BACKGROUND: Dioxins and PCBs accumulate in the food chain and might exert toxic effects in animals and humans. In large epidemiologic studies, exposure estimates of these compounds based on analyses of biological material might not be available or affordable. OBJECTIVES: To develop and then validate models for predicting concentrations of dioxins and PCBs in blood using a comprehensive food frequency questionnaire and blood concentrations. METHODS: Prediction models were built on data from one study (n=195), and validated in an independent study group (n=66). We used linear regression to develop predictive models for dioxins and PCBs, both sums of congeners and 33 single congeners (7 and 10 polychlorinated dibenzo-p-dioxins and furans (PCDDs/PCDFs), 12 dioxin-like polychlorinated biphenyls (PCBs: 4 non-ortho and 8 mono-ortho), sum of all the 29 dioxin-like compounds (total TEQ) and sum of 4 non dioxin-like PCBs (∑ CB-101, 138, 153, 183=PCB(4)). We used the blood concentration and dietary intake of each of the above as dependent and independent variables, while sex, parity, age, place of living, smoking status, energy intake and education were covariates. We validated the models in a new study population comparing the predicted blood concentrations with the measured blood concentrations using correlation coefficients and Weighted Kappa (К(W)) as measures of agreement, considering К(W)>0.40 as successful prediction. RESULTS: The models explained 78% (sum dioxin-like compounds), 76% (PCDDs), 76% (PCDFs), 74% (no-PCBs), 69% (mo-PCBs), 68% (PCB(4)) and 63% (CB-153) of the variance. In addition to dietary intake, age and sex were the most important covariates. The predicted blood concentrations were highly correlated with the measured values, with r=0.75 for dl-compounds 0.70 for PCB(4), (p<0.001) and 0.66 (p<0.001) for CB-153. К(W) was 0.68 for sum dl-compounds 0.65 for both PCB(4) and CB-153. Out of 33 congeners 16 (13dl-compounds and 3 ndl PCBs) had К(W)>0.40. CONCLUSIONS: The models developed had high power to predict blood levels of dioxins and PCBs and to correctly rank subjects according to high or low exposure based on dietary intake and demographic information. These models underline the value of dietary intake data for use in investigations of associations between dioxin and PCB exposure and health outcomes in large epidemiological studies with limited biomaterial for chemical analysis.
BACKGROUND:Dioxins and PCBs accumulate in the food chain and might exert toxic effects in animals and humans. In large epidemiologic studies, exposure estimates of these compounds based on analyses of biological material might not be available or affordable. OBJECTIVES: To develop and then validate models for predicting concentrations of dioxins and PCBs in blood using a comprehensive food frequency questionnaire and blood concentrations. METHODS: Prediction models were built on data from one study (n=195), and validated in an independent study group (n=66). We used linear regression to develop predictive models for dioxins and PCBs, both sums of congeners and 33 single congeners (7 and 10 polychlorinated dibenzo-p-dioxins and furans (PCDDs/PCDFs), 12 dioxin-like polychlorinated biphenyls (PCBs: 4 non-ortho and 8 mono-ortho), sum of all the 29 dioxin-like compounds (total TEQ) and sum of 4 non dioxin-like PCBs (∑ CB-101, 138, 153, 183=PCB(4)). We used the blood concentration and dietary intake of each of the above as dependent and independent variables, while sex, parity, age, place of living, smoking status, energy intake and education were covariates. We validated the models in a new study population comparing the predicted blood concentrations with the measured blood concentrations using correlation coefficients and Weighted Kappa (К(W)) as measures of agreement, considering К(W)>0.40 as successful prediction. RESULTS: The models explained 78% (sum dioxin-like compounds), 76% (PCDDs), 76% (PCDFs), 74% (no-PCBs), 69% (mo-PCBs), 68% (PCB(4)) and 63% (CB-153) of the variance. In addition to dietary intake, age and sex were the most important covariates. The predicted blood concentrations were highly correlated with the measured values, with r=0.75 for dl-compounds 0.70 for PCB(4), (p<0.001) and 0.66 (p<0.001) for CB-153. К(W) was 0.68 for sum dl-compounds 0.65 for both PCB(4) and CB-153. Out of 33 congeners 16 (13dl-compounds and 3 ndl PCBs) had К(W)>0.40. CONCLUSIONS: The models developed had high power to predict blood levels of dioxins and PCBs and to correctly rank subjects according to high or low exposure based on dietary intake and demographic information. These models underline the value of dietary intake data for use in investigations of associations between dioxin and PCB exposure and health outcomes in large epidemiological studies with limited biomaterial for chemical analysis.
Authors: Lori S Ahrenhoerster; Everett R Tate; Peter A Lakatos; Xuexia Wang; Michael D Laiosa Journal: Toxicol Appl Pharmacol Date: 2014-04-04 Impact factor: 4.219
Authors: Michael D Laiosa; Everett R Tate; Lori S Ahrenhoerster; Yuhong Chen; Demin Wang Journal: Environ Health Perspect Date: 2015-10-23 Impact factor: 9.031