Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.
School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, China.
Thorax. 2024 Apr 15;79(5):465-471. doi: 10.1136/thorax-2023-220782.
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.
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.
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 infection among HIV-positive inpatients with negative IGRA.
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.
血清细胞因子与结核病（TB）的进展相关，并且是 HIV 感染者中 TB 复发的预测因子。我们研究了血清细胞因子生物标志物是否可用于诊断 HIV 阳性住院患者中的 TB。
我们招募了有 TB 症状的 HIV 阳性住院患者，并测量了包括白细胞介素 2（IL-2）、白细胞介素 4（IL-4）、白细胞介素 6（IL-6）、白细胞介素 10（IL-10）、肿瘤坏死因子-α（TNF-α）和干扰素-γ（IFN-γ）在内的炎症生物标志物的血清水平。然后，我们构建并测试了我们的 TB 预测模型。
第一队列纳入了 236 名 HIV 阳性住院患者，所有炎症生物标志物在微生物学确诊 TB 的参与者中均明显高于无 TB 的参与者。我们构建了一个二分类支持向量机（SVM）模型，纳入了四个生物标志物（IL-6、IL-10、TNF-α和 IFN-γ）的数据。在训练集（n=189）和验证集（n=47）中，SVM 模型的评估效能为曲线下面积（AUC）分别为 0.92（95%CI 0.88 至 0.96）和 0.85（95%CI 0.72 至 0.97）。在一个独立的测试集（n=110）中，SVM 模型的 AUC 为 0.85（95%CI 0.76 至 0.94），特异性为 78%（95%CI 68%至 87%），敏感性为 85%（95%CI 66%至 96%）。此外，SVM 模型在无论 CD4 T 细胞计数如何的晚期 HIV 阳性住院患者中均优于干扰素-γ释放试验（IGRA），这可能是一种替代方法，用于鉴定 IGRA 阴性的 HIV 阳性住院患者中的感染。
四种细胞因子生物标志物模型成功地在 HIV 阳性住院患者中鉴定出 TB。该诊断模型可能是一种替代方法，用于诊断 CD4 T 细胞计数较低的晚期 HIV 阳性住院患者中的 TB。
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