Figure 1 illustrates the daily rate of change in total PCBs by reproductive outcome as a function of women’s baseline concentrations. The dependence of daily rate of change on baseline concentration is illustrated under- scoring the need to adjust for baseline con- centration when estimating the mean daily rate of change. This ﬁnding supports the use of summary statistics beyond simple correla- tions to allow simultaneous modeling of important covariates. Discussion This prospective pregnancy study with pre- conception enrollment of women is the ﬁrst to demonstrate the possible instability of PCB concentrations over critical windows such as conception and implantation, as measured by hCG-detected pregnancy. Our findings sug- gest that both the overall and daily rate of change in serum PCB concentrations may be associated with reproductive outcome, though cautious interpretation is needed given the limited cohort size and reliance on only two measurements. Further supporting these observations is the lack of change in concen- trations for women who failed to achieve an hCG-confirmed pregnancy within 12 men- strual cycles, though admittedly with a limited number of women. To this end, use of pre- conception cohorts with ascertainment of all women regardless of reproductive outcomes is essential for understanding exposure profiles relevant for the assessment of time-dependent health effects. Last, our study findings remained consistent regardless of PCB group- ing suggesting that categorization did not drive our results. In the absence of a univer- sally accepted classification scheme for the assessment of human health effects, our a pri- ori classification allowed us to formalize our assumptions when characterizing the cohort’s exposure over time. The reasons for the early changes between preconception and hCG-detected pregnancy are unknown and somewhat puzzling given the alleged persistence of these chemicals in the body. We speculate that early homeostatic changes after human conception and early embryonic development may affect chemical mobilization from lipid reserves, possibly as a result of the many physiologic changes accom- panying pregnancy (Bernstein et al. 2001) that may affect the absorption, distribution, metab- olism, and excretion of exogenous compounds (Casarett et al. 1996). Another possible inter- pretation is laboratory noise given the observed distribution of concentrations including values below the LOD [Appendix 1, Supplemental Material (online at http://www.ehponline. org/docs/2007/10086/suppl.pdf)]. We do not believe that this potential source of bias accounts for our findings, given our use of observed values as previously discussed. Regardless of the underlying mechanisms, these findings underscore the potential for variability arising from epidemiologic studies that rely on a single blood measurement of exposure, especially if captured at varying times during pregnancy or if relying only on women with clinically viable pregnancies. The utility and feasibility of prospective pregnancy studies with preconception enrollment of women or couples has been discussed previ- ously (Buck et al. 2004). In essence, preg- nancy loss may be viewed as a competing risk for birth if higher exposures or exposures dur- ing critical windows systematically are associ- ated with pregnancy outcome. Bias also may arise from collider stratiﬁcation (Hernan et al. 2004) that may arise when an intermediate variable such as reproductive outcome is used to stratify the analysis. Because reproductive outcome could be directly affected by two or more other unmeasured variables, stratifying on it can generate spurious study findings. However, it is unlikely that this type of bias would explain the large differences observed in our study. Still, we recognize the need for the results to be interpreted within the con- text of this validity threat. Our results dis- courage continued reliance on a single PCB congener such as PCB-153 for assessing the reproductive or developmental toxicity of all PCBs or other persistent compounds in the absence of further empirical assessment. 0.15 0.10 )y 0.05 a d y b m 0.00 u r e s g /g–0.05 n ( s B C lP–0.10 a t o t n ie–0.15 g n a h c fo–0.20 e t a r y l ia–0.25 D –0.30 –0.35 PCBs and critical windows We are unaware of any prospective preg- nancy studies with preconception enrollment that quantified serum PCBs concentrations across critical windows. To this end, we believe our longitudinal cohort study is the ﬁrst to pro- vide empirical data regarding mean changes in serum concentrations of PCBs from the pre- conception-to-postnatal window of human development. A few previous investigators have assessed the stability of select PCB congeners across trimesters of clinically established preg- nancies and reported them to be highly cor- related (Longnecker et al. 1999), as do investigators comparing maternal concentra- tions during established clinical pregnancies with cord or postnatal concentrations (Ayotte et al. 2003; Jarrell et al. 2005). However, such correlation coefﬁcients cannot be adjusted for time intervals or other relevant covariates. The median prenatal total PCB concentration for the 48 women giving birth in our study is com- parable to that for the 67 ﬁrst trimester women in the study by Longnecker et al. (1999), sug- gesting similarity in study cohorts with regard to exposure status (4.5 ng/g serum and 4.4 µg/L serum, assuming nanograms per gram ≈ micrograms per liter ≈ parts per billion). Of added note is the capture of all hCG pregnancies in our study with the use of home pregnancy test kits and women failing to conceive along with clinical pregnancies. A priori, we were interested in obtaining both Live birth (n = 48) Early pregnancy loss (n = 10) × Infertility (n = 9) –2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 Centered baseline total PCBs (ng/g serum) Figure 1. Daily rate of change in total PCB concentrations as a function of baseline PCB concentrations. Solid line, linear regression best ﬁt line for live delivery group; dashed line, linear regression best ﬁt line for early loss group; dotted line, linear regression best ﬁt line for infertile group. Environmental Health Perspectives • VOLUME 115 | NUMBER 9 | September 2007 1323