Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China.
Int J Infect Dis. 2020 Aug;97:190-196. doi: 10.1016/j.ijid.2020.05.109. Epub 2020 Jun 2.
Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. This study aimed to investigate a diagnostic model based on a combination of iron metabolism and the TB-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) in T-SPOT.TB assay for differentiation between ATB and LTBI.
A total of 345 participants with ATB (n=191) and LTBI (n=154) were recruited based on positive T-SPOT.TB results at Tongji hospital between January 2017 and January 2020. Iron metabolism analysis was performed simultaneously. A diagnostic model for distinguishing ATB from LTBI was established according to multivariate logistic regression.
The TBAg/PHA ratio showed 64.00% sensitivity and 90.10% specificity in distinguishing ATB from LTBI when a threshold of 0.22 was used. All iron metabolism biomarkers in the ATB group were significantly different from those in the LTBI group. Specifically, serum ferritin and soluble transferrin receptor in ATB were significantly higher than LTBI. On the contrary, serum iron, transferrin, total iron binding capacity, and unsaturated iron binding capacity in ATB were significantly lower than LTBI. The combination of iron metabolism indicators accurately predicted 60.00% of ATB cases and 91.09% of LTBI subjects, respectively. Moreover, the combination of iron metabolism indexes and TBAg/PHA ratio resulted in a sensitivity of 88.80% and specificity of 90.10%. Furthermore, the performance of models established in the Qiaokou cohort was confirmed in the Caidian cohort.
The data suggest that the combination of iron metabolism indexes and TBAg/PHA ratio could serve as a biomarker to distinguish ATB from LTBI in T-SPOT-positive individuals.
鉴别活动性结核病(ATB)与潜伏性结核感染(LTBI)仍然具有挑战性。本研究旨在探讨一种基于铁代谢和 T 细胞斑点试验(T-SPOT.TB)中结核特异性抗原/植物血凝素比值(TBAg/PHA 比值)组合的诊断模型,用于区分 ATB 和 LTBI。
根据 2017 年 1 月至 2020 年 1 月在同济医院 T-SPOT.TB 阳性结果的患者,共招募了 345 名 ATB(n=191)和 LTBI(n=154)患者。同时进行铁代谢分析。根据多变量逻辑回归建立区分 ATB 和 LTBI 的诊断模型。
当阈值为 0.22 时,TBAg/PHA 比值在区分 ATB 和 LTBI 时显示出 64.00%的敏感性和 90.10%的特异性。ATB 组的所有铁代谢生物标志物均与 LTBI 组显著不同。具体而言,ATB 中的血清铁蛋白和可溶性转铁蛋白受体明显高于 LTBI。相反,ATB 中的血清铁、转铁蛋白、总铁结合能力和未饱和铁结合能力明显低于 LTBI。铁代谢指标的组合分别准确预测了 60.00%的 ATB 病例和 91.09%的 LTBI 患者。此外,铁代谢指标与 TBAg/PHA 比值的组合灵敏度为 88.80%,特异性为 90.10%。此外,在桥口队列中建立的模型性能在蔡甸队列中得到了验证。
数据表明,铁代谢指标与 TBAg/PHA 比值的组合可作为 T-SPOT.TB 阳性个体中区分 ATB 和 LTBI 的生物标志物。