Mitochondrial methylcytosines as blood-based biomarkers for Alzheimer's disease dementia prognosis

Alzheimer's Disease Dementia (ADD) prognosis is an unmet medical need. Mitochondrial dysfunction is an early AD etiopathogenic factor. The present study analyzed mitochondrial DNA (mtDNA) methylation patterns in blood samples from patients with mild cognitive impairment (MCI) who progressed to...

Full description

Bibliographic Details
Authors: Gascón-Bayarri, Jordi, Mosquera Mayo, José Luís, Blanch Lozano, Marta, Martí Benaiges, Pau, Fontal Aina, Beatriz, Trapero Candela, Carla, Rojo Fité, Nuria, Rico, Inma, Campdelacreu i Fumadó, Jaume, Fowler, Cristopher, Laws, Simon M., Tort Merino, Adrià, Sánchez del Valle Díaz, Raquel, Bello, Joan, Fortea Ormaechea, Juan, Lleó Bisa, Alberto, Mehanian, Courosh, Swerdlow, Russell H., Reñé Ramírez, Ramon, Barrachina, Marta
Format: article
Status:Published version
Publication Date:2025
Country:España
Institution:Universidad de Barcelona
Repository:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/224918
Online Access:https://hdl.handle.net/2445/224918
Access Level:Open access
Keyword:ADN mitocondrial
Malaltia d'Alzheimer
Neurociències
Metilació
Neurologia
Mitochondrial DNA
Alzheimer's disease
Neurosciences
Methylation
Neurology
Description
Summary:Alzheimer's Disease Dementia (ADD) prognosis is an unmet medical need. Mitochondrial dysfunction is an early AD etiopathogenic factor. The present study analyzed mitochondrial DNA (mtDNA) methylation patterns in blood samples from patients with mild cognitive impairment (MCI) who progressed to ADD (P), MCI remained stable (NP), and Cognitively Normal (CN) individuals. Differentially methylated sites were identified in the D-loop region in both CN vs. NP and NP vs. P comparisons, even before β-amyloid positivity. A Random Forest model was developed using mtDNA methylation data combined with cognitive and risk factor features. Model's performance was assessed by cross-validation and tested on an independent set, achieving 84.4% accuracy in training and 83.2% (95% CI: 75.2%-89.4%) in testing. For identifying P patients, sensitivity and specificity were 95.1% and 70.7%, respectively. The AUC-ROC was 90.3%. The developed model demonstrates predictive capacity in distinguishing cognitive decline and stability in MCI individuals, independently of their β-amyloid status.

Similar Items