Preliminary Communication

School for Physical & Chemical Sciences, Human Metabolomics, North-West University (Potchefstroom Campus), Private Bag x6001, Box 269, Potchefstroom 2531, South Africa

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Katharina Ronacher

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

Mater Medical Research Institute, The University of Queensland, Brisbane, Australia

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Gerhard Walzl

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Du Toit Loots

*Author for correspondence: Tel.: +27 18 299 1818; Fax: +27 18 299 1823;

E-mail Address: dutoit.loots@nwu.ac.za

School for Physical & Chemical Sciences, Human Metabolomics, North-West University (Potchefstroom Campus), Private Bag x6001, Box 269, Potchefstroom 2531, South Africa

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Published Online:27 Nov 2017https://doi.org/10.2217/bmm-2017-0133

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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.

Keywords:

Papers of special note have been highlighted as: • of interest; •• of considerable interest

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Vol. 11, No. 12

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History

Received 21 April 2017

Accepted 6 September 2017

Published online 27 November 2017

Published in print December 2017

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Keywords

Author contributions

DT Loots conceptualized the study design; G Walzl and K Ronacher performed patient classification into treatment response groups for analysis, and provided all urine samples. L Luies performed the GC data acquisition/analysis and MV Reenen performed the statistical analysis. L Luies, DT Loots and MV Reenen interpreted the data and drafted the article. All authors approved the final version to be submitted.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

Ethical approval for this investigation, conducted according to the Declaration of Helsinki and International Conference on Harmonization Guidelines, was obtained from the Ethics Committee of the North-West University, South Africa (reference number NWU-00127–11-A1), as well as from Stellenbosch University Health Research Ethics Committee (reference number 99/039) and Cape Town City Health. All participants gave written informed consent for study participation and HIV testing.

Declaration of interest

The authors declare that there are no conflicts of interest, and that this manuscript, and the work described therein, is unpublished and has not been submitted for publication elsewhere.

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Predicting tuberculosis treatment outcome using metabolomics

Abstract

References