Evidence map›Paper›PMID 41258035›Full record

ArticleScientific reports2025

Benchtop NMR urine metabolomics for diagnosing pulmonary tuberculosis.

Patricia Comella-Del-Barrio, Ignacio Rodríguez, John S Bimba, Okoedoh Osazuwa, Ramota Alaran, Pilar Alonso-Moreno, Zuriñe Blasco-Iturri, Ana B Miguel-Coello, Luis E Cuevas, Jesús Ruiz-Cabello and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Patricia Comella-Del-Barrio *Institut d'Investigació Germans Trias i Pujol, 08916, Badalona, Barcelona, Spain.
Ignacio Rodríguez *Department of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040, Madrid, Spain.
John S BimbaZankli Research Centre and Department of Community Medicine, Bingham University, Karu, 961105, Nigeria.
Okoedoh OsazuwaZankli Research Centre and Department of Community Medicine, Bingham University, Karu, 961105, Nigeria.
Ramota AlaranCentre for Drugs and Diagnostics, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK.
Pilar Alonso-MorenoDepartment of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040, Madrid, Spain.
Zuriñe Blasco-IturriCIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029, Madrid, Spain.
Ana B Miguel-CoelloCIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029, Madrid, Spain.
Luis E CuevasCentre for Drugs and Diagnostics, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK.
Jesús Ruiz-CabelloDepartment of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040, Madrid, Spain.
José Luis Izquierdo-Garcia *Department of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040, Madrid, Spain. jlizquierdo@ucm.es.
José Domínguez *Institut d'Investigació Germans Trias i Pujol, 08916, Badalona, Barcelona, Spain. jadominguez@igtp.cat.

Funding

Basque Government Elkartek 2024 Program (bmG24)Comunidad de Madrid PIPF-2022/SAL-GL-24751Ministerio de Ciencia e Innovación FJC2021-047271-ISpanish Ministry of Science and Innovation PID2021-123238OB-I00
6 · The paper itself

Abstract

Tuberculosis (TB) remains a major global health issue, especially in resource-limited settings, where rapid and accurate diagnosis is critical yet often lacking. Nuclear magnetic resonance (NMR)-based metabolomics has emerged as a promising tool for identifying disease-specific biomarkers. This study assessed the diagnostic potential of urinary metabolomics using high-resolution (HR-)NMR and benchtop NMR (bNMR) in Nigerian adults with presumed pulmonary TB, including individuals with and without HIV. Urine samples were analysed by HR-NMR and bNMR. Multivariate analysis was used to develop classification models based on confirmed TB status. Key discriminant metabolites were identified. In HIV-negative individuals, HR-NMR achieved 84% sensitivity and 86% specificity, while bNMR reached 83% and 84%, respectively. In people living with HIV, HR-NMR reached 75% sensitivity and 70% specificity; bNMR achieved 90% and 46%. Both models met or exceeded WHO sensitivity criteria for non-sputum-based TB diagnostics. Thirteen discriminatory metabolites were identified, with eight consistent with previous studies. These findings suggest common metabolic signatures of TB, regardless of HIV status. Urinary NMR-based metabolomics shows strong potential for TB diagnosis in high TB/HIV burden settings. HR-NMR provides excellent accuracy, while bNMR offers a cost-effective alternative. Further studies are needed to validate and optimise this approach for clinical use.

Indexed as

MetabolomicsTuberculosis, PulmonaryAdultBiomarkersFemaleHIV InfectionsHumansMagnetic Resonance SpectroscopyMaleMetabolomeMiddle AgedSensitivity and SpecificityYoung AdultBiomarkers

Identifiers

PMID41258035
PMCPMC12630618

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.