Background Serum cytokines correlate with tuberculosis (TB) progression and are predictors of TB recurrence in people living with HIV. We investigated whether serum cytokine biosignatures could diagnose TB among HIV-positive inpatients. Methods We recruited HIV-positive inpatients with symptoms of TB and measured serum levels of inflammation biomarkers including IL-2, IL-4, IL-6, IL-10, tumour necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ). We then built and tested our TB prediction model. Results 236 HIV-positive inpatients were enrolled in the first cohort and all the inflammation biomarkers were significantly higher in participants with microbiologically confirmed TB than those without TB. A binary support vector machine (SVM) model was built, incorporating the data of four biomarkers (IL-6, IL-10, TNF-α and IFN-γ). Efficacy of the SVM model was assessed in training (n=189) and validation (n=47) sets with area under the curve (AUC) of 0.92 (95% CI 0.88 to 0.96) and 0.85 (95% CI 0.72 to 0.97), respectively. In an independent test set (n=110), the SVM model yielded an AUC of 0.85 (95% CI 0.76 to 0.94) with 78% (95% CI 68% to 87%) specificity and 85% (95% CI 66% to 96%) sensitivity. Moreover, the SVM model outperformed interferon-gamma release assay (IGRA) among advanced HIV-positive inpatients irrespective of CD4+ T-cell counts, which may be an alternative approach for identifying Mycobacterium tuberculosis infection among HIV-positive inpatients with negative IGRA. Conclusions The four-cytokine biosignature model successfully identified TB among HIV-positive inpatients. This diagnostic model may be an alternative approach to diagnose TB in advanced HIV-positive inpatients with low CD4+ T-cell counts. Data are available on reasonable request.

中文翻译：

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血清细胞因子生物特征用于识别 HIV 阳性住院患者中的结核病

背景 血清细胞因子与结核病 (TB) 进展相关，并且是 HIV 感染者结核病复发的预测因子。我们研究了血清细胞因子生物特征是否可以诊断 HIV 阳性住院患者的结核病。方法 我们招募了有结核病症状的 HIV 阳性住院患者，并测量了炎症生物标志物的血清水平，包括 IL-2、IL-4、IL-6、IL-10、肿瘤坏死因子-α (TNF-α) 和干扰素-γ。干扰素-γ）。然后我们构建并测试了结核病预测模型。结果 第一个队列纳入了 236 名 HIV 阳性住院患者，经微生物学确诊的结核病患者的所有炎症生物标志物均显着高于未患结核病的患者。建立了二元支持向量机（SVM）模型，结合了四种生物标志物（IL-6、IL-10、TNF-α和IFN-γ）的数据。 SVM 模型的功效在训练组 (n=189) 和验证组 (n=47) 中进行评估，曲线下面积 (AUC) 分别为 0.92（95% CI 0.88 至 0.96）和 0.85（95% CI 0.72 至 0.97） ， 分别。在独立测试集 (n=110) 中，SVM 模型产生的 AUC 为 0.85（95% CI 0.76 至 0.94），特异性为 78%（95% CI 68% 至 87%），特异性为 85%（95% CI 66%）。至 96%）的灵敏度。此外，无论 CD4+ T 细胞计数如何，SVM 模型在晚期 HIV 阳性住院患者中均优于干扰素γ释放测定（IGRA），这可能是在 IGRA 阴性的 HIV 阳性住院患者中识别结核分枝杆菌感染的替代方法。结论 四细胞因子生物印记模型成功地识别了 HIV 阳性住院患者中的结核病。该诊断模型可能是诊断 CD4+ T 细胞计数低的晚期 HIV 阳性住院患者结核病的替代方法。可根据合理要求提供数据。