by Izquierdo-Garcia, José Luis, Comella-del-Barrio, Patricia, Campos-Olivas, Ramón, Villar-Hernández, Raquel, Prat-Aymerich, Cristina, De Souza-Galvão, Maria Luiza, Jiménez-Fuentes, Maria Angeles, Ruiz-Manzano, Juan, Stojanovic, Zoran, González, Adela, Serra-Vidal, Mar, García-García, Esther, Muriel-Moreno, Beatriz, Millet, Joan Pau, Molina-Pinargote, Israel, Casas, Xavier, Santiago, Javier, Sabriá, Fina, Martos, Carmen, Herzmann, Christian, Ruiz-Cabello, Jesús and Domínguez, José
Abstract:
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.
Reference:
Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker (Izquierdo-Garcia, José Luis, Comella-del-Barrio, Patricia, Campos-Olivas, Ramón, Villar-Hernández, Raquel, Prat-Aymerich, Cristina, De Souza-Galvão, Maria Luiza, Jiménez-Fuentes, Maria Angeles, Ruiz-Manzano, Juan, Stojanovic, Zoran, González, Adela, Serra-Vidal, Mar, García-García, Esther, Muriel-Moreno, Beatriz, Millet, Joan Pau, Molina-Pinargote, Israel, Casas, Xavier, Santiago, Javier, Sabriá, Fina, Martos, Carmen, Herzmann, Christian, Ruiz-Cabello, Jesús and Domínguez, José), In Scientific Reports, volume 10, 2020.
Bibtex Entry:
@article{izquierdo-garcia_discovery_2020,
	title = {Discovery and validation of an {NMR}-based metabolomic profile in urine as {TB} biomarker},
	volume = {10},
	issn = {2045-2322},
	url = {http://www.nature.com/articles/s41598-020-78999-4},
	doi = {10.1038/s41598-020-78999-4},
	abstract = {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.},
	language = {en},
	number = {1},
	urldate = {2020-12-19},
	journal = {Scientific Reports},
	author = {Izquierdo-Garcia, José Luis and Comella-del-Barrio, Patricia and Campos-Olivas, Ramón and Villar-Hernández, Raquel and Prat-Aymerich, Cristina and De Souza-Galvão, Maria Luiza and Jiménez-Fuentes, Maria Angeles and Ruiz-Manzano, Juan and Stojanovic, Zoran and González, Adela and Serra-Vidal, Mar and García-García, Esther and Muriel-Moreno, Beatriz and Millet, Joan Pau and Molina-Pinargote, Israel and Casas, Xavier and Santiago, Javier and Sabriá, Fina and Martos, Carmen and Herzmann, Christian and Ruiz-Cabello, Jesús and Domínguez, José},
	month = dec,
	year = {2020},
	pages = {22317},
	file = {Izquierdo-Garcia et al. - 2020 - Discovery and validation of an NMR-based metabolom.pdf:/Users/jruizcabello/Zotero/storage/Z8A3PUZ9/Izquierdo-Garcia et al. - 2020 - Discovery and validation of an NMR-based metabolom.pdf:application/pdf}
}