Yousuf Ahmed J, Parekh Gita, Farrow Malcolm, Ball Graham, Graziadio Sara, Wilson Kevin, Lendrem Clare, Carr Liesl, Watson Lynne, Parker Sarah, Finch Joanne, Glover Sarah, Mistry Vijay, Porter Kate, Duvoix Annelyse, O'Brien Linda, Rees Sarah, Lewis Keir E, Davis Paul, Brightling Christopher E
Institute for Lung Health, NIHR BRC Respiratory Medicine, Department of Respiratory Sciences, University of Leicester, Leicester, UK.
Mologic LTD (trading as Global Access Diagnostics), Bedford, UK.
ERJ Open Res. 2025 Jun 2;11(3). doi: 10.1183/23120541.00797-2024. eCollection 2025 May.
COPD exacerbations cause considerable morbidity and mortality. We sought to identify a panel of urine biomarkers that can distinguish between stable and exacerbation states and predict risk of future exacerbations.
A retrospective discovery study was done measuring 35 biomarkers implicated in COPD pathogenesis in paired urine samples from 55 COPD subjects during stable and exacerbation states. A logistic regression model combining the 10 most discriminatory biomarkers in distinguishing between stable and exacerbation states was developed as a near-patient dipstick test with an opto-electronic reader. This biomarker panel was tested in a prospective study of 105 COPD subjects who undertook daily home urine testing over 6 months. The regression model was validated in paired samples from 26 individuals out of 105. An artificial neural network (ANN) using the urine biomarkers from 85 out of 105 subjects was developed and tested as a clinical decision tool to predict risk of an exacerbation.
The 10-biomarker panel (NGAL, TIMP1, CRP, fibrinogen, CC16, fMLP, TIMP2, A1AT, B2M and MMP8) was able to distinguish exacerbation stable state in the discovery study (ROC with an AUC 0.84, 95% CI 0.76-0.92; p <0.01) and validation study (AUC 0.81, 95% CI 0.70-0.92, p<0.01). The ANN model predicted an exacerbation within a 13-day window frame with an AUC 0.89 (95% CI 0.89-0.90) and identified an exacerbation median (interquartile range) 7 (5-9) days prior to clinical diagnosis.
We identified a panel of biomarkers that can distinguish between stable and exacerbation state, and using an ANN model, it can predict exacerbations before symptoms occur.
慢性阻塞性肺疾病（COPD）急性加重会导致相当高的发病率和死亡率。我们试图确定一组尿液生物标志物，以区分稳定状态和急性加重状态，并预测未来急性加重的风险。
进行了一项回顾性发现研究，在55例COPD患者处于稳定状态和急性加重状态时的配对尿液样本中，检测了35种与COPD发病机制相关的生物标志物。开发了一种逻辑回归模型，该模型结合了区分稳定状态和急性加重状态时最具鉴别力的10种生物标志物，作为一种带有光电阅读器的即时检验。该生物标志物组合在一项对105例COPD患者进行的前瞻性研究中进行了测试，这些患者在6个月内每天在家进行尿液检测。回归模型在105例中的26例个体的配对样本中得到验证。利用105例患者中85例患者的尿液生物标志物开发了一种人工神经网络（ANN），并将其作为一种临床决策工具进行测试，以预测急性加重的风险。
在发现研究（受试者工作特征曲线下面积[AUC]为0.84，95%置信区间[CI]为0.76 - 0.92；p<0.01）和验证研究（AUC为0.81，95%CI为0.70 - 0.92，p<0.01）中，10种生物标志物组合（中性粒细胞明胶酶相关脂质运载蛋白[NGAL]、基质金属蛋白酶组织抑制因子1[TIMP1]、C反应蛋白[CRP]、纤维蛋白原、 Clara细胞分泌蛋白[CC16]、N-甲酰甲硫氨酸-亮氨酸-苯丙氨酸[fMLP]、基质金属蛋白酶组织抑制因子2[TIMP2]、α1抗胰蛋白酶[A1AT]、β2微球蛋白[B2M]和基质金属蛋白酶8[MMP8]）能够区分急性加重和稳定状态。人工神经网络模型在13天的时间窗内预测急性加重的AUC为0.89（95%CI为0.89 - 0.90），并在临床诊断前中位数（四分位间距）7（5 - 9）天识别出急性加重。
我们确定了一组能够区分稳定状态和急性加重状态的生物标志物，并且利用人工神经网络模型，能够在症状出现前预测急性加重。
