The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis

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 and Nicolai J. Wewer Albrechtsen ()
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Annelaura Bach Nielsen: Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital
Lasse Fjordside: Rigshospitalet
Lylia Drici: University of Copenhagen
Maud Eline Ottenheijm: Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital
Christine Rasmussen: Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital
Anna J. Henningsson: Linköping University
Lene Holm Harritshøj: Copenhagen University Hospital
Matthias Mann: University of Copenhagen
Helene Mens: Rigshospitalet
Anne-Mette Lebech: Rigshospitalet
Nicolai J. Wewer Albrechtsen: Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-64903-z

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