Casadevall Carme, Agranovich Bella, Enríquez-Rodríguez Cesar Jesse, Faner Rosa, Pascual-Guàrdia Sergi, Castro-Acosta Ady, Camps-Ubach Ramon, Garcia-Aymerich Judith, Barreiro Esther, Monsó Eduard, Seijo Luis, Soler-Cataluña Juan José, Santos Salud, Peces-Barba Germán, López-Campos José Luis, Casanova Ciro, Agustí Alvar, Cosío Borja G, Abramovich Ifat, Gea Joaquim
Hospital del Mar Research Institute, Servei de Pneumologia, Hospital del Mar, MELIS Department, Universitat Pompeu Fabra, 08013 Barcelona, Spain.
Centro de Investigación Biomédica en Red, Área de Enfermedades Respiratorias (CIBERES), Instituto de Investigación Carlos III (ISCiii), 28029 Madrid, Spain.
Int J Mol Sci. 2025 May 9;26(10):4526. doi: 10.3390/ijms26104526.
The analysis of blood metabolites may help identify individuals at risk of having COPD and offer insights into its underlying pathophysiology. This study aimed to identify COPD-related metabolic alterations and generate a biological signature potentially useful for screening purposes. Plasma metabolomic profiles from 91 COPD patients and 91 controls were obtained using complementary semi-targeted and untargeted LC-MS approaches. Univariate analysis identified metabolites with significant differences between groups, and enrichment analysis highlighted the most affected metabolic pathways. Multivariate analysis, including ROC curve assessment and machine learning algorithms, was applied to assess the discriminatory capacity of selected metabolites. After adjustment for major potential confounders, 56 metabolites showed significant differences between COPD patients and controls. The enrichment analysis revealed that COPD-associated metabolic alterations primarily involved lipid metabolism (especially fatty acids and acylcarnitines), followed by amino acid pathways and xenobiotics. A panel of 10 metabolites, mostly related to lipid metabolism, demonstrated high discriminatory performance for COPD (ROC-AUC: 0.916; 90.1% sensitivity and 89% specificity). These findings may contribute to improving screening strategies and a better understanding of COPD-related metabolic changes. However, our findings remain exploratory and should be interpreted with caution, needing further validation and mechanistic studies.
血液代谢物分析可能有助于识别慢性阻塞性肺疾病（COPD）风险个体，并深入了解其潜在病理生理学。本研究旨在识别与COPD相关的代谢改变，并生成可能有助于筛查的生物标志物。使用互补的半靶向和非靶向液相色谱-质谱联用（LC-MS）方法获得了91例COPD患者和91例对照的血浆代谢组学图谱。单变量分析确定了组间有显著差异的代谢物，富集分析突出了受影响最大的代谢途径。应用多变量分析，包括ROC曲线评估和机器学习算法，以评估所选代谢物的鉴别能力。在对主要潜在混杂因素进行调整后，56种代谢物在COPD患者和对照之间显示出显著差异。富集分析显示，与COPD相关的代谢改变主要涉及脂质代谢（尤其是脂肪酸和酰基肉碱），其次是氨基酸途径和外源性物质。一组10种主要与脂质代谢相关的代谢物对COPD表现出较高的鉴别性能（ROC-AUC：0.916；敏感性90.1%，特异性89%）。这些发现可能有助于改进筛查策略，并更好地理解与COPD相关的代谢变化。然而，我们发现仍具有探索性，应谨慎解读，需要进一步验证和机制研究。
