A metabonomics approach as a means for identification of potential biomarkers for early diagnosis of endometriosis

Abstract

Our present study focuses on the identification of predictive biomarkers in serum for the early diagnosis of endometriosis in a minimally invasive manner using ¹H-NMR based metabonomics. PLS-DA modeling of bins obtained from CPMG spectra of serum samples discriminated endometriosis patients from controls with sensitivity and specificity levels of about 80% and 90%, respectively. Compared with those from controls, serum samples from endometriosis patients showed increased levels of lactate, 3-hydroxybutyrate, alanine, leucine, valine, threonine, lysine, glycerophosphatidylcholine, succinic acid and 2-hydroxybutyrate as well as decreased levels of lipids, glucose, isoleucine and arginine. Our work offers valuable information for non-invasive diagnosis of endometriosis and may be of potential benefit to understand pathogenesis of the disease.