Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease

Platero Dueñas, Carlos ORCID: https://orcid.org/0000-0003-3712-8297 (2022). Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease. "Journal of Neuroscience Methods", v. 374 ; pp. 1-6. ISSN 0165-0270. https://doi.org/10.1016/j.jneumeth.2022.109581.

Descripción

Título: Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Neuroscience Methods
Fecha: Mayo 2022
ISSN: 0165-0270
Volumen: 374
Materias:
ODS:
Palabras Clave Informales: Alzheimers disease; Biomarkers; Cognition; conversion; enrichment; longitudinal analysis; MARKERS; MCI; MRI; Predictive models; Alzheimer Disease; Alzheimer's Disease; Alzheimer’S Disease; Amyloid beta-Peptides; Biomarkers; Clinical-Trials; Cognitive dysfunction; Disease Progression; Humans; longitudinal analysis; Magnetic Resonance Imaging; Mild Cognitive Impairment; Predictive models
Escuela: E.T.S.I. Diseño Industrial (UPM)
Departamento: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Background
A preclinical stage of Alzheimer’s disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset.
New method
We propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI.

Results
A very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms.

Comparison with existing methods
Unlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models.

Conclusions
The estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/Aβ. Furthermore, pTAU/Aβ, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.

Más información

ID de Registro: 91865
Identificador DC: https://oa.upm.es/91865/
Identificador OAI: oai:oa.upm.es:91865
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/9906549
Identificador DOI: 10.1016/j.jneumeth.2022.109581
URL Oficial: https://www.sciencedirect.com/science/article/pii/...
Depositado por: iMarina Portal Científico
Depositado el: 14 Nov 2025 07:32
Ultima Modificación: 14 Nov 2025 08:33