Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway.
Department of Microbiology, Haukeland University Hospital, University of Bergen, Bergen, Norway.
Front Immunol. 2023 Jan 11;13:1051963. doi: 10.3389/fimmu.2022.1051963. eCollection 2022.
A large proportion of the global tuberculosis (TB) burden is asymptomatic and not detectable by symptom-based screening, driving the TB epidemic through continued transmission. Currently, no validated tools exist to diagnose incipient and subclinical TB. Nested within a large prospective study in household contacts of pulmonary TB cases in Southern India, we assessed 35 incipient TB and 12 subclinical TB cases, along with corresponding household active TB cases (n=11), and household controls (n=39) using high throughput methods for transcriptional and protein profiling. We split the data into training and test sets and applied a support vector machine classifier followed by a Lasso regression model to identify signatures. The Lasso regression model identified an 11-gene signature (, and ) that distinguished subclinical TB from incipient TB with a very good discriminatory power by AUCs in both training and test sets. Further, we identified an 8-protein signature comprising b-FGF, IFNγ, IL1RA, IL7, IL12p70, IL13, PDGF-BB, and VEGF that differentiated subclinical TB from incipient TB with good and moderate discriminatory power by AUCs in the training and test sets, respectively. The identified 11-gene signature discriminated well between the distinct stages of the TB disease spectrum, with very good discriminatory power, suggesting it could be useful for predicting TB progression in household contacts. However, the high discriminatory power could partly be due to over-fitting, and validation in other studies is warranted to confirm the potential of the immune biosignatures for identifying subclinical TB.
很大一部分全球结核病（TB）负担是无症状的，无法通过基于症状的筛查检测到，从而通过持续传播推动了结核病疫情。目前，没有经过验证的工具可以诊断初期和亚临床结核病。在印度南部肺结核病例家庭接触者的一项大型前瞻性研究中，我们评估了 35 例初期结核病和 12 例亚临床结核病病例，以及相应的家庭活动性结核病病例（n=11）和家庭对照（n=39），使用转录组和蛋白质谱的高通量方法。我们将数据分为训练集和测试集，并应用支持向量机分类器和 Lasso 回归模型来识别特征。Lasso 回归模型确定了一个由 11 个基因组成的特征（、和），该特征通过训练集和测试集的 AUC 非常好地区分了亚临床结核病和初期结核病。此外，我们确定了一个由 8 种蛋白质组成的特征，包括 b-FGF、IFNγ、IL1RA、IL7、IL12p70、IL13、PDGF-BB 和 VEGF，该特征通过 AUC 在训练集和测试集分别具有良好和中等的区分能力来区分亚临床结核病和初期结核病。确定的 11 个基因特征很好地区分了结核病疾病谱的不同阶段，具有很好的区分能力，这表明它可能有助于预测家庭接触者中结核病的进展。然而，高区分能力可能部分归因于过度拟合，需要在其他研究中进行验证，以确认免疫生物标志物识别亚临床结核病的潜力。
