Development and validation of prediction models for blood concentrations of dioxins and PCBs using dietary intakes

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• 作者：Helen Engelstad Kvalem^a ; ^b ; ^{helen.engelstad@fhi.no} ; Anne Lise Brantsæ ; ter^a ; Helle Margrete Meltzer^a ; Hein Stigum^a ; Cathrine Thomsen^a ; Margaretha Haugen^a ; Jan Alexander^a ; Helle K. Knutsen^a
• 关键词：2 ; 3 ; 7 ; 8-TCDD ; 2 ; 3 ; 7 ; 8-tetrachlorodibenzo-p-dioxin ; bw ; body weight ; dl-compounds ; dioxin-like polychlorinated biphenyls and polychlorinated dibenzo-p-dioxins/furans ; dl-PCBs ; dioxin-like polychlorinated biphenyls ; ndl-PCBs ; non-dioxin-like polychlorinated
• 刊名：Environment International
• 出版年：2012
• 出版时间：1 December, 2012
• 年：2012
• 卷：50
• 期：Complete
• 页码：15-21
• 全文大小：518 K

文摘

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 (¡Æ CB-101, 138, 153, 183 = PCB₄). 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 (§¬_W) as measures of agreement, considering §¬_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 % (PCB₄) 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 PCB₄, (p < 0.001) and 0.66 (p < 0.001) for CB-153. §¬_W was 0.68 for sum dl-compounds 0.65 for both PCB₄ and CB-153. Out of 33 congeners 16 (13 dl-compounds and 3 ndl PCBs) had §¬_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.