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Environ Health. 2019 Aug 28;18(1):76. doi: 10.1186/s12940-019-0515-1.

An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length.

Author information

1
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
2
Department of Environmental and Occupational Health, George Washington University Milken Institute School of Public Health, Washington, DC, USA.
3
Occupational Health, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
4
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
5
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
6
Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.
7
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. mk3961@cumc.columbia.edu.

Abstract

BACKGROUND:

Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome.

METHODS:

With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001-2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect.

RESULTS:

Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners.

CONCLUSIONS:

Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions.

KEYWORDS:

Chemical mixtures; Dimension reduction; Environmental mixtures; Multi-pollutant; Variable selection

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