300 ms,TE 2.9 ms,层厚1 mm,层数208,层间距0 mm,矩阵256×256,FOV 256 mm×256 mm,扫描时间5 min 12 s;(2)3D-FLAIR序列,TR 4800 ms,TE 441 ms,TI 1650 ms,层厚1 mm,层数160,层间距0 mm,矩阵256×256,FOV 256 mm×256 mm,扫描时间5 min 33 s。

1.3 影像学数据处理及分析

       首先,原始3D-T1WI及3D-FLAIR数据利用Dcm2niigui(http://www.mricro.com)软件由DICOM格式转换成Nifti格式,然后基于矩阵实验室MALTAB R2018a(Matrix Laboratory, R2018a)数据处理平台,采用SPM12工具(http://www.fil.ion.ucl.ac.uk/spm)中的CAT12(http://www.neuro.uni-jena.de)工具对所采集的磁共振图像进行质量分析和全脑体积计算,选择图像质量评分在B-及以上(噪声对比度<4%,非均匀性对比度<80%,图像体素分辨率<1 mm)的数据进行下一步分析[15, 16]。使用SPM12的LST工具(version 3.0.0,http://www.statistical-modelling.de/lst.html)中的病灶预测算法(lesion prediction algorithm, LPA)自动分割被试者3D-FLAIR图像中的WMH,病灶图像阈值设为0.5,获得被试者WMH的病灶分布图和全脑WMH体积[11]。应用脑功能磁共振成像(functional magnetic resonance imaging of the brain, FMRIB)数据软件库(FSL, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL),将病灶分布图和对应的3D-FLAIR图像共配准到MNI152_T1_1mm模板上。然后使用LST工具对配准后的分布图进行二进制转换,利用Mricron(www.mricro.com)软件将ESRD组及对照组的病灶分布图分别进行整合得到两组的病灶分布图。研究对象纳入排除流程见图1

图1  研究对象入组流程图。ESRD:终末期肾病。
Fig. 1  Process flowchart for enrollment of study population. ESRD: end-stage renal disease.

1.4 认知功能评分

       采用简易智能状态检查(Mini-Mental State Examination, MMSE)量表以及蒙提利尔认知评估量表(Montreal Cognitive Assessment, MoCA)对受试者进行认知功能评估[17, 18]。MMSE量表总分30分,分值越高表示认知功能越佳,评分>28为正常。MoCA量表总分30分,教育年限≤12年加1分,分值越高表示认知功能越佳,评分>26为正常。

1.5 样本量计算

       本研究为随机对照试验,试验组和对照组比例为1∶1,主要结局指标为研究对象WMH体积。根据既往文献报道数据[19],设β=0.2,把握度(Power=1-β)=80%,显著性水准双侧α=0.05,k=1,估算各组样本量为60,考虑10%脱落率,则各组需要样本量为66。

1.6 统计学分析

       使用SPSS 22.0统计学软件完成分析。对两组间年龄、教育水平、WMH严重程度(全脑WMH体积与全脑体积比)及认知功能评分进行Kolmogorov-Smirnov正态性检验,符合正态分布的以平均数±标准差(x¯±s)表示,采用两独立样本t检验,非正态分布的数据以中位数(四分间距)表示,采用Mann-Whitney U检验,两组间性别采用卡方检验,双侧P<0.05表示差异有统计学意义。

       使用Mricron软件将两组图像分别叠加到标准模板上显示,再利用其中的非参数映射(non-parametric mapping, NPM)软件对两组图片进行基于体素的病灶-症状映射分析(voxel-based lesion-symptom mapping, VLSM),两组数据采用Liebermeister检验,忽略10%个体,结果经过FDR校正,以P<0.05为差异有统计学意义,得到病灶分布差异图[20, 21]

