WASHINGTON--(BUSINESS WIRE)--The Women’s Brain Project, an international non-profit organization studying gender and sex determinants to brain and mental health and Altoida, a precision neurology company pioneering non-invasive brain health diagnostics using AI and augmented reality (AR), today announced results from a study showing sex-based differences using digital biomarker data collected from Altoida’s digital cognitive assessment platform.
Published in EPMA Journal, the study explored sex differences in Altoida’s digital cognitive assessment platform in a sample of 568 subjects consisting of a clinical dataset (mild cognitive impairment and dementia due to Alzheimer’s Disease) and a healthy population. The study results found that a biological sex classifier built on digital biomarker features, captured using Altoida’s application, achieved a 75% performance rate in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females.
The discernible differences seem to decline in subjects with mild cognitive impairment (MCI) or overt Alzheimer’s Disease (AD), independent of age. In the healthy population, the primary differentiating features are micro hand gestures detectable on a wearable, that measured accelerometric data. In this assessment domain, the accuracy reached 80 percent versus the overall neurocognitive Altoida performance. The study found that sex differences can be observed via digital biomarkers, which has the potential to impact diagnosis and treatment of AD.
“Our research shows how digital biomarkers can detect sex-based differences which are often overlooked when using standardized cognitive neuropsychological assessment,” said Antonella Santuccione Chadha, M.D., CEO and Co-Founder Women's Brain Project and Chief Medical Officer at Altoida. “These findings support the need for researchers and drug developers to account for sex-based characteristics in investigating prospective treatments for Alzheimer’s Disease.”
“Our ultimate goal is to build an integrated framework for sex-based cognitive assessment to predict, monitor and provide precision treatment of neurodegenerative disease,” said Travis Bond, CEO, Altoida. “Such a framework could be used for early detection of the disease, and enables both targeted prevention strategies and personalized Alzheimer’s treatment for patients. By integrating sex with risk stratification based on genetics and individual risk factors with the use of digital biomarker monitoring applications, this will enable the early detection and treatment of symptoms, when a patient has MCI, before development into Alzheimer’s.”
Study results highlight sex-based differences
An MCI diagnosis is determined often later in females, compared to males. This study suggests that using sex-adjusted tools for diagnosis (or sex-adjusted cut-offs) may be needed to improve diagnostic precision. Predictive diagnostics using AD biomarkers in the pre-symptomatic or oligosymptomatic (MCI) stage, followed by targeted preventions and treatment personalized to those individuals considered high risk, are increasingly considered to represent the best chance at successful AD management.
The performance dropped when this classifier was applied to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. The 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.
The results may also enable researchers to better understand the pathophysiological mechanisms of the disease, which might differ between sexes, with opportunities for personalized treatment. From a predictive medicine perspective, including sex differences might make predictions more precise, especially with algorithms that incorporate multiple variables. In particular, considering sex differences may improve the ability to predict fast decliners in MCI patients, which is a key element for planning therapy and care options. From a precision medicine perspective, whether a patient is a male or female makes a difference, based on the study data. More data on sex differences could guide future clinical practice, informing choices for ad-hoc prevention, diagnosis and treatment options.
These findings should be integrated with the most powerful recent developments in digital medicine to build models of disease development that can fully integrate the effect of sex, digital biomarker technology being one of the most promising tools when developing drugs or digital therapeutics in AD. The study was conducted to show the research community that there are potential sex differences in cognitive testing in Alzheimer’s, in order to implement measures to mitigate potential biases in clinical application.
