In-hospital metabolite changes in infective endocarditis—a longitudinal 1H NMR-based study

  • Christine Falk KleinEmail author
  • Sarah Louise Kjølhede Holle
  • Malene Højgaard Andersen
  • Anders Pedersen
  • Henning Bundgaard
  • Kasper Karmark Iversen
  • Anders MalmendalEmail author
Original Article


Treatment of infective endocarditis (IE) is a 4–6-week provided course of intravenously administered antibiotics. The aim of this study was to investigate how serum metabolites as measured by proton nuclear magnetic resonance (1H NMR) spectroscopy are changing over time during the active phase of IE, and to see whether these metabolite changes might be used to monitor recovery in these patients. Patients hospitalized with first-time IE at Herlev Hospital, Denmark, from September 2015 to June 2017 were included. Longitudinal blood sampling was performed and serum was analyzed using 1H NMR. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was used to separate sample groups and analyze differences in metabolite profiles. Thirteen patients were included in the study (77% men, median age 62 years (IQR 53–77)). All patients were cured during the hospitalization without any relapse during 6 months of follow-up. We analyzed 61 serum samples (median 5 samples, range 2–8 per person) drawn in the treatment period after IE diagnosis. The main changes during the in-hospital period were decreased levels of glucose, mannose, leucine, isoleucine, phenylalanine, tyrosine, and signals from polyols and N-acetylated protein. The metabolomic changes could in contrast to the routinely used parameters CRP and leucocyte levels distinguish between the early and late stages of disease treatment. We present the first longitudinal study of 1H NMR metabolomics in patients with infective endocarditis. The metabolomic changes show a promising strength compared to routinely used clinical parameters.


Infective endocarditis Antibiotic treatment Metabolomics NMR Longitudinal study 



The authors gratefully acknowledge support from Dr. Christian Pihl and the medical student Mia Pries-Heje for helping with the sample collection process.

Author contributions

HB, KI, AM, and CFK conceived and designed the study. CFK, SLKH, and MHA included the patients, and collected and prepared the blood samples. CFK, AM, and AP made the NMR spectral analyses. AM made the statistical analyses. CFK drafted the manuscript, and SLKH, MHA, AP, HB, KI, and AM revised the manuscript critically for intellectual content, and have read and approved the final manuscript for submission.


This work was supported by the Herlev Hospital Research Council and FUKAP (research fund of The Department of Cardiology, Copenhagen University Hospital Gentofte). The funder did not have any role in designing or conducting the study, neither data collection, data analyses, nor manuscript approval.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the Danish Data Protection Agency ( 2012-58-0004, local HGH-2015-010, I-suite number: 03923) and the Danish Scientific Ethics Committee (protocol number: H-15009681).

Informed consent

All patients included in this study participated after having given informed consent.

Supplementary material

10096_2019_3586_MOESM1_ESM.docx (2.7 mb)
ESM 1 (DOCX 2772 kb)


