Plasma biomarkers to detect prevalent or predict progressive tuberculosis associated with Human Immunodeficiency Virus

Plasma biomarkers to detect prevalent or predict progressive tuberculosis associated with Human Immunodeficiency Virus–1. Lesosky, M., Rangaka, M. X, Pienaar, C., Coussens, A. K, Goliath, R., Mathee, S., Mwansa-Kambafwile, J., Maartens, G., Wilkinson, R. J, & Wilkinson, K. A Clinical Infectious Diseases, 69(2):295–305, sep, 2019.

Paper doi abstract bibtex

Background The risk of HIV-1 infected individuals developing TB is high while both prognostic and diagnostic tools remain insensitive. The predictive performance of plasma biomarkers to identify HIV-1 infected individuals likely to progress to active disease is unknown. Methods Thirteen preselected analytes were determined from QuantiFERON® Gold in-tube (QFT) plasma samples in 421 HIV-1 infected persons recruited within the screening and enrolment phases of a randomised controlled trial of isoniazid preventive therapy. Blood for QFT was obtained pre-randomisation. Individuals were classified into prevalent TB, incident TB and controls. Comparisons between groups, supervised learning methods and weighted correlation network analyses were applied utilising the unstimulated and background-corrected plasma analyte concentrations. Results Unstimulated samples showed higher analyte concentrations in prevalent and incident TB compared to controls. The largest differences were seen for CXCL10, IL-2, IL-1 and TGF-. Predictive model analysis using unstimulated analytes discriminated better between controls and prevalent TB (Area Under the Curve AUC= 0˙9), reasonably between incident and prevalent TB (AUC \textgreater 0˙8), but poorly between controls and incident TB. Unstimulated IL-2 and IFN-$γ$ were ranked at or near the top for all comparisons except the comparison between controls vs incident TB. Models using background adjusted values performed poorly. Conclusions Single plasma biomarkers are unlikely to distinguish between disease states in HIV-1 co-infected individuals and combinations of biomarkers are required. The ability to detect prevalent TB is potentially important, as no blood test hitherto has suggested utility to detect prevalent TB amongst HIV-1 co-infected persons.

@article{Lesosky2018,
abstract = {Background The risk of HIV-1 infected individuals developing TB is high while both prognostic and diagnostic tools remain insensitive. The predictive performance of plasma biomarkers to identify HIV-1 infected individuals likely to progress to active disease is unknown. Methods Thirteen preselected analytes were determined from QuantiFERON{\textregistered} Gold in-tube (QFT) plasma samples in 421 HIV-1 infected persons recruited within the screening and enrolment phases of a randomised controlled trial of isoniazid preventive therapy. Blood for QFT was obtained pre-randomisation. Individuals were classified into prevalent TB, incident TB and controls. Comparisons between groups, supervised learning methods and weighted correlation network analyses were applied utilising the unstimulated and background-corrected plasma analyte concentrations. Results Unstimulated samples showed higher analyte concentrations in prevalent and incident TB compared to controls. The largest differences were seen for CXCL10, IL-2, IL-1 and TGF-. Predictive model analysis using unstimulated analytes discriminated better between controls and prevalent TB (Area Under the Curve AUC= 0{\textperiodcentered}9), reasonably between incident and prevalent TB (AUC {\textgreater} 0{\textperiodcentered}8), but poorly between controls and incident TB. Unstimulated IL-2 and IFN-$\gamma$ were ranked at or near the top for all comparisons except the comparison between controls vs incident TB. Models using background adjusted values performed poorly. Conclusions Single plasma biomarkers are unlikely to distinguish between disease states in HIV-1 co-infected individuals and combinations of biomarkers are required. The ability to detect prevalent TB is potentially important, as no blood test hitherto has suggested utility to detect prevalent TB amongst HIV-1 co-infected persons.},
author = {Lesosky, Maia and Rangaka, Molebogeng X and Pienaar, Cara and Coussens, Anna K and Goliath, Ren{\'{e}} and Mathee, Shaheed and Mwansa-Kambafwile, Judith and Maartens, Gary and Wilkinson, Robert J and Wilkinson, Katalin A},
doi = {10.1093/cid/ciy823},
isbn = {1537-6591 1058-4838},
issn = {1058-4838},
journal = {Clinical Infectious Diseases},
keywords = {OA,fund{\_}ack,original},
mendeley-tags = {OA,fund{\_}ack,original},
month = {sep},
number = {2},
pages = {295--305},
pmid = {30256919},
title = {{Plasma biomarkers to detect prevalent or predict progressive tuberculosis associated with Human Immunodeficiency Virus–1}},
url = {https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciy823/5106993},
volume = {69},
year = {2019}
}

