The meta-analysis for ideal cytokines to distinguish the latent and active TB infection

BACKGROUND: One forth whole-world population is infected with Mycobacterium tuberculosis (Mtb), but 90% of them are asymptotic latent infection without any symptoms but positive result in IFN-γ release assay. There is lack of ideal strategy to distinguish active tuberculosis (TB) and latent tuberculosis infection (LTBI). Some scientist had focused on a set of cytokines as biomarkers besides interferon- gamma (IFN-γ) to distinguish active TB and LTBI, but with considerable variance of results. This meta-analysis aimed to evaluate the overall discriminative ability of potential immune molecules to distinguish active TB and LTBI. METHODS: PubMed, the Cochrane Library, and Web of Science databases were searched to identify studies assessing diagnostic roles of cytokines for distinguishing active TB and LTBI published up to August 2018. The quality of enrolled studies was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled diagnostic sensitivity and specificity of each cytokine was calculated by using Meta-DiSc software. Area under the summary receiver operating characteristic curve (AUC) was used to summarize the overall diagnostic performance of each biomarker. RESULTS: Fourteen studies with 982 subjects met the inclusion criteria, including 526 active TB and 456 LTBI patients. Pooled sensitivity, specificity and AUC for discriminating between active TB and LTBI were analyzed for IL-2 (0.87, 0.61 and 0.9093), IP-10 (0.77, 0.73 and 0.8609), IL-5 (0.64, 0.75 and 0.8533), IL-13 (0.75, 0.71 and 0.8491), IFN-γ (0.67, 0.75 and 0.8031), IL-10 (0.68, 0.74 and 0.7957) and TNF-α (0.67, 0.64 and 0.7783). The heterogeneous subgroup analysis showed that cytokine detection assays, TB incidence, and stimulator with Mtb antigens are main influence factors for their diagnostic performance. CONCLUSIONS: The meta-analysis showed cytokine production could assist the distinction between active TB and LTBI, IL-2 with the highest overall accuracy. No single biomarker is likely to show sufficiently diagnostic performance due to limited sensitivity and specificity. Further prospective studies are needed to identify the optimal combination of biomarkers to enhanced diagnostic capacity in clinical practice.