Xu Rong, Zhang Ying, Li Zhaodong, He Mingjie, Lu Hailin, Liu Guizhen, Yang Min, Fu Liang, Chen Xinchun, Deng Guofang, Wang Wenfei
Endocrinology Department, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Department of Endocrinology, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, China.
Front Mol Biosci. 2024 Aug 13;11:1436135. doi: 10.3389/fmolb.2024.1436135. eCollection 2024.
Individuals with diabetes mellitus (DM) are at an increased risk of (Mtb) infection and progressing from latent tuberculosis (TB) infection to active tuberculosis disease. TB in the DM population is more likely to go undiagnosed due to smear-negative results.
Exhaled breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry. An eXtreme Gradient Boosting (XGBoost) model was utilized for breathomics analysis and TB detection.
XGBoost model achieved a sensitivity of 88.5%, specificity of 100%, accuracy of 90.2%, and an area under the curve (AUC) of 98.8%. The most significant feature across the entire set was m106, which demonstrated a sensitivity of 93%, specificity of 100%, and an AUC of 99.7%.
The breathomics-based TB detection method utilizing m106 exhibited high sensitivity and specificity potentially beneficial for clinical TB screening and diagnosis in individuals with diabetes.
糖尿病患者感染结核分枝杆菌（Mtb）以及从潜伏性结核（TB）感染进展为活动性结核病的风险增加。由于涂片阴性结果，糖尿病患者中的结核病更有可能未被诊断出来。
收集呼出气体样本，并使用高压光子电离飞行时间质谱进行分析。采用极端梯度提升（XGBoost）模型进行呼吸组学分析和结核病检测。
XGBoost模型的灵敏度为88.5%，特异性为100%，准确率为90.2%，曲线下面积（AUC）为98.8%。整个数据集中最显著的特征是m106，其灵敏度为93%，特异性为100%，AUC为99.7%。
利用m106的基于呼吸组学的结核病检测方法表现出高灵敏度和特异性，可能对糖尿病患者的临床结核病筛查和诊断有益。
