Abstract
Lyme borreliosis (LB) and its disseminated nervous system manifestation, Lyme neuroborreliosis (LNB), presents diagnostic challenges, especially in seropositive and ambiguous clinical cases. In this study, we applied mass spectrometry (MS)-based metabolomics combined with machine learning (ML) to analyze serum samples from patients with definite acute LNB (n = 34), treated LNB (n = 34), together with Borrelia antibody-negative (non-LNB) controls (n = 62). Importantly, pre- and post-treatment samples were collected from the same individuals, enabling within-patient comparisons that enhance sensitivity to LNB-related metabolic changes. The non-LNB control group was age- and sex-matched (n = 34), and treated LNB patients served as a practical substitute for postinfectious recovery. Strong discriminatory performance was observed across all pairwise group comparisons. ML model classifiers yielded accuracy rates significantly above those expected by chance, with a perfect classification (1.00) achieved between treated LNB and non-LNB controls. This high separation, independent of antibody status, highlights the potential of MS-based metabolomics as a complementary diagnostic strategy. Receiver operating characteristic curve (ROC) analyses further supported robust performance, with high sensitivity and specificity. Although variance explained in unsupervised ordination was limited (PERMANOVA 4%), the supervised models demonstrated diagnostic value. These findings support the feasibility of metabolomic profiling combined with ML models for LNB diagnosis.