IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-64903-z.html
   My bibliography  Save this article

The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis

Author

Listed:
  • Annelaura Bach Nielsen

    (Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital
    University of Copenhagen)

  • Lasse Fjordside

    (Rigshospitalet)

  • Lylia Drici

    (University of Copenhagen)

  • Maud Eline Ottenheijm

    (Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital
    University of Copenhagen)

  • Christine Rasmussen

    (Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital)

  • Anna J. Henningsson

    (Linköping University
    Region Jönköping County)

  • Lene Holm Harritshøj

    (Copenhagen University Hospital
    University of Copenhagen)

  • Matthias Mann

    (University of Copenhagen
    Max Planck Institute of Biochemistry)

  • Helene Mens

    (Rigshospitalet)

  • Anne-Mette Lebech

    (Rigshospitalet
    University of Copenhagen)

  • Nicolai J. Wewer Albrechtsen

    (Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital
    University of Copenhagen
    Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital)

Abstract

Lyme neuroborreliosis (LNB), a nervous system infection caused by tick-borne spirochetes of the Borrelia burgdorferi sensu lato complex, is among the most frequent bacterial infections of the nervous system in Europe. Early diagnosis and continuous monitoring remain challenging due to limited sensitivity and specificity of current methods and requires invasive lumbar punctures, underscoring the need for improved, less invasive diagnostic tools. Here, we apply mass spectrometry-based proteomics to analyse 308 cerebrospinal fluid (CSF) samples and 207 plasma samples from patients with LNB, viral meningitis, controls and other manifestations of Lyme borreliosis. Diagnostic panels of regulated proteins are identified and evaluated through machine learning-assisted proteome analyses. In CSF, the classifier distinguishes LNB from viral meningitis and controls with AUCs of 0.92 and 0.90, respectively. In plasma, LNB is distinguished from controls with an AUC of 0.80. Our findings suggest a potential diagnostic role for machine learning-assisted proteomics in adults with LNB.

Suggested Citation

  • Annelaura Bach Nielsen & Lasse Fjordside & Lylia Drici & Maud Eline Ottenheijm & Christine Rasmussen & Anna J. Henningsson & Lene Holm Harritshøj & Matthias Mann & Helene Mens & Anne-Mette Lebech & Ni, 2025. "The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64903-z
    DOI: 10.1038/s41467-025-64903-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-64903-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-64903-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64903-z. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.