Cargando…
Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis
OBJECTIVES: With a marginally effective vaccine and no significant breakthroughs in new treatments, a sensitive and specific method to distinguish active tuberculosis from latent tuberculosis infection (LTBI) would allow for early diagnosis and limit the spread of the pathogen. The analysis of multi...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542925/ https://www.ncbi.nlm.nih.gov/pubmed/33033030 http://dx.doi.org/10.1136/bmjopen-2020-039501 |
_version_ | 1783591634770853888 |
---|---|
author | Qiu, Beibei Liu, Qiao Li, Zhongqi Song, Huan Xu, Dian Ji, Ye Jiang, Yan Tian, Dan Wang, Jianming |
author_facet | Qiu, Beibei Liu, Qiao Li, Zhongqi Song, Huan Xu, Dian Ji, Ye Jiang, Yan Tian, Dan Wang, Jianming |
author_sort | Qiu, Beibei |
collection | PubMed |
description | OBJECTIVES: With a marginally effective vaccine and no significant breakthroughs in new treatments, a sensitive and specific method to distinguish active tuberculosis from latent tuberculosis infection (LTBI) would allow for early diagnosis and limit the spread of the pathogen. The analysis of multiple cytokine profiles provides the possibility to differentiate the two diseases. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Cochrane Library, Clinical Key and EMBASE databases were searched on 31 December 2019. ELIGIBILITY CRITERIA: We included case–control studies, cohort studies and randomised controlled trials considering IFN-γ, TNF-α, IP-10, IL-2, IL-10, IL-12 and VEGF as biomarkers to distinguish active tuberculosis and LTBI. DATA EXTRACTION AND SYNTHESIS: Two students independently extracted data and assessed the risk of bias. Diagnostic OR, sensitivity, specificity, positive and negative likelihood ratios and area under the curve (AUC) together with 95% CI were used to estimate the diagnostic value. RESULTS: Of 1315 records identified, 14 studies were considered eligible. IL-2 had the highest sensitivity (0.84, 95% CI: 0.72 to 0.92), while VEGF had the highest specificity (0.87, 95% CI: 0.73 to 0.94). The highest AUC was observed for VEGF (0.85, 95% CI: 0.81 to 0.88), followed by IFN-γ (0.84, 95% CI: 0.80 to 0.87) and IL-2 (0.84, 95% CI: 0.81 to 0.87). CONCLUSION: Cytokines, such as IL-2, IFN-γ and VEGF, can be utilised as promising biomarkers to distinguish active tuberculosis from LTBI. PROSPERO REGISTRATION NUMBER: CRD42020170725. |
format | Online Article Text |
id | pubmed-7542925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-75429252020-10-19 Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis Qiu, Beibei Liu, Qiao Li, Zhongqi Song, Huan Xu, Dian Ji, Ye Jiang, Yan Tian, Dan Wang, Jianming BMJ Open Epidemiology OBJECTIVES: With a marginally effective vaccine and no significant breakthroughs in new treatments, a sensitive and specific method to distinguish active tuberculosis from latent tuberculosis infection (LTBI) would allow for early diagnosis and limit the spread of the pathogen. The analysis of multiple cytokine profiles provides the possibility to differentiate the two diseases. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Cochrane Library, Clinical Key and EMBASE databases were searched on 31 December 2019. ELIGIBILITY CRITERIA: We included case–control studies, cohort studies and randomised controlled trials considering IFN-γ, TNF-α, IP-10, IL-2, IL-10, IL-12 and VEGF as biomarkers to distinguish active tuberculosis and LTBI. DATA EXTRACTION AND SYNTHESIS: Two students independently extracted data and assessed the risk of bias. Diagnostic OR, sensitivity, specificity, positive and negative likelihood ratios and area under the curve (AUC) together with 95% CI were used to estimate the diagnostic value. RESULTS: Of 1315 records identified, 14 studies were considered eligible. IL-2 had the highest sensitivity (0.84, 95% CI: 0.72 to 0.92), while VEGF had the highest specificity (0.87, 95% CI: 0.73 to 0.94). The highest AUC was observed for VEGF (0.85, 95% CI: 0.81 to 0.88), followed by IFN-γ (0.84, 95% CI: 0.80 to 0.87) and IL-2 (0.84, 95% CI: 0.81 to 0.87). CONCLUSION: Cytokines, such as IL-2, IFN-γ and VEGF, can be utilised as promising biomarkers to distinguish active tuberculosis from LTBI. PROSPERO REGISTRATION NUMBER: CRD42020170725. BMJ Publishing Group 2020-10-07 /pmc/articles/PMC7542925/ /pubmed/33033030 http://dx.doi.org/10.1136/bmjopen-2020-039501 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Epidemiology Qiu, Beibei Liu, Qiao Li, Zhongqi Song, Huan Xu, Dian Ji, Ye Jiang, Yan Tian, Dan Wang, Jianming Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
title | Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
title_full | Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
title_fullStr | Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
title_full_unstemmed | Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
title_short | Evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
title_sort | evaluation of cytokines as a biomarker to distinguish active tuberculosis from latent tuberculosis infection: a diagnostic meta-analysis |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542925/ https://www.ncbi.nlm.nih.gov/pubmed/33033030 http://dx.doi.org/10.1136/bmjopen-2020-039501 |
work_keys_str_mv | AT qiubeibei evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT liuqiao evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT lizhongqi evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT songhuan evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT xudian evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT jiye evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT jiangyan evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT tiandan evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis AT wangjianming evaluationofcytokinesasabiomarkertodistinguishactivetuberculosisfromlatenttuberculosisinfectionadiagnosticmetaanalysis |