December 26, 2023
Oita University
Eisai Co., Ltd.

DEVELOPMENT OF PREDICTION MODEL FOR BRAIN AMYLOID-BETA ACCUMULATIONUSING WRISTBAND SENSOR
Oita University and Eisai Co., Ltd. (Eisai) hereby announce the development of the world's first machinelearning model to predict amyloid beta*1 (Aβ) accumulation in the brain using a wristband sensor. Thismodel is expected to enable screening for brain Aβ accumulation, which is an important pathological factorof Alzheimer's disease*2 (AD), simply by collecting biological and lifestyle data from daily life.
The details of this model were published in the online edition of the peer-reviewed medical journal
Alzheimer's Research & Therapy on December 12, 2023.
In AD, which is said to account for over 60% of the causes of dementia, Aβ begins to accumulate in thebrain about 20 years before the onset of the disease. This has prompted the development of new
therapeutic drugs targeting Aβ, leading to the approval of an humanized anti-soluble aggregated Aβ
monoclonal antibody in Japan. The key to maximizing treatment effects of the medicine is detecting Aβ
accumulation in the brain of patients with mild cognitive impairment before the onset of symptoms. Currently,
although brain Aβ accumulation can be detected by positron emission tomography*3 (amyloid PET) andcerebrospinal fluid testing*4 (CSF testing), the number of medical institutes able to perform those tests islimited, and the high cost and invasiveness of these tests are considered issues. Therefore, developmentof an inexpensive and easy-to-use screening method has been sought after to identify those who needamyloid PET or CSF testing.
Although lifestyle factors, including lack of exercise, social isolation, and sleep disorders, as well as
diseases, including hypertension, diabetes, and cardiovascular disease are known risk factors for AD, thus
far, studies applying machine learning models to predict brain Aβ accumulation have used only cognitive
function tests, blood tests, and brain imaging tests. In contrast, this is the first machine learning study tofocus on "biological data" and "lifestyle data."
This study integrated "biological data" collected by wristband sensors, such as physical activity, sleep, andheart rate, "lifestyle data" obtained from medical consultations, such as number of household members,
employment status, frequency of going outdoors, means of transportation, number of days participating incommunity activity, and “subjects’ background”, such as age, education history, history of alcohol intake,and medical history (hypertension, stroke, diabetes, heart disease, thyroid disease) to create a machinelearning model to predict individuals who are likely to test positive by brain amyloid PET, and evaluated themodel’s performance. The result of this study showed that the Area Under the Curve (AUC), an evaluatioinindex of the prediction model consisting of “biological data”, “lifestyle data” and “subjects’ background”, was0.79, leading to the decision that the model has sufficient capability for screening. This machine learningmodel is able to predict brain Aβ accumulation using readily available non-invasive variables. As a result,this model appears widely applicable as a pre-screening for people living in areas with little access toamyloid PET and CSF testing, and to reduce financial and physical burden to patients, as well as the costsof clinical studies.
【Glossary of Terms】*1 Amyloid beta: A protein viewed as a cause of Alzheimer's disease, which accumulates in the brain forabout 20 years prior to the onset of the disease and forms senile plaques
2*

Alzheimer's disease: the most common cause of dementia, and its pathological characteristics include
senile plaques, neurofibrillary tangles, and neuronal cell death3*

