ArticleAlzheimer's research & therapy2025
Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease.
Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Transcriptomic insights into the co-occurring psychological symptoms and cardiovascular risks among military service members and veterans with mild traumatic brain injury: A LIMBIC-CENC study.Brain, behavior, & immunity - health · 2026Article
- Interpretable machine learning for cognitive impairment assessment: integration of clinical and radiomic white matter hyperintensities features.Journal of translational medicine · 2026Article
- Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross-Platform Validation.Annals of clinical and translational neurology · 2026Article
- Exploratory RNA Sequencing Reveals Systemic Metabolic Dysregulation in Alzheimer's Disease: Insights from a Diverse Latin American Cohort.Molecular neurobiology · 2026Article
- Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression.Frontiers in bioinformatics · 2026Article
- Conserved Blood Transcriptome Patterns Highlight microRNA and Hub Gene Drivers of Neurodegeneration.Genes · 2025Article
- Mitochondrial methylcytosines as blood-based biomarkers for Alzheimer's disease dementia prognosis.iScience · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Doxycycline: An essential tool for Alzheimer's disease.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2025Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEffective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.
methodsBlood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models.
resultsWe discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.
conclusionsBlood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.