Abstract
Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.
Highlights
Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics
Three hundred and thirty-two participants were included in this study and classified into the following study groups: 189 active TB patients, 42 pneumococcal pneumonia patients, 61 latent TB infection (LTBI) individuals, and 40 uninfected individuals
We have identified and characterised a metabolic profile of TB in urine from a high-field nuclear magnetic resonance (NMR) spectrometer and detected the same profile with a low-field NMR spectrometer
Summary
Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location This provides a new approach in the search for possible biomarkers for the diagnosis of TB. Metabolomics has emerged from the ‘omics’ technologies as a tool to obtain a fingerprint of all the metabolites present in a cellular system, allowing discrimination between samples with a different biological s tatus[9] In this approach, metabolomics has been applied to study the metabolites affected by host–pathogen interactions and identify diagnostic markers to improve diagnosis of different respiratory infectious d iseases[10]. A benchtop NMR spectrometer has been developed as a potential tool for point-of-care diagnostics in urine samples due to its high performance in a compact s ize[15,16]
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