A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children
Background: Our study aimed to identify a host cytokine biosignature that could distinguish childhood tuberculosis (TB) from other respiratory diseases (OD). Methods: Cytokine responses in prospectively recruited children with symptoms suggestive of TB were measured in whole blood assay supernatants...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-08-01
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| Series: | EBioMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235239642030284X |
| _version_ | 1811222746531627008 |
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| author | Toyin Togun Clive J. Hoggart Schadrac C. Agbla Marie P. Gomez Uzochukwu Egere Abdou K. Sillah Binta Saidy Francis Mendy Madhukar Pai Beate Kampmann |
| author_facet | Toyin Togun Clive J. Hoggart Schadrac C. Agbla Marie P. Gomez Uzochukwu Egere Abdou K. Sillah Binta Saidy Francis Mendy Madhukar Pai Beate Kampmann |
| author_sort | Toyin Togun |
| collection | DOAJ |
| description | Background: Our study aimed to identify a host cytokine biosignature that could distinguish childhood tuberculosis (TB) from other respiratory diseases (OD). Methods: Cytokine responses in prospectively recruited children with symptoms suggestive of TB were measured in whole blood assay supernatants, harvested after overnight incubation, using a Luminex platform. We used logistic regression models with Least Absolute Shrinkage and Selection Operator (LASSO) penalty to identify the optimal biosignature associated with confirmed TB disease in the training set. We subsequently assessed its performance in the test set. Findings: Of the 431 children included in the study, 44 had bacteriologically confirmed TB, 60 had clinically diagnosed TB while 327 had OD. All children were HIV-negative. Application of LASSO regression models to the training set (n = 260) resulted in the combination of IL-1ra, IL-7 and IP-10 from unstimulated samples as the optimally discriminant cytokine biosignature associated with bacteriologically confirmed TB. In the test set (n = 171), this biosignature distinguished children diagnosed with TB disease, irrespective of microbiological confirmation, from OD with area under the receiver operator characteristic curve (AUC) of 0•74 (95% CI: 0•67, 0•81), and demonstrated sensitivity and specificity of 72•2% (95% CI: 60•4, 82•1%) and 75•0% (95% CI: 64•9, 83•4%) respectively, with its performance independent of their age group and their age- and sex-adjusted nutritional status. Interpretation: This novel biosignature of childhood TB derived from unstimulated supernatants is promising. Independent validation with further optimisation will improve its performance and translational potential. Funding: Steinberg Fellowship (McGill University); Grand Challenges Canada; MRC Program Grant. |
| first_indexed | 2024-04-12T08:20:09Z |
| format | Article |
| id | doaj.art-ba572dcb42c44838bca87d386c5d7005 |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-04-12T08:20:09Z |
| publishDate | 2020-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-ba572dcb42c44838bca87d386c5d70052022-12-22T03:40:36ZengElsevierEBioMedicine2352-39642020-08-0158102909A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian childrenToyin Togun0Clive J. Hoggart1Schadrac C. Agbla2Marie P. Gomez3Uzochukwu Egere4Abdou K. Sillah5Binta Saidy6Francis Mendy7Madhukar Pai8Beate Kampmann9Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia; The Vaccine Centre, and Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom; Corresponding author.Section of Paediatrics, Imperial College London, St Mary's Campus, London, United Kingdom; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USADepartment of Health Data Sciences, University of Liverpool, Liverpool, United Kingdom; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, United KingdomVaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The GambiaCommunity Health Systems Group, Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, United KingdomVaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The GambiaVaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The GambiaVaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The GambiaMcGill International TB Centre, and Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Manipal-McGill Centre for Infectious Diseases, Manipal University, Manipal, IndiaVaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia; The Vaccine Centre, and Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom; Corresponding author.Background: Our study aimed to identify a host cytokine biosignature that could distinguish childhood tuberculosis (TB) from other respiratory diseases (OD). Methods: Cytokine responses in prospectively recruited children with symptoms suggestive of TB were measured in whole blood assay supernatants, harvested after overnight incubation, using a Luminex platform. We used logistic regression models with Least Absolute Shrinkage and Selection Operator (LASSO) penalty to identify the optimal biosignature associated with confirmed TB disease in the training set. We subsequently assessed its performance in the test set. Findings: Of the 431 children included in the study, 44 had bacteriologically confirmed TB, 60 had clinically diagnosed TB while 327 had OD. All children were HIV-negative. Application of LASSO regression models to the training set (n = 260) resulted in the combination of IL-1ra, IL-7 and IP-10 from unstimulated samples as the optimally discriminant cytokine biosignature associated with bacteriologically confirmed TB. In the test set (n = 171), this biosignature distinguished children diagnosed with TB disease, irrespective of microbiological confirmation, from OD with area under the receiver operator characteristic curve (AUC) of 0•74 (95% CI: 0•67, 0•81), and demonstrated sensitivity and specificity of 72•2% (95% CI: 60•4, 82•1%) and 75•0% (95% CI: 64•9, 83•4%) respectively, with its performance independent of their age group and their age- and sex-adjusted nutritional status. Interpretation: This novel biosignature of childhood TB derived from unstimulated supernatants is promising. Independent validation with further optimisation will improve its performance and translational potential. Funding: Steinberg Fellowship (McGill University); Grand Challenges Canada; MRC Program Grant.http://www.sciencedirect.com/science/article/pii/S235239642030284XChildrenTuberculosisCytokineBiosignatureDiagnosis |
| spellingShingle | Toyin Togun Clive J. Hoggart Schadrac C. Agbla Marie P. Gomez Uzochukwu Egere Abdou K. Sillah Binta Saidy Francis Mendy Madhukar Pai Beate Kampmann A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children EBioMedicine Children Tuberculosis Cytokine Biosignature Diagnosis |
| title | A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children |
| title_full | A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children |
| title_fullStr | A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children |
| title_full_unstemmed | A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children |
| title_short | A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children |
| title_sort | three marker protein biosignature distinguishes tuberculosis from other respiratory diseases in gambian children |
| topic | Children Tuberculosis Cytokine Biosignature Diagnosis |
| url | http://www.sciencedirect.com/science/article/pii/S235239642030284X |
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