Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Communications Medicine
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. communications medicine
  3. articles
  4. article
Plasma and pericardial fluid metabolomic signatures of patients with ischemic heart disease
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 21 January 2026

Plasma and pericardial fluid metabolomic signatures of patients with ischemic heart disease

  • Federica De Castro  ORCID: orcid.org/0000-0001-5060-293X1 na1,
  • Chiara Coppola2 na1,
  • Egeria Scoditti  ORCID: orcid.org/0000-0003-2753-84873,
  • Giuseppe Santarpino4,5,6,
  • Stefania Marazia  ORCID: orcid.org/0000-0003-4456-53727,
  • Francesco Paolo Fanizzi1 &
  • …
  • Michele Maffia  ORCID: orcid.org/0000-0003-0665-45348,9 

Communications Medicine , Article number:  (2026) Cite this article

  • 738 Accesses

  • 2 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Diagnostic markers
  • Heart failure

Abstract

Background

Ischemic heart disease is the leading cause of global mortality. Despite advances in clinical management, current diagnostic tools do not capture early metabolic disturbances associated with myocardial ischemia. Understanding these alterations may provide new insights into disease mechanisms.

Methods

A metabolomic approach based on magnetic resonance spectroscopy is used to characterize the metabolic profile of patients with ischemic heart disease compared with non-ischemic individuals. Plasma and pericardial fluid collected during cardiac surgery are analyzed to investigate both systemic and heart-proximal molecular changes. Small-molecule concentrations are quantified and statistically evaluated to identify metabolic differences associated with ischemia.

Results

Here we show that ischemic heart disease is associated with a distinct metabolic pattern. We observe increased concentrations of 3-hydroxybutyrate in both biological fluids, together with elevated succinate in pericardial fluid, indicating alterations in mitochondrial energy metabolism. Additional changes involve pathways linked to substrate utilization and redox balance.

Conclusions

These findings highlight a metabolic response to myocardial ischemia detectable in both systemic and locally collected fluids. The identified alterations offer a deeper understanding of the biochemical environment associated with ischemic heart disease.

Plain language summary

Ischemic heart disease happens when the heart does not receive enough blood and oxygen. It is one of the main causes of death around the world. To better understand what happens inside the heart during this condition, we studied small molecules found in blood and in the fluid surrounding the heart during surgery. These molecules, called metabolites, help us understand how the body produces and uses energy. We found specific changes in these molecules in people with ischemic heart disease. In particular, we observed signs that heart cells may switch to different energy sources when oxygen is low. These results help us better understand what happens during heart damage and may support future research aimed at improving early detection and patient care.

Similar content being viewed by others

Mitophagy in ischemic heart disease: molecular mechanisms and clinical management

Article Open access 30 December 2024

Rapid mitochondrial repolarization upon reperfusion after cardiac ischemia

Article Open access 11 December 2025

Assessment of left ventricular tissue mitochondrial bioenergetics in patients with stable coronary artery disease

Article Open access 07 August 2023

Data availability

All the metabolomic data used to generate the here presented statistical models and figures are available as Supplementary Data.

References

  1. Khan, M. A. et al. Global epidemiology of ischemic heart disease: results from the Global Burden of Disease Study. Cureus 12, e9349 (2025).

  2. Kyu, H. H. et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet 392, 1859–1922 (2018).

    Google Scholar 

  3. Nowbar, A. N., Gitto, M., Howard, J. P., Francis, D. P. & Al-Lamee, R. Mortality from ischemic heart disease. Circ. Cardiovasc. Qual. Outcomes 12, e005375 (2019).

    Google Scholar 

  4. Virani, S. S. et al. Heart disease and stroke statistics—2020 update: a report from the American Heart Association. Circulation 141, e139–e596 (2020).

    Google Scholar 

  5. Roth, G. A. et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76, 2982–3021 (2020).

    Google Scholar 

  6. Heusch, G. Myocardial ischemia: lack of coronary blood flow or myocardial oxygen supply/demand imbalance?. Circ. Res. 119, 194–196 (2016).

