Environmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels

<div><p>Objective</p><p>A growing body of evidence suggests that environmental pollutants, such as heavy metals, persistent organic pollutants and plasticizers play an important role in the development of chronic diseases. Most epidemiologic studies have examined environmental pollutants individually, but in real life, we are exposed to multi-pollutants and pollution mixtures, not single pollutants. Although multi-pollutant approaches have been recognized recently, challenges exist such as how to estimate the risk of adverse health responses from multi-pollutants. We propose an “Environmental Risk Score (ERS)” as a new simple tool to examine the risk of exposure to multi-pollutants in epidemiologic research.</p><p>Methods and Results</p><p>We examined 134 environmental pollutants in relation to serum lipids (total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides) using data from the National Health and Nutrition Examination Survey between 1999 and 2006. Using a two-stage approach, stage-1 for discovery (n = 10818) and stage-2 for validation (n = 4615), we identified 13 associated pollutants for total cholesterol, 9 for HDL, 5 for LDL and 27 for triglycerides with adjustment for sociodemographic factors, body mass index and serum nutrient levels. Using the regression coefficients (weights) from joint analyses of the combined data and exposure concentrations, ERS were computed as a weighted sum of the pollutant levels. We computed ERS for multiple lipid outcomes examined individually (single-phenotype approach) or together (multi-phenotype approach). Although the contributions of ERS to overall risk predictions for lipid outcomes were modest, we found relatively stronger associations between ERS and lipid outcomes than with individual pollutants. The magnitudes of the observed associations for ERS were comparable to or stronger than those for socio-demographic factors or BMI.</p><p>Conclusions</p><p>This study suggests ERS is a promising tool for characterizing disease risk from multi-pollutant exposures. This new approach supports the need for moving from a single-pollutant to a multi-pollutant framework.</p></div

Estimated environmental risk score (ERS) weights for environmental pollutants selected for each phenotype.

<p>HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; PAHs, polycyclic aromatic hydrocarbons; PFCs, perfluorinated compounds; PCBs, polychlorinated biphenyls; TCDD, tetrachlorodibenzodioxin; PnCDF, pentachlorodibenzofuran; HxCDF, hexachlorodibenzofuran; HpCDF, heptachlorodibenzofuran; PnCB, pentachlorobiphenyl; HxCB, hexachlorobiphenyl; DDT, dichlorodiphenyltrichloroethane.</p><p>All models were adjusted for age, gender, race/ethnicity, education, BMI and phenotype-specific nutrients shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098632#pone.0098632.s006" target="_blank">Table S3</a>.</p>a<p>Weights were estimated using the training data (n = 11586).</p>b<p>ERS constructed with coefficient estimates from single-pollutant models as weights.</p>c<p>ERS constructed with coefficient estimates from multi-pollutant models as weights.</p>#<p><i>p</i>-value<0.001,</p><p>*0.001≤<i>p</i>-value<0.01, and ^∧0.01≤<i>p</i>-value<0.05.</p

Population characteristics by two stage samples.

<p>HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.</p

Odds ratios (95% CIs) for environmental risk score (ERS) categorized by quintile^a (n = 3847).

a<p>Odds ratios for dichotomized phenotype (high vs. low) comparing subjects with ERS in the top 20% to those in the bottom 20%, adjusted for covariates and micronutrients.</p>b<p>Dichotomization thresholds: 200 mg/dL for total cholesterol, 40 mg/dL (male) or 50 mg/dL (female) for HDL, 130 mg/dL for LDL and 150 mg/dL for triglyceride.</p>c<p>Pollutants selected by single-phenotype regression (n = 13, 9, 5 and 27 for total cholesterol, HDL, LDL and triglyceride, respectively) to construct ERS, adjusted for phenotype-specific micronutrients.</p>d<p>Pollutants selected by multi-phenotype regression (n = 45) to construct ERS, adjusted for union of selected micronutrients (n = 14).</p>e<p>ERS constructed with coefficient estimates from single-pollutant models as weights.</p>f<p>ERS constructed with coefficient estimates from multi-pollutant models as weights.</p

Schematic plot of statistical methods for Environmental Risk Score.

