Gut-brain axis volatile organic compounds derived from breath distinguish between schizophrenia and major depressive disorder.

BACKGROUND: Signatures from the metabolome and microbiome have already been introduced as candidates for diagnostic and treatment support. The aim of this study was to investigate the utility of volatile organic compounds (VOCs) from the breath for detection of schizophrenia and depression. METHODS: Patients with a diagnosis of major depressive disorder (MDD) or schizophrenia, as well as healthy controls, were recruited to participate. After being clinically assessed and receiving instruction, each participant independently collected breath samples for subsequent examination by proton transfer-reaction mass spectrometry. RESULTS: The sample consisted of 104 participants: 36 patients with MDD, 34 patients with schizophrenia and 34 healthy controls. Through mixed-model and deep learning analyses, 5 VOCs contained in the participants' breath samples were detected that significantly differentiated between diagnostic groups and healthy controls, namely VOCs with mass-to-charge ratios (m/z) 60, 69, 74, 88 and 90, which had classification accuracy of 76.8% to distinguish participants with MDD from healthy controls, 83.6% to distinguish participants with schizophrenia from healthy controls and 80.9% to distinguish participants with MDD from those with schizophrenia. No significant associations with medication, illness duration, age of onset or time in hospital were detected for these VOCs. LIMITATIONS: The sample size did not allow generalization, and confounders such as nutrition and medication need to be tested. CONCLUSION: This study established promising results for the use of human breath gas for detection of schizophrenia and MDD. Two VOCs, 1 with m/z 60 (identified as trimethylamine) and 1 with m/z 90 (identified as butyric acid) could then be further connected to the interworking of the microbiota-gut-brain axis.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations.

Background: 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 heterogeneous 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. Methods: Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis. Results: 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 (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, pFDR=0.0006), 3-methylpyridine (p = 0.0002, pFDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, pFDR = 0.0174), valeric acid (p = 0.005, pFDR = 0.0162) and isoprene (p = 0.038, pFDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and 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). Conclusion: According to the results of this study, 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.