    Google Scholar 

  7. Libby, P. The changing landscape of atherosclerosis. Nature 592, 524–533 (2021).

    Google Scholar 

  8. Andersson, C. & Vasan, R. S. Epidemiology of cardiovascular disease in young individuals. Nat. Rev. Cardiol. 15, 230–240 (2018).

    Google Scholar 

  9. Bekelis, K., Roberts, D. W., Zhou, W. & Skinner, J. S. Fragmentation of care and the use of head computed tomography in patients with ischemic stroke. Circ. Cardiovasc. Qual. Outcomes 7, 430–436 (2014).

    Google Scholar 

  10. Dineva, S. et al. Comparative efficacy and safety of chlorthalidone and hydrochlorothiazide-meta-analysis. J. Hum. Hypertens. 33, 766–774 (2019).

    Google Scholar 

  11. Melak, T. & Baynes, H. W. Myocardial ischemia/reperfusion: translational pathophysiology of ischemic heart disease. EJIFCC 30, 179–194 (2019).

    Google Scholar 

  12. Heusch, G. Myocardial ischemia/reperfusion: translational pathophysiology of ischemic heart disease. Med 5, 10–31 (2024).

    Google Scholar 

  13. Condrat, C. E. et al. miRNAs as Biomarkers in disease: latest findings regarding their role in diagnosis and prognosis. Cells 9, 276 (2020).

    Google Scholar 

  14. Pan, H. et al. Atherosclerosis is a smooth muscle cell–driven tumor-like disease. Circulation 149, 1885–1898 (2024).

    Google Scholar 

  15. Hodas, R., Polexa, ȘtefaniaA., Rareș, M. & Benedek, T. Coronary computed tomography angiography for assesment of stable coronary artery disease – a cost-effectiveness perspective. J. Interdiscip. Med. 6, 37–42 (2021).

    Google Scholar 

  16. Meng, H. et al. New progress in early diagnosis of atherosclerosis. Int. J. Mol. Sci. 23, 8939 (2022).

    Google Scholar 

  17. De Castro, F. et al. NMR-based metabolomic approach to investigate the antitumor effects of the novel [Pt(η 1-C2H4OMe)(DMSO)(Phen)]+(Phen = 1,10-Phenanthroline) compound on neuroblastoma cancer cells. Bioinorg. Chem. Appl. 2022, 8932137 (2022).

  18. De Matteis, S. et al. Metabolic profile evolution in relapsed/refractory B-cell non-hodgkin lymphoma patients treated with CD19 chimeric antigen receptor t-cell therapy and implications in clinical outcome. Haematologica https://doi.org/10.3324/haematol.2024.285154 (2024).

    Google Scholar 

  19. Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).

    Google Scholar 

  20. Del Coco, L. et al. Blood metabolite profiling of Antarctic expedition members: an 1H NMR spectroscopy-based study. Int. J. Mol. Sci. 24, 8459 (2023).

  21. Dalamaga, M. Clinical metabolomics: useful insights, perspectives and challenges. Metab. Open 22, 100290 (2024).

    Google Scholar 

  22. De Castro, F., Benedetti, M., Del Coco, L. & Fanizzi, F. P. NMR-based metabolomics in metal-based drug research. Molecules 24, 2240 (2019).

  23. De Castro, F. et al. Response of cisplatin resistant SkOV-3 Cells to [PT(O,O0-Acac)(γ-ACaC)(DMS)] treatment revealed by a metabolomic 1H-NMR Study. Molecules 23, 2301 (2018).

  24. Gonzalez-Covarrubias, V., Martínez-Martínez, E. & del Bosque-Plata, L. The potential of metabolomics in biomedical applications. Metabolites 12, 194 (2022).

    Google Scholar 

  25. Taegtmeyer, H. et al. Assessing cardiac metabolism: a scientific statement from the American Heart Association. Circ. Res. 118, 1659–1701 (2016).

    Google Scholar 

  26. Li, Q. et al. Energy metabolism: a critical target of cardiovascular injury. Biomed. Pharmacother. Biomed. Pharmacother 165, 115271 (2023).

    Google Scholar 

  27. Bodi, V. et al. Metabolomic profile of human myocardial ischemia by nuclear magnetic resonance spectroscopy of peripheral blood serum. JACC 59, 1629–1641 (2012).