       使用偏相关分析,将年龄和性别作为控制变量,检验ESRD患者WMH严重程度分别与认知功能评分、血清白蛋白和肾小球滤过率的相关性,P<0.05为差异有统计学意义。使用线性回归分析,将ESRD组的年龄、性别作为自变量,WMH严重程度、血清白蛋白和肾小球滤过率分别作为因变量,计算ESRD组WMH严重程度、血清白蛋白和肾小球滤过率的残差,并绘制ESRD组WMH严重程度残差与血清白蛋白残差、WMH严重程度残差与肾小球滤过率残差的散点图。

       将WMH严重程度作为检验变量,两组类别作为状态变量绘制ROC曲线。计算曲线下面积(area under the curve, AUC)、敏感度、特异度、约登指数。

2 结果

2.1 一般临床资料、WMH严重程度及认知功能评分统计学比较

       本研究共纳入ESRD组81例,对照组77例,两组间一般临床资料的差异性分析结果显示ESRD组年龄与对照组年龄差异无统计学意义(P=0.358);ESRD组男女性别比与对照组间差异无统计学意义(P=0.567),ESRD组认知功能评分低于对照组(MoCA:P<0.001;MMSE:P<0.001);ESRD组WMH严重程度(全脑WMH体积与全脑体积比)高于对照组(P<0.001)。详见表1

表1  ESRD组和对照组一般临床资料、认知功能评分及WMH严重程度分析结果
Tab. 1  Analysis results of general clinical data, cognitive function scores and severity of WMH in the ESRD group and the control group

2.2 ESRD组和对照组WMH分布图比较

       ESRD组和对照组病灶分布图分别叠加到标准模板上显示。两组间病灶分布图比较分析,结果显示双侧室周WMH分布比例差异具有统计学意义(Z值:1.914~6.483,P<0.05,FDR矫正)(图2)。

图2  终末期肾病(ESRD)组和对照组脑白质高信号(WMH)分布结果图。2A:对照组病灶分布图,n表示病灶重叠数量;2B:ESRD组病灶分布图,n表示病灶重叠数量;2C:对照组与ESRD组病灶分布差异图(经FDR校正,Z值为1.914~6.483,P<0.05),高亮区域表示ESRD组WMH比例高于对照组,集中于侧脑室周围。
Fig. 2  Plots of white matter hyperintensities (WMH) distribution results for end-stage renal disease (ESRD) and control groups. 2A: Lesion distribution map of the control group, n represents the number of overlapping lesions; 2B: Lesion distribution map of ESRD group, n represents the number of overlapping lesions; 2C: The difference in lesion distribution between the control group and the ESRD group (FDR corrected, Z value: 1.914 to 6.483, P < 0.05), the highlighted area indicates that the proportion of WMH in the ESRD group is significantly higher than that in the control group, and it is concentrated around the lateral ventricle.

2.3 WMH严重程度与临床生化指标及认知功能评分相关性分析

       将年龄、性别作为控制变量,分别对ESRD组WMH严重程度与血清白蛋白、肾小球滤过率和认知功能评分进行偏相关分析,结果显示WMH严重程度与血清白蛋白水平呈负相关(r=-0.337,P=0.002),与肾小球滤过率呈负相关(r=-0.231,P=0.041),与MoCA和MMSE评分无关(P=0.922,P=0.761),见图3

图3  脑白质高信号(WMH)严重程度与临床生化指标的残差散点图。
Fig. 3  Residual scatter plot of white matter hyperintensities (WMH) severity and clinical biochemical indicators.

2.4 WMH严重程度的诊断效能分析

       将WMH严重程度作为检验变量,两组类别作为状态变量绘制ROC曲线。结果显示WMH严重程度诊断ESRD具有良好诊断效能(AUC=0.817;95% CI:0.751~0.884),最大约登指数为0.571,相应的敏感度为76.5%,特异度为80.6%(图4)。

图4  脑白质高信号(WMH)严重程度ROC曲线。ROC:受试者工作特征;AUC:曲线下面积。
Fig. 4  ROC curve of white matter hyperintensities (WMH) severity. ROC: receiver operating characteristic; AUC: area under the curve.

