Earwax: a neglected body secretion or a step ahead in clinical diagnosis? A pilot study

Abstract

This work combines the advantages of volatile metabolites profiling as a young growing research field with a non-invasive sampling technique using earwax “a neglected body secretion” for detection and monitoring of biomarkers for diabetes mellitus (types 1 and 2). Earwax samples were collected from 26 diabetic patients of both types, analyzed by headspace gas chromatography mass spectrometry and confronted to the volatile earwax composition of 33 healthy individuals. Data mining analysis was conducted using different models to discriminate the healthy individuals from the diabetic patients and to discriminate between both types of diabetes as well. The model with the best discriminating ability was found to be partial least squares discriminant analysis (PLS-DA) after variable selection. The 6 most important biomarkers were ethanol, acetone, methoxyacetone, hydroxyurea, isobutyraldehyde, and acetic acid. The multivariate model constructed was validated using a test data set and was able to correctly predict all the samples. The receiver operating characteristic (ROC) curves were built for the 6 variables for diabetes types 1 and 2 diagnoses. Among the 6 variables selected, methoxyacetone was the only biomarker able solely to perfectly discriminate between diabetes types 1 and 2. The method is simple, non-invasive, accurate, and highly accepted by patients.