    Google Scholar 

  28. Zhang, B. & Schmidlin, T. Recent advances in cardiovascular disease research driven by metabolomics technologies in the context of systems biology. Npj Metab. Health Dis. 2, 1–12 (2024).

    Google Scholar 

  29. McGranaghan, P. et al. Predictive value of metabolomic biomarkers for cardiovascular disease risk: a systematic review and meta-analysis. Biomark. Biochem. Indic. Expo. Response Susceptibility Chem. 25, 101–111 (2020).

    Google Scholar 

  30. Cardiovascular Research. Predictive Metabolites for Incident Myocardial Infarction: A Two-Step Meta-Analysis of Individual Patient Data from Six Cohorts Comprising 7897 Individuals from the Consortium of METabolomics Studies. https://academic.oup.com/cardiovascres/article/119/17/2743/7273623 (2025).

  31. Lind, L., Fall, T., Ärnlöv, J., Elmståhl, S. & Sundström, J. Large-scale metabolomics and the incidence of cardiovascular disease. J. Am. Heart Assoc. 12, e026885 (2023).

    Google Scholar 

  32. Menaker, Y. et al. Stratification of atherosclerosis based on plasma metabolic states. J. Clin. Endocrinol. Metab. 109, 1250–1262 (2024).

    Google Scholar 

  33. Ali, S. E., Farag, M. A., Holvoet, P., Hanafi, R. S. & Gad, M. Z. A Comparative metabolomics approach reveals early biomarkers for metabolic response to acute myocardial infarction. Sci. Rep. 6, 36359 (2016).

    Google Scholar 

  34. Hasselbalch, R. B. et al. Metabolomics of early myocardial ischemia. Metabolomics Off. J. Metabolomic Soc. 19, 33 (2023).

    Google Scholar 

  35. Chu, C. et al. The interactions and biological pathways among metabolomics products of patients with coronary heart disease. Biomed. Pharmacother. Biomed. Pharmacother 173, 116305 (2024).

    Google Scholar 

  36. Vignoli, A. et al. High-throughput metabolomics by 1D NMR. Angew. Chem. Int. Ed Engl. 58, 968–994 (2019).

    Google Scholar 

  37. Frontiers. Physiology of Pericardial Fluid Production and Drainage. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2015.00062/full (2025).

  38. Trindade, F., Vitorino, R., Leite-Moreira, A. & Falcão-Pires, I. Pericardial fluid: an underrated molecular library of heart conditions and a potential vehicle for cardiac therapy. Basic Res. Cardiol. 114, 10 (2019).

    Google Scholar 

  39. Yang, Y. et al. Metabolic signatures in pericardial fluid and serum are associated with new-onset atrial fibrillation after isolated coronary artery bypass grafting. Transl. Res. 256, 30–40 (2023).

    Google Scholar 

  40. Fatehi Hassanabad, A. et al. Pericardial fluid of patients with coronary artery disease can drive fibrosis via TGF-beta pathway. JACC Basic Transl. Sci. 9, 1329–1344 (2024).

    Google Scholar 

  41. El-Sherbini, A. H. et al. The role of natriuretic peptides in pericardial fluid in predicting cardiovascular disorders: a systematic review. Cardiol. Rev. https://doi.org/10.1097/CRD.0000000000000779 (2024).

    Google Scholar 

  42. Chighine, A. et al. Metabolomics investigation of post-mortem human pericardial fluid. Int. J. Legal Med. 137, 1875–1885 (2023).

    Google Scholar 

  43. Julkunen, H. et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat. Commun. 14, 604 (2023).

    Google Scholar 

  44. Chong, J.; Xia, J. Using metaboanalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data. In Computational Methods and Data Analysis for Metabolomics, (ed. Li, S.) 337–360 (Humana, 2020).

  45. Brewer, L. C., Svatikova, A. & Mulvagh, S. L. The challenges of prevention, diagnosis and treatment of ischemic heart disease in women. Cardiovasc. Drugs Ther. 29, 355–368 (2015).

    Google Scholar 

  46. Panchal, V. R. et al. Reduced pericardial levels of endostatin correlate with collateral development in patients with ischemic heart disease. J. Am. Coll. Cardiol. 43, 1383–1387 (2004).

