BiBi Fatemeh Nobakht Mothlagh Ghoochani - Department of Basic Medical Sciences, Neyshabur University of Medical Sciences, Neyshabur, Iran
Rasoul Aliannejad - Pulmonary Department, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Afsaneh Arefi Oskuei - Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Mostafa Rezaei-Tavirani - Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Shiva Kalantari - Chronic Kidney Disease Research Center, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Mohammad Taghi Naseri - Department of Chemistry, Faculty of Sciences, Tarbiat Modares University, Tehran, Iran
Alireza Bagheban - Physiotherapy Research Center, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Hadi Parastar - Department of Chemistry, Sharif University of Technology, Tehran, Iran
Ghazale Aliakbarzadeh - Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran

Objective(s): This study aims to evaluate combined proton nuclear magnetic resonance (۱H NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) metabolic profiling approaches, for discriminating between mustard airway diseases (MADs) and healthy controls and for providing biochemical information on this disease. Materials and Methods: In the present study, analysis of serum samples collected from ۱۷ MAD subjects and ۱۲ healthy controls was performed using NMR. Of these subjects, ۱۴ (۸ patients and ۶ controls) were analyzed by GC-MS. Then, their spectral profiles were subjected to principal component analysis (PCA) and orthogonal partial least squares regression discriminant analysis (OPLS-DA). Results: A panel of twenty eight metabolite biomarkers was generated for MADs, sixteen  NMR-derived metabolites (۳-methyl-۲-oxovaleric acid, ۳-hydroxyisobutyrate, lactic acid, lysine, glutamic acid, proline, hydroxyproline, dimethylamine, creatine, citrulline, choline, acetic acid, acetoacetate, cholesterol, alanine, and lipid (mainly VLDL)) and twelve GC-MS-derived metabolites (threonine, phenylalanine, citric acid, myristic acid, pentadecanoic acid, tyrosine, arachidonic acid, lactic acid, propionic acid, ۳-hydroxybutyric acid, linoleic acid, and oleic acid). This composite biomarker panel could effectively discriminate MAD subjects from healthy controls, achieving an area under receiver operating characteristic curve (AUC) values of ۱ and ۰.۷۹ for NMR and GC-MS, respectively. Conclusion: In the present study, a robust panel of twenty-eight biomarkers for detecting MADs was established. This panel is involved in three metabolic pathways including aminoacyl-tRNA biosynthesis, arginine, and proline metabolism, and synthesis and degradation of ketone bodies, and could differentiate MAD subjects from healthy controls with a higher accuracy.

GC-MS, Metabolomics, Multivariate analysis, NMR spectroscopy, Sulfur mustard