3 讨论

       本研究首次将LST工具应用于ESRD患者的3D-FLAIR影像分析,并对WMH空间分布及严重程度进行定量分析,发现ESRD患者相较于对照组更容易出现室周WMH,且WMH严重程度与血清白蛋白水平和肾小球滤过率存在相关性,表明ESRD能引起脑室周白质变化,提示血清白蛋白可能参与WMH形成机制,为“肾-脑轴”理论提供了影像学依据,支持ESRD系统性代谢紊乱对脑白质的特异性影响。

3.1 ESRD患者室周WMH的病因分析

       本研究发现ESRD患者与对照组相比更容易出现室周WMH。YUSIPOV等[22]通过分析慢性肾病患者炎症/免疫生物标志物的差异表达,发现ESRD患者具有一致的加速衰老表型;PLANTONE等[19]发现慢性肾病患者WMH体积增加,且与年龄存在相关;PHUAH等[13]将WMH分为5种独特的空间模式:额叶深部、脑室周围、皮质旁、顶叶和后部,并发现额叶深部WMH与小动脉硬化(高血压、糖尿病)的危险因素有关,而脑室周围WMH只与年龄有关,与脑小血管疾病危险因素和认知障碍无关;BACHMANN等[23]在轻度认知障碍的患者中发现侧脑室后角和深部WMH与肾脏疾病有关。既往研究结果进一步支持ESRD患者更容易出现室周WMH的结果,提示ESRD存在加速患者衰老的机制[13, 15, 22]。ESRD患者由于肾功能严重减退,一方面血液内的尿毒素不能经尿液清除,导致ESRD患者脑血管内皮功能障碍,血脑屏障被破坏,尿毒素在脑内蓄积;另一方面,ESRD患者促红细胞生成素减少,慢性贫血导致代偿性脑灌注增加,进一步加重尿毒素在脑内蓄积[24]。尿毒素通过血脑屏障进入大脑不同区域,包括位于脑室下区的神经祖细胞壁龛[25]。位于脑室下区的神经祖细胞有助于抵消代谢功能障碍或炎症过程,具有促进髓鞘生成的功能[26]。尿毒素中的丙酮醛会诱导神经祖细胞死亡,阻止髓鞘生成,发展为脑白质脱髓鞘,最终导致室周WMH形成[27, 28]。因此,推测ESRD患者由于尿毒症毒素在脑中蓄积,干扰神经祖细胞的生理功能,导致更易出现室周WMH。

3.2 ESRD患者WMH与临床生化指标及认知功能的相关性

       本研究结果表明,在ESRD患者中,血清白蛋白水平与WMH严重程度呈负相关,提示ESRD患者脑白质损伤可能与代谢紊乱有关。ZAHR等[29]研究发现在酒精使用障碍和艾滋病患者中,血清白蛋白与室周WMH体积呈负相关,与本研究结果相符。血清白蛋白可调节胶体渗透压,影响生理循环系统功能,还具有抗炎、抗氧化作用[30]。ESRD患者因肾小球受损,尿中排出大量蛋白尿;或伴有肝功能异常,合成蛋白减少,通常患有低白蛋白血症[31]。当ESRD患者体内血清白蛋白长期减少,血浆胶体渗透压降低,水分渗出增多,积存于脑血管周围及细胞间质,在MRI上表现为WMH[32]。此外,本研究发现肾小球滤过率与WMH严重程度呈负相关,提示肾功能下降会加重WMH,与WEI等[33]的研究结果相符。结合既往文献报道和本研究结果,推测在ESRD患者中,血清白蛋白和肾功能下降会增加肾病患者脑损伤的风险。本研究结果表明,ESRD患者认知功能与对照组之间存在显著差异,但在控制年龄和性别后未发现ESRD患者WMH的严重程度与认知功能的相关性,与既往相关研究结果类似[34]。推测ESRD患者WMH的形成可能受多种因素影响,未来需要更进一步加以研究。