  1. 1.
    Prendergast BD (2006) The changing face of infective endocarditis. Heart 92:879–885. CrossRefPubMedGoogle Scholar
  2. 2.
    Moreillon P, Que Y-A (2004) Infective endocarditis. Lancet 363(9403):139–149. CrossRefPubMedGoogle Scholar
  3. 3.
    Cahill TJ, Prendergast BD (2016) Infective endocarditis. Lancet 387(10021):882–893. CrossRefPubMedGoogle Scholar
  4. 4.
    Durack D, Lukes A, Bright D (1994) New criteria for diagnosis of infective endocarditis: utilization of specific echocardiographic findings. Am J Med 96:200–209. CrossRefPubMedGoogle Scholar
  5. 5.
    Habib G, Lancellotti P, Antunes M et al (2015) 2015 ESC guidelines for the management of infective endocarditis.
  6. 6.
    Baddour LM, Wilson WR, Bayer AS et al (2015) Infective endocarditis in adults: diagnosis, antimicrobial therapy, and management of complications.
  7. 7.
    Dickerman SA, Abrutyn E, Barsic B et al (2007) The relationship between the initiation of antimicrobial therapy and the incidence of stroke in infective endocarditis: an analysis from the ICE Prospective Cohort Study (ICE-PCS). Am Heart J 154(6):1086–1094. CrossRefPubMedGoogle Scholar
  8. 8.
    Verhagen DWM, Hermanides J, Korevaar JC et al (2008) Prognostic value of serial C-reactive protein measurements in left-sided native valve endocarditis. Arch Intern Med 168(3):302–307. CrossRefPubMedGoogle Scholar
  9. 9.
    Nicholson JK, Lindon JC, Holmes E (1999) “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11):1181–1189. CrossRefPubMedGoogle Scholar
  10. 10.
    Nicholsen JK, Lindon JC (2008) Systems biology: Metabonomics Nature 455(7216):1054-1056.
  11. 11.
    Long Y, Sanchez-Espiridion B, Lin M et al (2017) Global and targeted serum metabolic profiling of colorectal cancer progression. Cancer 123(20):4066–4074. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Slupsky CM, Rankin KN, Fu H et al (2009) Pneumococcal pneumonia : potential for diagnosis through a urinary metabolic profile Research articles. 5550–5558Google Scholar
  13. 13.
    Bothwell JHF, Griffin JL (2011) An introduction to biological nuclear magnetic resonance spectroscopy. Biol Rev 86(2):493–510. CrossRefPubMedGoogle Scholar
  14. 14.
    Grant DM, Harris RM (2002) Encyclopedia of magnetic resonance. John Wiley & Sons, New York isbn: 978-0-471-49082-1Google Scholar
  15. 15.
    Mallol R, Rodriguez MA, Brezmes J, Masana L, Correig X (2013) Human serum/plasma lipoprotein analysis by NMR: application to the study of diabetic dyslipidemia. Prog Nucl Magn Reson Spectrosc 70:1–24. CrossRefPubMedGoogle Scholar
  16. 16.
    Faber JH, Malmodin D, Toft H et al (2007) Metabonomics in diabetes research. J Diabetes Sci Technol 1(4):549–557. CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Iversen K, Ihlemann N, Gill SU et al (2018) Partial oral versus intravenous antibiotic treatment of endocarditis. N Engl J Med NEJMoa1808312.
  18. 18.
    Dona AC, Jiménez B, Schäfer H et al (2014) Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem 86(19):9887–9894. CrossRefPubMedGoogle Scholar
  19. 19.
    Kaluarachchi M, Boulangé CL, Karaman I et al (2018) A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS. Metabolomics 14(3):32. CrossRefPubMedGoogle Scholar
  20. 20.
    iNMR Webpage. Accessed 20 May 2019
  21. 21.
    Savorani F, Tomasi G, Engelsen SB (2010) icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson 202(2):190–202. CrossRefGoogle Scholar
  22. 22.
    Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemom 16(3):119–128. CrossRefGoogle Scholar
  23. 23.
    Ghini V, Saccenti E, Tenori L, Assfalg M, Luchinat C (2015) Allostasis and resilience of the human individual metabolic phenotype.
  24. 24.
    Albrich WC, Harbarth S (2015) Pros and cons of using biomarkers versus clinical decisions in start and stop decisions for antibiotics in the critical care setting. Intensive Care Med 41(10):1739–1751. CrossRefPubMedGoogle Scholar
  25. 25.
    Fontela PS, O’Donnell S, Papenburg J (2018) Can biomarkers improve the rational use of antibiotics? Curr Opin Infect Dis 1.
  26. 26.
    Johnson CH, Gonzalez FJ (2018) Challenges and opportunities of metabolomics. J Cell Physiol 227(8):2975–2981. CrossRefGoogle Scholar
  27. 27.
    Balog CIA, Meissner A, Goaler S et al (2011) Metabonomic investigation of human Schistosoma mansoni infectionw. Mol BioSyst Mol BioSyst 7(7):1473–1480. CrossRefPubMedGoogle Scholar
  28. 28.
    Yang K, Zhang F, Han P, Kui W, Yuan D (2018) Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer. Metabolomics 14(9):1–9. CrossRefGoogle Scholar
  29. 29.
    Nevedomskaya E, Mayboroda OA (2011) Molecular BioSystems Cross-platform analysis of longitudinal data in metabolomics w. 3214–3222.
  30. 30.
    Lewis GD, Asnani A, Gerszten RE (2008) Application of metabolomics to cardiovascular biomarker and pathway discovery. J Am Coll Cardiol 52(2):117–123. CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Stage C, Jürgens G, Dalhoff KP, Rasmussen HB (2014) Metabolomics kan potentielt forbedre lægemiddelterapien. Ugeskr Laeger 176(6):525–528. CrossRefGoogle Scholar
  32. 32.
    Kafsack B, Llinas M (2010) Eating at the table of another: metabolomics of host/parasite interactions. Cell Host Microbe 7(2):90–99. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CardiologyHerlev Gentofte HospitalCopenhagenDenmark
  2. 2.The Swedish NMR CentreUniversity of GothenburgGöteborgSweden
  3. 3.Department of CardiologyCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
  4. 4.Copenhagen Health Science PartnersKøbenhavnDenmark
  5. 5.Department of ChemistryLund UniversityLundSweden

Personalised recommendations