Azim Adnan, Rezwan Faisal I, Barber Clair, Harvey Matthew, Kurukulaaratchy Ramesh J, Holloway John W, Howarth Peter H
Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK.
NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK.
J Pers Med. 2022 Oct 2;12(10):1635. doi: 10.3390/jpm12101635.
The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions.
呼出气体中挥发性有机化合物（VOCs）的测量（呼吸组学）代表了一种用于气道疾病的令人兴奋的生物标志物基质，早期研究表明其对气道炎症具有敏感性。任何临床生物标志物分析有效性的关键方面之一是对测量短期重复性的理解。我们在14名经过广泛临床特征描述的重度哮喘患者中连续5天收集呼出气体样本。对所有呼吸样本中VOC丰度进行主成分分析，结果显示采样日之间不存在差异。来自同一患者的样本聚集在一起，并且根据T2炎症标志物存在一些分离。在70个样本中计算了每个VOC的受试者内和受试者间变异性，发现30.35%的VOC不稳定：在受试者之间可变，在同一受试者中也可变。从机器学习方法中排除这些不稳定的VOC后，未发现基础数据的结构明显损失或与显著临床特征的关系丧失。此外，通过轮廓系数进行的聚类评估表明聚类更明显。我们能够描述重度哮喘人群中呼吸样本的短期重复性，并证实其对气道炎症的敏感性。我们还描述了一种基于方差的新型特征选择工具，当应用于更大规模的临床研究时，可以改善机器学习模型的预测。