3.3 WMH严重程度的诊断效能

       本研究通过ROC曲线分析WMH严重程度对于ESRD的诊断效能,结果显示AUC为0.817(95% CI:0.751~0.884),最大约登指数为0.571,相应的敏感度为76.5%,特异度为80.6%。WMH严重程度对于ERSD的诊断效能良好,提示WMH严重程度对鉴别ESRD具有一定的参考价值,说明WMH对于诊断ESRD脑白质损害具有重要价值。WMH严重程度与其他肾功能指标对ESRD的诊断效能比较有待进一步研究。

3.4 本研究的局限性及展望

       本研究存在一定的局限性:(1)纳入研究的样本量较少,未对研究对象是否透析及透析类型、是否患有认知障碍进一步分组讨论。未来可通过多中心大样本、队列研究等方式探索治疗干预对ESRD患者WMH的影响,并分析WMH与其他肾功能指标对ESRD的联合诊断价值。(2)本研究未对不同设备间WMH体积进行一致性检验,未来需要扩充样本量,比较不同设备参数对WMH检测的影响。本研究通过自动化识别分割WMH发现ESRD患者WMH分布存在一定特征性,未来可以进一步探索通过机器学习等方式联合AI进行辅助诊断分析的可能性。

4 结论

       综上所述,本研究通过LST工具自动分割ESRD患者和对照组3D-FLAIR图像中的WMH,结果显示ESRD患者脑室周围白质易于受到损伤,且与肾功能和血清白蛋白下降密切相关,WMH可作为有效判别ESRD脑白质损害的重要影像学指标。

