Alteration of metabolic profile during the progression of Alzheimer’s disease

Abstract

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder that threatens the population health of older adults. However, the mechanisms of how altered metabolism involves AD pathology are poorly understood. This study intends to provide novel insights into the role of metabolic alteration in the pathology and identify potential metabolic biomarkers for AD diagnosisMethodThe Gas chromatography‐mass spectrometry (GC‐MS) was used to measure the concentrations of serum metabolites in a cohort of subjects with AD (n = 88) and cognitively normal control (CN) (n = 85). The patients were classified as very mild (n = 25), mild (n = 27), moderate (n = 25), and severe (n = 11). Serum metabolic profiles were analyzed by multivariate (principal component analysis) and univariate approaches (t‐tests, false discovery, receiver operating characteristic curves, and correlation analysis). The artificial neural network model (ANN) was applied to identify potential biomarkers of AD. Biofunctional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes.ResultThe results revealed considerable separation between the AD and CN patients. Edetic acid, (S)‐3,4‐Dihydroxybutyric acid, and arachidonic acid were identified as potential AD diagnostic biomarkers using an ANN model. And the classification model could predict the risk for AD with high accuracy (AUC = 0.93). The most significant changes in the metabolite’s composition occur during the very mild to the mild stage. The metabolic enrichment analysis revealed that carbohydrate metabolism deficiency and disturbance of amino acids, fatty acids, and lipids metabolism are involved in AD progression. Especially, pathway analysis highlighted that l−glutamate participated in four crucial nervous system pathways (including GABAergic synapse, glutamatergic synapse, retrograde endocannabinoid signaling and synaptic vesicle cycle).ConclusionSerum metabolomic analysis demonstrated that the metabolic alterations had already arisen at the very mild stages of dementia. Disrupt energy metabolism may be the core of the vicious cycle in AD, resulting in a wide range of metabolic disorders, including abnormal glucose metabolism, TCA cycle impairments, amino acids, fatty acids, and lipids dysregulation. Furthermore, a metabolic diagnostic model of AD was constructed using ANN, which could robustly differentiate AD patients from CN. Our study identified new markers for AD diagnosis and highlighted the roles of metabolic changes on the progression of AD.