Construction of Environmental Risk Score Beyond Standard Linear Models Using Machine Learning Methods: Application to Metal Mixtures, Oxidative Stress and Cardiovascular Disease in NHANES

Details

• Personal Author:
  Mukherjee B ; Park SK ; Zhao Z
• Description:
  Background: There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress [gamma-glutamyl transferase (GGT)], a common disease pathway linking environmental exposure and numerous health endpoints. Methods: We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003-2004 to 2013-2014, n = 9664). We randomly split the data evenly into training and testing sets and constructed ERS's of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints. Results: ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS's showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS's showed non-significant positive associations with mortality outcomes. Conclusions: ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints. [Description provided by NIOSH]
• Subjects:
  Air Pollutants, Occupational Air Pollution Biomarkers Cardiovascular Diseases Cardiovascular System Environment Environmental Exposure Environmental Pollution Enzymes Epidemiology Metals Occupational Health Quality Control Risk Assessment Safety Staff Development Surveys And Questionnaires

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• Keywords:
  Air Pollutants Air Pollution Algorithms Analytical Methods Analytical Processes Author Keywords: Bayesian Additive Regression Tree BART Bayesian Kernel Machine Regression BKMR Blood Pressure Cardiovascular Disease Data Processing Elastic-net Environmental Exposure Environmental Factors Environmental Pollution Environmental Risk Score ERS Health Effects Health Surveys Hypertension Machine Learning Mathematical Models Metal Compounds Mixed Exposures Mixtures Multipollutants Oxidative Stress Performance Tests Pollutants Pollution Quality Assurance Quality Control Regression Analysis Risk Analysis Risk Assessment Statistical Analysis Super Learner Training

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• ISSN:
  1476-069X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Michigan ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  16
• NIOSHTIC Number:
  nn:20052520
• Citation:
  Environ Health 2017 Sep; 16:102
• Contact Point Address:
  Sung Kyun Park, Departments of Epidemiology and Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
• Email:
  sungkyun@umich.edu
• CAS Registry Number:
  Barium (CAS RN 7440-39-3) ; Cadmium (CAS RN 7440-43-9) ; Cobalt (CAS RN 7440-48-4) ; Methylarsonic acid (CAS RN 124-58-3)
• Federal Fiscal Year:
  2017
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Environmental Health: A Global Access Science Source
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:ded76351d537ef6b1b666e907eb2296b685d8b7d1cb15bef26912bd99c66e7a954433a126deffb2589111049b77ab26bb9c0d62e5054972c6b1c93db96a4db66
• Download URL:
  https://stacks.cdc.gov/view/cdc/212033/cdc_212033_DS1.pdf
• File Type:
  [PDF - 1.22 MB ]