Plasma proteomics for biomarker discovery in childhood tuberculosis.

Abstract

Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, thus a global priority is the identification of novel non-sputum-based TB biomarkers. Here we use high-throughput proteomics to measure the plasma proteome for 511 children, with and without HIV, and across 4 countries, to distinguish TB status using standardized definitions. By employing a machine learning approach, we derive four parsimonious biosignatures encompassing 3 to 6 proteins that achieve AUCs of 0.87-0.88 and which all reach the minimum WHO target product profile accuracy thresholds for a TB screening test. This work provides insights into the unique host response in pediatric TB disease, as well as a non-sputum biosignature that could reduce delays in TB diagnosis and improve the detection and management of TB in children worldwide.