Rapid Diagnosis of Tuberculosis Meningitis by Detecting Mycobacterium tuberculosis Cell-Free DNA in Cerebrospinal Fluid.

OBJECTIVES: Tuberculosis meningitis (TBM) is one of the most severe forms of tuberculosis. However, TBM diagnosis is quite challenging due to nonspecific clinical presentation and the paucity of the pathogen in cerebrospinal fluid (CSF) samples. In this study, we report a new method for detecting cell-free Mycobacterium tuberculosis DNA (cf-TB) in CSF and evaluate its clinical value for TBM diagnosis. METHODS: Of 68 patients prospectively recruited, 46 were diagnosed as having TBM and 22 as non-TBM. We compared the cf-TB method with CSF smear microscopy, mycobacterial culture, and the Xpert MTB/RIF assay (Xpert) using the consensus case definition for TBM proposed in 2009 as a reference standard. RESULTS: The sensitivity of the cf-TB test was 56.5% (26/46) in patients with TBM, and it was significantly higher than other methods: microscopy (2.2%, 1/46; P < .001), mycobacterial culture (13.0%, 6/46; P < .001), and Xpert (23.9%, 11/46; P = .001). For specificity, none of the four methods reported false-positive results in the non-TBM group. CONCLUSIONS: The new method detecting cell-free M tuberculosis DNA in CSF is rapid and accurate for diagnosis of TBM and easily incorporated into regular laboratory tests.

1H nuclear magnetic resonance-based metabolic profiling of cerebrospinal fluid to identify metabolic features and markers for tuberculosis meningitis.

BACKGROUND: Tuberculosis meningitis (TBM) is the most severe form of tuberculosis, and currently lacks efficient diagnostic approaches. Metabolomics has the potential to differentiate patients with TBM from those with other forms of meningitis and meningitis-negative individuals. However, no systemic metabolomics research has compared the cerebrospinal fluid (CSF) of these patients. METHODS: 1H nuclear magnetic resonance (NMR) was used for CSF metabolic profiling. Principal component analysis and orthogonal signal correction-partial least squares-discriminant analysis (OPLS-DA) were used to screen for important variables. The Human Metabolome Database was used to identify metabolites, and MetaboAnalyst 4.0 was used for pathway analysis and over-representation analysis. RESULTS: OPLS-DA modeling could distinguish TBM from other forms of meningitis, and several significantly changed metabolites were identified. Additionally, 23, 6, and 21 metabolites were able to differentiate TBM from viral meningitis, bacterial meningitis, and meningitis-negative groups, respectively. Pathway analysis indicated that these metabolites were mainly involved in carbohydrate and amino acid metabolism, and over-representation analysis indicated that some of these pathways were over-represented. CONCLUSIONS: The metabolites identified have the potential to serve as biomarkers for TBM diagnosis, and carbohydrate and amino acid metabolism are perturbed in the CSF of patents with TBM. Metabolomics is a valuable approach for screening TBM biomarkers. With further investigation, the metabolites identified in this study could aid in TBM diagnosis.