| Dai Qingchi,Xi Cong,Li Shuang,et al.Construction and validation of biodosimetry models for dose estimation and classification based on absolute quantification of radiation-sensitive metabolites in rat plasma[J].Chinese Journal of Radiological Medicine and Protection,2026,46(3):230-237 |
| Construction and validation of biodosimetry models for dose estimation and classification based on absolute quantification of radiation-sensitive metabolites in rat plasma |
| Received:October 28, 2025 |
| DOI：10.3760/cma.j.cn112271-20251028-00382 |
| KeyWords:Metabolomics Ionizing radiation Biomarker Dose estimation Receiver operating characteristic (ROC) |
| FundProject:国家自然科学基金(82003393);中国疾控中心辐射安全所青年科学研究所长基金(2023-02) |
| Author Name | Affiliation | E-mail | | Dai Qingchi | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Xi Cong | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Li Shuang | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Cai Tianjing | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Yan Yukang | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Lu Xue | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Liu Qingjie | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | | | Zhao Hua | China CDC Key Laboratory of Radiological Protection and Nuclear Emergency, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beiing 100088, China | zhaohua@nirp.chinacdc.cn |
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| Abstract:: |
| Objective To explore the feasibility of radiation-sensitive metabolites as biomarkers, this study established and validated radiation dose estimation and classification models for rat plasma samples by performing absolute quantification of 15 radiation-sensitive metabolites. Methods Fifty rats were randomly divided into 6 groups(n = 8 or 9) and exposed to total-body irradiation (TBI) using 60Co γ-rays at doses of 0, 1, 2, 3, 5, and 8 Gy, respectively. Plasma samples were collected 3 d post-irradiation, and absolute quantification of 15 radiation-sensitive metabolites was performed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Based on the quantification result, fifteen univariate general linear models and a multivariate linear regression model incorporating metabolite panel were developed. For external validation, an additional cohort of 25 rats were radomly divided into 5 groups(n = 5) and irradiated at doses of 0, 0.5, 2.5, 4, and 6 Gy, respectively. Following the same protocol, plasma was collected 3 d after exposure and the same 15 metabolites were absolutely quantified to evaluate the predictive accuracy and robustness of the established models. Results At 3 d post-irradiation, we observed a significant dose-dependent increase in metabolites phenylalanine and taurine (R2 > 0.8, P < 0.05), while the metabolites butyrylcarnitine, citrulline, N-acetylornithine, and proline exhibited a significant dose-dependent decrease (R2 > 0.8, P < 0.05). The general linear models for citrulline, phenylalanine, butyrylcarnitine, N-acetylornithine, proline, and taurine all achieved R2 > 0.8 in the univariate analysis. The final multivariate linear regression model was constructed as: y = -0.237x1+0.046x2+0.058x3-3.246x4+5.126 (x1: citrulline, x2:threonine, x3: creatine, x4:N-acetylornithine, R2 = 0.941). Model validation within 0-6 Gy irradiation revealed that 48% of sample predictions had a relative standard deviation (RSD) below 40% in both the univariate model of citrulline and the multivariate model, with the latter demonstrating superior stability. The combined metabolite panel (citrulline, proline, butyrylcarnitine, creatine, hexanoylcarnitine, palmitoylcarnitine) achieved AUC values of 0.923 and 0.910 in the training and validation sets, respectively, for discriminating between 0 Gy and >0 Gy samples. Similarly, for distinguishing <2 Gy from ≥2 Gy samples, the panel yielded AUCs of 0.994 and 1.000 in the training and validation sets. Conclusions The metabolite panels consisting of citrulline, threonine, creatine and N-acetylornithine, as well as the combination of citrulline, proline, butyrylcarnitine, creatine, hexanoylcarnitine, and palmitoylcarnitine, show potential as biomarkers for specific radiation dose estimation and classification, respectively. |
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