The Early Detection of Neurodegenerative diseases initiative: an international and multidisciplinary effort for transforming the early detection of dementia‐causing diseases
Authors: Marinaro Federica , Morvan Claire , Au Rhoda , Bond Simon , Burkhart Michael C. , Carlebach Nomi , Chan Dennis , Delbarre Daniel , Farier Lisa , Lacey Aaron , LaMonica Haley , Lancaster Claire L , Lee Liz Yuanxi , Llewellyn David J , Mallon Ann‐Marie , Martins Ralph N , Ardle Ríona Mc , Mummery Catherine J. , Naismith Sharon L , Oldham Mike , Rainey‐Smith Stephanie , Santos Luis , Slight Sarah , Smith Nadia , Thomas Spencer , Venton Jenny , Tolley Clare , Whitaker Kirstie , Wright Joshua , Wilson Sarah , Wing Kieran , Crawford John , Dagum Paul , Routledge Carol , Walker Zuzana , Everson Richard , Hinds Chris , Kourtzi Zoe 
Published: June 2023

Background

Around 50 million people have dementia worldwide, with nearly 10 million new cases every year. Diagnosis is complex and often relies on expensive and invasive measures, with most patients accessing medical support when they already experience symptoms.

Method

The Early Detection of Neurodegenerative diseases (EDoN) initiative, spearheaded by Alzheimer’s Research UK, brings together over 60 experts from 49 universities, research projects, patient cohorts and technology providers to create machine learning models to detect the earliest stages of dementia‐causing diseases. EDoN has reviewed behavioural and physiological modalities with the strongest association with pre‐clinical disease.

Result

Over 140 modalities were identified from the review and were shortlisted to create the version 1 digital toolkit. This first version includes Mezurio and Longevity smartphone apps, a Fitbit charge 4 activity tracker and Dreem 3 sleep headband. This Toolkit was further refined through patient and public involvement studies and collects 26 measures related to 7 aspects of behaviour and physiology (cognition, neural activity, physical activity, heart rate, fine motor movement, sleep, language and speech). The Toolkit is now being used to collect digital data in four international cohorts (Boston University Alzheimer’s Disease Research Center ‐ BU ADRC; The predictors of COgnitive DECline in attenders of memory clinic using digital devices ‐ CODEC‐2; Western Australia Memory Study ‐ WAMS; Healthy Brain Aging ‐ HBA), alongside prospective and retrospective clinical data, to inform the development of machine learning models.

Conclusion

EDoN will build models with digital markers, validating them against other biomarkers to predict dementia subtypes and individualised disease trajectories. Based on the outputs of the initial models, EDoN will go through a series of iterations of cohort engagement, modality and tool refinement, and data collection. Workstreams are underway to inform data security, privacy, ethics and open policy research, as well as considering the integration of the final EDoN Toolkit into healthcare systems globally. EDoN aims to deliver a cost‐effective, low burden and population‐wide method for early detection of dementia‐causing diseases that will benefit the public, patients, carers, researchers and clinicians, as well as the broader healthcare system and the delivery of new therapies.