Metabolomics has been shown to have utility in assessing responses to exposure by ionizing radiation (IR) in easily accessible biofluids such as urine. Most studies to date from our laboratory and others have employed γ-irradiation at relatively high dose rates (HDR), but many environmental exposure scenarios will probably be at relatively low dose rates (LDR). There are well-documented differences in the biologic responses to LDR compared to HDR, so an important question is to assess LDR effects at the metabolomics level. Our study took advantage of a modern mass spectrometry approach in exploring the effects of dose rate on the urinary excretion levels of metabolites 2 days after IR in mice. A wide variety of statistical tools were employed to further focus on metabolites, which showed responses to LDR IR exposure (0.00309 Gy/min) distinguishable from those of HDR. From a total of 709 detected spectral features, more than 100 were determined to be statistically significant when comparing urine from mice irradiated with 1.1 or 4.45 Gy to that of sham-irradiated mice 2 days post-exposure. The results of this study show that LDR and HDR exposures perturb many of the same pathways such as TCA cycle and fatty acid metabolism, which also have been implicated in our previous IR studies. However, it is important to note that dose rate did affect the levels of particular metabolites. Differences in urinary excretion levels of such metabolites could potentially be used to assess an individual's exposure in a radiobiological event and thus would have utility for both triage and injury assessment.

In a major radiological event, rapid screening of radiation-exposed individuals for possible medical intervention is critical. Here we suggest a high-throughput, non-invasive approach to identify radiation biomarkers in urine and demonstrate a proof of principle in mice. Mice were whole-body irradiated (8 Gy X rays), and urine samples were collected from both irradiated and control mice for 7 days after exposure. (1)H nuclear magnetic resonance (NMR) spectra of all the urine samples were acquired on a spectrometer operating at a proton frequency of 600 MHz. The multivariate data were analyzed by principal component analysis (PCA). The resulting biomarkers revealed a broad range of metabolism changes, including creatine, succinate, methylamine, citrate, 2-oxoglutarate, taurine, N-methyl-nicotinamide, hippurate and choline. The temporal dependence of several biomarkers on radiation exposure was also explored. Combining several metabolomic biomarkers with different temporal dependence could provide an estimate of when the radiation exposure occurred. These results will be helpful in projecting metabolomic "fingerprints" in humans exposed to radiation.