Register
Forgot your password?

Metabolomic profiling in cardio-oncology: current state of the problem and clinical perspectives

Authors: Khabarova N.V., Kozhevnikova M.V., Kirichenko Yu.Yu., Ilgisonis I.S., Appolonova S.A., Shestakova K.M., Korobkova E.O., Belenkov Yu.N.

Company: I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation

For correspondence:  Sign in or register.

Type:  Reviews


DOI: https://doi.org/10.24022/1997-3187-2026-20-1-7-25

For citation: Khabarova N.V., Kozhevnikova M.V., Kirichenko Yu.Yu., Ilgisonis I.S., Appolonova S.A., Shestakova K.M., Korobkova E.O., Belenkov Yu.N. Metabolomic profiling in cardio-oncology: current state of the problem and clinical perspectives. Creative Cardiology. 2026; 20 (1): 7–25 (in Russ.). DOI: 10.24022/1997-3187-2026-20-1-7-25

Received / Accepted:  12.12.2025 / 11.03.2026

Keywords: cardio-oncology heart failure cardiotoxicity anticancer therapy metabolomics metabolomic profiling risk stratification personalized medicine



Subscribe 🔒

 

Abstract

The increasing survival of patients with malignant neoplasms over recent decades has been accompanied by a growing burden of cardiovascular complications related to anticancer therapy. Heart failure and other manifestations of cardiotoxicity frequently determine long-term prognosis, limit the feasibility of optimal oncological treatment, and represent a substantial medical and public health challenge. In this context, cardio-oncology has emerged as a key interdisciplinary field focused on preserving both survival and quality of life in cancer patients.

Despite advances in cardiac imaging and the use of conventional biomarkers, current diagnostic strategies are largely oriented toward the detection of established structural and functional myocardial damage. Early metabolic alterations that precede clinically overt cardiotoxicity remain insufficiently characterized and are rarely integrated into routine clinical practice.

Metabolomic profiling, which reflects the integrated state of systemic and cellular metabolism, is increasingly recognized as a promising approach for the early identification of cardiovascular toxicity and risk stratification in cardio-oncology. This review summarizes contemporary evidence on the methodological principles of metabolomic analysis, key metabolic mechanisms underlying anticancer therapy-related cardiotoxicity, and characteristic metabolomic signatures associated with heart failure in oncology patients. The diagnostic and prognostic value of metabolomic profiles and their potential role in monitoring cardioprotective strategies are discussed.

Integration of metabolomics with imaging techniques, biomarkers, and artificial intelligence–based algorithms may substantially enhance personalized management of cardio-oncology patients and support the development of more effective strategies to reduce cardiovascular and cancer-related mortality.

