Ménière’s disease is a debilitating disorder of the inner ear which can cause extreme vertigo, dizziness and eventual hearing loss. Like a number of diseases of the ear, the etiology is not well understood. In addition, determination of the presence of the disease occurs primarily through “diagnosis by exclusion” in which the disease is distinguished from others that have similar symptoms by the process of elimination. As the diagnostic workup can take months to years, an accurate and more rapid method of diagnosis is highly desirable. In this study, the potential of the chemical composition of earwax to serve as a reporter of Ménière’s disease was investigated. The chemical signature of earwax from both healthy donors and patients with Ménière’s disease was analyzed using direct analysis in real time – high-resolution mass spectrometry (DART-HRMS). The acquired mass spectra were then subjected to the multivariate statistical analysis technique random forest, to develop a model for earwax class prediction. Assessment of the prediction capability of the model using leave-one-out cross validation yielded an accuracy of 63%. Nevertheless, external validation samples in the form of unknowns were correctly classified by the model 100% of the time. It was found that the two groups (Ménière’s and non-Ménière’s samples) could be differentiated based on fatty acid profile distinctions that were subsequently characterized by GC-MS. Overall, the results demonstrate that earwax can serve as a viable and readily accessible matrix that can be analyzed to reveal a disease state. This paves the way for the development of a rapid and accurate Ménière’s disease diagnosis approach.

Utilization of mass spectrometric data for the development of an approach for the diagnosis of Ménière’s disease

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