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.
- infectious diseases & infestations
- diagnostic microbiology
- public health
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Contributors BQ and JW conceived, initiated and led the study. BQ, QL, ZL, HS, DX, YeJ, YaJ and DT collected the data. BQ and QL analysed the data with input from all of the authors. BQ and JW prepared the manuscript. All authors reviewed and approved the manuscript.
Funding This study was supported by the National Natural Science Foundation of China (81973103), National Key R&D Program of China (2017YFC0907000), Qing Lan Project of Jiangsu Province (2019) and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The funding agencies had no role in the study design, data collection, analysis, decision to publish or preparation of the manuscript.
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval The ethical approval can be exempted as this is a systematic review and meta-analysis of data from already published studies.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information. All data generated or analysed during this study are included in this published article.
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