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全基因组测序和机器学习揭示耐利福平结核病痰菌转阴延迟的关键驱动因素。

Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis.

作者信息

Fang Qing, Li Xiangchen, Lu Yewei, Gao Junshun, Wu Yvette, Chen Yi, Che Yang

机构信息

Departments of Pulmonary Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.

Jiaxing Key Laboratory of Clinical Laboratory Diagnostics and Translational Research, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China.

出版信息

Front Cell Infect Microbiol. 2025 Aug 7;15:1641385. doi: 10.3389/fcimb.2025.1641385. eCollection 2025.

DOI:10.3389/fcimb.2025.1641385
PMID:40851801
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12367658/
Abstract

Rifampicin-resistant tuberculosis (RR-TB) remains a major global health challenge, with delayed sputum culture conversion (SCC) predicting poor treatment outcomes. This study integrated whole-genome sequencing (WGS) and machine learning to identify clinical and genomic determinants of SCC failure in 150 RR-TB patients (2019-2023). Phenotypic and genotypic analysis revealed high rates of isoniazid resistance (74.0%) and mutations (97.3%, predominantly Ser450Leu), with 90% of strains belonging to Lineage 2 (Beijing family). While 64.7% achieved 2-month SCC, 18.0% remained culture-positive at 6 months. Univariate analysis linked 2-month SCC failure to smear positivity, resistance to isoniazid, amikacin, capreomycin, and levofloxacin, and pre-XDR-TB status, though only smear positivity (aOR=2.41, P=0.008) and levofloxacin resistance (aOR=2.83, P=0.003) persisted as independent predictors in multivariable analysis. A Random Forest model achieved robust prediction of SCC failure (AUC: 0.86 ± 0.06 at 2 months; 0.76 ± 0.10 at 6 months), identifying levofloxacin resistance (feature importance: 6.37), _p.Met306Ile (5.94), and smear positivity (5.12) as top 2-month predictors, while _p.Ser315Thr (4.85) and _p.Asp94Gly (3.43) dominated 6-month predictions. These findings underscore smear positivity, levofloxacin resistance, and specific resistance mutations as critical drivers of SCC failure, guiding targeted RR-TB treatment strategies.

摘要

耐利福平结核病(RR-TB)仍然是一项重大的全球卫生挑战,痰培养转阴延迟(SCC)预示着治疗效果不佳。本研究整合了全基因组测序(WGS)和机器学习,以确定150例RR-TB患者(2019 - 2023年)SCC失败的临床和基因组决定因素。表型和基因型分析显示异烟肼耐药率(74.0%)和突变率(97.3%,主要是Ser450Leu)很高,90%的菌株属于2型谱系(北京家族)。虽然64.7%的患者在2个月时实现了SCC,但18.0%的患者在6个月时仍培养阳性。单因素分析将2个月SCC失败与涂片阳性、对异烟肼、阿米卡星、卷曲霉素和左氧氟沙星的耐药性以及pre-XDR-TB状态相关联,不过在多变量分析中,只有涂片阳性(调整后比值比[aOR]=2.41,P=0.008)和左氧氟沙星耐药(aOR=2.83,P=0.003)作为独立预测因素持续存在。随机森林模型对SCC失败实现了可靠预测(2个月时曲线下面积[AUC]:0.86±0.06;6个月时0.76±0.10),确定左氧氟沙星耐药(特征重要性:6.37)、p.Met306Ile(5.94)和涂片阳性(5.12)为2个月时的前三大预测因素,而p.Ser315Thr(4.85)和p.Asp94Gly(3.43)在6个月时的预测中占主导地位。这些发现强调涂片阳性、左氧氟沙星耐药和特定耐药突变是SCC失败的关键驱动因素,为针对性的RR-TB治疗策略提供了指导。

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本文引用的文献

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Time to sputum culture conversion and its associated factors among drug-resistant tuberculosis patients: a systematic review and meta-analysis.时间对耐药结核病患者痰培养转换及其相关因素的影响:系统评价和荟萃分析。
BMC Infect Dis. 2024 Feb 7;24(1):169. doi: 10.1186/s12879-024-09009-5.
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Emerging applications of machine learning in genomic medicine and healthcare.
机器学习在基因组医学和医疗保健中的新兴应用。
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Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis.耐利福平结核所致疾病的全球负担:一项数学建模分析。
Nat Commun. 2023 Oct 4;14(1):6182. doi: 10.1038/s41467-023-41937-9.
5
Effectiveness of Bedaquiline Use beyond Six Months in Patients with Multidrug-Resistant Tuberculosis.贝达喹啉治疗耐多药结核病患者超过六个月的疗效。
Am J Respir Crit Care Med. 2023 Jun 1;207(11):1525-1532. doi: 10.1164/rccm.202211-2125OC.
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WHO's Global Tuberculosis Report 2022.世界卫生组织《2022年全球结核病报告》。
Lancet Microbe. 2023 Jan;4(1):e20. doi: 10.1016/S2666-5247(22)00359-7. Epub 2022 Dec 12.
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Assessing Pretomanid for Tuberculosis (APT), a Randomized Phase 2 Trial of Pretomanid-Containing Regimens for Drug-Sensitive Tuberculosis: 12-Week Results.评估用于治疗结核病的普瑞马尼德(APT),一项针对药物敏感型结核病的含普瑞马尼德治疗方案的随机2期试验:12周结果。
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