Precision Prediction of Alzheimer's Disease: Integrating Mitochondrial Energy Metabolism and Immunological Insights

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is characterized by mitochondrial dysfunction and immune dysregulation. This study is aimed at developing a risk prediction model for AD by integrating multi-omics data and exploring the interplay between mitochondrial energy metabolism-related genes (MEMRGs) and immune cell dynamics. We integrated four GEO datasets (GSE132903, GSE29378, GSE33000, GSE5281) for differential gene expression analysis, functional enrichment, and weighted gene co-expression network analysis (WGCNA). We identified two key gene modules (turquoise and magenta) significantly correlated with AD. Subsequently, we constructed a risk prediction model incorporating five MEMRGs (MRPL15, RBP4, ABCA1, MPV17, and MRPL37) and clinical factors using LASSO regression. The model demonstrated robust predictive performance (AUC > 0.815) in both internal and external validation (GSE44770) cohorts. Downregulation of MRPL15, RBP4, MPV17, and MRPL37 in AD brain regions (validated using AlzData and qRT-PCR) suggests impaired mitochondrial function. Conversely, ABCA1 upregulation may represent a compensatory response. Furthermore, significant differences in immune cell proportions, particularly gamma delta T cells (p = 0.002) and activated CD4 memory T cells (p = 0.027), were found between AD and non-demented samples. We observed significant correlations between MEMRG expression and specific immune cell fractions, indicating a potential link between mitochondrial dysfunction and immune dysregulation in AD. Our study provides a reliable risk prediction model for AD and highlights the crucial roles of MEMRGs and immune responses in disease pathogenesis, offering potential targets for therapeutic interventions.