Robbert L Harms, Alberto Ferrari, Irene B Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas, Antonella Santuccione Chadha
{"title":"Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach.","authors":"Robbert L Harms, Alberto Ferrari, Irene B Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas, Antonella Santuccione Chadha","doi":"10.1007/s13167-022-00284-3","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-022-00284-3.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203627/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epma Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13167-022-00284-3","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
PMA Journal is a journal of predictive, preventive and personalized medicine (PPPM). The journal provides expert viewpoints and research on medical innovations and advanced healthcare using predictive diagnostics, targeted preventive measures and personalized patient treatments. The journal is indexed by PubMed, Embase and Scopus.