References

  1. Lyon A.R., López-Fernández T., Couch L.S. et al. 2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio- Oncology Society (IC-OS). Eur. Heart J. Cardiovasc. Imaging. 2022; 23 (10): e333–e465. DOI: 10.1093/ehjci/jeac106
  2. Lyon A.R., Dent S., Stanway S. et al. Baseline cardiovascular risk assessment in cancer patients scheduled to receive cardiotoxic cancer therapies: a position statement and new risk assessment tools from the Cardio-Oncology Study Group of the Heart Failure Association of the European Society of Cardiology in collaboration with the International Cardio-Oncology Society. Eur. J. Heart Fail. 2020; 22 (11): 1945– 1960. DOI: 10.1002/ejhf.1920
  3. Camilli M., Cipolla C.M., Dent S. et al. Anthracycline cardiotoxicity in adult cancer patients: JACC: CardioOncology State-of-the-Art Review. JACC CardioOncol. 2024; 6 (5): 655–677. DOI: 10.1016/j.jaccao.2024.07.016
  4. Rhee J.W., Ky B., Armenian S.H. et al. Primer on biomarker discovery in cardio-oncology: application of omics technologies. JACC CardioOncol. 2020; 2 (3): 379–384. DOI: 10.1016/j.jaccao.2020.07.006
  5. Asnani A., Shi X., Farrell L. et al. Changes in citric acid cycle and nucleoside metabolism are associated with anthracycline cardiotoxicity in patients with breast cancer. J. Cardiovasc. Transl. Res. 2020; 13 (3): 349–356. DOI: 10.1007/s12265-019-09897-y
  6. Thonusin C., Nawara W., Khuanjing T. et al. Blood metabolomes as non-invasive biomarkers and targets of metabolic interventions for doxorubicin and trastuzumab-induced cardiotoxicity. Arch. Toxicol. 2023; 97 (2): 603–618. DOI: 10.1007/s00204-022-03412-0
  7. Thonusin C., Osataphan N., Leemasawat K. et al. Changes in blood metabolomes as potential markers for severity and prognosis in doxorubicin- induced cardiotoxicity: a study in HER2-positive and HER2-negative breast cancer patients. J. Transl. Med. 2024; 22 (1): 398. DOI: 10.1186/s12967-024-05088-9
  8. Singh A., Bakhtyar M., Jun S.R. et al. A narrative review of metabolomics approaches in identifying biomarkers of doxorubicin-induced cardiotoxicity. Metabolomics. 2025; 21 (3): 68. DOI: 10.1007/s11306-025-02258-8
  9. Baskhanova S.N., Moskaleva N.E., Shestakova K.M. et al. Targeted metabolomics for cardiovascular disease: validation of a high-throughput HPLC-MS/MS assay. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 2025; 1264: 124732. DOI: 10.1016/j.jchromb.2025.124732
  10. Moskaleva N.E., Shestakova K.M., Kukharenko A.V. et al. Target metabolome profiling-based machine learning as a diagnostic approach for cardiovascular diseases in adults. Metabolites. 2022; 12 (12): 1185. DOI: 10.3390/metabo12121185
  11. Kirichenko Yu.Yu., Varsieva V.G., Shestakova K.M. et al. Metabolomic profiling as a potential tool for predicting cancer therapy-related cardiovascular toxicity: a pilot single-center study. Kardiologiia. 2025; 65 (6): 3–11 (in Russ.). DOI: 10.18087/cardio.2025.6.n2936
  12. Alseekh S., Aharoni A., Brotman Y. et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat. Methods. 2021; 18 (7): 747–756. DOI: 10.1038/s41592-021-01197-1
  13. Powers R., Andersson E.R., Bayless A.L. et al. Best practices in NMR metabolomics: current state. Trends Analyt. Chem. 2024; 171: 117478. DOI: 10.1016/j.trac.2023.117478
  14. Fazzini L., Caggiari L., Deidda M. et al. Metabolomic profiles on antiblastic cardiotoxicity: new perspectives for early diagnosis and cardioprotection. J. Clin. Med. 2022; 11 (22): 6745. DOI: 10.3390/jcm11226745
  15. Ding J., Feng X., Xu Z., Xu H. Metabolomic profiling and biomarker identification for early detection and therapeutic targeting of doxorubicin- induced cardiotoxicity. Front. Cell. Dev. Biol. 2025; 13: 1543636. DOI: 10.3389/fcell.2025.1543636
  16. Bowen T.J., Hall A.R., Lloyd G.R. et al. Discovering a predictive metabolic signature of drug-induced structural cardiotoxicity in cardiac microtissues. Arch. Toxicol. 2025; 99 (8): 3379–3392. DOI: 10.1007/s00204-025-04074-4
  17. Mosley J.D., Dunn W.B., Kuligowski J. et al. Metabolomics 2023 workshop report: moving toward consensus on best QA/QC practices in LC–MS-based untargeted metabolomics. Metabolomics. 2024; 20 (4): 73. DOI: 10.1007/s11306-024-02135-w
  18. Medina-Hernández D., Cádiz L., Mastrangelo A. et al. SGLT2i therapy prevents anthracycline-induced cardiotoxicity in a large animal model by preserving myocardial energetics. JACC CardioOncol. 2025; 7 (2): 171–184. DOI: 10.1016/j.jaccao.2024.12.004
  19. Lippa K.A., Aristizabal-Henao J.J., Beger R.D. et al. Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC). Metabolomics. 2022; 18 (4): 24. DOI: 10.1007/s11306-021-01848-6
  20. Tonry C., Russell-Hallinan A., McCune C. et al. Circulating biomarkers for management of cancer therapeutics-related cardiac dysfunction. Cardiovasc. Res. 2023; 119 (3): 710–728. DOI: 10.1093/cvr/cvac087
  21. Chen Y., Tang Y., Zhang Y.C. et al. A metabolomic study of rats with doxorubicin-induced cardiomyopathy and Shengmai injection treatment. PLoS One. 2015; 10 (5): e0125209. DOI: 10.1371/journal.pone.0125209
