BACKGROUND: Post-treatment Lyme disease symptoms/syndrome (PTLDS) occurs in approximately 10% of Lyme disease patients following antibiotic treatment. Biomarkers or specific clinical symptoms to identify PTLDS patients do not currently exist and the PTLDS classification is based on the report of persistent, subjective symptoms for ≥ 6 months following antibiotic treatment for Lyme disease. METHODS: Untargeted liquid chromatography-mass spectrometry metabolomics was used to determine longitudinal metabolic responses and biosignatures in PTLDS and clinically cured non-PTLDS Lyme patients. Evaluation of biosignatures included: 1) defining altered classes of metabolites; 2) elastic net regularization to define metabolites that most strongly defined PTLDS and non-PTLDS patients at different timepoints; 3) changes in the longitudinal abundance of metabolites; 4) linear discriminant analysis to evaluate robustness in a second patient cohort. RESULTS: This study determined that observable metabolic differences exist between PTLDS and non-PTLDS patients at multiple timepoints. The metabolites with differential abundance included those from glycerophospholipid, bile acid and acylcarnitine metabolism. Distinct longitudinal patterns of metabolite abundance indicated a greater metabolic variability in PTLDS vs non-PTLDS patients. Small numbers of metabolites (6-40) could be used to define PTLDS vs. non-PTLDS patients at defined time points, and the findings were validated in a second cohort of PTLDS and non-PTLDS patients. CONCLUSIONS: These data provide evidence that an objective metabolite-based measurement can distinguish patients with PTLDS and help understand the underlying biochemistry of PTLDS.

Metabolites detectible in human biofluids are attractive biomarkers for the diagnosis of early Lyme disease (ELD), a vector-borne infectious disease. Urine represents an easily obtained clinical sample that can be applied for diagnostic purposes. However, few studies have explored urine for biomarkers of ELD. In this study, metabolomics approaches were applied to evaluate small molecule metabolites in urine from patients with ELD (n = 14), infectious mononucleosis (n = 14) and healthy controls (n = 14). Metabolic biosignatures for ELD versus healthy controls and ELD versus infectious mononucleosis were generated using untargeted metabolomics. Pathway analyses and metabolite identification revealed the dysregulation of several metabolic processes in ELD as compared to healthy controls or mononucleosis, including metabolism of tryptophan. Linear discriminant analyses demonstrated that individual metabolic biosignatures can correctly discriminate ELD from the other patient groups with accuracies of 71 to 100%. These data provide proof-of-concept for use of urine metabolites as biomarkers for diagnostic classification of ELD.

Standard two-tiered testing (STTT) is the recommended algorithm for laboratory diagnosis of Lyme disease (LD). Several limitations are associated with STTT that include low sensitivity in the early stages of disease, as well as technical complexity and subjectivity associated with second-tier immunoblots; therefore, modified two-tiered testing (MTTT) algorithms that utilize two sequential first-tier tests and eliminate immunoblots have been evaluated. Recently, a novel MTTT that uses a VlsE chemiluminescence immunoassay followed by a C6 enzyme immunoassay has been proposed. The purpose of this study was to evaluate the performance of the VlsE/C6 MTTT using well-characterized serum samples. Serum samples from the CDC Lyme Serum Repository were tested using three MTTTs: VlsE/C6, whole cell sonicate (WCS)/C6 and WCS/VlsE, and three STTTs (immunoblots preceded by three different first-tier assays: VlsE, C6 and WCS). Significant differences were not observed between the MTTTs assessed; however, the VlsE/C6 MTTT resulted in the highest specificity (100%) when other diseases were tested and the lowest sensitivity (75%) for LD samples as compared to the other MTTTs evaluated. Significant differences were present between various MTTTs and STTTs evaluated. Specifically, all MTTTs resulted in higher sensitivities for all LD groups combined when compared to the STTTs and were significantly more accurate (i.e. higher proportion of correct classifications) for this group with the exception of the WCS/ViraStripe STTT. Additionally, when other diseases were tested, only the VlsE/C6 MTTT differed significantly from the WCS/ViraStripe STTT with the VlsE/C6 MTTT resulting in a 6.2% higher accuracy. Overall, the VlsE/C6 MTTT offers an additional laboratory testing algorithm for LD with equivalent or enhanced performance to the other MTTTs and STTTs evaluated in this study.

Lyme disease, the most commonly reported vector-borne disease in the United States, results from infection with Borrelia burgdorferi. Early clinical diagnosis of this disease is largely based on the presence of an erythematous skin lesion for individuals in high-risk regions. This, however, can be confused with other illnesses including southern tick-associated rash illness (STARI), an illness that lacks a defined etiological agent or laboratory diagnostic test, and is coprevalent with Lyme disease in portions of the eastern United States. By applying an unbiased metabolomics approach with sera retrospectively obtained from well-characterized patients, we defined biochemical and diagnostic differences between early Lyme disease and STARI. Specifically, a metabolic biosignature consisting of 261 molecular features (MFs) revealed that altered N-acyl ethanolamine and primary fatty acid amide metabolism discriminated early Lyme disease from STARI. Development of classification models with the 261-MF biosignature and testing against validation samples differentiated early Lyme disease from STARI with an accuracy of 85 to 98%. These findings revealed metabolic dissimilarity between early Lyme disease and STARI, and provide a powerful and new approach to inform patient management by objectively distinguishing early Lyme disease from an illness with nearly identical symptoms.