Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism - PubMed
3 hours ago
- #Alzheimer's disease
- #Cholesterol metabolism
- #Machine learning
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing global prevalence, projected to reach 152 million cases by 2050.
- The study utilized Mendelian randomization and machine learning to analyze transcriptomic data from AD patients, identifying differentially expressed genes (DEGs).
- 29 genes were associated with AD, 21 of which are linked to cholesterol metabolism, highlighting its role in AD pathogenesis.
- An 8-gene diagnostic signature (CHSY1, FIBP, DHCR24, HVCN1, KIFAP3, KLHL21, LETMD1, SLC25A29) was developed, outperforming existing AD diagnostic models.
- Animal experiments validated the biological relevance of identified genes, supporting their roles in AD pathology.
- The research proposes novel pathways for early diagnosis and potential therapeutic interventions in AD management.