  22. Palaskas N.L., Segura A., Lelenwa L. et al. Immune checkpoint inhibitor myocarditis: elucidating the spectrum of disease through endomyocardial biopsy. Eur. J. Heart Fail. 2021; 23 (10): 1725–1735. DOI: 10.1002/ejhf.2265
  23. Cozma A., Sporis N.D., Lazar A.L. et al. Cardiac toxicity associated with immune checkpoint inhibitors: a systematic review. Int. J. Mol. Sci. 2022; 23 (18): 10948. DOI: 10.3390/ijms231810948
  24. Minegishi S., Horita N., Ishigami T., Hibi K. Cardiotoxicity associated with immune checkpoint inhibitors. Cancers (Basel). 2023; 15 (22): 5487. DOI: 10.3390/cancers15225487
  25. Dai L., Duan Y., Xiong Q. From tumor immunotherapy to myocardial injury: a mechanistic discussion of immune checkpoint inhibitor-related myocarditis (Review). Mol. Med. Rep. 2025; 32 (6): 332. DOI: 10.3892/mmr.2025.13697
  26. Dunn W.B., Kuligowski J., Lewis M.R. Metabolomics 2022 workshop report: state of QA/QC best practices in LC–MS-based untargeted metabolomics., informed through mQACC community engagement initiatives. Metabolomics. 2023 (19): 93. DOI: 10.1007/s11306-023-02060-4
  27. Gomes C., Geels J., Debray T.P.A. et al. Risk prediction models for cancer therapy related cardiac dysfunction in patients with cancer and cancer survivors: systematic review and meta-analysis. BMJ. 2025; 390: e084062. DOI: 10.1136/bmj-2025-084062
  28. Nganou-Gnindjio C.N., Okobalemba E.A., Tasong L.A. et al. Evaluation of the cardiotoxicity risk based on the HFA-ICOS score in a group of sub-Saharan African women before breast cancer treatment by chemotherapy and/or radiotherapy: a cross-sectional study in a group of Cameroonian women. Cardiooncology. 2025; 11 (1): 89. DOI: 10.1186/s40959-025-00390-x
  29. Unger K., Li Y., Yeh C. et al. Plasma metabolite biomarkers predictive of radiation induced cardiotoxicity. Radiother. Oncol. 2020; 152: 133–145. DOI: 10.1016/j.radonc.2020.04.018
  30. González-Domínguez Á., Estanyol-Torres N., Brunius C. et al. QComics: recommendations and guidelines for robust, easily implementable and reportable quality control of metabolomics data. Anal. Chem. 2024; 96 (3): 1064–1072. DOI: 10.1021/acs.analchem.3c03660
  31. Schiffer W., Pedersen L.N., Lui M. et al. Advan ces in screening for radiation-associated cardiotoxicity in cancer patients. Curr. Cardiol. Rep. 2023; 25 (11): 1589–1600. DOI: 10.1007/s11886-023-01971-x
  32. Evans A.M., O’Donovan C., Playdon M. et al. Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners. Metabolomics. 2020; 16 (10): 113. DOI: 10.1007/s11306-020-01728-5
  33. Addison D., Neilan T.G., Barac A. et al. Cardiovascular imaging in contemporary cardio-oncology: a scientific statement from the American Heart Association. Circulation. 2023; 148 (16): 1271–1286. DOI: 10.1161/CIR.0000000000001174
  34. Tamaki N., Manabe O., Hirata K. Cardiovascular imaging in cardio-oncology. Jpn. J. Radiol. 2024; 42 (12): 1372–1380. DOI: 10.1007/s11604-024-01636-x
  35. Kaboré E.G., Macdonald C., Kaboré A. et al. Risk prediction models for cardiotoxicity of chemotherapy among patients with breast cancer: a systematic review. JAMA Netw Open. 2023; 6 (2): e230569. DOI: 10.1001/jamanetworkopen.2023.0569
  36. Mosley J.D., Schock T.B., Beecher C.W. et al. Establishing a framework for best practices for quality assurance and quality control in untargeted metabolomics. Metabolomics. 2024; 20 (2): 20. DOI: 10.1007/s11306-023-02080-0
  37. Cochran D., NourEldein M., Bezdekova D. et al. A reproducibility crisis for clinical metabolomics studies. Trends Analyt. Chem. 2024; 180: 117918. DOI: 10.1016/j.trac.2024.117918
  38. Dougherty B.V., Moore C.J., Rawls K.D. et al. Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism. PLoS Comput. Biol. 2024; 20 (2): e1011919. DOI: 10.1371/journal.pcbi.1011919
  39. De Jonge N.F., Mildau K., Meijer D. et al. Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools. Metabolomics. 2022; 18 (12): 103. DOI: 10.1007/s11306-022-01963-y
  40. Broeckling C.D., Beger R.D., Cheng L.L. et al. Current practices in LC-MS untargeted metabolomics: a scoping review on the use of pooled quality control samples. Anal. Chem. 2023; 95 (51): 18645–18654. DOI: 10.1021/acs.analchem.3c02924
  41. Wishart D.S., Cheng L.L., Copié V. et al. NMR and metabolomics – a roadmap for the future. Metabolites. 2022; 12 (8): 678. DOI: 10.3390/metabo12080678

About Authors

  • Natalya V. Khabarova, Cand. Med. Sci., Associate Professor, Cardiologist; ORCID
  • Mariya V. Kozhevnikova, Dr. Med. Sci., Professor, Cardiologist; ORCID
  • Yuliya Yu. Kirichenko, Cand. Med. Sci., Associate Professor, Cardiologist; ORCID
  • Irina S. Ilgisonis, Cand. Med. Sci., Professor, Hematologist; ORCID
  • Svetlana A. Appolonova, Cand. Chem. Sci., Head of the Center for Biopharmaceutical Analysis and Metabolomic Research; ORCID
  • Kseniya M. Shestakova, Cand. Pharm. Sci., Head of Laboratory; ORCID
  • Ekaterina O. Korobkova, Cand. Med. Sci., Associate Professor, Cardiologist; ORCID
  • Yuriy N. Belenkov, Dr. Med. Sci., Professor, Academician of RAS, Chief of Chair, Cardiologist; ORCID

Chief Editor

Elena Z. Golukhova, MD, PhD, DSc, Professor, Academician of Russian Academy of Sciences, Director of Bakoulev National Medical Research Center for Cardiovascular Surgery


Sort by