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

• Published in: Journal of neuroscience methods Vol. 374; p. 109581
• Main Author: Platero, Carlos
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.05.2022
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer’s disease; Amyloid beta-Peptides; Biomarkers; Cognitive Dysfunction - pathology; Copper; Disease Progression; Humans; Longitudinal analysis; Magnetic Resonance Imaging; Mild cognitive impairment; Predictive models; Predictive models; Longitudinal analysis; Alzheimer’s disease; Mild cognitive impairment
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2022.109581

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. 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. 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. 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. 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. [Display omitted] •A new methodology to obtain a natural history of AD progression is exposed.•Subsets of markers are proposed from the categorical predictive models.•For each vector, a history of preclinical AD using disease progression modeling approaches is presented.•The selected vector depends on the temporal ordering of the patients based on the clinical symptoms.•And from estimates of convert times for patients progressing to MCI/dementia.•These models combined MRI-based data, CSF markers, and cognitive tests using a longitudinal study.