children of nonsmoking mothers. However, this association was enhanced in children of smoking mothers. This relation seemed to be mediated by an influence on weight gain dur- ing the preschool period. Second, PCBs were also associated with higher BMI SDS values. This association was not modified by smok- ing of the mother, nor did it change with the age of the child. The association between PCBs and BMI SDS seemed to be mediated by a negative influence on birth weight and length and on length between 1 and 3 years. Our results should be considered in light of some important study limitations. We found significant associations and inter- actions with maternal smoking ever, which includes mothers who smoked before preg- nancy or during pregnancy, but not with smoking during pregnancy alone. This result could be attributable to insufficient power. Second, although it has been suggested that smoking could increase susceptibility to pollutants by interfering with immune function and by promoting oxidative stress, which could sensitize cells to pollutants, it is also known that mothers who quit smok- ing before pregnancy gain more weight dur- ing pregnancy (Favaretto et al. 2007), which could modulate both birth weight/length and cord blood pollutant levels. This could sug- gest that pharmacokinetic factors are respon- sible for these associations. Unfortunately, we did not record maternal weight gain dur- ing pregnancy in our study, which should be considered as a major study limitation. This could also suggest that maternal smoking has long-term health effects. These limitations require elucidation in future studies. A second limitation of this study is that we related reported results to BMI SDS until a child reached 3 years of age. Although there is a correlation between BMI during pre- school age and adult BMI (Nader et al. 2006; Sachdev et al. 2005), we do not know whether the correlation with BMI will persist into later periods and whether these children will actually become overweight or obese. Third, although we found no differences in confound- ers between this cohort and the larger cohort from which it originates, there are significant regional differences in exposure to these pol- lutants in Flanders (Koppen G, Den Hond E, Nelen V, Van De Mieroop E, Bruckers L, Bilau M, et al., unpublished data). Therefore, our findings cannot be generalized to the entire Flemish population. Another limitation is that the exact duration of breast-feeding was not available, so we had to create two crude vari- ables to assess postnatal exposure. Finally, as in every longitudinal study, missing data are a hindrance. However, we were able to confirm our results by multiple imputation. The preschool period is a critical time in the development of obesity, because it Environmental Health Perspectives • volume 117 | number 1 | January 2009 Childhood BMI and environmental pollution coincides with the so-called adiposity rebound phenomenon. Indeed, BMI rises in infancy but declines during the preschool years, increas- ing again during the primary school years (Rolland-Cachera et al. 1984). Even though we did not assess adiposity rebound in this study, it is accepted that higher BMI SDS dur- ing this period is associated with an increased risk of obesity in adult life (Whitaker et al. 1998). Furthermore, there is a known correla- tion between BMI during the preschool years and adult BMI (Nader et al. 2006; Sachdev et al. 2005). This is the first study demonstrat- ing that environmental pollution may influ- ence BMI during these critical years. DDE and PCBs may influence BMI by interfering with several endocrine mediators. For instance, PCB and DDE levels are related to lower thyroid hormone levels in pregnant women (Takser et al. 2005). DDT may also have estrogenic, antiandrogenic, and antiprogestin effects (Jurewicz et al. 2006; Kelce and Wilson 1997). PCBs may exert estrogenic, antiandrogenic, or antiestrogenic effects (Koppe et al. 2006). Other studies provide evidence that PCBs are associated with increased insulin resistance in adults (Lee et al. 2007; Vasiliu et al. 2006). Although endocrine disruption caused by these pollutants has been well documented, few studies have investigated the influence of pre- natal exposure to pollutants and BMI later in life. One follow-up study found that increased transplacental exposure to DDE is associated with a significantly higher weight adjusted for height in adolescent boys (Gladen et al. 2000). However, a study of the same group in adoles- cent boys with higher exposure to DDT and DDE failed to replicate this finding (Gladen et al. 2004). A study by Karmaus and Eneli (2004) demonstrates that intrauterine exposure to DDE, but not to PCBs, is associated with a higher weight and BMI in adult females. Table 4. Least-square means for BMI SDS computed according to maternal smoking status, for different levels of DDE (10th and 90th percentiles and median) at the ages of 1, 2, and 3 years. Child’s age 1 year 2 years 3 years 63.7 ng/g lipid (10th) Smoking Mean ± SE 138.9 ng/g lipid (median) Smoking Mean ± SE 450.0 ng/g lipid (90th) Smoking Mean ± SE No Yes No Yes No Yes 0.74 ± 0.15 1.42 ± 0.17 0.11 ± 0.14 0.38 ± 0.17 –0.53 ± 0.21 –0.66 ± 0.24 No Yes No Yes No Yes 0.80 ± 0.13 1.38 ± 0.14 0.15 ± 0.13 0.43 ± 0.14 –0.50 ± 0.19 –0.51 ± 0.20 No Yes No Yes No Yes 1.03 ± 0.15 1.21 ± 0.30 0.32 ± 0.15 0.65 ± 0.30 –0.39 ± 0.20 0.10 ± 0.40 Table 5. The association between PCBs and BMI SDS between 1 and 3 years. Table 6. Association between PCBs and birth weight and birth length SDS. Parameter Intercept Birth weight SDS Maternal smoking ever Maternal BMI (kg/m2) PCBs (ng/g lipid) Age of the child Parameter Birth weight SDS Intercept Maternal smoking ever Paternal weight (kg) PCBs (ng/g lipids) PCBs × maternal smoking ever Birth length SDS Intercept Maternal smoking ever Paternal height (m) Maternal height (m) PCBs (ng/g lipid) PCBs × maternal smoking ever Measure Birth weight SDS Birth length SDS Table 7. Least-square means for birth weight and birth length SDS computed according to maternal smok- ing status and for different levels of PCBs (10th and 90th percentiles and median). 48.6 ng/g lipid (10th) Smoking Mean ± SE 95.5 ng/g lipid (median) Smoking Mean ± SE 197 ng/g lipid (90th) Smoking Mean ± SE No Yes No Yes –0.29 ± 0.14 0.13 ± 0.17 0.04 ± 0.14 0.41 ± 0.17 No Yes No Yes –0.25 ± 0.11 –0.13 ± 0.13 0.08 ± 0.11 0.19 ± 0.13 No Yes No Yes β ± SE 0.758 ± 0.588 0.333 ± 0.045 0.368 ± 0.172 0.046 ± 0.022 0.003 ± 0.001 –0.766 ± 0.069 β ± SE –1.088 ± 0.537 0.369 ± 0.152 0.014 ± 0.006 –0.002 ± 0.001 –0.003 ± 0.001 –12.244 ± 2.780 0.327 ± 0.156 0.037 ± 0.012 0.036 ± 0.013 –0.002 ± 0.001 –0.003 ± 0.001 p-Value 0.2 0.001 0.03 0.04 0.03 < 0.001 p-Value 0.05 0.02 0.03 0.03 0.004 < 0.001 0.04 0.002 0.006 0.08 0.02 –0.17 ± 0.15 –0.69 ± 0.18 0.15 ± 0.16 –0.27 ± 0.18 125