Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, New York, United States of America.
Bureau of Health Services, Fire Department of New York, Brooklyn, New York, United States of America.
PLoS Comput Biol. 2021 Jul 21;17(7):e1009144. doi: 10.1371/journal.pcbi.1009144. eCollection 2021 Jul.
Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV1, %Pred) binary logistic regression had AUCROC [0.90(0.84-0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker-PEDF, an antiangiogenic agent-is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers-GRO, MCP-1, MDC, MIP-4-reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.
生物标志物可预测世贸中心肺部损伤（WTC-LI）；然而，我们用于表型 WTC 疾病的血清细胞因子、趋化因子和高通量平台数据中仍然存在未解决的多重共线性问题。为了解决这个问题，我们使用了自动化、机器学习、高维数据修剪，并验证了确定的生物标志物。母队列由患有 WTC-LI（FEV1，%Pred<正常值下限（LLN）的男性、从不吸烟的消防员组成；n=100）和对照组成（n=127），并对他们的生物标志物进行了评估。病例和对照（n=15/组）进行了非靶向代谢组学分析，然后对代谢物、细胞因子、趋化因子和临床数据进行特征选择。通过 5 倍交叉验证的二元逻辑回归在不重叠的母队列中验证了细胞因子、趋化因子和临床生物标志物。代谢物（n=580）、临床生物标志物（n=5）和以前检测到的细胞因子、趋化因子（n=106）的随机森林确定，对分类分离最重要的前 5%生物标志物包括色素上皮衍生因子（PEDF）、巨噬细胞衍生趋化因子（MDC）、收缩压、巨噬细胞炎性蛋白-4（MIP-4）、生长调节致癌基因蛋白（GRO）、单核细胞趋化蛋白-1（MCP-1）、载脂蛋白-AII（Apo-AII）、细胞膜代谢物（鞘脂、磷脂）和支链氨基酸。通过混杂因素调整（9/11 年龄、BMI、暴露和 9/11 前 FEV1、%Pred）的二元逻辑回归验证的模型具有 AUCROC [0.90（0.84-0.96）]。PEDF 和 MIP-4 减少以及 Apo-AII 增加与 WTC-LI 的可能性增加相关。GRO、MCP-1 增加和同时 MDC 减少与 WTC-LI 的可能性降低相关。总之，自动化数据修剪确定了新的 WTC-LI 生物标志物；在独立队列中进行了性能验证。一种生物标志物-PEDF，一种抗血管生成剂-是一种与颗粒物质相关肺部疾病相关的新型预测性生物标志物。其他生物标志物-GRO、MCP-1、MDC、MIP-4-揭示了免疫细胞在 WTC-LI 发病机制中的参与。我们自动化生物标志物识别的发现需要进一步研究这些潜在的药物治疗靶点。
