Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach

Abstract

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer’s, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida’s digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida’s application, achieved a 75% ROC-AUC (receiver operating characteristic — area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.

Sex and gender considerations in Alzheimer’s disease: The Women’s Brain Project contribution

“…The WBP-led study analyzed data from clinical settings ( N = 438 patients from Italy, Greece, and Spain) and data from the Japanese population ( N = 130 patients) ( Harms et al, 2022 ). In this study, the clinical data consists of controlled tests of elderly subjects with MCI, AD or Aβ (+) biomarkers or young healthy controls.…”

“…We used all of this data (which was devoid of anagraphical information) to train an algorithm to determine the sex of the patient based on the data received. The sex classifier successfully determined the sex of the patient among healthy patients, but its power faded in MCI and AD populations ( Harms et al, 2022 ). Notably, the successful differentiation in healthy individuals shows that males and females have distinct differences in their neurocognition as detected by the NMI.…”

An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease

“…One well-known project taking advantage of AI in neurology is called Altoida (Harms et al, 2022 ), which aims to identify digital biomarkers of MCI and AD, focusing on sex-based differences in these biomarkers. As reported by Harms et al ( 2022 ) and Livingston et al ( 2020 ), ~ 40% of dementias are estimated to be a result of modifiable risk factors.…”

“…Projects around the world are emerging to take advantage of the large amounts of data collected daily by the technology we use and wear, including mobile phones, digital watches, heart rate monitors, and sleep bands. The passively collected and actively inputted data has great potential to be analyzed using machine learning (ML) and artificial intelligence (AI) to detect subtle changes in a person's symptoms, including vocal inflection and eye gaze, which can be linked to known AD biomarkers (Harms et al, 2022 ). With the advent of blockchain technology, the encryption and data structuring within this format is being tested to offer more privacy and to store massive amounts of information for analysis (Hort et al, 2021 ).…”

Definitions of digital biomarkers: a systematic mapping of the biomedical literature

“…These data types, though still a key component of research and clinical care that may be stored digitally, are not digitally measured or truly dependent on software." Harms et al (2022) 32 "Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements.…”