Biomarker Signatures of TB Infection in Young Children With and Without HIV

每年，全球估计有100万儿童患结核病（TB），1500多万儿童患结核病。 据估计，儿童暴露于结核分枝杆菌（Mtb）。病死率很高， 5岁以下儿童。目前的方法来诊断和管理的幼儿是密切 与结核病病例的接触不足。那些有症状的人可能会接受基于肿瘤的诊断 耐受性不佳（如胃吸出物），需要进入参考实验室， 因为结核病可能是少杆菌性的或肺外的。因此，经验性多药抗结核 治疗在许多地方占主导地位。渐近的管理也是次优的。给定 IGRA和TST在该年龄组中表现较差，大多数采用异烟肼预防性治疗 （IPT）。在成人中，无症状（亚临床结核病）至少与活动性结核病一样常见，并且不会被发现 通过目前的筛查。我们不知道这种情况在暴露的儿童中有多常见， 然而，在这些国家中，IPT是不够。此外，这个年龄组中约19%的儿童患有潜伏性结核病， 感染（LTBI）将进展为活动性TB，通常在接下来的3-6年内，如果没有IPT（IPT仅 63%有效）。因此，需要发现一种或多种生物标志物来识别那些< 5岁的儿童。 患有亚临床TB（尽管接受IPT，仍可能进展）的年龄<10岁的患者;以及 可能会进步。这些生物标志物将允许IPT和ATT适当靶向那些可能 效益这个研究者联盟正在对儿童（< 5岁）进行诊断和队列研究。 年龄）密切接触者的结核病病例，包括严格的细菌学参考标准适用 无症状和非痰病毒的诊断和评价， 基于方法。我们现在建议对5岁以下的结核病密切接触者进行评估 一组多样且互补的细菌、基于宿主的和成像非痰生物标志物， 已经显示出作为成年人疾病进展的预测指标的前景。此外，我们还将发现相关的生物标志物， 通过使用蛋白质组学、单细胞组学、T细胞 活化标记物，抗原特异性抗体谱，Mtb外泌体测定，计算机辅助检测 (CAD)用于胸部X线解读和床旁超声（POCUS）。我们的目标是描述一个 符合最低目标表现特征的生物标志物或生物标志物组，以识别患有 亚临床TB和/或处于进展的高风险。我们将应用先进的机器学习和集成 多组学，以确定这些生物标志物签名与结核病风险变量的组合， 预测进展的精确度。这些结果将为<5岁儿童的风险分层提供新的方法 年龄的目标管理的预防性治疗和ATT。

Per year, globally an estimated one million children develop tuberculosis (TB) and more than 15 million children are estimated to be exposed to Mycobacterium tuberculosis (Mtb). The case fatality rate is high in children < 5 years of age. Current approaches to diagnosis and management of young children that are close contacts to a TB case are inadequate. Those that are symptomatic may undergo sputum-based diagnostics that are not well tolerated (eg gastric aspirates), require access to a reference laboratory, and are not sensitive because TB may be paucibacillary or extrapulmonary. For that reason empirical multidrug anti-TB treatment predominates in many locales. Management of the asymptomatics is sub-optimal as well. Given the poor performance of IGRAs and TST in this age group, most are treated with isoniazid preventive therapy (IPT). In adults, asymptomatic (subclinical TB) is at least as common as active TB and will not be detected by current symptom-based screening. We do not know how often this is the case in exposed children, however, IPT, would be inadequate in them. Further, about 19% of children in this age group with latent TB infection (LTBI) will progress to active TB, usually within the next 3-6 in the absence of IPT (and IPT is only 63% effective). The need therefore is to discover a biomarker or biomarkers that identify those children < 5 years of age with subclinical TB (likely to progress despite IPT); and those without subclinical TB that are likely to progress. These biomarkers would allow appropriate targeting of IPT and ATT to those likely to benefit. This consortium of investigators have on-going diagnostic and cohort studies of child (< 5 years of age) close contacts of TB cases in Uganda that include a rigorous bacteriologic reference standard applied to asymptomatic as well as symptomatics and evaluation of novel diagnostics and discovery of non-sputum- based approaches. We propose now to evaluate in children < 5years old that are close contacts of a TB case a diverse and complementary panel of bacterial, host-based and imaging non-sputum biomarkers that have shown promise as predictors of progression in adults. Further, we will discover relevant biomarkers in this population through an unbiased multi-omics approach using proteomics, single-cell omics, T-cell activation markers, antigen-specific antibody profiling, Mtb exosomal assays, computer-aided detection (CAD) for chest X-ray interpretation and point-of-care ultrasound (POCUS). Our goal is to characterize a biomarker or group of biomarkers that meet a minimal target performance profile to identify children with subclinical TB and/or at high risk of progression. We will apply advanced machine learning and integrative multiomics to identify combinations of these biomarker signatures alongside TB risk variables to improve precision of predicting progression. These results will provide novel approaches to risk-stratify children <5 years of age for targeting the administration of preventive therapy and ATT.