Cellular Senescence of Patient-derived Fibroblasts Reveals the Mid-old Stage as a Critical Window for Transcriptomic Signatures Linked to Alzheimer's Disease Biomarkers and Classification
Aging cells from Alzheimer's patients show distinctive gene patterns that might lead to simpler blood-based diagnostic tests.
Using replicative senescence models of patient-derived fibroblasts (13 AD vs 13 controls), this study identifies a 'mid-old' transcriptomic window where ML classification of AD achieves >90% accuracy and three genes correlate with established AD biomarkers. This approach links cellular aging biology to AD diagnostics, with potential implications for accessible peripheral biomarker discovery.
What the study was
- Study design
- Experimental study with machine learning classifier (N=26 patients/controls)
- Population
- 13 Alzheimer's dementia patients and 13 healthy controls (fibroblasts)
- Sample size
- 26
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Clinical Psychopharmacology and Neuroscience
Why it surfaced
Novel approach linking senescence transcriptomics to AD biomarkers with ML; small n=26 limits inference but mechanistic findings are novel and diagnostically interesting.
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