Laneke Luies1, Mari van Reenen1, Katharina Ronacher2,3, Gerhard Walzl2, Du Toit Loots1. 1. School for Physical & Chemical Sciences, Human Metabolomics, North-West University (Potchefstroom Campus), Private Bag x6001, Box 269, Potchefstroom 2531, South Africa. 2. Division of Molecular Biology & Human Genetics, Faculty of Medicine & Health Sciences, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/MRC Centre for Molecular & Cellular Biology, Stellenbosch University, Tygerberg 7505, South Africa. 3. Mater Medical Research Institute, The University of Queensland, Brisbane, Australia.
Abstract
AIM: Predicting a poor treatment outcome would offer significant benefits for patient care and for new drug development. Materials, methods & results: Urine samples from tuberculosis-positive patients with a successful and unsuccessful treatment outcome were collected at baseline and analyzed. The identified metabolites were used in a forward logistic regression model, which achieved a receiver operating characteristic area under the curve of 0.94 (95% CI: 0.84-1) and cross-validated well in a leave-one-out context, with an area under the curve of 0.89 (95% CI: 0.7-1). Two possible predictors were identified, which are associated with a gut microbiota imbalance. DISCUSSION & CONCLUSION: Our findings show the capacity of metabolomics to predict treatment failure at the time of diagnosis, which potentially offers significant benefits for the use in new drug development clinical trials and individualized patient care.
AIM: Predicting a poor treatment outcome would offer significant benefits for patient care and for new drug development. Materials, methods & results: Urine samples from tuberculosis-positivepatients with a successful and unsuccessful treatment outcome were collected at baseline and analyzed. The identified metabolites were used in a forward logistic regression model, which achieved a receiver operating characteristic area under the curve of 0.94 (95% CI: 0.84-1) and cross-validated well in a leave-one-out context, with an area under the curve of 0.89 (95% CI: 0.7-1). Two possible predictors were identified, which are associated with a gut microbiota imbalance. DISCUSSION & CONCLUSION: Our findings show the capacity of metabolomics to predict treatment failure at the time of diagnosis, which potentially offers significant benefits for the use in new drug development clinical trials and individualized patient care.
Entities:
Keywords:
M. tuberculosis; metabolomics; predicting treatment outcome; treatment failure; tuberculosis
Authors: Lauren S Peetluk; Felipe M Ridolfi; Peter F Rebeiro; Dandan Liu; Valeria C Rolla; Timothy R Sterling Journal: BMJ Open Date: 2021-03-02 Impact factor: 2.692