Rupak Shivakoti1,2, John W Newman3,4,5, Luke Elizabeth Hanna6, Artur T L Queiroz7,8, Kamil Borkowski5, Akshay N Gupte9, Mandar Paradkar10, Pattabiraman Satyamurthi6, Vandana Kulkarni10, Murugesh Selva6, Neeta Pradhan10, Shri Vijay Bala Yogendra Shivakumar11, Saravanan Natarajan6, Ramesh Karunaianantham6, Nikhil Gupte9,10, Kannan Thiruvengadam6, Oliver Fiehn5, Renu Bharadwaj12, Anju Kagal12, Sanjay Gaikwad12, Shashikala Sangle12, Jonathan E Golub9, Bruno B Andrade7,8,13,14,15,16, Vidya Mave9,10, Amita Gupta9,10,17, Chandrasekaran Padmapriyadarsini6,17. 1. Dept of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA rs3895@cumc.columbia.edu. 2. Dept of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA. 3. Obesity and Metabolism Research Unit, Western Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, Davis, CA, USA. 4. Dept of Nutrition, University of California, Davis, CA, USA. 5. West Coast Metabolomics Center, University of California, Davis, CA, USA. 6. National Institute for Research in Tuberculosis, Chennai, India. 7. Instituto Goncalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil. 8. Multinational Organization Network Sponsoring Translational and Epidemiological Research, Salvador, Brazil. 9. Dept of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 10. Byramjee-Jeejeebhoy Medical College-Johns Hopkins University Clinical Research Site, Pune, India. 11. Johns Hopkins University, India office (Center for Clinical Global Health Education), Pune, India. 12. Byramjee-Jeejeebhoy Government Medical College, Pune, India. 13. Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Brazil. 14. Curso de Medicina, Faculdade de Tecnologia e Ciências, Salvador, Brazil. 15. Curso de Medicina, Universidade Salvador (UNIFACS), Laureate International Universities, Salvador, Brazil. 16. Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador, Brazil. 17. Equal contribution.
Abstract
INTRODUCTION: Host lipids play important roles in tuberculosis (TB) pathogenesis. Whether host lipids at TB treatment initiation (baseline) affect subsequent treatment outcomes has not been well characterised. We used unbiased lipidomics to study the prospective association of host lipids with TB treatment failure. METHODS: A case-control study (n=192), nested within a prospective cohort study, was used to investigate the association of baseline plasma lipids with TB treatment failure among adults with pulmonary TB. Cases (n=46) were defined as TB treatment failure, while controls (n=146) were those without failure. Complex lipids and inflammatory lipid mediators were measured using liquid chromatography mass spectrometry techniques. Adjusted least-square regression was used to assess differences in groups. In addition, machine learning identified lipids with highest area under the curve (AUC) to classify cases and controls. RESULTS: Baseline levels of 32 lipids differed between controls and those with treatment failure after false discovery rate adjustment. Treatment failure was associated with lower baseline levels of cholesteryl esters and oxylipin, and higher baseline levels of ceramides and triglycerides compared to controls. Two cholesteryl ester lipids combined in a unique classifier model provided an AUC of 0.79 (95% CI 0.65-0.93) in the test dataset for prediction of TB treatment failure. CONCLUSIONS: We identified lipids, some with known roles in TB pathogenesis, associated with TB treatment failure. In addition, a lipid signature with prognostic accuracy for TB treatment failure was identified. These lipids could be potential targets for risk-stratification, adjunct therapy and treatment monitoring.
INTRODUCTION: Host lipids play important roles in tuberculosis (TB) pathogenesis. Whether host lipids at TB treatment initiation (baseline) affect subsequent treatment outcomes has not been well characterised. We used unbiased lipidomics to study the prospective association of host lipids with TB treatment failure. METHODS: A case-control study (n=192), nested within a prospective cohort study, was used to investigate the association of baseline plasma lipids with TB treatment failure among adults with pulmonary TB. Cases (n=46) were defined as TB treatment failure, while controls (n=146) were those without failure. Complex lipids and inflammatory lipid mediators were measured using liquid chromatography mass spectrometry techniques. Adjusted least-square regression was used to assess differences in groups. In addition, machine learning identified lipids with highest area under the curve (AUC) to classify cases and controls. RESULTS: Baseline levels of 32 lipids differed between controls and those with treatment failure after false discovery rate adjustment. Treatment failure was associated with lower baseline levels of cholesteryl esters and oxylipin, and higher baseline levels of ceramides and triglycerides compared to controls. Two cholesteryl ester lipids combined in a unique classifier model provided an AUC of 0.79 (95% CI 0.65-0.93) in the test dataset for prediction of TB treatment failure. CONCLUSIONS: We identified lipids, some with known roles in TB pathogenesis, associated with TB treatment failure. In addition, a lipid signature with prognostic accuracy for TB treatment failure was identified. These lipids could be potential targets for risk-stratification, adjunct therapy and treatment monitoring.
Authors: Fantahun Biadglegne; Johannes R Schmidt; Kathrin M Engel; Jörg Lehmann; Robert T Lehmann; Anja Reinert; Brigitte König; Jürgen Schiller; Stefan Kalkhof; Ulrich Sack Journal: Biomedicines Date: 2022-03-27