Zhang Lingyu, Wang Danhua, Dai Yibei, Wang Xuchu, Cao Ying, Liu Weiwei, Tao Zhihua
Department of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Front Aging Neurosci. 2022 May 13;14:863673. doi: 10.3389/fnagi.2022.863673. eCollection 2022.
Predicting amnestic mild cognitive impairment (aMCI) in conversion and Alzheimer's disease (AD) remains a daunting task. Standard diagnostic procedures for AD population are reliant on neuroimaging features (positron emission tomography, PET), cerebrospinal fluid (CSF) biomarkers (Aβ1-42, T-tau, P-tau), which are expensive or require invasive sampling. The blood-based biomarkers offer the opportunity to provide an alternative approach for easy diagnosis of AD, which would be a less invasive and cost-effective screening tool than currently approved CSF or amyloid β positron emission tomography (PET) biomarkers.
We developed and validated a sensitive and selective immunoassay for total Tau in plasma. Robust signatures were obtained based on several clinical features selected by multiple machine learning algorithms between the three participant groups. Subsequently, a well-fitted nomogram was constructed and validated, integrating clinical factors and total Tau concentration. The predictive performance was evaluated according to the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. Decision curve analysis and calibration curves are used to evaluate the net benefit of nomograms in clinical decision-making.
Under optimum conditions, chemiluminescence analysis (CLIA) displays a desirable dynamic range within Tau concentration from 7.80 to 250 pg/mL with readily achieved higher performances (LOD: 5.16 pg/mL). In the discovery cohort, the discrimination between the three well-defined participant groups according to Tau concentration was in consistent agreement with clinical diagnosis (AD vs. non-MCI: AUC = 0.799; aMCI vs. non-MCI: AUC = 0.691; AD vs. aMCI: AUC = 0.670). Multiple machine learning algorithms identified Age, Gender, EMPG, Tau, ALB, HCY, VB12, and/or Glu as robust signatures. A nomogram integrated total Tau concentration and clinical factors provided better predictive performance (AD vs. non-MCI: AUC = 0.960, AD vs. aMCI: AUC = 0.813 in discovery cohort; AD vs. non-MCI: AUC = 0.938, AD vs. aMCI: AUC = 0.754 in validation cohort).
The developed assay and a satisfactory nomogram model hold promising clinical potential for early diagnosis of aMCI and AD participants.
预测遗忘型轻度认知障碍（aMCI）向阿尔茨海默病（AD）的转化仍然是一项艰巨的任务。AD人群的标准诊断程序依赖于神经影像学特征（正电子发射断层扫描，PET）、脑脊液（CSF）生物标志物（Aβ1-42、T-tau、P-tau），这些检查昂贵或需要侵入性采样。基于血液的生物标志物为AD的简易诊断提供了一种替代方法，它将是一种比目前批准的CSF或淀粉样β正电子发射断层扫描（PET）生物标志物侵入性更小且成本效益更高的筛查工具。
我们开发并验证了一种用于检测血浆中总Tau的灵敏且特异的免疫测定法。基于多个机器学习算法在三个参与者组中选择的几个临床特征获得了稳健的特征。随后，构建并验证了一个拟合良好的列线图，整合了临床因素和总Tau浓度。根据受试者工作特征（ROC）曲线和曲线下面积（AUC）统计量评估预测性能。决策曲线分析和校准曲线用于评估列线图在临床决策中的净效益。
在最佳条件下，化学发光分析（CLIA）在Tau浓度为7.80至250 pg/mL范围内显示出理想的动态范围，并且易于实现更高的性能（检测限：5.16 pg/mL）。在发现队列中，根据Tau浓度对三个明确的参与者组进行的区分与临床诊断一致（AD与非MCI：AUC = 0.799；aMCI与非MCI：AUC = 0.691；AD与aMCI：AUC = 0.670）。多个机器学习算法将年龄、性别、EMPG、Tau、ALB、HCY、VB12和/或Glu识别为稳健的特征。整合总Tau浓度和临床因素的列线图提供了更好的预测性能（在发现队列中，AD与非MCI：AUC = 0.960，AD与aMCI：AUC = 0.813；在验证队列中，AD与非MCI：AUC = 0.938，AD与aMCI：AUC = 0.754）。
所开发的检测方法和令人满意的列线图模型在早期诊断aMCI和AD参与者方面具有广阔的临床应用前景。
