Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction

Autor: Yaoxiang Li, Shivani Bansal, Vijayalakshmi Sridharan, Sunil Bansal, Meth M. Jayatilake, Jose A. Fernández, John H. Griffin, Marjan Boerma, Amrita K. Cheema
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Metabolites, Vol 13, Iss 4, p 525 (2023)
Druh dokumentu: article
ISSN: 2218-1989
DOI: 10.3390/metabo13040525
Popis: 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.
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