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...

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Detalles Bibliográficos
Autores: 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
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/224918
Acceso en línea:https://hdl.handle.net/2445/224918
Access Level:acceso abierto
Palabra clave:ADN mitocondrial
Malaltia d'Alzheimer
Neurociències
Metilació
Neurologia
Mitochondrial DNA
Alzheimer's disease
Neurosciences
Methylation
Neurology
Descripción
Sumario: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.

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