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Blood for QFT was obtained pre-randomisation. Individuals were classified into prevalent TB, incident TB and controls. Comparisons between groups, supervised learning methods and weighted correlation network analyses were applied utilising the unstimulated and background-corrected plasma analyte concentrations. Results Unstimulated samples showed higher analyte concentrations in prevalent and incident TB compared to controls. The largest differences were seen for CXCL10, IL-2, IL-1 and TGF-. Predictive model analysis using unstimulated analytes discriminated better between controls and prevalent TB (Area Under the Curve AUC= 0˙9), reasonably between incident and prevalent TB (AUC \\textgreater 0˙8), but poorly between controls and incident TB. Unstimulated IL-2 and IFN-$γ$ were ranked at or near the top for all comparisons except the comparison between controls vs incident TB. Models using background adjusted values performed poorly. Conclusions Single plasma biomarkers are unlikely to distinguish between disease states in HIV-1 co-infected individuals and combinations of biomarkers are required. The ability to detect prevalent TB is potentially important, as no blood test hitherto has suggested utility to detect prevalent TB amongst HIV-1 co-infected persons.","author":[{"propositions":[],"lastnames":["Lesosky"],"firstnames":["Maia"],"suffixes":[]},{"propositions":[],"lastnames":["Rangaka"],"firstnames":["Molebogeng","X"],"suffixes":[]},{"propositions":[],"lastnames":["Pienaar"],"firstnames":["Cara"],"suffixes":[]},{"propositions":[],"lastnames":["Coussens"],"firstnames":["Anna","K"],"suffixes":[]},{"propositions":[],"lastnames":["Goliath"],"firstnames":["René"],"suffixes":[]},{"propositions":[],"lastnames":["Mathee"],"firstnames":["Shaheed"],"suffixes":[]},{"propositions":[],"lastnames":["Mwansa-Kambafwile"],"firstnames":["Judith"],"suffixes":[]},{"propositions":[],"lastnames":["Maartens"],"firstnames":["Gary"],"suffixes":[]},{"propositions":[],"lastnames":["Wilkinson"],"firstnames":["Robert","J"],"suffixes":[]},{"propositions":[],"lastnames":["Wilkinson"],"firstnames":["Katalin","A"],"suffixes":[]}],"doi":"10.1093/cid/ciy823","isbn":"1537-6591 1058-4838","issn":"1058-4838","journal":"Clinical Infectious Diseases","keywords":"OA,fund_ack,original","mendeley-tags":"OA,fund_ack,original","month":"sep","number":"2","pages":"295–305","pmid":"30256919","title":"Plasma biomarkers to detect prevalent or predict progressive tuberculosis associated with Human Immunodeficiency Virus–1","url":"https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciy823/5106993","volume":"69","year":"2019","bibtex":"@article{Lesosky2018,\r\nabstract = {Background The risk of HIV-1 infected individuals developing TB is high while both prognostic and diagnostic tools remain insensitive. The predictive performance of plasma biomarkers to identify HIV-1 infected individuals likely to progress to active disease is unknown. Methods Thirteen preselected analytes were determined from QuantiFERON{\\textregistered} Gold in-tube (QFT) plasma samples in 421 HIV-1 infected persons recruited within the screening and enrolment phases of a randomised controlled trial of isoniazid preventive therapy. Blood for QFT was obtained pre-randomisation. Individuals were classified into prevalent TB, incident TB and controls. Comparisons between groups, supervised learning methods and weighted correlation network analyses were applied utilising the unstimulated and background-corrected plasma analyte concentrations. Results Unstimulated samples showed higher analyte concentrations in prevalent and incident TB compared to controls. The largest differences were seen for CXCL10, IL-2, IL-1 and TGF-. Predictive model analysis using unstimulated analytes discriminated better between controls and prevalent TB (Area Under the Curve AUC= 0{\\textperiodcentered}9), reasonably between incident and prevalent TB (AUC {\\textgreater} 0{\\textperiodcentered}8), but poorly between controls and incident TB. Unstimulated IL-2 and IFN-$\\gamma$ were ranked at or near the top for all comparisons except the comparison between controls vs incident TB. Models using background adjusted values performed poorly. Conclusions Single plasma biomarkers are unlikely to distinguish between disease states in HIV-1 co-infected individuals and combinations of biomarkers are required. The ability to detect prevalent TB is potentially important, as no blood test hitherto has suggested utility to detect prevalent TB amongst HIV-1 co-infected persons.},\r\nauthor = {Lesosky, Maia and Rangaka, Molebogeng X and Pienaar, Cara and Coussens, Anna K and Goliath, Ren{\\'{e}} and Mathee, Shaheed and Mwansa-Kambafwile, Judith and Maartens, Gary and Wilkinson, Robert J and Wilkinson, Katalin A},\r\ndoi = {10.1093/cid/ciy823},\r\nisbn = {1537-6591 1058-4838},\r\nissn = {1058-4838},\r\njournal = {Clinical Infectious Diseases},\r\nkeywords = {OA,fund{\\_}ack,original},\r\nmendeley-tags = {OA,fund{\\_}ack,original},\r\nmonth = {sep},\r\nnumber = {2},\r\npages = {295--305},\r\npmid = {30256919},\r\ntitle = {{Plasma biomarkers to detect prevalent or predict progressive tuberculosis associated with Human Immunodeficiency Virus–1}},\r\nurl = {https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciy823/5106993},\r\nvolume = {69},\r\nyear = {2019}\r\n}\r\n","author_short":["Lesosky, M.","Rangaka, M. 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