Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data

Document Type

Article

Abstract

BACKGROUND: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults. OBJECTIVE: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults. METHODS: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.e., age, sex, education), NPS (i.e., Neuropsychiatric Inventory Questionnaire; Beck Depression and Anxiety Inventories), at least one AD biomarker (i.e., plasma-, neuroimaging- and/or cerebrospinal fluid [CSF]-derived), and at least 2 repeated neuropsychological assessments. We trained and tested ML models using a stepwise feature addition approach to predict decline versus non-decline in global and domain-specific (i.e., memory, language, visuospatial, and attention/executive function) cognitive scores. RESULTS: ML models had better performance when NPS were included along with a) neuroimaging biomarkers for predicting decline in global cognition, as well as language and visuospatial skills; b) plasma-derived biomarkers for predicting decline in visuospatial skills; and c) CSF-derived biomarkers for predicting decline in attention/executive function, language, and memory. CONCLUSIONS: NPS, added to ML models including demographic and AD biomarker data, improves prediction of downward trajectories in global and domain-specific cognitive scores among community-dwelling older adults, albeit effect sizes are small. These preliminary findings need to be confirmed by future cohort studies.

Medical Subject Headings

Humans; Machine Learning; Alzheimer Disease (psychology, diagnosis, cerebrospinal fluid); Male; Female; Aged; Biomarkers (cerebrospinal fluid, blood); Cognitive Dysfunction (diagnosis, psychology, cerebrospinal fluid); Neuropsychological Tests; Middle Aged; Aged, 80 and over

Publication Date

2-1-2025

Publication Title

Journal of Alzheimer's disease : JAD

E-ISSN

1875-8908

Volume

103

Issue

3

First Page

833

Last Page

843

PubMed ID

39791216

Digital Object Identifier (DOI)

10.1177/13872877241306654

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