{"doi":"10.3390/metabo13040525","title":"Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction","abstract":"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.","journal":"Metabolites","year":2023,"id":360463,"datarank":0.4116102889875736,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.06622252503846672,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.06622252503846672,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":6,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9552,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":369550,"name":"Shivani Bansal","orcid":"0000-0002-5095-5687","position":1,"is_corresponding":false},{"id":369551,"name":"Vijayalakshmi Sridharan","orcid":"0000-0003-2956-4953","position":2,"is_corresponding":false},{"id":730985,"name":"Sunil Bansal","orcid":"0000-0001-6066-7841","position":3,"is_corresponding":false},{"id":490990,"name":"Meth Jayatilake","orcid":"0000-0002-5780-9391","position":4,"is_corresponding":false},{"id":483071,"name":"José A. Fernández","orcid":"0000-0002-0804-1865","position":5,"is_corresponding":false},{"id":379313,"name":"John H. Griffin","orcid":"0000-0002-4302-2547","position":6,"is_corresponding":false},{"id":277094,"name":"Marjan Boerma","orcid":"0000-0003-2249-5203","position":7,"is_corresponding":false},{"id":87920,"name":"Amrita K. Cheema","orcid":"0000-0003-4877-7583","position":8,"is_corresponding":false},{"id":369549,"name":"Yaoxiang Li","orcid":"0000-0001-9200-1016","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:14:01.896928Z","pmid":"37110184","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}