In this study, plasma metabolic characteristics in hematopoietic stem cell transplantation patients undergoing total body ionizing irradiation were investigated using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. High dose radiation caused metabolic disorders involving dysregulation of gut microflora, shift in energy supply, and protein synthesis and metabolism. Four metabolic markers (PC-O-38:5, urate, ornithine, and GCDCS) were chosen as potential biomarkers for radiation injury. Specific metabolic changes were found in specific patients following high doses of exposure. These findings provide valuable information for selecting metabolic biomarker panels and understanding radiation pathology.
Despite some advances in the study of radiation injuries, effective methods of prevention and treatment of severe acute radiation syndrome or illness (ARS) are still lacking. Therefore, an in-depth understanding of the biological characteristics associated with high dose radiation is essential to reveal the mechanisms underlying the varied biological processes following high dose radiation and the development of novel potent radioprotective agents. In the present study, plasma metabolic characteristics were investigated using hematopoietic stem cell transplantation patients (n = 36) undergoing total body ionizing irradiation (TBI) utilizing gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). Plasma was collected pre-irradiation, 3 days after completion of fractionated radiation therapy with a total dose of 12 Gy delivered at a dose rate of 8 cGy min(-1). These metabolic disorders involve the dysregulation of the gut microflora, a shift in energy supply from aerobic respiration toward ketogenesis, protein synthesis and metabolism in response to TBI. Furthermore, the panel of four metabolic markers with most potential consisting of PC (O-38:5), urate, ornithine, and GCDCS for radiation injury was chosen by combining multiple methods of data processing that included univariate analysis, partial least squares discriminant analysis (PLS-DA), and multivariable stepwise linear regression analysis. While similar patterns of metabolic alterations were observed in patients of different genders, disease types and ages, specific changes were also found in specific patients following high doses of exposure. These findings provide valuable information for selecting metabolic biomarker panels for radiation injury, clues for radiation pathology and therapeutic interventions involved in high-dose radiation exposure.
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