Boehringer Ingelheim Pharmaceuticals, Inc, 900 Ridgebury Road, Ridgefield, CT, 06877, USA.
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Respir Res. 2024 Oct 23;25(1):383. doi: 10.1186/s12931-024-03015-6.
Blood biomarkers predictive of the progression of idiopathic pulmonary fibrosis (IPF) would be of value for research and clinical practice. We used data from the IPF-PRO Registry to investigate whether the addition of "omics" data to risk prediction models based on demographic and clinical characteristics improved prediction of the progression of IPF.
The IPF-PRO Registry enrolled patients with IPF at 46 sites across the US. Patients were followed prospectively. Median follow-up was 27.2 months. Prediction models for disease progression included omics data (proteins and microRNAs [miRNAs]), demographic factors and clinical factors, all assessed at enrollment. Data on proteins and miRNAs were included in the models either as raw values or based on clusters in various combinations. Least absolute shrinkage and selection operator (Lasso) Cox regression was applied for time-to-event composite outcomes and logistic regression with L1 penalty was applied for binary outcomes assessed at 1 year. Model performance was assessed using Harrell's C-index (for time-to-event outcomes) or area under the curve (for binary outcomes).
Data were analyzed from 231 patients. The models based on demographic and clinical factors, with or without omics data, were the top-performing models for prediction of all the time-to-event outcomes. Relative changes in average C-index after incorporating omics data into models based on demographic and clinical factors ranged from 1.7 to 3.2%. Of the blood biomarkers, surfactant protein-D, serine protease inhibitor A7 and matrix metalloproteinase-9 (MMP-9) were among the top predictors of the outcomes. For the binary outcomes, models based on demographics alone and models based on demographics plus omics data had similar performances. Of the blood biomarkers, CC motif chemokine 11, vascular cell adhesion protein-1, adiponectin, carcinoembryonic antigen and MMP-9 were the most important predictors of the binary outcomes.
We identified circulating protein and miRNA biomarkers associated with the progression of IPF. However, the integration of omics data into prediction models that included demographic and clinical factors did not materially improve the performance of the models.
ClinicalTrials.gov; No: NCT01915511; registered August 5, 2013; URL: www.
gov .
血液生物标志物对特发性肺纤维化(IPF)进展的预测具有重要价值,无论是对研究还是临床实践都是如此。我们利用来自 IPF-PRO 登记处的数据,研究了在基于人口统计学和临床特征的风险预测模型中加入“组学”数据是否可以改善对 IPF 进展的预测。
IPF-PRO 登记处招募了美国 46 个地点的 IPF 患者。患者进行前瞻性随访。中位随访时间为 27.2 个月。疾病进展的预测模型包括组学数据(蛋白质和 microRNAs [miRNAs])、人口统计学因素和临床因素,所有这些因素均在入组时进行评估。蛋白质和 microRNAs 数据要么作为原始值,要么根据各种组合的聚类包含在模型中。采用最小绝对收缩和选择算子(Lasso)Cox 回归进行时间事件复合结局分析,采用 L1 惩罚的逻辑回归进行 1 年时的二项结局分析。采用 Harrell's C 指数(用于时间事件结局)或曲线下面积(用于二项结局)评估模型性能。
对 231 名患者进行了数据分析。基于人口统计学和临床因素的模型,无论是否包含组学数据,都是所有时间事件结局预测的最佳模型。将组学数据纳入基于人口统计学和临床因素的模型后,平均 C 指数的相对变化范围为 1.7%至 3.2%。在血液生物标志物中,表面活性剂蛋白-D、丝氨酸蛋白酶抑制剂 A7 和基质金属蛋白酶-9(MMP-9)是预测结局的重要标志物。对于二项结局,仅基于人口统计学的模型和基于人口统计学加组学数据的模型具有相似的性能。在血液生物标志物中,CC 基序趋化因子 11、血管细胞黏附蛋白-1、脂联素、癌胚抗原和 MMP-9 是预测二项结局的最重要标志物。
我们确定了与 IPF 进展相关的循环蛋白和 miRNA 生物标志物。然而,将组学数据整合到包含人口统计学和临床因素的预测模型中,并没有显著改善模型的性能。
ClinicalTrials.gov;编号:NCT01915511;注册日期:2013 年 8 月 5 日;网址:www.clinicaltrials.gov。
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