[1]
FLYTHE J E, WATNICK S. Dialysis for chronic kidney failure: a review[J]. JAMA, 2024, 332(18): 1559-1573. DOI: 10.1001/jama.2024.16338.
[2]
DECARLI C, RAJAN K B, JIN L W, et al. WMH contributions to cognitive impairment: rationale and design of the diverse VCID study[J]. Stroke, 2025, 56(3): 758-776. DOI: 10.1161/STROKEAHA.124.045903.
[3]
MAILLARD P, FLETCHER E, CARMICHAEL O, et al. Cerebrovascular markers of WMH and infarcts in ADNI: a historical perspective and future directions[J]. Alzheimers Dement, 2024, 20(12): 8953-8968. DOI: 10.1002/alz.14358.
[4]
WU X Q, YA J Y, ZHOU D, et al. Pathogeneses and imaging features of cerebral white matter lesions of vascular origins[J]. Aging Dis, 2021, 12(8): 2031-2051. DOI: 10.14336/AD.2021.0414.
[5]
JIANG Y H, DU W, LI Y, et al. Disturbed dynamic brain activity and neurovascular coupling in end-stage renal disease assessed with MRI[J]. J Magn Reson Imaging, 2025, 61(4): 1831-1844. DOI: 10.1002/jmri.29597.
[6]
ZHENG K, ZHOU Y Z, QIAN Y J, et al. Increased premature cerebral small vessel diseases in dialysis patients: a retrospective cross-sectional study[J]. Nephron, 2021, 145(4): 330-341. DOI: 10.1159/000513121.
[7]
FISHER M. Mechanisms of cerebral microvascular disease in chronic kidney disease[J/OL]. J Stroke Cerebrovasc Dis, 2021, 30(9): 105404 [2024-04-22]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8164637. DOI: 10.1016/j.jstrokecerebrovasdis.2020.105404.
[8]
GRECO F, QUARTA L G, PARIZEL P M, et al. Relationship between chronic kidney disease and cerebral white matter hyperintensities: a systematic review[J]. Quant Imaging Med Surg, 2023, 13(11): 7596-7606. DOI: 10.21037/qims-22-707.
[9]
KUMMARI S, BURRA K G, REDDY V R K, et al. Determination of efficiency of 3D fluid-attenuated inversion recovery (FLAIR) in the imaging of multiple sclerosis in comparison with 2D FLAIR at 3-tesla MRI[J/OL]. Cureus, 2023, 15(11): e48136 [2024-04-22]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10693390. DOI: 10.7759/cureus.48136.
[10]
SCHMIDT P, GASER C, ARSIC M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis[J]. Neuroimage, 2012, 59(4): 3774-3783. DOI: 10.1016/j.neuroimage.2011.11.032.
[11]
RIBALDI F, ALTOMARE D, JOVICICH J, et al. Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: a European multi-site 3T study[J]. Magn Reson Imaging, 2021, 76: 108-115. DOI: 10.1016/j.mri.2020.11.008.
[12]
STRINGER M S, BLAIR G W, KOPCZAK A, et al. Cerebrovascular function in sporadic and genetic cerebral small vessel disease[J]. Ann Neurol, 2025, 97(3): 483-498. DOI: 10.1002/ana.27136.
[13]
PHUAH C L, CHEN Y, STRAIN J F, et al. Association of data-driven white matter hyperintensity spatial signatures with distinct cerebral small vessel disease etiologies[J/OL]. Neurology, 2022, 99(23): e2535-e2547 [2024-04-22]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9754646. DOI: 10.1212/wnl.0000000000201186.
[14]
CHEN H J, QIU J, XU X L, et al. Abnormal white matter along fibers by automated fiber quantification in patients undergoing hemodialysis[J]. Neurol Sci, 2023, 44(12): 4499-4509. DOI: 10.1007/s10072-023-06912-8.
[15]
CAO X S, SUI B, WU B L, et al. MR study on white matter injury in patients with acute diquat poisoning[J]. Neurotoxicology, 2025, 106: 37-45. DOI: 10.1016/j.neuro.2024.12.002.
[16]
AMAN A, HOSKOTE A, JADHAV K S, et al. Comparative analysis of brain volumetric measurements between contrast-enhanced and non-contrast MRI images[J/OL]. Neurosci Lett, 2025, 848: 138118 [2025-02-01]. https://www.sciencedirect.com/science/article/pii/S0304394025000060via%3Dihub. DOI: 10.1016/j.neulet.2025.138118.
[17]
CARTON C, CALAFIORE M, CAUET C, et al. Montreal Cognitive Assessment (MoCA) use in general practice for the early detection of cognitive impairment: a feasibility study[J/OL]. BJGP Open, 2025, 9(1): BJGPO.2024.0039 [2025-02-01]. https://bjgpopen.org/content/9/1/BJGPO.2024.0039.long. DOI: 10.3399/BJGPO.2024.0039.
[18]
SEJUNAITE K, BELAL Y, LANZA C, et al. Different meanings of a three-point decline in MMSE score in Alzheimer's disease and depressive disorder[J/OL]. BJPsych Open, 2024, 10(5): e145 [2025-02-01]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11698181. DOI: 10.1192/bjo.2024.732.
