Application of Mass Spectrometry-Based Metabolomics and Machine Learning in the Diagnostics of Lyme Neuroborreliosis

Abstract

<p> 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 <em>Borrelia</em> 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. <br></p>