Predicting cognitive decline in Alzheimer's disease: A real-life proof-of-principle study on multimodal assessment

BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disorder. While AD diagnosis traditionally relies on clinical criteria, recent trends favor a precise biological definition. Existing biomarkers efficiently detect AD pathology but inadequately reflect the extent of cognitive impairment or disease heterogeneity. Alternative tools, such as neuroimaging and neurophysiological techniques, might better assess actual functional impairment.ObjectiveTo explore a multimodal approach-combining quantitative electroencephalography (EEG), cerebrospinal fluid (CSF), and cognitive assessment-to identify the most effective predictors of cognitive decline in AD patients.MethodsIn this observational study, 28 biologically confirmed AD patients underwent baseline evaluations including high-density EEG, CSF biomarker analysis, and cognitive assessment (Mini-Mental State Examination, MMSE). Cognitive assessment was repeated after one year. The rate of cognitive decline was calculated as monthly MMSE score change.ResultsMedian baseline age was 71.2 years. Median monthly MMSE decline was 0.25 points. Patients were classified into slow (≤0.25 MMSE/month) or fast (>0.25 MMSE/month) progressors. Fast progressors had significantly lower baseline individual alpha frequency (IAF) (6.8 Hz versus 9.1 Hz; p = 0.003), lower MMSE at one year (19 versus 24; p = 0.02), and more frequent diabetes and cardiovascular history. A multivariate regression analysis adjusted for age revealed that baseline IAF (p = 0.002), initial MMSE score (p = 0.028), and the p-tau/Aβ<sub>42</sub> ratio (p < 0.01) significantly predicted monthly cognitive decline.ConclusionsCombining quantitative EEG-derived IAF, baseline cognitive status, and CSF biomarkers (p-tau/Aβ<sub>42</sub> ratio) enhances prediction of AD progression. This integrated approach better captures disease heterogeneity, highlighting the need for multimodal strategies in the prognosis and management of AD.

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Journal
Journal of Alzheimer s Disease
Published
2025-09-10
DOI
https://doi.org/10.1177/13872877251376676
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
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article

Predicting cognitive decline in Alzheimer's disease: A real-life proof-of-principle study on multimodal assessment

Fioravante Capone, Gianmarco Iaccarino, Mariagrazia Rossi, Francesco Motolese et al.
Journal of Alzheimer s Disease
Dementia and Cognitive Impairment Research
article

Predicting cognitive decline in Alzheimer's disease: A real-life proof-of-principle study on multimodal assessment

Fioravante Capone, Gianmarco Iaccarino, Mariagrazia Rossi, Francesco Motolese, Alessandro Cruciani, Vincenzo Di Lazzaro, Fabio Pilato, Davide Norata, Elisabetta Sapio, Silvia Lodico, Maria Luisa Di Giesi
article en

Abstract

BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disorder. While AD diagnosis traditionally relies on clinical criteria, recent trends favor a precise biological definition. Existing biomarkers efficiently detect AD pathology but inadequately reflect the extent of cognitive impairment or disease heterogeneity. Alternative tools, such as neuroimaging and neurophysiological techniques, might better assess actual functional impairment.ObjectiveTo explore a multimodal approach-combining quantitative electroencephalography (EEG), cerebrospinal fluid (CSF), and cognitive assessment-to identify the most effective predictors of cognitive decline in AD patients.MethodsIn this observational study, 28 biologically confirmed AD patients underwent baseline evaluations including high-density EEG, CSF biomarker analysis, and cognitive assessment (Mini-Mental State Examination, MMSE). Cognitive assessment was repeated after one year. The rate of cognitive decline was calculated as monthly MMSE score change.ResultsMedian baseline age was 71.2 years. Median monthly MMSE decline was 0.25 points. Patients were classified into slow (≤0.25 MMSE/month) or fast (>0.25 MMSE/month) progressors. Fast progressors had significantly lower baseline individual alpha frequency (IAF) (6.8 Hz versus 9.1 Hz; p = 0.003), lower MMSE at one year (19 versus 24; p = 0.02), and more frequent diabetes and cardiovascular history. A multivariate regression analysis adjusted for age revealed that baseline IAF (p = 0.002), initial MMSE score (p = 0.028), and the p-tau/Aβ<sub>42</sub> ratio (p < 0.01) significantly predicted monthly cognitive decline.ConclusionsCombining quantitative EEG-derived IAF, baseline cognitive status, and CSF biomarkers (p-tau/Aβ<sub>42</sub> ratio) enhances prediction of AD progression. This integrated approach better captures disease heterogeneity, highlighting the need for multimodal strategies in the prognosis and management of AD.

Journal of Alzheimer s DiseaseVol. 108(1)
Università Campus Bio-Medico (IT), Campus Bio Medico University Hospital (IT)
Openalex Percentile: Top 72%
Dementia and Cognitive Impairment Research
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