    Google Scholar 

  47. Kalogeris, T., Baines, C. P., Krenz, M. & Korthuis, R. J. Cell biology of ischemia/reperfusion injury. Int. Rev. Cell Mol. Biol. 298, 229–317 (2012).

    Google Scholar 

  48. Aubert, G. et al. The failing heart relies on ketone bodies as a fuel. Circulation 133, 698–705 (2016).

    Google Scholar 

  49. Christensen, K. H. et al. Circulating 3-hydroxy butyrate predicts mortality in patients with chronic heart failure with reduced ejection fraction. ESC Heart Fail 11, 837–845 (2024).

    Google Scholar 

  50. Wei, S. et al. β-hydroxybutyrate in cardiovascular diseases: a minor metabolite of great expectations. Front. Mol. Biosci. 9, 823602 (2022).

    Google Scholar 

  51. Munt, B. I., Moss, R. R., Thompson, C. R. Pericardial Disease*. In The Practice of Clinical Echocardiography (Third Edition), (eds. Otto, C. M., Ed., W. B.) 710–734 (Remedica, 2004).

  52. Homilius, C. et al. Ketone body 3-hydroxybutyrate elevates cardiac output through peripheral vasorelaxation and enhanced cardiac contractility. Basic Res. Cardiol. 118, 37 (2023).

    Google Scholar 

  53. Chouchani, E. T. et al. Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS. Nature 515, 431–435 (2014).

    Google Scholar 

  54. Zhang, W., Lang, R. Succinate metabolism: a promising therapeutic target for inflammation, ischemia/reperfusion injury and cancer. Front. Cell Dev. Biol. 11, 1266973 (2023).

  55. Zhang, J. et al. Accumulation of succinate in cardiac ischemia primarily occurs via canonical Krebs cycle activity. Cell Rep. 23, 2617–2628 (2018).

    Google Scholar 

  56. Pell, V. R., Chouchani, E. T., Murphy, M. P., Brookes, P. S. & Krieg, T. Moving forwards by blocking back-flow. Circ. Res. 118, 898–906 (2016).

    Google Scholar 

  57. Leite, L. N. et al. Pharmacological characterization of the mechanisms underlying the vascular effects of succinate. Eur. J. Pharmacol. 789, 334–343 (2016).

    Google Scholar 

  58. Milliken, A. S., Nadtochiy, S. M. & Brookes, P. S. Inhibiting succinate release worsens cardiac reperfusion injury by enhancing mitochondrial reactive oxygen species generation. J. Am. Heart Assoc. 11, e026135 (2022).

    Google Scholar 

  59. Prag, H. A. et al. Mechanism of succinate efflux upon reperfusion of the ischaemic heart. Cardiovasc. Res. 117, 1188–1201 (2021).

    Google Scholar 

  60. Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

    Google Scholar 

  61. Iwakura, A. et al. Pericardial fluid from patients with ischemic heart disease induces myocardial cell apoptotis via an oxidant stress-sensitive p38 mitogen-activated protein kinase pathway. J. Mol. Cell. Cardiol. 33, 419–430 (2001).

    Google Scholar 

  62. Markin, S. S. et al. A novel preliminary metabolomic panel for IHD diagnostics and pathogenesis. Sci. Rep. 14, 2651 (2024).

    Google Scholar 

  63. Bäckström, T., Goiny, M., Lockowandt, U., Liska, J. & Franco-Cereceda, A. Cardiac outflow of amino acids and purines during myocardial ischemia and reperfusion. J. Appl. Physiol. Bethesda Md 1985 94, 1122–1128 (2003).

    Google Scholar 

  64. Song, D., O’Regan, M. H. & Phillis, J. W. Mechanisms of amino acid release from the isolated anoxic/reperfused rat heart. Eur. J. Pharmacol. 351, 313–322 (1998).

    Google Scholar 

  65. Newgard, C. B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9, 311–326 (2009).

    Google Scholar 

  66. Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

    Google Scholar 

  67. Shah, S. H. et al. Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ. Cardiovasc. Genet. 3, 207–214 (2010).

    Google Scholar 

  68. Du, X. et al. Relationships between circulating branched chain amino acid concentrations and risk of adverse cardiovascular events in patients with STEMI treated with PCI. Sci. Rep. 8, 15809 (2018).

