Mitochondrial methylcytosines as blood-based biomarkers for Alzheimer's disease dementia prognosis
Gascon-Bayarri, Jordi (Hospital Universitari de Bellvitge)
Mosquera, José Luis (ADmit Therapeutics (Barcelona))
Blanch Lozano , Marta (ADmit Therapeutics (Barcelona))
Martí, Pau (ADmit Therapeutics (Barcelona))
Fontal, Beatriz (ADmit Therapeutics (Barcelona))
Trapero, Carla (ADmit Therapeutics (Barcelona))
Rojo, Nuria (Hospital Universitari de Bellvitge)
Rico Delgado, Inmaculada (Hospital Universitari de Bellvitge)
Campdelacreu, Jaume (Hospital Universitari de Bellvitge)
Fowler, Cristopher (The University of Melbourne (Austràlia))
Laws, Simon M. (Edith Cowan University (Joondalup, Austràlia))
Tort-Merino, Adrià (Hospital Clínic i Provincial de Barcelona)
Sanchez-Valle, Raquel (Universitat de Barcelona)
Bello, Joan (Complex Hospitalari Moisès Broggi (Sant Joan Despí, Barcelona))
Fortea, Juan (Institut de Recerca Sant Pau)
Lleó, Alberto (Institut de Recerca Sant Pau)
Mehanian, Courosh (University of Oregon)
Swerdlow, Russell H. (The University of Kansas Alzheimer's Disease Research Center)
Reñé-Ramírez, Ramón (Hospital Universitari de Bellvitge)
Barrachina, Marta (ADmit Therapeutics (Barcelona))
Universitat Autònoma de Barcelona

Date:	2025 Alzheimer's Disease Dementia (ADD) prognosis is an unmet medical need. Mitochondrial dysfunction is anearly 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 4.4% 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, indepen- dently of their β-amyloid status
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Medicine ; Neurology ; Neuroscience ; Molecular neuroscience
Published in:	iScience, Vol. 28 (august 2025) , ISSN 2589-0042

DOI: 10.1016/j.isci.2025.113418
PMID: 40978150

22 p, 4.1 MB

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