Background
Facial palsy is the most common cranial neuropathy in children, frequently caused by neuro-borreliosis or Bell's palsy. Differentiation requires lumbar puncture to assess cerebrospinal fluid (CSF) cell count and Borrelia burgdorferi antibody index. We aimed to investigate CSF and plasma proteomics to explore the potential for early, accurate, non-invasive diagnostics.

Methods
Samples were collected from 129 children with facial palsy at the first hospital visit. The causal disease was later diagnosed as by Bell's palsy, neuroborreliosis, unverified aetiology, or Ramsay Hunt syndrome. Mass spectrometry was applied to measure the proteomes of each sample. Differential abundance was assessed between (i) facial palsy of unverified aeti-ology and definite neuroborreliosis, (ii) Bell's palsy and neuroborreliosis, and (iii) neuroborrelio-sis with and without facial palsy, using multivariate linear model corrected for sex and age. Functional interpretation was carried out through enrichment analysis and tree-based cluster-ing of enriched terms. A plasma-based diagnostic classifier was developed using machine learning with feature selection and cross-validation across 14 algorithms.

Results
Proteomic profiles indicated that children with facial palsy of unverified aetiology likely repre-sent neuroborreliosis, supporting empirical treatment. Neuroborreliosis showed significantly elevated CSF proteins related to immune pathways, whereas those with Bell's palsy had ele-vated CSF proteins associated with extracellular matrix organization. Within neuroborreliosis, patients without facial palsy showed stronger enrichment of immune and transport pathways. A diagnostic classifier based on 14 features achieved AUROC of 0.77 and MCC of 0.56, with CD248 as the strongest contributor, supporting a diagnostic step-down from lumbar puncture to plasma-based diagnostic screening.

Co-authors: Joakim Bloch, Annelaura Bach Nielsen, Ulrikka Nygaard, Nicolai J. Wewer Albrechtsen