    Google Scholar 

  69. Cheng, M.-L. et al. Metabolic disturbances identified in plasma are associated with outcomes in patients with heart failure: diagnostic and prognostic value of metabolomics. J. Am. Coll. Cardiol. 65, 1509–1520 (2015).

    Google Scholar 

  70. Wang, Y. et al. Association of circulating branched-chain amino acids with risk of cardiovascular disease: a systematic review and meta-analysis. Atherosclerosis 350, 90–96 (2022).

    Google Scholar 

  71. Choi, B. H., Hyun, S. & Koo, S.-H. The role of BCAA metabolism in metabolic health and disease. Exp. Mol. Med. 56, 1552–1559 (2024).

    Google Scholar 

  72. Li, T. et al. Defective branched-chain amino acid catabolism disrupts glucose metabolism and sensitizes the heart to ischemia-reperfusion injury. Cell Metab 25, 374–385 (2017).

    Google Scholar 

  73. Brand, K. Metabolism of 2-oxoacid analogues of leucine, valine and phenylalanine by heart muscle, brain and kidney of the Rat. Biochim. Biophys. Acta BBA Gen. Subj. 677, 126–132 (1981).

    Google Scholar 

  74. Uddin, G. M. et al. Deletion of BCATm increases insulin-stimulated glucose oxidation in the heart. Metabolism 124, 154871 (2021).

    Google Scholar 

  75. Lai, L. et al. Energy metabolic reprogramming in the hypertrophied and early stage failing heart: a multisystems approach. Circ. Heart Fail. 7, 1022–1031 (2014).

    Google Scholar 

  76. American Heart Association. Catabolic defect of branched-chain amino acids promotes heart failure. Circulation 133, 21 (2025).

  77. Sardar, S. W., Nam, J., Kim, T. E., Kim, H. & Park, Y. H. Identification of novel biomarkers for early diagnosis of atherosclerosis using high-resolution metabolomics. Metabolites 13, 1160 (2023).

    Google Scholar 

  78. Jauhiainen, R. et al. The association of 9 amino acids with cardiovascular events in finnish men in a 12 year follow-up study. J. Clin. Endocrinol. Metab. 106, 3448–3454 (2021).

    Google Scholar 

  79. Würtz, P. et al. High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis. Eur. Heart J. 33, 2307–2316 (2012).

    Google Scholar 

  80. Mu, H. et al. The association of aromatic amino acids with coronary artery disease and major adverse cardiovascular events in a chinese population. Int. J. Food Sci. Nutr. 75, 825–834 (2024).

    Google Scholar 

  81. Anand, S. K. et al. Amino acid metabolism and atherosclerotic cardiovascular disease. Am. J. Pathol. 194, 510–524 (2024).

    Google Scholar 

  82. Williams, I. H., Sugden, P. H. & Morgan, H. E. Use of aromatic amino acids as monitors of protein turnover. Am. J. Physiol. Endocrinol. Metab. 240, E677–E681 (1981).

    Google Scholar 

  83. Peuhkurinen, K. J., Takala, T. E., Nuutinen, E. M. & Hassinen, I. E. Tricarboxylic acid cycle metabolites during ischemia in isolated perfused rat heart. Am. J. Physiol. 244, H281–H288 (1983).

    Google Scholar 

  84. Dong, S. et al. Lactate and myocardiac energy metabolism. Front. Physiol. 12, 715081 (2021).

  85. Lopaschuk, G. D., Ussher, J. R., Folmes, C. D. L., Jaswal, J. S. & Stanley, W. C. Myocardial fatty acid metabolism in health and disease. Physiol. Rev. 90, 207–258 (2010).

    Google Scholar 

  86. Lv, J. et al. Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease. Nat. Commun. 15, 5729 (2024).

    Google Scholar 

  87. Markin, S. S. et al. Targeted metabolomic profiling of acute ST-segment elevation myocardial infarction. Sci. Rep. 14, 23838 (2024).

