Metabolomic Profiling of the Blood of Patients with Chronic Consciousness Disorders - A. A. Orlova; E. A. Kondrat'eva; Ya. A. Dubrovskii; N. V. Dryagina; E. V. Verbitskaya; S. A. Kondratev; A. A. Kostareva; A. N. Kondratev (Общая реаниматология)
The main variants of chronic consciousness disorder (CCD) developing in adverse coma outcome are vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimal consciousness state (MCS).   The aim of the study was to investigate the main differences in metabolomic abnormalities in patients with VS / UWS and MCS, as well as to identify changes in metabolomics depending on sleep or wakefulness phase.   Materials and Methods. Untargeted metabolome analysis of blood plasma of 10 patients in VS / UWS (group 1) and 6 patients in MCS (group 2) was performed using reversed-phase and hydrophilic chromatography methods. The underlying conditions of brain injury were TBI (2 in group 1 and 5 in group 2) and hypoxia (8 in group 1 and 1 in group 2). The internal jugular vein was catheterized in all patients, and blood was collected while awake during the daytime for 2 days. Aliquots of pooled plasma samples were purified from protein components and analyzed by high-performance liquid chromatography in two modes: reversed-phase and hydrophilic ones. Mass-spectrometric detection was performed in full ion current scanning mode: registration of positively charged ions in the m/z range from 50 to 1300 a.u. Data were adjusted and normalized using MS-DIAL software ver. 4.70 software; differences were identified using analysis of variance, discriminant and cluster analysis. The data were analyzed and visualized using MetaboAnalyst 5.0 software (https://www.metaboanalyst.ca).   Results. Four major metabolites (at VIP > 0.5), which content was most modulated depending on the study group, were identified including 4 (m/z 124.0867, Rt = 17.67, p < 0.01), 33 (m/z 782.5722, Rt = 17.69, p < 0.01), 6 (m/z 125.0904, Rt = 18.43, p < 0.01) and 1 (m/z 463.2304, Rt = 15.78, p < 0.01), with no significant differences between daytime and nighttime blood samples. Significant quantitative differences were shown for three metabolites in the groups, 14 (m/z 162.1126, Rt = 10.28, p < 0.01), 35 (m/z 780.5483, Rt = 7.65, p < 0.01), and 41 (m/z 806.5649, Rt = 7.58, p < 0.01), and four metabolites when comparing the daytime and nighttime samples: 14 (m/z 162.1126, Rt = 10.28, p = 0.0201), 35 (m/z 780.5483, Rt = 7.65, p < 0.01), 41 (m/z 806.5649, Rt = 7.58, p < 0.01), and 48 (m/z 848.5354, Rt = 7.65, p < 0.01).   Conclusion. Untargeted metabolomic analysis confirmed the hypothesis of likely significant quantitative and qualitative differences in metabolite composition depending on the type of CCD and circadian rhythm. The study established a set of metabolites that are potential biomarkers for differential diagnosis of VS/UWS and MCS including 4, 33, 6, 1 (in the experiment on the reversed-phase column) and 14, 35, 41, 48 (in the experiment on the hydrophilic column), based on their significant contribution to intergroup and intragroup differences. Further studies will be aimed to characterize the identified metabolites.