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

BackgroundMajor 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.MethodsBreath 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.ResultsA 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).ConclusionAccording 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

Estimation of central blood pressure waveform from femoral blood pressure waveform by blind sources separation

BackgroundCentral blood pressure (cBP) is a better indicator of cardiovascular morbidity and mortality than peripheral BP (pBP). However, direct cBP measurement requires invasive techniques and indirect cBP measurement is based on rigid and empirical transfer functions applied to pBP. Thus, development of a personalized and well-validated method for non-invasive derivation of cBP from pBP is necessary to facilitate the clinical routine. The purpose of the present study was to develop a novel blind source separation tool to separate a single recording of pBP into their pressure waveforms composing its dynamics, to identify the compounds that lead to pressure waveform distortion at the periphery, and to estimate the cBP. The approach is patient-specific and extracts the underlying blind pressure waveforms in pBP without additional brachial cuff calibration or any a priori assumption on the arterial model.MethodsThe intra-arterial femoral BPfe and intra-aortic pressure BPao were anonymized digital recordings from previous routine cardiac catheterizations of eight patients at the German Heart Centre Berlin. The underlying pressure waveforms in BPfe were extracted by the single-channel independent component analysis (SCICA). The accuracy of the SCICA model to estimate the whole cBP waveform was evaluated by the mean absolute error (MAE), the root mean square error (RMSE), the relative RMSE (RRMSE), and the intraclass correlation coefficient (ICC). The agreement between the intra-aortic and estimated parameters including systolic (SBP), diastolic (DBP), mean arterial pressure (MAP), and pulse pressure (PP) was evaluated by the regression and Bland–Altman analyses.ResultsThe SCICA tool estimated the cBP waveform non-invasively from the intra-arterial BPfe with an MAE of 0.159 ± 1.629, an RMSE of 5.153 ± 0.957 mmHg, an RRMSE of 5.424 ± 1.304%, and an ICC of 0.94, as well as two waveforms contributing to morphological distortion at the femoral artery. The regression analysis showed a strong linear trend between the estimated and intra-aortic SBP, DBP, MAP, and PP with high coefficient of determination R2 of 0.98, 0.99, 0.99, and 0.97 respectively. The Bland–Altman plots demonstrated good agreement between estimated and intra-aortic parameters with a mean error and a standard deviation of difference of −0.54 ± 2.42 mmHg [95% confidence interval (CI): −5.28 to 4.20] for SBP, −1.97 ± 1.62 mmHg (95% CI: −5.14 to 1.20) for DBP, −1.49 ± 1.40 mmHg (95% CI: −4.25 to 1.26) for MAP, and 1.43 ± 2.79 mmHg (95% CI: −4.03 to 6.90) for PP.ConclusionsThe SCICA approach is a powerful tool that identifies sources contributing to morphological distortion at peripheral arteries and estimates cBP