Predicting Amyloid Risk by Machine Learning Algorithms Based on the A4 Screen Data: Application to the Japanese Trial-Ready Cohort Study

Overview

Journal Alzheimers Dement (N Y)

Date 2021 Mar 29

PMID 33778148

Citations 11

Authors

Kenichiro Sato

Ryoko Ihara

Kazushi Suzuki

Yoshiki Niimi

Tatsushi Toda

Gustavo Jimenez-Maggiora

Oliver Langford

Michael C Donohue

Rema Raman

Paul S Aisen

Reisa A Sperling

Atsushi Iwata

Takeshi Iwatsubo

Affiliations

Soon will be listed here.

Abstract

Background: Selecting cognitively normal elderly individuals with higher risk of brain amyloid deposition is critical to the success of prevention trials for Alzheimer's disease (AD).

Methods: Based on the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease study data, we built machine-learning models and applied them to our ongoing Japanese Trial-Ready Cohort (J-TRC) webstudy participants registered within the first 9 months ( = 3081) of launch to predict standard uptake value ratio (SUVr) of amyloid positron emission tomography.

Results: Age, family history, online Cognitive Function Instrument and CogState scores were important predictors. In a subgroup of J-TRC webstudy participants with known amyloid status ( = 37), the predicted SUVr corresponded well with the self-reported amyloid test results (area under the curve = 0.806 [0.619-0.992]).

Discussion: Our algorithms may be usable for automatic prioritization of candidate participants with higher amyloid risks to be preferentially recruited from the J-TRC webstudy to in-person study, maximizing efficiency for the identification of preclinical AD participants.

Citing Articles

Blood-based biomarker prescreening with different testing combinations and cutoffs: A simulation study examining efficacy and cost-effectiveness in Alzheimer's disease prevention studies.

Sato K, Niimi Y, Ihara R, Iwata A, Suzuki K, Iwatsubo T Alzheimers Dement (N Y). 2025; 11(1):e70065.

PMID: 40065919 PMC: 11891560. DOI: 10.1002/trc2.70065.

Public perceptions related to healthcare preparedness to anti-amyloid therapies for Alzheimer's Disease in Japan.

Sato K, Niimi Y, Ihara R, Iwata A, Suzuki K, Nemoto K Alzheimers Res Ther. 2024; 16(1):205.

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Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.

Lu X, Yang C, Liang L, Hu G, Zhong Z, Jiang Z J Am Med Inform Assoc. 2024; 31(11):2749-2759.

PMID: 39259922 PMC: 11491624. DOI: 10.1093/jamia/ocae243.

Combining plasma Aβ and p-tau217 improves detection of brain amyloid in non-demented elderly.

Niimi Y, Janelidze S, Sato K, Tomita N, Tsukamoto T, Kato T Alzheimers Res Ther. 2024; 16(1):115.

PMID: 38778353 PMC: 11112892. DOI: 10.1186/s13195-024-01469-w.

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors.

Kimura N, Aota T, Aso Y, Yabuuchi K, Sasaki K, Masuda T Alzheimers Res Ther. 2023; 15(1):212.

PMID: 38087316 PMC: 10714506. DOI: 10.1186/s13195-023-01363-x.

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