ArticleResearch square2025
Multicohort assessment of plasma metabolic signatures of tuberculosis disease in children.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Current microbiological tests for tuberculosis (TB) disease in children have suboptimal accuracy and rely on respiratory samples which may be challenging to obtain. We sought to use high-resolution metabolomics (HRM) to identify blood-based biomarkers associated with TB disease in children. We analyzed plasma samples from 438 children 0-14 years being evaluated for TB disease in India, Peru, Uganda, The Gambia, and South Africa. All children underwent a standard clinical evaluation and were followed up after 3 months. Children were classified as Confirmed (n = 104), Unconfirmed (n = 108), or Unlikely TB (n = 226) as per NIH consensus definitions. We used liquid chromatography/mass spectrometry for HRM analysis of plasma samples. Differentially regulated metabolic pathways in children with confirmed versus unlikely TB in at least three of the five countries included purine, linoleate, arginine and proline, aspartate and asparagine, and tryptophan metabolism. Controlling for age and study site, we found creatine, alanine, retinol, citrulline, fumarate, and tryptophan to be significantly decreased in children with Confirmed TB disease versus those with Unlikely TB, while cortisol, nicotinamide, and butyrylcarnitine were increased (FDR-corrected p-value < 0.2). Using logistic regression, we found this nine-metabolite signature had an area under the receiver operator characteristic curve (AUC) of 0.72 in the test set of participants with Confirmed and Unlikely TB and an AUC of 0.49 in the Unconfirmed TB group. Of the five cohorts examined, the model performed best among Indian children with Confirmed TB (AUC = 0.84). These results show a nine-metabolite plasma signature has moderate accuracy for identification of Confirmed TB disease in children and could potentially be combined with other non-sputum biomarkers to inform future TB diagnostics.
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