PEDF, a Pleiotropic WTC-LI Biomarker: Machine Learning Biomarker Identification and Validation

Details

• Personal Author:
  Crowley G ; Kim J ; Kwon S ; Lam R ; Liu M ; Nolan A ; Prezant DJ
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Automation Biomarkers Cohort Studies Firefighters Lung Occupational Health Respiratory System Safety September 11 Terrorist Attacks
• Keywords:
  Cohort Studies Fire Fighters Pathogenesis World Trade Center WTC
• ISSN:
  1553-734X
• Document Type:
  Text
• Funding:
  Cooperative Agreement ; Contract
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  17
• Issue:
  7
• NIOSHTIC Number:
  nn:20063916
• Citation:
  PLoS Comput Biol 2021 Jul; 17(7):e1009144
• Contact Point Address:
  Anna Nolan, 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
• Email:
  anna.nolan@med.nyu.edu
• Federal Fiscal Year:
  2021
• Performing Organization:
  New York University School of Medicine
• Peer Reviewed:
  True
• Start Date:
  20170701
• Source Full Name:
  Public Library of Science Computational Biology
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:456c079b61a9aab42442ec380aef9be5f4457f7713a5c6a02c959c9bf96d67fb3696ce8134cb50d1d31ace5a311b9f32eac113b0e7793dfb5aac4fc1b75b9398
• Download URL:
  https://stacks.cdc.gov/view/cdc/222454/cdc_222454_DS1.pdf
• File Type:
  [PDF - 1.62 MB ]