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. 2019 Jan 15;53(2):719-732.
doi: 10.1021/acs.est.8b04056. Epub 2018 Dec 24.

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Caroline L Ring  1   2 , Jon A Arnot  3   4   5 , Deborah H Bennett  6 , Peter P Egeghy  7 , Peter Fantke  8 , Lei Huang  9 , Kristin K Isaacs  7 , Olivier Jolliet  9 , Katherine A Phillips  7 , Paul S Price  7 , Hyeong-Moo Shin  10 , John N Westgate  3 , R Woodrow Setzer  1 , John F Wambaugh  1
Affiliations

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Caroline L Ring et al. Environ Sci Technol. .

Abstract

Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based exposure pathway models range from 73 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ∼0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479 926 chemicals, mostly with minimal exposure information. This approach identifies 1880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 μg/kg BW/day for 474572 compounds.

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Figures

Figure 1:
Figure 1:
The Systematic Empirical Evaluation of Models (SEEM) framework combines predictors of exposure (πi,k) according to how well they explain the available intake rates (Ri). In the top half of the figure we describe how the overall average (grand mean) a0, the pathway averages aj, and the model weights wj,k are determined with Bayesian analysis. Each wj,k is an evaluation of each predictor, as well as a calibration of how to align that predictor with the intake rates. In the bottom half of the figure we explain how for chemicals without intake rates, we extrapolate the averages and weights from the Bayesian analysis to combine the predictors into a consensus prediction. The predictors are centered such that if no predictor is available, the average value is used. The pathway indicators δi,j, are predicted using the Random Forest algorithm (Table 3).
Figure 2:
Figure 2:
Exposure predictors are organized to give a consensus prediction of intake rate based upon the exposure pathway(s) associated with a chemical. The exposure pathway indicators (δij) determine whether (1) or not (0) each pathway is associated --- if 1 (“yes”), then the predictors to the right will modify the estimated intake rate. The pathway means (ai) indicate overall relative changes in intake rate associated with each pathway. Each exposure predictor and the NHANES intake rates were scaled so that their mean was zero and any value indicates the number of standard deviations above or below the mean. When a given predictor is unavailable for a given chemical, the mean value is used.
Figure 3:
Figure 3:
A pathway-based SEEM meta-analysis allows disparate sources of exposure information (e.g., expert estimates of intake rate and high-throughput exposure estimates, both described here as “models”). Chemicals with no predictions for a given model are assigned either the average exposure predicted for that model or zero, depending on whether or not a chemical is predicted to have exposure via the pathway relevant to that model. Most of the 114 NHANES chemicals analyzed are predicted to have exposure via multiple pathways and are distributed according to Table 2. The unexplained chemical-to-chemical variability is an empirical estimate of the uncertainty of our calibrated predictions. The dashed line indicates identity (perfect predictor) while the solid line indicates a least squares regression on the medians (with gray shaded region indicating standard error).
Figure 4:
Figure 4:
The 95% credible interval (vertical line) and median predicted exposure (points in Panel A) for 479,926 chemicals. The 1880 chemicals whose confidence intervals exceed 0.1 mg/kg BW/day are ranked on a logarithmic scale (Panel a) and all remaining chemicals plotted on an arithmetic scale (panel b). The shape of each plot-point in Panel a indicates the predicted (>50% probability) or assumed (training set) exposure pathways. Chemicals may have exposure by none (i.e., “unknown” pathway), one, or more than one of the four pathways. The upper limit of the 95% interval for the vast majority of chemicals is less than 1 μg/kg BW/day. The upper limit of the credible interval for the first four chemicals in panel A is truncated for plotting clarity.
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