Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease.
Por:
Botello-Marabotto M, Martínez-Bisbal MC, Calero M, Bernardos A, Pastor AB, Medina M and Martínez-Máñez R
Publicada:
15 oct 2023
Ahead of Print:
26 sep 2023
Resumen:
Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15?years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance ( 1 H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n?=?51), mild cognitive impairment (MCI, n?=?27), and cognitively healthy controls (HC, n?=?50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n?=?9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n?=?14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n?=?4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1 H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.
Filiaciones:
Botello-Marabotto M:
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Valencia, Spain
Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE), Universitat Politècnica de València, Valencia, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
Martínez-Bisbal MC:
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Valencia, Spain
Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE), Universitat Politècnica de València, Valencia, Spain
Departamento de Química-Física, Universitat de València, Valencia, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
Unidad Mixta UPV-CIPF de Investigación en Mecanismos de Enfermedades y Nanomedicina, Universitat Politècnica de València, Centro de Investigación Príncipe Felipe, Valencia, Spain
Calero M:
Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
CIEN Foundation, Queen Sofia Foundation Alzheimer Research Center, Madrid, Spain
Instituto de Salud Carlos III, Madrid, Spain
Bernardos A:
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Valencia, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
Unidad Mixta UPV-CIPF de Investigación en Mecanismos de Enfermedades y Nanomedicina, Universitat Politècnica de València, Centro de Investigación Príncipe Felipe, Valencia, Spain
Departamento de Química, Universitat Politècnica de València, Valencia, Spain
Pastor AB:
CIEN Foundation, Queen Sofia Foundation Alzheimer Research Center, Madrid, Spain
Medina M:
Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
CIEN Foundation, Queen Sofia Foundation Alzheimer Research Center, Madrid, Spain
:
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Valencia, Spain
Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE), Universitat Politècnica de València, Valencia, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
Unidad Mixta UPV-CIPF de Investigación en Mecanismos de Enfermedades y Nanomedicina, Universitat Politècnica de València, Centro de Investigación Príncipe Felipe, Valencia, Spain
Departamento de Química, Universitat Politècnica de València, Valencia, Spain
gold
|