Perinatal exposure to polychlorinated biphenyls and dioxins through dietary intake

Polychlorinated biphenyls (PCBs) and dioxins (polychlorinated dibenzo p-dioxins and dibenzofurans) are potentially hazardous compounds. Since food is the major source (>90%) for the accumulation of PCBs and dioxins in the human body, food habits in women determine the degree of fetal exposure and levels in human milk. In order to investigate an association between dietary intake and PCB and dioxin levels in human milk and PCB levels in maternal and cord plasma, the food intake of 418 Dutch women during pregnancy was recorded using semi-quantitative food frequency questionnaires. After adjusting for covariates, a weak association was found between the estimated dietary intake of 2,3,7,8-tetrachlorodibenzo p-dioxin (2,3,7,8-TCDD), dioxins, and planar PCBs and their corresponding levels in breast milk. The estimated dietary intake of 2,3,7,8-TCDD, dioxins, and planar PCBs was also related to the PCB levels in maternal and cord plasma. Dairy products accounted for about half and industrial oils for about a quarter of the estimated 2,3,7,8-TCDD, dioxin, and the planar PCB intake. It is concluded that the contribution of a pregnancy related diet to PCB and dioxin levels in human milk and to PCB levels in maternal and cord plasma is relatively low. Decrease of exposure to PCBs and dioxins of the fetus and the neonate probably requires long-term reduction of the intake of these pollutants. Substitution of normal cheese by low-fat cheese and the use of vegetable oils instead of fish oils in the preparation of foodstuffs by the food industry could contribute to a reduced intake of PCBs and dioxins

Perinatal exposure to polychlorinated biphenyls and dioxins and its effect on neonatal neurological development

Polychlorinated biphenyls (PCBs) and dioxins (polychlorinated dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs)) are widespread environmental contaminants which are neurotoxic in animals. Perinatal exposure to PCBs, PCDDs, and PCDFs occurs prenatally via the placenta and postnatally via breast milk. To investigate whether such an exposure affects the neonatal neurological condition, the neurological optimality of 418 Dutch newborns was evaluated with the Prechtl neurological examination. Half of the infants were breast-fed, the other half were formula-fed, representing a relatively high against a relatively low postnatally exposed group, respectively. As an index of prenatal exposure, four non-planar PCBs in cord and maternal plasma were used. These PCB levels were not related to neurological function. As measures of combined pre- and early neonatal exposure, 17 dioxin congeners, three planar, and 23 non-planar PCB congeners were determined in human milk in the second week after delivery. Higher levels of PCBs, PCDDs, and PCDFs in breast milk were related to reduced neonatal neurological optimality. Higher levels of planar PCBs in breast milk were associated with a higher incidence of hypotonia. This study confirms previous reports about the neurotoxic effects of these compounds on the developing brain of newborn infants

The Mantel-Haenszel Procedure Revisited:Models and Generalizations

<p>Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented.</p>