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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., Significance: Our method involves a volatolomic approach by headspace gas chromatography coupled with mass spectrometry as a single analytical technique combined with multivariate data analysis to detect biomarkers of diabetes in earwax samples. Our method was able to discriminate with high accuracy between 33 healthy controls and 26 diabetic patients as well as its types (1 and 2). Our method employing earwax, a "neglected biological matrix" not only has the advantage of non-invasive sampling but also overcomes the limitations of the applied procedures in other biological samples, involving no or minimum sample pretreatment, no external contamination and utilizing a simple sample collection technique., (Copyright © 2017 Elsevier B.V. All rights reserved.)