<p>Schematic plot of statistical methods for Environmental Risk Score.</p

Risk prediction by continuous environmental risk score (ERS) using single-phenotype approach^a (n = 3847).

a<p>Pollutants selected by single-phenotype regression (n = 13, 9, 5 and 27 for total cholesterol, HDL, LDL and triglyceride, respectively) to construct ERS which was computed in the validation data (n = 3847), with adjustment for base covariates and phenotype-specific micronutrients.</p>b<p>Continuous phenotypes dichotomized to be high vs. low by thresholds: 200 mg/dL for CHOL, 40 mg/dL (male) or 50 mg/dL (female) for HDL, 130 mg/dL for LDL and 150 mg/dL for TRIG.</p>c<p>adjusted for base covariates and phenotype-specific micronutrients.</p>d<p>Model 1 plus ERS constructed with coefficient estimates from single-pollutant models as weights.</p>e<p>Model 1 plus ERS constructed with coefficient estimates from multi-pollutant models as weights.</p>f<p>Predicted residual sums of squares.</p>g<p>Area under the receiver operating characteristic (ROC) curve and its 95% confidence interval computed with 2000 stratified bootstrap replicates.</p

Median (25^th, 75^th percentiles) concentrations of plasma inflammation biomarkers and urinary phthalate metabolites by study visit in weighted study population.

<p>*<i>P</i> < 0.05 for difference in biomarker concentration at visit 2,3, or 4 compared to visit 1.</p><p>^aCalculated from linear mixed effect (LME) models with subject specific random intercepts and slopes with biomarker predicted by study visit (continuous)</p><p>Median (25^th, 75^th percentiles) concentrations of plasma inflammation biomarkers and urinary phthalate metabolites by study visit in weighted study population.</p

Odds ratios (95% confidence intervals) of having adverse levels of HDL (40 mg/dL for men and 50 mg/dL for women) and LDL (130 mg/dL) comparing the highest vs. the lowest quintiles of ERS and individual pollutants that compose the ERS.

<p>Models were adjusted for age, gender, race/ethnicity, education, BMI, and phenotype-specific micronutrients.</p

Associations between Maternal Biomarkers of Phthalate Exposure and Inflammation Using Repeated Measurements across Pregnancy

<div><p>Phthalate exposure is prevalent in populations worldwide, including pregnant women. Maternal urinary metabolite concentrations have been associated with adverse reproductive outcomes, but underlying mechanisms remain unclear. Here we investigate inflammation as a possible pathway by examining phthalates in association with inflammation biomarkers, including C-reactive protein (CRP) and a panel of cytokines (IL-1β, IL-6, IL-10, and TNF-α) in a repeated measures analysis of pregnant women (N = 480). Urinary phthalate metabolites and plasma inflammation biomarkers were measured from samples collected at up to four visits per subject during gestation (median 10, 18, 26, and 35 weeks). Associations were examined using mixed models to account for within-individual correlation of measures. Few statistically significant associations or clear trends were observed, although in full models mono-carboxypropyl phthalate (MCPP) was significantly (percent change with interquartile range increase in exposure [%Δ] = 8.89, 95% confidence interval [CI] = 3.28, 14.8), and mono-benzyl phthalate (MBzP) was suggestively (%Δ = 6.79, 95%CI = -1.21, 15.4) associated with IL-6. Overall these findings show little evidence of an association between phthalate exposure and peripheral inflammation in pregnant women. To investigate inflammation as a mechanism of phthalate effects in humans, biomarkers from target tissues or fluids, though difficult to measure in large-scale studies, may be necessary to detect effects.</p></div

Residual plots from generalized additive mixed models of the associations between mono-benzyl phthalate (MBzP) and mono-carboxypropyl phthalate (MCPP) and IL-6.

<p>Residual plots from generalized additive mixed models of the associations between mono-benzyl phthalate (MBzP) and mono-carboxypropyl phthalate (MCPP) and IL-6.</p