Machine learning prediction of tau‐PET in Alzheimer's disease using plasma, MRI, and clinical data

Linda Karlsson; Jacob W. Vogel; Ida Arvidsson; Kalle Åström; Olof Strandberg; Jakob Seidlitz; Richard A. I. Bethlehem; Erik Stomrud; Rik Ossenkoppele; Nicholas J. Ashton; Henrik Zetterberg; Kaj Blennow; Sebastian Palmqvist; Ruben Smith; Shorena Janelidze; Renaud La Joie; Gil D. Rabinovici; Alexa Pichet Binette; Niklas Mattsson; Oskar Hansson

doi:10.1002/alz.14600

Key Points

This research aims to determine how accessible variables can estimate tau tangle load in Alzheimer's disease.
Utilized plasma phosphorylated tau-217 and MRI data as predictors for tau-PET.
Developed machine learning models to analyze data from various Alzheimer’s disease cohorts.
Assessed model generalizability across different cohorts.
High predictive accuracy for tau-PET observed using plasma p-tau217 and MRI data.
Machine learning models demonstrated strong generalizability across various Alzheimer's disease cohorts.

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Implication

Highlights plasma and MRI as key predictors of tau-PET in Alzheimer's Disease, suggesting new diagnostic approaches.

Abstract

Accessible variables showed potential in estimating tau tangle load and distribution. Plasma phosphorylated tau-217 (p-tau217) and magnetic resonance imaging (MRI) were the best predictors of different tau-PET (positron emission tomography) composites. Machine learning models demonstrated high generalizability across AD cohorts.

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Cite This Study

Karlsson et al. (2025) studied this question.

synapsesocial.com/papers/69df23e358b92af24d7a108a https://doi.org/10.1002/alz.14600

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Machine learning prediction of tau‐PET in Alzheimer's disease using plasma, MRI, and clinical data

Key Points

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Implication

Abstract

Discussion

Cite This Study

Machine learning prediction of tau‐PET in Alzheimer's disease using plasma, MRI, and clinical data

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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