Eisai working on wristband sensor for brain amyloid



Eisai has joined forces with Oita University in Japan to develop a machine-learning tool that may be able to predict whether someone is at risk of developing Alzheimer’s disease using a wrist-worn sensor.

The device is designed to predict the accumulation in the brain of amyloid beta, which aggregates to form distinctive plaques in the brains of people who develop Alzheimer’s and some other forms of dementia.

The wristband measures a range of biological data such as physical activity, sleep, and heart rate, and is analysed by the ML algorithm in conjunction with lifestyle data obtained from medical consultations.

The lifestyle data is broad, covering medical history as well as the number of household members, employment status, frequency of going outdoors, means of transportation, number of days participating in community activity, and other factors such as age, education history, and history of alcohol intake.

Eisai said the proof-of-concept Usuki study has found that the sensor/algorithm combination has “sufficient capability for screening” and could be a low-cost, easy-to-use alternative to current methods of testing for amyloid in the brain such as PET imaging and cerebrospinal fluid (CSF) testing.

It was put through its paces in a cohort of 120 subjects aged 65 or over with mild cognitive impairment that had not progressed to full-blown dementia, who were followed for up to three years. Over the period, it was shown to match results generated using conventional cognitive testing and annual PET scans.

Interestingly, the study also identified which lifestyle factors seemed to be most predictive of amyloid accumulation, pointing to physical activity, sleep, heart rate, amount of conversation, age, length of education, living with or without children, means of transportation, the presence of an accompanying person for hospital visits, communication frequencies, and number of outings.

Eisai said the results were particularly important as “the key to maximising treatment effects of the medicine is detecting [amyloid beta] accumulation in the brain of patients with mild cognitive impairment before the onset of symptoms.”

Along with partner Biogen, Eisai brought the first amyloid-targeting drugs to market as therapy for Alzheimer’s – notably Leqembi (lecanemab) which has now been approved in the US and Japan – but has also looked beyond the medicine in its approach to dementia.

Along with this sensor/algorithm approach, the company looked at other digital means of identifying people at risk of dementia and supporting them after a diagnosis, and in September launched a dedicated subsidiary company – Theoria Technologies – to create a “digital ecosystem” for dementia.

Its activity in this area goes back several years. A collaboration with NTT IT led to the development of a cloud-based system to allow doctors and caregivers to share treatment plans and medical and lifestyle information for elderly patients being treated at home, and that was followed by the launch of a smartphone app – called NouKNOW – that allows people to self-assess their cognitive performance and spot declines that may signal the onset of dementia.

Last year, it partnered with Tokyo-based Lifenet Insurance Co. to develop a series of insurance products specifically for people with dementia and other ageing-related diseases, and in June, Eisai announced it was taking part in a two-year research project in the UK to develop digital tools that could be used alongside approved therapies for people with dementia.



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