[19]
VEMURI P, DAVEY C, JOHANSEN K L, et al. Chronic kidney disease associated with worsening white matter disease and ventricular enlargement[J]. J Alzheimers Dis, 2021, 83(4): 1729-1740. DOI: 10.3233/JAD-210604.
[20]
PLANTONE D, VOLLONO C, PARDINI M, et al. A voxel-based lesion symptom mapping analysis of chronic pain in multiple sclerosis[J]. Neurol Sci, 2021, 42(5): 1941-1947. DOI: 10.1007/s10072-020-04745-3.
[21]
MOON H I, JEONG Y J, SUH J H. Voxel-based lesion symptom mapping analysis for dysphagia in stroke patients with isolated cerebellar lesions[J]. J Neural Transm (Vienna), 2022, 129(1): 65-74. DOI: 10.1007/s00702-021-02438-5.
[22]
YUSIPOV I, KONDAKOVA E, KALYAKULINA A, et al. Accelerated epigenetic aging and inflammatory/immunological profile (ipAGE) in patients with chronic kidney disease[J]. Geroscience, 2022, 44(2): 817-834. DOI: 10.1007/s11357-022-00540-4.
[23]
BACHMANN D, VON RICKENBACH B, BUCHMANN A, et al. White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition[J/OL]. Alzheimers Res Ther, 2024, 16(1): 67 [2025-02-01]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10983708. DOI: 10.1186/s13195-024-01435-6.
[24]
吴龙, 郑罡, 张龙江, 等. 终末期肾病患者脑血流灌注改变: 基于pCASL法MRI研究及与认知功能障碍的相关性[J]. 放射学实践, 2017, 32(8): 808-811. DOI: 10.13609/j.cnki.1000-0313.2017.08.005.
WU L, ZHENG G, ZHANG L J, et al. Brain blood perfusion changes in patients with end-stage renal disease: a MRI study using pseudo continuous arterial spin labeling(pCASL) technique and the correlation of these changes with cognitive dysfunction[J]. Radiol Pract, 2017, 32(8): 808-811. DOI: 10.13609/j.cnki.1000-0313.2017.08.005.
[25]
VIGGIANO D, WAGNER C A, MARTINO G, et al. Mechanisms of cognitive dysfunction in CKD[J]. Nat Rev Nephrol, 2020, 16(8): 452-469. DOI: 10.1038/s41581-020-0266-9.
[26]
ZHANG Y, SONG Z H, WU R, et al. PRRC2B modulates oligodendrocyte progenitor cell development and myelination by stabilizing Sox2 mRNA[J/OL]. Cell Rep, 2024, 43(3): 113930 [2025-02-01]. https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00258-4_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124724002584%3Fshowall%3Dtrue. DOI: 10.1016/j.celrep.2024.113930.
[27]
LI H, JACOB M A, CAI M F, et al. Regional cortical thinning, demyelination and iron loss in cerebral small vessel disease[J]. Brain, 2023, 146(11): 4659-4673. DOI: 10.1093/brain/awad220.
[28]
OLIVEIRA K B, DE SOUZA F M A, DE SÁ L B M, et al. Potential mechanisms underlying COVID-19-mediated central and peripheral demyelination: roles of the RAAS and ADAM-17[J]. Mol Neurobiol, 2025, 62(1): 1151-1164. DOI: 10.1007/s12035-024-04329-8.
[29]
ZAHR N M, PFEFFERBAUM A. Serum albumin and white matter hyperintensities[J/OL]. Transl Psychiatry, 2024, 14: 233 [2025-02-01]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11144249. DOI: 10.1038/s41398-024-02953-5.
[30]
MANOLIS A A, MANOLIS T A, MELITA H, et al. Low serum albumin: a neglected predictor in patients with cardiovascular disease[J]. Eur J Intern Med, 2022, 102: 24-39. DOI: 10.1016/j.ejim.2022.05.004.
[31]
PARHI K K, VIJAYASAMUNDEESWARI C K, KANCHAN R K, et al. Association of serum albumin and C-reactive protein with outcomes in ESRD patients undergoing hemodialysis at a tertiary care center in Salem, Tamil Nadu[J]. J Pharm Bioallied Sci, 2024, 16(Suppl 5): S4540-S4543. DOI: 10.4103/jpbs.jpbs_1147_24.
[32]
KIM J W, BYUN M S, LEE J H, et al. Serum albumin and beta-amyloid deposition in the human brain[J/OL]. Neurology, 2020, 95(7): e815-e826 [2024-04-22]. https://pmc.ncbi.nlm.nih.gov/articles/PMC7605506. DOI: 10.1212/wnl.0000000000010005.
[33]
WEI C S, YAN C Y, YU X R, et al. Association between white matter hyperintensities and chronic kidney disease: a systematic review and meta-analysis[J/OL]. Front Med (Lausanne), 2022, 9: 770184 [2024-04-22]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9112853. DOI: 10.3389/fmed.2022.770184.
[34]
PRINS N D, SCHELTENS P. White matter hyperintensities, cognitive impairment and dementia: an update[J]. Nat Rev Neurol, 2015, 11(3): 157-165. DOI: 10.1038/nrneurol.2015.10.

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