NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK.
NHMRC Centre of Research Excellence in Pulmonary Fibrosis, Camperdown, NSW, Australia; Centre for Respiratory Health, School of Biomedical Sciences, University of Western Australia, Nedlands, WA, Australia; Cell Biology Group, Institute for Respiratory Health, Nedlands, WA, Australia; Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia.
Lancet Respir Med. 2024 Sep;12(9):681-692. doi: 10.1016/S2213-2600(24)00147-4. Epub 2024 Jul 15.
Pulmonary fibrosis results from alveolar injury, leading to extracellular matrix remodelling and impaired lung function. This study aimed to classify patients with pulmonary fibrosis according to blood biomarkers to differentiate distinct disease patterns, known as endotypes.
In this cluster analysis, we first classified patients from the PROFILE study, a multicentre, prospective, observational cohort of individuals with incident idiopathic pulmonary fibrosis or non-specific interstitial pneumonia in the UK (Nottingham University Hospitals, Nottingham; and Royal Brompton Hospital, London). 13 blood biomarkers representing extracellular matrix remodelling, epithelial stress, and thrombosis were measured by ELISA in the PROFILE study. We classified patients by unsupervised consensus clustering. To evaluate generalisability, a machine learning classifier trained on biomarker signatures derived from consensus clustering was applied to a replication dataset from the Australian Idiopathic Pulmonary Fibrosis Registry (AIPFR). Biomarker associations with mortality and change in percentage of predicted forced vital capacity (FVC%) were assessed, adjusting for age, gender, baseline FVC%, and antifibrotic treatment and steroid treatment before and after baseline. Mortality risk associated with the clusters in the PROFILE cohort was evaluated with Cox proportional hazards models, and mixed-effects models were used to analyse how clustering was associated with longitudinal FVC% in the PROFILE and AIPFR cohorts.
455 of 580 participants from the PROFILE study (348 [76%] men and 107 [24%] women; mean age 72·4 years [SD 8·3]) were included in the analysis. Within this group, three clusters were identified based on blood biomarkers. A basement membrane collagen (BM) cluster (n=248 [55%]) showed high concentrations of PRO-C4, PRO-C28, C3M, and C6M, whereas an epithelial injury (EI) cluster (n=109 [24%]) showed high concentrations of MMP-7, SP-D, CYFRA211, CA19-9, and CA-125. The third cluster (crosslinked fibrin [XF] cluster; n=98 [22%]) had high concentrations of X-FIB. In the replication dataset (117 of 833 patients from AIPFR; 87 [74%] men and 30 [26%] women; mean age 72·9 years [SD 7·9]), we identified the same three clusters (BM cluster, n=93 [79%]; EI cluster, n=8 [7%]; XF cluster, n=16 [14%]). These clusters showed similarities with clusters in the PROFILE dataset regarding blood biomarkers and phenotypic signatures. In the PROFILE dataset, the EI and XF clusters were associated with increased mortality risk compared with the BM cluster (EI vs BM: adjusted hazard ratio [HR] 1·88 [95% CI 1·42-2·49], p<0·0001; XF vs BM: adjusted HR 1·53 [1·13-2·06], p=0·0058). The EI cluster showed the greatest annual FVC% decline, followed by the BM and XF clusters. A similar FVC% decline pattern was observed in these clusters in the AIPFR replication dataset.
Blood biomarker clustering in pulmonary fibrosis identified three distinct blood biomarker signatures associated with lung function and prognosis, suggesting unique pulmonary fibrosis biomarker patterns. These findings support the presence of pulmonary fibrosis endotypes with the potential to guide targeted therapy development.
None.
肺纤维化是由肺泡损伤引起的，导致细胞外基质重塑和肺功能受损。本研究旨在根据血液生物标志物对肺纤维化患者进行分类，以区分不同的疾病模式，即表型。
在这项聚类分析中，我们首先对来自 PROFILE 研究的患者进行分类，这是一项多中心、前瞻性、观察性队列研究，纳入了英国的特发性肺纤维化或非特异性间质性肺炎患者（诺丁汉大学医院，诺丁汉；和皇家布朗普顿医院，伦敦）。在 PROFILE 研究中，通过 ELISA 测量了 13 种代表细胞外基质重塑、上皮应激和血栓形成的血液生物标志物。我们通过无监督共识聚类对患者进行分类。为了评估普遍性，将基于共识聚类衍生的生物标志物特征的机器学习分类器应用于来自澳大利亚特发性肺纤维化登记处（AIPFR）的复制数据集。评估了生物标志物与死亡率和预测用力肺活量（FVC%）的百分比变化之间的关联，调整了年龄、性别、基线 FVC%以及基线前后的抗纤维化治疗和皮质类固醇治疗。使用 Cox 比例风险模型评估 PROFILE 队列中各聚类的死亡风险，并使用混合效应模型分析聚类与 PROFILE 和 AIPFR 队列的纵向 FVC%之间的关联。
纳入了来自 PROFILE 研究的 580 名参与者中的 455 名（348 名[76%]男性和 107 名[24%]女性；平均年龄 72.4 岁[8.3]）进行分析。在这一组中，根据血液生物标志物确定了三个聚类。基底膜胶原蛋白（BM）聚类（n=248[55%]）显示出 PRO-C4、PRO-C28、C3M 和 C6M 的高浓度，而上皮损伤（EI）聚类（n=109[24%]）显示出 MMP-7、SP-D、CYFRA211、CA19-9 和 CA-125 的高浓度。第三个聚类（交联纤维蛋白[XF]聚类；n=98[22%]）具有高浓度的 X-FIB。在复制数据集（来自 AIPFR 的 833 名患者中的 117 名；87 名[74%]男性和 30 名[26%]女性；平均年龄 72.9 岁[7.9]）中，我们确定了相同的三个聚类（BM 聚类，n=93[79%]；EI 聚类，n=8[7%]；XF 聚类，n=16[14%]）。这些聚类在血液生物标志物和表型特征方面与 PROFILE 数据集的聚类具有相似性。在 PROFILE 数据集中，EI 和 XF 聚类与 BM 聚类相比，与死亡率风险增加相关（EI 与 BM：调整后的危险比[HR]1.88[95%CI 1.42-2.49]，p<0.0001；XF 与 BM：调整后的 HR 1.53[1.13-2.06]，p=0.0058）。EI 聚类的 FVC%年下降幅度最大，其次是 BM 和 XF 聚类。在 AIPFR 复制数据集中，这些聚类中也观察到了类似的 FVC%下降模式。
肺纤维化中的血液生物标志物聚类确定了三个与肺功能和预后相关的不同血液生物标志物特征，提示存在具有潜在靶向治疗开发潜力的肺纤维化表型。
肺纤维化患者的血液生物标志物聚类可识别出与肺功能和预后相关的三个不同的血液生物标志物特征，表明存在不同的肺纤维化生物标志物模式。这些发现支持存在肺纤维化表型的可能性，这可能有助于指导靶向治疗的开发。
无。
