A metabolomics profiling approach was conducted to identify diagnostic biomarkers of PD from sebum, a non-invasively available biofluid. In this study, we used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Partial least squares-discriminant analysis (PLS-DA) models based on this sebum metabolome had correct classification rates of 70.4% and 69.7% to distinguish between drug naïve PD and medicated PD from control, respectively. Variable importance in projection (VIP) scores indicate compounds with significance belonged to sphingolipid, triacylglycerol and fatty acid/ester lipid classes. Pathway enrichment analysis showed alterations in lipid metabolism and mitochondrial dysfunction viz. the carnitine shuttle, sphingolipid metabolism and arachidonic acid metabolism. This study unveiled novel diagnostic sebum-based biomarkers for PD, and provides insight towards our current understanding of the pathogenesis of PD.