ABSTRACT/PROJECT SUMMARY
Ménière’s disease is a chronic, incurable vestibular disorder that produces a recurring set of symptoms as
a result of abnormally large amounts of endolymph in the inner ear. Manifestations of the disease include
recurrent episodes of vertigo, tinnitus, imbalance, nausea and/or vomiting, a feeling of fullness or pressure
in the ear, and fluctuating, progressive low-frequency hearing loss. Diagnosis is difficult because other
neurological conditions present some of the same symptoms. Thus, Ménière’s disease diagnosis, which is
challenging, imprecise, and time consuming, involves the painstaking process of excluding other diseases
with overlapping symptoms. Because it has no known chemical or radiographic markers, diagnosis is based
on the observation of a clinical compendium of symptoms, and misdiagnosis is fairly common. If chemical
markers of Ménière’s and other relevant neurological disorders could be determined, more rapid and
accurate diagnosis could be achieved based on assessment of the presence (or absence) of these relevant
compounds. It is hypothesized here that the chemical profile of cerumen can serve as a reporter of the
presence of Ménière’s disease and other neurological disorders with overlapping symptoms, and that
knowledge of these differential profiles can be leveraged to accurately and rapidly reveal the presence of
Ménière’s disease. This hypothesis will be investigated through pursuit of the following specific aims:
Specific Aim I: Collection and determination of the mass spectral chemical signatures of cerumen from
healthy donors, Ménière’s disease patients, and patients diagnosed with other neurotological disorders with
overlapping symptoms.
Specific Aim II: Development of machine learning prediction models that enable accurate determination
of the presence of Ménière’s disease and/or other neurotological disorders from cerumen chemical profiles,
and reveal the presence of the subset of compounds that are important for the ability to distinguish
Ménière’s disease samples from others.
Specific Aim III: Structural characterization of compounds revealed by the machine learning prediction
model(s) developed in Specific Aim II, to be associated with Ménière’s disease.
The results of this work will reveal whether there is a correlation between the lipid profile of earwax and
the presence of particular disease states. Structural information will be acquired on the molecules that are
responsible for the differences in healthy and Ménière’s disease patients. The information revealed would
provide the opportunity for development of a potential non-invasive method for the rapid diagnosis of
Ménière’s disease.
