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.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00284-3.

Keywords: Alzheimer’s disease; Classifier; Digital biomarkers; Predictive preventive personalized medicine (PPPM); Sex.

Fig. 1

The motoric functioning tasks in the Altoida test. These are executed one after another. Using their index finger of their dominant hand, from left to right, the task is to (1) draw a circle, (2) draw a square, (3) draw a rotated W shape within 7 s, (4) draw as many circles as possible within 7 s, (5) tap the highlighted buttons (left, right, left, right, etc.), and (6) tap the highlighted button as fast as possible, the buttons highlight at random

Fig. 2

Illustration of the augmented reality (AR) task in the Altoida test. During the AR test, the subject is asked to place and find three virtual objects in the room. To do so, the subject is required to walk around the room holding a tablet or smartphone device in front of him/her. While doing so, the camera of the device records the environment and displays it back to the user on the screen, augmented with virtual objects (in this illustration, a teddy bear). The user needs to place the objects on flat surfaces and later recall their position by walking back to that location

Fig. 3

Receiver operating characteristic (ROC) curve with five-fold cross-validation results for the digital biomarker sex classifier

Fig. 4

Receiver operating characteristics (ROC) area under curve (AUC) for different subgroups. A Comparison over different stages of Alzheimer’s progression. B Comparison over different age groups in the healthy population. Error bars show standard error of the mean (SEM). P-values are computed using the Mann Whitney U test

Fig. 5

Feature importance of the sex classifier in healthy individuals. A The top five feature groups according to the SHAP method. Each bar represents the summed SHAP value of the features in that feature group. B A feature value SHAP distribution plot for the top five contributing features. Subject specific SHAP values were computed for each datapoint in the classifier training data. For each feature, we then plot for each datapoint a dot with the feature value of that datapoint, with the dot color coded by the relative feature value. The position of each dot on the SHAP value x-axis represents the magnitude and the direction of the contribution of that specific feature value of that specific datapoint towards classifying as female (− 1) or male (+ 1). Acronyms in the plots are augmented reality (AR), fast Fourier transform (FFT), SHapley Additive exPlanations (SHAP), accelerometer (ACC), variance (var), first part of a single test (1st), or second part of a single test (2nd)

Fig. 6

Comparative histograms of the top five contributing features (according to the SHAP results in Fig. 5) for the group of healthy subjects, with male data in blue and female data in red. Acronyms are augmented reality (AR), fast Fourier transform (FFT), accelerometer (ACC), variance (var), first part of a single test (1st), or second part of a single test (2nd)

Fig. 7

Comparative histograms of the top five contributing features (according to the SHAP results in Fig. 5) for the combined group of MCI and AD subjects, with male data in blue and female data in red. Acronyms are augmented reality (AR), fast Fourier transform (FFT), accelerometer (ACC), variance (var), first part of a single test (1st), or second part of a single test (2nd)