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Currently, no validated biomarkers can predict early DIC. Identifying novel biomarkers is essential for detecting patients at higher risk and enable timely interventions before irreversible cardiac injury occurs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Twenty-seven breast cancer patients treated with DOX-containing chemotherapy were stratified by change in left ventricular ejection fraction (LVEF): 19 patients who maintained normal cardiac function (normal, decline\u2009&lt;\u200910%) and 8 who developed cardiotoxicity (abnormal, decline\u2009&gt;\u200910%). Plasma samples were collected at baseline and after chemotherapy for untargeted metabolomic profiling. Both baseline and pre\u2013post designs were employed to capture static and dynamic metabolic alterations associated with DIC. Stepwise logistic regression was used to filter non-informative metabolites, and predictive performance was further validated using Random Forest modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A well-marked separation of plasma metabolomic profiles was observed between normal and abnormal cardiotoxicity groups at baseline (T0). Statistical analysis identified 100 significant metabolites at baseline (T0) and 78 metabolites after the first cycle of chemotherapy (T0-T1), with 10 metabolites common to both time-points: 3-phosphoglycerate, 2-hydroxyphenylacetate, inosine, taurine, suberate (C8-DC), sebacate (C10-DC), sphingadienine, oxindolylalanine. Machine learning models identified key metabolites (e.g., sebacate [C10-DC], 2-hydroxyhippurate, orotate, picolinate, and suberate [C8-DC]) as candidate predictors of cardiotoxicity, achieving moderate discriminatory performance in cross-validation, with higher specificity than sensitivity, indicating limited detection of abnormal cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Metabolomic profiling shows potential for early detection of DIC in breast cancer patients, supporting personalized interventions to prevent irreversible cardiac damage.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11306-026-02469-7","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:11:29Z","timestamp":1783145489000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabolic phenotypes of doxorubicin-induced cardiotoxicity among patients with breast cancer"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1762-6686","authenticated-orcid":false,"given":"Amarnath","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2681-3950","authenticated-orcid":false,"given":"Se-Ran","family":"Jun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine","family":"Wallis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8346-043X","authenticated-orcid":false,"given":"Renny","family":"S. 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