Biomarkers of iron metabolism facilitate clinical diagnosis in Mycobacterium tuberculosis infection
Biomarkers of iron metabolism facilitate clinical diagnosis in Mycobacterium tuberculosis infection
复制标题
铁代谢的生物标志物有助于结核分枝杆菌感染的临床诊断
DOI：
10.1136/thoraxjnl-2018-212557
复制
发表时间：
2019-12-01
期刊：
影响因子：
10
通讯作者：
Chen, Xinchun
中科院分区：
文献类型：
--
作者：
Dai, Youchao;Shan, Wanshui;Chen, Xinchun
背景铁稳态紊乱是结核病进展的危险因素，也是结核病治疗失败和死亡的指标。很少有研究评价铁稳态作为结核病诊断生物标志物。方法我们招募了TB、潜伏性TB感染（LTBI）、治愈的TB（RxTB）、肺炎（PN）和健康对照（HC）的参与者。通过测定血清铁、铁蛋白和转铁蛋白三种铁生物标志物的水平，建立并验证了预测模型。结果3种铁标志物水平与肺结核、HC、LTBI、RxTB或PN患者的病情、肺损害程度和菌负荷相关。然后，我们建立了一个结核病预测模型，神经网络（NNET），结合了三种铁生物标志物的数据。该模型显示出良好的TB诊断性能，在训练数据集（n=663）中具有83%（95%CI 77至87）的灵敏度和86%（95%CI 83至89）的特异性，在测试数据集（n=220）中具有70%（95%CI 58至79）的灵敏度和92%（95%CI 86至96）的特异性。区分TB与HC、LTBI、RxTB和PN的NNET模型的曲线下面积（AUC）均>0.83。在单独队列（n=967）中对NNET模型进行独立验证，得出AUC为0.88（95% CI 0.85 - 0.91），灵敏度为74%（95% CI 71 - 77），特异性为92%（95% CI 87 - 96）。结论建立的NNET结核病预测模型在一个大的队列中区分了结核病与HC、LTBI、RxTB和PN。该诊断测定可增强当前TB诊断。
Background Perturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker. Methods We recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model. Results We observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity. Conclusions The established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics.