Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes

Author:

Riseberg Emily12ORCID,Melamed Rachel D.3ORCID,James Katherine A.4ORCID,Alderete Tanya L.5,Corlin Laura16ORCID

Affiliation:

1. Department of Public Health and Community Medicine , Tufts University , Boston , MA , USA

2. Department of Nutrition , Harvard T.H. Chan School of Public Health , Boston , MA , USA

3. Biological Sciences , University of Massachusetts , Lowell , MA , USA

4. Department of Environmental and Occupational Health, Colorado School of Public Health , University of Colorado, Anschutz Medical Campus , Aurora , CO , USA

5. Department of Integrative Physiology , University of Colorado , Boulder , CO , USA

6. Department of Civil and Environmental Engineering , Tufts University , Medford , MA , USA

Abstract

Abstract Objectives Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes. Methods We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models. Results From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose. Conclusions We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.

Funder

National Institute of Environmental Health Sciences

Tufts Institute of the Environment

Tufts University Department of Public Health and Community Medicine

Eunice Kennedy Shriver National Institute of Child Health and Human Development

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Epidemiology

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