Verhulst et al. ± 0.98, 0.02 ± 0.89, and –0.32 ± 0.79, respec- tively. Table 2 presents results of a Spearman correlation analysis between the pollutants and possible confounders. All pollutants are signifi- cantly intercorrelated: Correlation coefficients among DDE, PCBs, and hexachlorobenzene are between 0.6 and 0.7. The correlation between the latter pollutants and dioxin-like compounds are between 0.2 and 0.3. Association of chemical toxins with BMI SDS for children between 1 and 3 years. Table 3 presents the model for the association between DDE and BMI SDS. The param- eter estimates and p-values of this model did not change when we excluded birth weight Table 1. Subject characteristics. Variable No. BMI at 1 year of age (kg/m2) BMI SDS at 1 year of age (kg/m2) BMI at 3 years of age (kg/m2) BMI SDS at 3 years of age (kg/m2) Percent boys (%) Percent from rural agglomerations (%) Birth weight (kg) Birth weight SDS Birth length (cm) Birth length SDS Age of mother (years) BMI of mother (kg/m2) BMI of father (kg/m2) Households with low income (%) Maternal smoking ever (%) Maternal smoking during pregnancy (%) Breast-feeding ever (%) Breast-feeding for at least 6 months (%) Hexachlorobenzene (ng/g lipids) DDE (ng/g lipids) Dioxin-like compounds (pg CALUX-TEQ/g lipids) PCBs (ng/g lipids) Values are mean ± SD or percent. Possible confounder Female sex Rural Flanders Age of mother (years) Birth weight SDS BMI of mother (kg/m2) BMI of father (kg/m2) Low socioeconomic status Maternal smoking ever (%) Maternal smoking during pregnancy (%) *p < 0.05; **p < 0.01. 0.29** DDE –0.04 0.16 –0.00 0.03 –0.02 –0.10 –0.17 0.00 Parameter Intercept Birth weight SDS Smoking ever DDE (ng/g lipids) DDE × maternal smoking ever Age of the child Maternal smoking ever × age of the child DDE × age of the child Maternal smoking ever × DDE × age of the child 124 SDS from this analysis. The model is com- plex because of significant interactions among DDE, smoking ever, and child’s age. Table 4 further outlines the results of the model with least-square means of BMI SDS calculated according to the mother’s smoking status, for various ages of the child (1, 2, and 3 years) and levels of DDE (10th percentile, median, and 90th percentile). We can summarize the results for DDE as follows: At 1 year of age, children of smoking mothers had a higher BMI SDS compared with children of nonsmoking moth- ers. However, there was a nonsignificant trend such that this difference diminished as DDE levels increased. At the end of the study, at Value 138 17.2 ± 1.5 1.14 ± 1.23 15.7 ± 1.8 –0.36 ± 1.56 53.3 38.7 3.4 ± 0.4 –0.25 ± 0.92 50.45 ± 1.78 0.07 ± 0.96 30.8 ± 4.1 23.19 ± 3.95 24.25 ± 3.12 13.6 44.4 9.6 66.4 23.4 34.4 ± 19.0 (range, 4.3–108.3) 212 ± 243 (range, 24–1,816) 31.0 ± 19.2 (range, 6.0–78.7) 117 ± 76 (range, 9–442) 3 years of age, the observed difference in chil- dren’s BMI SDS between smoking and non- smoking mothers was reduced, because of the faster rate of decline in BMI SDS in subjects of the former group. This relationship held for all children except those with high levels of DDE. Finally, the effect of increasing DDE levels on BMI SDS at 3 years of age was small in children of nonsmoking mothers (the differ- ence in BMI SDS for a DDE level of 450 ng/g and a DDE level of 63.7 ng/g = 0.13). On the other hand, smoking enhanced the effect of DDE on a child’s BMI SDS at 3 years of age (difference = 0.76). In the multiple imputation analysis, the bor- derline significant interaction between age and DDE became insignificant (p = 0.3). The inter- action among age, DDE, and maternal smok- ing ever remained significant. Furthermore, the effect of DDE on BMI SDS at 1 year of age increased (0.001 ± 0.001; p = 0.08). Increasing concentrations of PCBs were associated with higher BMI SDS values between 1 and 3 years of age (Table 5). For PCBs, we found no significant interactions with sex and the smoking status of the mother. Results for PCBs did not change during the multiple imputation analysis. We also performed a simi- lar analysis for the TEQ factor of the single dioxin-like PCB that we measured, PCB-118 (Van den Berg et al. 2006), but this factor was not correlated with BMI Z-score. Finally, we found no association between BMI SDS and hexachlorobenzene and dioxin-like compounds. Associations at and after birth between DDE and PCBs levels and weight and length SDS. Both maternal smoking and PCBs were associated with birth weight SDS, although their effects depended on each other (Tables 6 and 7). We observed similar effects for birth length SDS (Tables 6 and 7). DDE was not associated with birth weight or length. In a model similar to the analysis presented in Table 3, including weight SDS for children between 1 and 3 years as an outcome variable, DDE was not associated with weight SDS at 1 year of age (p = 0.5). We found indica- tions that smoking ever was associated with increased weight SDS at 1 year of age (β ± SE = 0.653 ± 0.348; p = 0.06). Similar to data presented in Table 3, we found significant interactions between maternal smoking and age (–0.253 ± 0.129; p = 0.045), DDE and age (0.001 ± 0.000; p = 0.05), and maternal smok- ing and DDE and age (0.001 ± 0.000; p = 0.04). PCBs did not influence weight SDS. Finally, PCBs were associated with a sys- tematic lower length SDS for children between 1 and 3 years of age (–0.002 ± 0.001; p = 0.04). DDE was not associated with length SDS. Discussion In this study we found that DDE had a small association with BMI SDS in 3-year-old ovlume 117 | number 1 | January 2009 • Environmental Health Perspectives Table 2. Spearman correlation analysis between pollutants and possible confounders. Hexachlorobenzene Dioxin-like compounds Table 3. The association between DDE and BMI SDS of children between 1 and 3 years of age. 0.00 0.19* 0.23* –0.04 0.10 0.08 –0.05 –0.17 0.08 PCBs –0.01 –0.01 0.40** –0.13 –0.22* –0.01 0.17 0.03 0.11 β ± SE 2.643 ± 0.298 0.310 ± 0.095 1.259 ± 0.378 –0.002 ± 0.001 –0.003 ± 0.001 –1.116 ± 0.146 –0.497 ± 0.188 0.001 ± 0.001 0.001 ± 0.001 –0.02 –0.08 –0.07 –0.03 –0.10 0.13 0.17 –0.11 –0.09 p-Value < 0.001 0.001 0.001 0.2 0.06 < 0.001 0.01 0.047 0.04