Development of a Machine Learning Prediction Model for the Detection of Meniere's Disease from Cerumen Chemical Profiles

State University Of New York At Albany, Albany NY

Investigators

Abstract

ABSTRACT/PROJECT SUMMARY for Supplement Request on Alzheimer’s Disease and Related Dementias [NOT-AG-22-025] (Parent Project: R21DC02056501) Alzheimer’s disease and its related dementias (ADRD) afflict ~50 million people worldwide. Although testing methodologies to diagnose and differentiate different forms of dementia have been investigated for decades, the only definitive means to confirm a diagnosis of ADRD is at autopsy. Diagnosis is slow and is often based on exclusionary criteria along with the results of multiple forms of costly testing. However, if more readily accessible chemical markers of ADRD can be identified, rapid and accurate diagnoses could be accomplished based on assessment of the presence (or absence) of relevant compounds. Such an achievement would revolutionize ADRD diagnosis in terms of methods and cost, and could even reveal other dimensions of disease pathogenesis and progression that might shed light on disease etiology, and lead to alternative, more effective treatments. It is hypothesized here that based on research findings that reveal that ADRD manifests in part in terms of changes in lipid profiles, the chemical profile of the lipid-rich cerumen matrix may serve as a reporter of the presence of ADRD, and that cerumen profiles may differ as a function of dementia type. Knowledge of these differential profiles can be leveraged to accurately and rapidly reveal the presence of ADRD via the application of machine learning algorithms to the chemical data. This hypothesis will be investigated through pursuit of the following specific aims: Specific Aim I: Determination of the mass spectrum-derived chemical signatures of cerumen from healthy donors, Alzheimer’s disease (AD) patients, and patients diagnosed with other dementias. Specific Aim II: Development of machine learning prediction models that enable accurate determination of the presence of Alzheimer’s disease and/or other dementias based on features common to all types of dementia but distinct from cerumen from healthy donors. Specific Aim III: Development of machine learning prediction models to distinguish Alzheimer’s disease samples from other types of dementia using cerumen chemical profiles, and determination of the subset of compounds that are unique to each type of dementia. Specific Aim IV: Structural characterization of biomarkers revealed by the machine learning prediction model(s) developed in Specific Aims II and III. The results of this work will reveal whether there is a correlation between the lipid profile of cerumen and the presence of Alzheimer’s disease and related dementias. Structural information will be acquired on the molecules that are responsible for the differences in healthy and dementia patients. The information revealed would provide the opportunity for future development of a potential non-invasive method for the rapid diagnosis of Alzheimer’s disease and related dementias.