Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer's disease.
Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer's disease.
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DOI：
10.3389/fnagi.2022.906519
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发表时间：
2022
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
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基于结构磁共振成像（sMRI）的多个视觉评分量表与阿尔茨海默病（AD）患者疾病严重程度和脑脊液（CSF）生物标志物之间的关系尚不明确。在这项研究中，共招募了438例临床诊断为AD的患者。所有参与者均接受脑sMRI扫描，并对内侧颞叶萎缩（MTA）、后部萎缩（PA）、整体脑萎缩-额叶子量表（GCA-F）和Fazekas评分进行视觉评价。同时，通过神经心理学测试评估疾病严重程度，如简易精神状态检查（MMSE）、蒙特利尔认知评估（莫卡）和临床痴呆评定（CDR）。其中，对95例患者进行了CSF核心生物标志物检测，包括Aβ1-42、Aβ1-40、Aβ1-42/Aβ1-40、p-tau和t-tau。结果表明，GCA-F和Fazekas量表与发病年龄呈显著正相关（分别为r = 0.181，p < 0.001; r = 0.411，p < 0.001）。晚发性AD（LOAD）患者的GCA-F和Fazekas评分较高（p < 0.001，p < 0.001）。MTA与GCA-F与病程呈正相关（r = 0.137，p < 0.05; r = 0.106，p < 0.05）。在疾病严重程度方面，疾病严重程度与MTA、PA GCA-F和Fazekas评分之间存在显著正相关（分别为p < 0.001、p <0.001、p <0.001、p < 0.05）。此外，在调整年龄、性别和APOE等位基因后，多变量logistic回归分析显示MTA量表对中重度AD的独立贡献具有统计学意义（p < 0.05）。结合年龄、性别和APOE等位基因的模型对中重度AD的预测效果最好（AUC = 0.712，敏感性= 51.5%，特异性= 84.6%）。此外，我们观察到MTA和Fazekas评分与较低的Aβ1-42浓度相关（分别为p < 0.031，p < 0.022）。总之，我们系统地分析了多种视觉评定量表在预测AD临床状态方面的益处。视力评定量表结合年龄、性别和APOE等位基因在预测AD严重程度方面表现最好。MRI生物标志物与CSF生物标志物组合可用于临床实践。
The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer’s disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aβ1–42, Aβ1–40, Aβ1–42/Aβ1–40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aβ1–42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.
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DOI：
10.1186/s13195-017-0328-9
发表时间：
2017-12-29
期刊：
Alzheimer's research & therapy
影响因子：
--
作者：
Bos I;Verhey FR;Ramakers IHGB;Jacobs HIL;Soininen H;Freund-Levi Y;Hampel H;Tsolaki M;Wallin ÅK;van Buchem MA;Oleksik A;Verbeek MM;Olde Rikkert M;van der Flier WM;Scheltens P;Aalten P;Visser PJ;Vos SJB
通讯作者：
Vos SJB
影响因子：
11.1
作者：
Ferreira, D.;Cavallin, L.;Westman, E.
通讯作者：
Westman, E.
影响因子：
4.2
作者：
Abdelnour, Carla;Ferreira, Daniel;Aarsland, Dag
通讯作者：
Aarsland, Dag
影响因子：
4
作者：
Mao, Chenhui;Sha, Longze;Gao, Jing
通讯作者：
Gao, Jing
影响因子：
4.2
作者：
Jiao, Bin;Liu, Xiaoyan;Shen, Lu
通讯作者：
Shen, Lu