Amyloid PET: a brain imaging test visualizing Aβ accumulation in the brain

4

Cerebrospinal fluid testing: A test analyzing cerebrospinal fluid for Aβ42, phosphorylated tau, and total*tau as biomarkers of Alzheimer's disease
Background and Outline of Research
As Japan has become a super-aging society with the rise in the number of dementia patients over the ageof 65, the development of new therapeutic agents for AD, the most common cause of dementia, is an urgentissue. Lifestyle habits such as lack of exercise, social isolation, and sleep disorders, as well as diseases
such as hypertension, diabetes, and cardiovascular disease, are known risk factors for AD. The
development of drugs targeting Aβ has seen progress in recent years, and this year, an anti-Aβ protofibril
antibody was approved in Japan. To maximize the effects of this medicine, it is essential to identify
individuals with pre-symptomatic mild cognitive impairment (MCI) who are likely to have elevated brain
amyloid accumulation. To date, although reports on machine learning models for predicting brain Aβ
accumulation by cognitive function tests, blood tests, and brain imaging tests have been available, noresearch has focused on biological data or lifestyle data. This research is the world’s first attempt to createa machine learning model to predict amyloid positivity in subjects by utilizing "biological data" collected witha wristband sensor, including daily activity, sleep, speech, and heart rate, and “lifestyle data” collected
through medical consultation.
Results and Significance of Research, Future Development
This research utilized data from a prospective cohort study on the elderly without dementia aged 65 andolder living in Usuki City, Oita Prefecture, conducted between August 2015 and September 2019. 122
individuals (54 men, 68 women, median age 75.50 years) with mild cognitive impairment or subjective
memory impairment wore a wristband sensor for approximately 7 days every 3 months. The study alsocollected lifestyle data through medical consultation, and subjects underwent regular amyloid PET (once ayear) examinations over the course of 3 years. The research evaluated a predictive model created withthree machine learning technologies, support vector machine, Elastic Net, and logistic regression, tointegrate "Biological data" collected by wristband sensors, such as physical activity, sleep, and heartbeat,and "lifestyle data" obtained from medical consultations, such as living with household members,
employment status, frequency of going outdoors, means of transportation, number of days participating incommunity activity, as well as the “subjects’ background”, such as age, education history, history of alcoholuse, and medical history (hypertension, stroke, diabetes, heart disease, thyroid disease). For instance,
while

the AUC of a predictive model created with only "biological data" collected by the wristband sensors

using Elastic Net was 0.70, the AUC of a predictive model created with additional "lifestyle data" and patient
background was 0.79, exhibiting improved performance. This research is the world’s first attempt to createa machine learning model to predict brain Aβ accumulation by utilizing "biological data" collected with awristband sensor, including daily activity, sleep, speech, and heart rate, and “lifestyle data” collected
through medical consultation, as well as “subjects’ background”.
Furthermore, using state-of-the-art algorithms the to identify multiple factors which contribute to predicting
Aβ accumulation, 22 common factors were identified that were common across the three learning machinetechnologies. Specifically identified were physical activity, sleep, heart rate, amount of conversation, age,length of education, living with or without children, means of transportation, presence of accompanying
person for hospital visits, communication frequencies, and number of outings.
Academic Paper:
Title:

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

Authors: Noriyuki Kimura (Department of Neurology, Faculty of Medicine, Oita University) 1,2, Tomoki Aota(Eisai Co., Ltd.) 1, Yasuhiro Aso(Oita Prefectural Hospital), Kenichi Yabuuchi (Department of Neurology,Faculty of Medicine, Oita University), Kotaro Sasaki (Eisai Co., Ltd.), Teruaki Masuda (Department ofNeurology, Faculty of Medicine, Oita University), Atsuko Eguchi(Department of Neurology, Faculty ofMedicine, Oita University), Yoshitaka Maeda (Eisai Co., Ltd.), Ken Aoshima (Eisai Co., Ltd./University ofTsukuba) 2, Etsuro Matsubara (Department of Neurology, Faculty of Medicine, Oita University)
1.

These authors contributed equally to the manuscript.

2.

Corresponding author.

Publisher:

Alzheimer's Research & Therapy

Please direct any interview requests or inquiries to the contact information provided below
For further information or any inquiries regarding this study
Noriyuki Kimura, Associate Professor, Department of Neurology, Faculty of Medicine, Oita University
TEL: +81-(0)97-586-5814, FAX: +81-(0)97-586-6502
Email：naika3@oita-u.ac.jp

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