While persistent environmental chemicals such as dioxin and PCBs have been associated with endometriosis, the weight of evidence has relied upon statistical analyses that focus either on an individual chemical or on a combination of selected congeners that have been simply summed and analyzed. We are unaware of any previous efforts attempting to analyze measured chemical concentrations, and more closely capturing individual exposure scenarios (irrespective of limits of detection), to explore potential mixtures in the context of other relevant biologic covariates and their effect on reproductive outcomes such as endometriosis. We utilized a BBN framework to explore the individual effects of PCB congeners and the odds of an endometriosis diagnosis so that the joint distribution of PCB concentrations and endometriosis could be assessed in the context of potential confounding among the measured concentrations. To this end, the BBN approach incorporates both a data-driven reduction approach to identify particular chemicals driving the effect conditional on all other exposures and biologically relevant covariates for endometriosis. Unlike other modeling approaches that may fail to reach convergence when sample size is limited relative to the number of chemicals being studied, the BBN approach is robust to this concern.
Our BBN identified a range of AORs for individual PCB congeners ranging from <1 to >3. PCB#114 conferred the largest effect on the odds of an endometriosis diagnosis in this study cohort among the 62 PCB congeners assessed. In fact, high (75th percentile) levels of PCB #114 conferred three times the odds of endometriosis in comparison to low (25th percentile) levels. More interestingly, the effect of PCB#114 was greatly enhanced in mixtures with elevated levels of PCB congeners #99, #101, #136, and #153. In the context of the 75th percentile for PCB congeners #99, #101, #136, #153, the AOR for PCB #114 increased nearly 11-fold in comparison to women with all concentrations in the 25th percentile. Moreover, conditional on mixtures with high levels of PCB #114, the AOR for the other four influential congeners was 1.65. Even though the associated CI included one, the relatively large nominal value of the AOR points toward a possible incremental effect of the PCBs when evaluated in the context of a mixture. Combined these findings underscore the need to consider chemical mixtures, particularly since there may be geographic differences in the types of mixtures to which study populations are exposed. We believe that a more thorough analysis of all measured compounds (and the presentation of such data) is informative not only for the evaluation of potential health risks, but in delineating how mixtures may vary and whether an individual compound is etiologic irrespective of study population.
There are important limitations underlying our work that need to be considered when interpreting the results. Our intent was to demonstrate the feasibility of the BBN approach for assessing chemical mixtures to identify signals that may inform etiologic or mechanistic research. To this end, the BBN approach can be viewed as an empirically based data reduction approach and not one for determining etiology, per se. This is important given our cohort size, though it is comparable to many published studies on this topic. Other data-reduction methods such as principal component analysis (PCA), canonical correlation analysis, factor analysis and structural equation techniques often require normality assumptions for optimal performance. The utility of the BBN approach is its lack of assumptions required for parametric models including the normality assumption among others. Thus, findings from the BBN are more robust compared to those obtained from methods that rely on parametric assumptions.
Previous approaches that sum congeners essentially assign equal weights to all PCBs and, thereby, may not fully account for influential compounds. Gennings et al. [10
] develop a novel method where differential weights are assigned to chemicals in a linear combination according to an optimization procedure, thereby allowing subsets of congeners to be differentially associated with the outcome. Methods such as PCA produce weighted sums of congeners on the basis of observed variability of the congeners in the mixture without accounting for specific of each congener on the outcome under study. Moreover, the weights in the linear combination need not be directly related to individual influences. In the BBN approach, the relative contribution of congeners in the mixture can be evaluated with regard to the analytic model as they are derived from the data resulting added flexibility. The BBN approach can serve as a tool for determining the relative influence of chemicals in a mixture when limited information is available to advise the investigator, and in generating testable hypotheses for future research.
Of all the PCB congeners assessed, #114 was most informative for endometriosis in this cohort of women. The extent to which #114 is etiologically associated with endometriosis awaits corroboration, but hopefully illustrates the utility of the BBN approach for identifying possible etiologic signals driving observed health effects for a particular cohort or study sample. Still, the biological interpretation of congeners identified as relevant to human health or disease outcomes through BBN approaches requires toxicologic and other biologic input. As with any modeling approach, our findings are dependent upon valid and reproducible laboratory measurements in the context of biologically plausible specified models. Clearly, a team science approach is needed in building the BBN and in interpreting the results. However, compounds identified as being important for disease outcomes may warrant subsequent experimental research aimed at determining mechanistic pathways to aid in the interpretation of findings including possible statistical artifacts.