Predicting tuberculosis treatment outcome using metabolomics.

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.

journal_name

Biomark Med

journal_title

Biomarkers in medicine

authors

Luies L,Reenen MV,Ronacher K,Walzl G,Loots DT

doi

10.2217/bmm-2017-0133

subject

Has Abstract

pub_date

2017-12-01 00:00:00

pages

1057-1067

issue

12

eissn

1752-0363

issn

1752-0371

journal_volume

11

pub_type

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