Evidence map›Paper›PMID 38425106›Full record

ArticleCurrent Alzheimer research2024

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

Carlos Platero, Jussi Tohka, Bryan Strange

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Article in Current Alzheimer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Carlos PlateroHealth Science Technology Group, Technical University of Madrid, 28012 Madrid, Spain.ORCID 0000-0003-3712-8297
Jussi TohkaA.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, FI-70211 Kuopio, Finland.ORCID 0000-0002-1048-5860
Bryan StrangeLaboratory for Clinical Neuroscience, CTB, Technical University of Madrid, IdISSC, Madrid, Spain.ORCID 0000-0001-6476-4091

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Comunidad de Madrid MINA CM P2022 BMD 7236NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

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

objectivesThe 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.

methodsThis 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.

resultsA 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.

conclusionBased 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.

Indexed as

Cognitive DysfunctionDementiaDisease ProgressionNeuropsychological TestsAgedAged, 80 and overCohort StudiesFemaleHumansLongitudinal StudiesMaleNeuroimagingAlzheimer’s diseaseAT(N) biomarkersdementia.disease progression modelingMild cognitive impairmentpredictive models

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.