Ilari Kuukkanen’s Doctoral Dissertation 20.5.2026

Lyme neuroborreliosis is a manifestation of Lyme borreliosis affecting the nervous system and is caused by Borrelia burgdorferi sensu lato bacteria. Its diagnosis can be challenging in clinical practice, partly because current diagnostic approaches may not always provide clear answers in complex or ambiguous cases. Kuukkanen’s study combined UHPLC–MS/MS-based metabolomics with machine learning to analyse patient samples collected before and after treatment. The samples were compared with control groups and, in part, with samples from patients with other central nervous system infections to assess which metabolic changes were specifically associated with Lyme neuroborreliosis.

The study found that Lyme neuroborreliosis is associated particularly with alterations in tryptophan metabolism, lipid-mediated signalling, and other amino acid metabolic pathways. Some of the findings detected in cerebrospinal fluid were also associated with CXCL13, a marker of neuroinflammation, supporting their potential relevance to the disease process. Machine learning-based classification models were able to distinguish biomarker profiles related to Lyme neuroborreliosis with good accuracy.

The results indicate that the identified biomarkers and biomarker panels may have potential to complement current diagnostic methods and support the development of new tools for diagnosis and treatment monitoring. Overall, the dissertation provides new insight into the metabolic changes associated with Lyme neuroborreliosis and lays an important foundation for further development of these methods toward future clinical application.