Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations - Gbaoui, Laila; Fachet, Melanie; Lüno, Marian; Meyer-Lotz, Gabriele; Frodl, Thomas; Hoeschen, Christoph (Frontiers in Psychiatry, 2022)
Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogenous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD. Breath samples were collected in Tadlar bags at awakening, 30 and 60 minutes after awakening from 26 patients with MDD and 25 HCs. A non-targeted breathomics analysis by proton transfer reaction mass spectrometry coupled to advanced machine learning approaches and metabolomic pathway analysis was performed to rank potentional volatile metabolites and investigate the metabolomics changes that occur in patients with MDD. A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis, branched chain amino acids valine, leucine and isoleucine biosynthesis, glycolysis and gluconeogenesis, nicotinate and nicotinamide metabolism and pyruvate metabolism. Moreover, the support vector machine predictive model showed that butylamine, 3-methylpyridine, endogenous aliphatic ethanol isotope, valeric acid and isoprene were potential metabolites discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96). According these results, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.