Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients

Carlos Platero; Jussi Tohka; Bryan Strange

doi:10.2174/0115672050295317240223162312

Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients

Curr Alzheimer Res. 2024 Feb 28. doi: 10.2174/0115672050295317240223162312. Online ahead of print.

Authors

Carlos Platero^{1

2

3

4}, Jussi Tohka², Bryan Strange^{3

4}

Affiliations

¹ Health Science Technology Group, Technical University of Madrid, 28012 Madrid, Spain.
² Biomedical Image Analysis Group, University of Eastern Finland, FI-70211 Kuopio, Finland.
³ Laboratory for Clinical Neuroscience, CTB, Technical University of Madrid, IdISSC, Madrid, Spain.
⁴ Alzheimer Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, Spain.

PMID: 38425106
DOI: 10.2174/0115672050295317240223162312

Abstract

Background: Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies.

Objectives: The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia.

Methods: This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques.

Results: A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects.

Conclusion: Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.

Keywords: AT(N) biomarkers; Alzheimer’s disease; Mild cognitive impairment; disease progression modeling.; predictive models.