Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia; The Vaccine Centre, and Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom.
Section of Paediatrics, Imperial College London, St Mary's Campus, London, United Kingdom; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
EBioMedicine. 2020 Aug;58:102909. doi: 10.1016/j.ebiom.2020.102909. Epub 2020 Jul 22.
Our study aimed to identify a host cytokine biosignature that could distinguish childhood tuberculosis (TB) from other respiratory diseases (OD).
Cytokine responses in prospectively recruited children with symptoms suggestive of TB were measured in whole blood assay supernatants, harvested after overnight incubation, using a Luminex platform. We used logistic regression models with Least Absolute Shrinkage and Selection Operator (LASSO) penalty to identify the optimal biosignature associated with confirmed TB disease in the training set. We subsequently assessed its performance in the test set.
Of the 431 children included in the study, 44 had bacteriologically confirmed TB, 60 had clinically diagnosed TB while 327 had OD. All children were HIV-negative. Application of LASSO regression models to the training set (n = 260) resulted in the combination of IL-1ra, IL-7 and IP-10 from unstimulated samples as the optimally discriminant cytokine biosignature associated with bacteriologically confirmed TB. In the test set (n = 171), this biosignature distinguished children diagnosed with TB disease, irrespective of microbiological confirmation, from OD with area under the receiver operator characteristic curve (AUC) of 0•74 (95% CI: 0•67, 0•81), and demonstrated sensitivity and specificity of 72•2% (95% CI: 60•4, 82•1%) and 75•0% (95% CI: 64•9, 83•4%) respectively, with its performance independent of their age group and their age- and sex-adjusted nutritional status.
This novel biosignature of childhood TB derived from unstimulated supernatants is promising. Independent validation with further optimisation will improve its performance and translational potential.
Steinberg Fellowship (McGill University); Grand Challenges Canada; MRC Program Grant.
本研究旨在确定一种宿主细胞因子生物标志物，以区分儿童结核病（TB）与其他呼吸道疾病（OD）。
采用 Luminex 平台，对疑似结核病症状的前瞻性招募儿童的全血检测上清液中的细胞因子反应进行测量。我们使用具有最小绝对收缩和选择算子（LASSO）惩罚的逻辑回归模型，在训练集中识别与确诊 TB 疾病相关的最佳生物标志物。随后，我们在测试集中评估其性能。
在纳入的 431 名儿童中，44 名患有细菌学确诊的 TB，60 名患有临床诊断的 TB，327 名患有 OD。所有儿童均为 HIV 阴性。将 LASSO 回归模型应用于训练集（n=260），结果显示，来自未刺激样本的 IL-1ra、IL-7 和 IP-10 的组合是与细菌学确诊 TB 相关的最佳区分细胞因子生物标志物。在测试集中（n=171），该生物标志物能够区分诊断为 TB 疾病的儿童，无论是否进行微生物学确认，与 OD 区分，其受试者工作特征曲线（ROC）下面积（AUC）为 0·74（95%CI：0·67，0·81），其敏感性和特异性分别为 72·2%（95%CI：60·4，82·1%）和 75·0%（95%CI：64·9，83·4%），其性能独立于其年龄组及其年龄和性别调整后的营养状况。
这种源自未刺激上清液的儿童 TB 新型生物标志物具有很大的应用前景。通过进一步优化和独立验证，可以提高其性能和转化潜力。
斯坦伯格奖学金（麦吉尔大学）；加拿大大挑战；MRC 项目资助。
