Development of a novel diagnostic model for Alzheimer's disease based on glymphatic system and metabolism-related genes.

[OBJECTIVES] Alzheimer's disease (AD), a common neurodegenerative disorder, is characterized by its complex pathogenesis and challenging early diagnosis; however, the role of the glymphatic system and metabolism-related genes (GS&MetabolismRGs) in AD remains poorly understood. Therefore, this study aimed to explore a potential diagnostic model and the molecular mechanisms of GS&MetabolismRGs in AD.

[MATERIALS AND METHODS] We obtained glymphatic system and metabolism-related differentially expressed genes (GS&MetabolismRDEGs) associated with AD by integrating of GEO and GeneCards databases. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analyses, and gene set enrichment analysis were performed to investigate the roles of GS&metabolismRDEGs in AD-related biological processes. Hub genes were identified using machine learning methods, resulting in the construction and validation of AD diagnostic models. AD samples were further stratified into high-score and low-score groups based on the median value of glymphatic system and Metabolism Score to investigate the underlying pathogenesis. Finally, immune infiltration analysis was conducted to explore the relationship between immune cell frequencies and hub genes.

[RESULTS] Six GS&MetabolismRDEGs were identified, which were predominantly enriched in biological processes, such as the PD-L1 expression, hyaluronan metabolic process, and the PD-1 checkpoint pathway in cancer. Further analysis identified six hub genes that were used to construct an AD diagnostic model. Immune infiltration analysis of the disease and control groups revealed significant associations among all eight immune cell types. The strongest negative correlation was found between the resting memory CD4 T cells and Tregs. Further analysis revealed a strong positive correlation between Tregs and in low-risk group and the most significant correlation between activated mast cells and in high-risk group.

[CONCLUSION] This study developed a novel diagnostic model based on six GS&MetabolismRDEGs, highlighting their potential as key biomarkers for early diagnosis and providing new insights into the molecular mechanisms driving AD.