Enríquez-Rodríguez César Jessé, Agranovich Bella, Pascual-Guàrdia Sergi, Faner Rosa, Camps-Ubach Ramon, Castro-Acosta Ady, López-Campos José Luis, Peces-Barba Germán, Seijo Luis, Caguana-Vélez Oswaldo Antonio, Rodríguez-Chiaradia Diego, Barreiro Esther, Monsó Eduard, Cosío Borja G, Abramovich Ifat, Agustí Alvar, Casadevall Carme, Gea Joaquim
Hospital del Mar Research Institute, Servei de Pneumologia, Hospital del Mar, 08003 Barcelona, Spain.
MELIS Department, Universitat Pompeu Fabra, 08003 Barcelona, Spain.
Int J Mol Sci. 2025 Jul 2;26(13):6373. doi: 10.3390/ijms26136373.
Chronic Obstructive Pulmonary Disease (COPD) is a complex condition with high mortality. Early identification of patients at increased risk of death remains a major clinical challenge. This pilot study aimed to explore whether plasma metabolomic profiling could aid in the prediction of long-term (7-year) mortality and provide insight into potential underlying mechanisms. Plasma samples from 54 randomly selected stable COPD patients were analyzed using both untargeted and semi-targeted LC-MS approaches. After excluding patients with unclear death data, non-COPD-related deaths and metabolomic outliers, 41 individuals were included in the final analysis. During follow-up, 13 patients (32%) died, and 28 survived. Univariate analysis identified 12 metabolites-mainly amino acids-that differed significantly between the two groups. Functional analysis suggested a significant disruption in energy production pathways. Predictive models developed using machine learning algorithms, consisting of either ten metabolites alone or nine metabolites plus FEV, achieved high accuracy for 7-year mortality prediction, with the latter model performing slightly better. Internal validation was conducted using five-fold cross-validation. While exploratory, these findings support the hypothesis that early metabolic alterations, particularly in energy pathways, may contribute to long-term mortality risk in stable COPD patients, and could complement traditional prognostic markers such as FEV.
慢性阻塞性肺疾病（COPD）是一种死亡率高的复杂病症。早期识别死亡风险增加的患者仍然是一项重大临床挑战。这项初步研究旨在探讨血浆代谢组学分析是否有助于预测长期（7年）死亡率，并深入了解潜在的潜在机制。使用非靶向和半靶向液相色谱-质谱联用方法分析了54例随机选择的稳定期COPD患者的血浆样本。在排除死亡数据不明确、非COPD相关死亡和代谢组学异常值的患者后，最终分析纳入了41名个体。在随访期间，13例患者（32%）死亡，28例存活。单因素分析确定了12种代谢物——主要是氨基酸——在两组之间存在显著差异。功能分析表明能量产生途径存在显著破坏。使用机器学习算法开发的预测模型，由单独的十种代谢物或九种代谢物加FEV组成，对7年死亡率预测具有较高的准确性，后一种模型表现略好。使用五折交叉验证进行内部验证。虽然是探索性的，但这些发现支持了这样的假设，即早期代谢改变，特别是能量途径中的改变，可能导致稳定期COPD患者的长期死亡风险，并可补充传统的预后标志物如FEV。
