OBJECTIVE Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) analysis is a promising non-linear regression model, characterized by its high flexibility and utility in clinical diagnosis. ANN simulates neuron learning procedures and it could provide good diagnostic performances in this complex and heterogeneous disease compared with linear regression analysis. DESIGN AND METHODS In our study, a new set of lipid peroxidation compounds were determined in urine and plasma samples from patients diagnosed with early Alzheimer Disease (n = 70) and healthy controls (n = 26) by means of ultra-performance liquid chromatography coupled with tandem mass-spectrometry. Then, a model based on ANN was developed to classify groups of participants. RESULTS The diagnostic performances obtained using an ANN model for each biological matrix were compared with the corresponding linear regression model based on partial least squares (PLS), and with the non-linear (radial and polynomial) support vector machine (SVM) models. Better accuracy, in terms of receiver operating characteristic-area under curve (ROC-AUC), was obtained for the ANN models (ROC-AUC 0.882 in plasma and 0.839 in urine) than for PLS and SVM models. CONCLUSION Lipid peroxidation and ANN constitute a useful approach to establish a reliable diagnosis when the prognosis is complex, multidimensional and non-linear.

中文翻译：

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在早期阿尔茨海默病诊断中评估脂质过氧化和人工神经网络模型。

目的脂质过氧化作用是阿尔茨海默病（AD）早期阶段涉及的分子机制，人工神经网络（ANN）分析是一种有前途的非线性回归模型，其特征是其高度的灵活性和在临床诊断中的实用性。人工神经网络模拟神经元学习程序，与线性回归分析相比，它可以在这种复杂的异质性疾病中提供良好的诊断性能。设计与方法在我们的研究中，通过超高效液相色谱耦合技术从诊断为早期阿尔茨海默病（n = 70）和健康对照（n = 26）的患者的尿液和血浆样品中测定了一套新的脂质过氧化化合物。与串联质谱联用 然后，开发了基于ANN的模型来对参与者的组进行分类。结果将使用ANN模型获得的每种生物基质的诊断性能与基于偏最小二乘（PLS）的相应线性回归模型以及非线性（径向和多项式）支持向量机（SVM）模型进行了比较。与PLS和SVM模型相比，就ANN模型（血浆中ROC-AUC为0.882，尿液中为0.839）而言，在接收器工作曲线下特征面积（ROC-AUC）方面，精度更高。结论当预后复杂，多维和非线性时，脂质过氧化和人工神经网络是建立可靠诊断的有用方法。以及非线性（径向和多项式）支持向量机（SVM）模型。与PLS和SVM模型相比，ANN模型（血浆中ROC-AUC为0.882，尿液中为0.839）在曲线下的接收器工作特征区域（ROC-AUC）方面具有更好的精度。结论当预后复杂，多维和非线性时，脂质过氧化和人工神经网络是建立可靠诊断的有用方法。以及非线性（径向和多项式）支持向量机（SVM）模型。与PLS和SVM模型相比，ANN模型（血浆中ROC-AUC为0.882，尿液中为0.839）在曲线下的接收器工作特征区域（ROC-AUC）方面具有更好的精度。结论当预后复杂，多维和非线性时，脂质过氧化和人工神经网络是建立可靠诊断的有用方法。