Abstract
Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD.Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively.Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.
Highlights
Alzheimer’s disease (AD) is the most common chronic neurodegenerative disease (Burns and Iliffe, 2009)
Individualized blood mRNA expression data of 711 patients were downloaded from the Gene Expression Omnibus (GEO) database, including 238 CONs, 189 mild cognitive impairment (MCI), and 284 AD patients
We found that mitochondrial dysfunction in the brain tissue of AD patients can be simultaneously detected in the peripheral system (Johri and Beal, 2012), suggesting that AD may be caused by abnormal gene expression or brain damage, as observed in peripheral blood (Johri and Beal, 2012; Leuner et al, 2012; Trushina et al, 2013; Pérez et al, 2017)
Summary
Alzheimer’s disease (AD) is the most common chronic neurodegenerative disease (Burns and Iliffe, 2009). In a European cohort study, a machine-learning approach identified 347 plasma metabolites associated with early diagnosis in AD with an area under curve (AUC) of about 0.85 (Stamate et al, 2019). In a study of circulating non-coding RNA in patients with AD, 21 disease-related features were identified using RT-qPCR, and 18 strongly correlated features were extracted using statistical learning methods to establish a machine learning model, with an AUC of about 0.86 (Herrero-Labrador et al, 2020). There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis.
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