Center for Translational Immunology (CTI), University Medical Center Utrecht, Utrecht, Netherlands.
Platform Immune Monitoring (PIM), University Medical Center Utrecht, Utrecht, Netherlands.
Front Immunol. 2021 Oct 7;12:725447. doi: 10.3389/fimmu.2021.725447. eCollection 2021.
INTRODUCTION: There is an urgent medical need to differentiate active tuberculosis (ATB) from latent tuberculosis infection (LTBI) and prevent undertreatment and overtreatment. The aim of this study was to identify biomarker profiles that may support the differentiation between ATB and LTBI and to validate these signatures. MATERIALS AND METHODS: The discovery cohort included adult individuals classified in four groups: ATB (n = 20), LTBI without prophylaxis (untreated LTBI; n = 20), LTBI after completion of prophylaxis (treated LTBI; n = 20), and healthy controls (HC; n = 20). Their sera were analyzed for 40 cytokines/chemokines and activity of adenosine deaminase (ADA) isozymes. A prediction model was designed to differentiate ATB from untreated LTBI using sparse partial least squares (sPLS) and logistic regression analyses. Serum samples of two independent cohorts (national and international) were used for validation. RESULTS: sPLS regression analyses identified C-C motif chemokine ligand 1 (CCL1), C-reactive protein (CRP), C-X-C motif chemokine ligand 10 (CXCL10), and vascular endothelial growth factor (VEGF) as the most discriminating biomarkers. These markers and ADA(2) activity were significantly increased in ATB compared to untreated LTBI (p ≤ 0.007). Combining CCL1, CXCL10, VEGF, and ADA2 activity yielded a sensitivity and specificity of 95% and 90%, respectively, in differentiating ATB from untreated LTBI. These findings were confirmed in the validation cohort including remotely acquired untreated LTBI participants. CONCLUSION: The biomarker signature of CCL1, CXCL10, VEGF, and ADA2 activity provides a promising tool for differentiating patients with ATB from non-treated LTBI individuals.
简介:区分活动性结核病(ATB)和潜伏性结核感染(LTBI),并防止治疗不足和过度治疗,这是一个紧迫的医学需求。本研究的目的是确定可能支持区分 ATB 和 LTBI 的生物标志物特征,并验证这些特征。
材料和方法:发现队列包括分为四组的成年个体:ATB(n=20)、未接受治疗的 LTBI(untreated LTBI;n=20)、完成预防治疗的 LTBI(treated LTBI;n=20)和健康对照(HC;n=20)。分析了他们的血清中的 40 种细胞因子/趋化因子和腺苷脱氨酶(ADA)同工酶的活性。使用稀疏偏最小二乘(sPLS)和逻辑回归分析设计了一个预测模型,以区分 ATB 和未治疗的 LTBI。使用两个独立队列(国内和国际)的血清样本进行验证。
结果:sPLS 回归分析确定 C-C 基序趋化因子配体 1(CCL1)、C 反应蛋白(CRP)、C-X-C 基序趋化因子配体 10(CXCL10)和血管内皮生长因子(VEGF)为最具区分性的生物标志物。与未治疗的 LTBI 相比,这些标志物和 ADA(2)活性在 ATB 中显著增加(p≤0.007)。将 CCL1、CXCL10、VEGF 和 ADA2 活性结合使用,可分别在区分 ATB 和未治疗的 LTBI 中达到 95%和 90%的灵敏度和特异性。这些发现在包括远程获取的未治疗 LTBI 参与者的验证队列中得到了证实。
结论:CCL1、CXCL10、VEGF 和 ADA2 活性的生物标志物特征为区分 ATB 和未治疗的 LTBI 患者提供了一种很有前途的工具。
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