    Google Scholar 

  88. Drake, K. J., Sidorov, V. Y., Mcguinness, O. P., Wasserman, D. H. & Wikswo, D. P. Amino acids as metabolic substrates during cardiac ischemia. Exp. Biol. Med. https://doi.org/10.1258/ebm.2012.012025 (2012).

    Google Scholar 

Download references

Acknowledgements

This research was financially supported by the POS Salute 2014–2020 of the Italian Ministry of Health, under Trajectory 3 “Regenerative, Predictive and Personalized Medicine”, Action Line 3.1, within the project “Integrated Health and Genetics System for Cardiovascular Diseases (SISAGEN-CARDIO)” (local project code T3-AN-18).

Author information

Author notes
  1. These authors contributed equally: Federica De Castro, Chiara Coppola.

Authors and Affiliations

  1. Department of Biological and Environmental Sciences and Technologies (DiSTeBA), University of Salento, Via Monteroni, Lecce, Italy

    Federica De Castro & Francesco Paolo Fanizzi

  2. Department of Mathematics and Physics “E. De Giorgi”, University of Salento, Via Lecce—Arnesano, Lecce, Italy

    Chiara Coppola

  3. Institute of Clinical Physiology (IFC), National Research Council (CNR), Lecce, Italy

    Egeria Scoditti

  4. Department of Cardiac Surgery, Città di Lecce Hospital, GVM Care & Research, Lecce, Italy

    Giuseppe Santarpino

  5. Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy

    Giuseppe Santarpino

  6. Department of Cardiac Surgery, Paracelsus Medical University, Nuremberg, Germany

    Giuseppe Santarpino

  7. Department of Cardiology, PO Fazzi, ASL_LE, Piazzale Muratore, Lecce, Italy

    Stefania Marazia

  8. Department of Experimental Medicine, University of Salento, Lecce, Via, Monteroni, Italy

    Michele Maffia

  9. Lab of Clinical Proteomic, PO Fazzi, ASL_LE Piazzale Muratore, Lecce, Italy

    Michele Maffia

Authors
  1. Federica De Castro
    View author publications

    Search author on:PubMed Google Scholar

  2. Chiara Coppola
    View author publications

    Search author on:PubMed Google Scholar

  3. Egeria Scoditti
    View author publications

    Search author on:PubMed Google Scholar

  4. Giuseppe Santarpino
    View author publications

    Search author on:PubMed Google Scholar

  5. Stefania Marazia
    View author publications

    Search author on:PubMed Google Scholar

  6. Francesco Paolo Fanizzi
    View author publications

    Search author on:PubMed Google Scholar

  7. Michele Maffia
    View author publications

    Search author on:PubMed Google Scholar

Contributions

F.D.C., C.C., M.M.: Conceptualization; F.D.C., C.C., E.S.:formal analysis, data curation, investigation; F.D.C., C.C., E.S. writing-original draft, F.D.C.: performed metabolomic analysis; C.C., E.S., G.S., S.M., M.M.: contributed patient samples and clinical data; F.D.C., C.C., E.S., M.M., F.P.F.: writing-reviewing and editing; F.P.F., M.M.: Supervision, Funding acquisition. All authors have read and agreed to the published.

Corresponding author

Correspondence to Michele Maffia.

Ethics declarations

Competing interests

The authors declare no competing interests

Peer review

Peer review information

Communications Medicine thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Description of Additional Supplementary files

Supplementary Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Castro, F., Coppola, C., Scoditti, E. et al. Plasma and pericardial fluid metabolomic signatures of patients with ischemic heart disease. Commun Med (2026). https://doi.org/10.1038/s43856-025-01353-0

Download citation

  • Received: 29 April 2025

  • Accepted: 19 December 2025

  • Published: 21 January 2026

  • DOI: https://doi.org/10.1038/s43856-025-01353-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Collections
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Journal Information
  • Open Access Fees and Funding
  • Journal Metrics
  • Editors
  • Editorial Board
  • Calls for Papers
  • Contact
  • Conferences
  • Editorial Values Statement
  • Posters
  • Editorial policies

Publish with us

  • For Authors
  • For Referees
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Communications Medicine (Commun Med)

ISSN 2730-664X (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing