Benchtop NMR-based metabolomic analysis as a diagnostic tool for tuberculosis in clinical urine samples

The ability to diagnose tuberculosis (TB) in its primary stages is an essential factor in the spreading control of this disease. However, reference methods present a low sensitivity, are slow or require a well equipped laboratory. This study aimed at developing a Nuclear Magnetic Resonance (NMR)-based metabolomic approach for the differential diagnosis of TB from urine samples. Translational potential has been also proved by the use of an affordable and portable low-field (LF) NMR spectrometer. Methods: Urine samples from adult patients diagnosed of TB (n=19), other respiratory infection (n=25) and healthy controls (n=29) were examined using a Bruker Avance high-field (HF) (16.4T) spectrometer and a Magritek Spinsolve LF (1.4T) spectrometer. Principal Component Analysis (PCA) was applied to identify metabolic differences between groups. Classificatory models of partial least squares discriminant analysis (PLS-DA) was developed for the diagnosis of TB. Results: The urine HF NMR spectra provide a high discrimination between the three groups. We identified 31 metabolic signals significantly diferent between groups. PLS-DA classification models, TB vs control and TB vs respiratory infection, provided a diagnosis accuracy of about 100% by test samples. A similar PLS-DA classification model was developed based on LF NMR spectra, providing a diagnosis accuracy of about 100% by test samples. As conclusions, we have shown that the metabolomic profile obtained by both high- and low-field NMR is sensitive to identify TB. The high diagnostic accuracy archive by LF NMR is of particular interest because it opens the door to an easy translation to a clinical environment.