Basler Sarah, Sievi Noriane A, Schmidt Felix, Fricke Kai, Arvaji Alexandra, Herth Jonas, Baur Diego M, Sinues Pablo, Ulrich Silvia, Kohler Malcolm
Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland.
Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
J Breath Res. 2024 Dec 16;19(1). doi: 10.1088/1752-7163/ad9ac4.
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) show high variability in individual susceptibility and promote disease progression; thus, accurate diagnosis and treatment is essential. Unravelling the molecular metabolic changes during AECOPD in breath could promote understanding of AECOPD and its treatment. Our objective was to investigate the metabolic breath profiles during AECOPD for biomarker detection. We conducted real-time breath analysis in patients with COPD during AECOPD and during subsequent stable phase. Molecular breath patterns were compared between AECOPD and stable phase by dimension reduction techniques and paired t-tests. Pathway enrichment analyses were performed to investigate underlying metabolic pathways. Partial least-squares discriminant analysis and XGboost were utilised to build a prediction model to differentiate AECOPD from stable state. 35 patients (60% male) with a mean age of 65 (10.2) yr with AECOPD were included. AECOPD could be predicted with a high sensitivity of 82.5% (95% confidence interval of 68.8%-93.8%) and an excellent discriminative power (AUC = 0.86). Metabolic changes in the linoleate, tyrosine, and tryptophan pathways during AECOPD were predominant. Significant metabolic changes occur during COPD exacerbations, predominantly in the linoleate, tyrosine, and tryptophan pathways, which are all linked to inflammation. Real-time exhaled breath analysis enables a good prediction of AECOPD compared to stable state and thus could enhance precision of AECOPD diagnosis and efficacy in clinical practice.
慢性阻塞性肺疾病急性加重期（AECOPD）在个体易感性方面表现出高度变异性，并促进疾病进展；因此，准确的诊断和治疗至关重要。揭示AECOPD期间呼出气体中的分子代谢变化有助于增进对AECOPD及其治疗的理解。我们的目标是研究AECOPD期间的代谢呼气谱以检测生物标志物。我们对COPD患者在AECOPD期间及随后的稳定期进行了实时呼气分析。通过降维技术和配对t检验比较了AECOPD和稳定期之间的分子呼气模式。进行通路富集分析以研究潜在的代谢通路。利用偏最小二乘判别分析和XGboost构建预测模型，以区分AECOPD和稳定状态。纳入了35例平均年龄为65（10.2）岁的AECOPD患者（60%为男性）。AECOPD的预测具有82.5%的高灵敏度（95%置信区间为68.8%-93.8%）和出色的判别能力（AUC = 0.86）。AECOPD期间亚油酸、酪氨酸和色氨酸途径的代谢变化最为显著。COPD加重期会出现显著的代谢变化，主要发生在亚油酸、酪氨酸和色氨酸途径，这些都与炎症有关。与稳定状态相比，实时呼气分析能够很好地预测AECOPD，因此可以提高AECOPD在临床实践中的诊断准确性和疗效。
