Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310009, P. R. China.
State Key Laboratory for Oncogenes and Related Genes School of Biomedical Engineering Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.
Adv Sci (Weinh). 2024 Oct;11(39):e2406233. doi: 10.1002/advs.202406233. Epub 2024 Aug 19.
Tuberculosis (TB) stands as the second most fatal infectious disease after COVID-19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics-based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle-enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost-effectiveness (approximately $3). A panel of 14 m z features is identified as biomarkers for TB diagnosis and a panel of 4 m z features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964-0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806-0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end-TB strategy.
结核病 (TB) 是仅次于 COVID-19 的第二大致命传染病，其有效治疗取决于准确的诊断和表型分析。代谢组学为识别疾病诊断和表型分析的差异代谢物提供了有价值的见解。然而，由于缺乏令人满意的代谢方法，TB 的诊断和表型分析仍然是巨大的挑战。本研究报告了一种基于代谢组学的快速 TB 检测诊断方法。通过自动化纳米颗粒增强激光解吸/电离质谱平台检测血清代谢指纹图谱，该平台具有快速检测速度（以秒为单位）、最小样本消耗（纳升）和成本效益（约 3 美元）的特点。鉴定出了 14 个 m/z 特征作为 TB 诊断的生物标志物，以及 4 个 m/z 特征作为 TB 表型分析的生物标志物。基于获得的生物标志物，通过先进的机器学习算法构建 TB 代谢模型。稳健的代谢模型在 TB 诊断中产生了 97.8%（95%置信区间 (CI)，0.964-0.986）的曲线下面积 (AUC)，在表型分析中产生了 85.7%（95% CI，0.806-0.891）的 AUC。在这项研究中，揭示了血清代谢生物标志物谱，并开发了一种具有理想诊断性能的准确代谢工具，用于 TB 诊断和表型分析，这可能会加速实施终结结核病战略。
