Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction

Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed...
Ausführliche Beschreibung

Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples. Ausführliche Beschreibung