ical mixture including high versus low levels is indicated by darker and larger nodes, respectively.

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 percenttile 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.

5. Conclusion

PCB congener #114 was identified by the BBN approach as the most influential compound within a mixture of 68 PCB congeners, conferring a 200% increased likelihood of an endometriosis diagnosis following laparoscopy. Thus, the BBN may be an approach for corroborating results across study populations, or in developing weighting schemes aimed at estimating the magnitude of effects exerted by individual compounds in keeping more closely with the manner in which human exposure arises.

6. Acknowledgements

The work was supported in part with funding from the American Chemistry Council and the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health & Human Development.

REFERENCES

  1. G. M. Buck Louis, C. D. Lynch and M. A. Cooney, “Environmental Influences on Female Fecundity and Fertility,” Seminars in Reproductive Medicine, Vol. 24, No. 3, 2006, pp. 147-155. doi:10.1055/s-2006-944421
  2. G. Torf, L. Hagmar, A. Giwercman and J. P. Bonde, “Epidemiological Evidence on Reproductive Effects of Persistent Organochlorines in Humans,” Reproductive Toxicology, Vol. 19, No. 1, 2004, pp. 5-26. doi:10.1016/j.reprotox.2004.05.006
  3. A. Mayani, S. Barel, S. Soback and M. Almagor, “Dioxin Concentrations in Women with Endometriosis,” Human Reproduction, Vol. 12, No. 2, 1997, pp. 373-375. doi:10.1093/humrep/12.2.373
  4. B. Trabert, A. J. De Roos, S. M. Schwartz, U. Peters, D. Scholes, D. B. Barr, et al., “Non-Dioxin-Like Polychlorinated Biphenyls and Risk of Endometriosis,” Environmental Health Perspectives, Vol. 118, 2010, pp. 1280- 1285. doi:10.1289/ehp.0901444
  5. K. B. Moysich, P. Mendola, E. F. Schisterman, J. L. Freudenheim, C. B. Ambrosone, J. E. Vena, et al., “An Evaluation of Proposed Frameworks for Grouping Polychlorinated Biphenyl (PCB) Congener Data into Meaningful Analytic Units,” American Journal of Industrial Medicine, Vol. 35, No. 3, 1999, pp. 223-231. doi:10.1002/(SICI)1097-0274(199903)35:3<223::AID-AJIM2>3.0.CO;2-L
  6. M. S. Wolff, D. Camann, M. Gammon and S. D. Stellman, “Proposed PCB Congener Groupings for Epidemiological Studies,” Environmental Health Perspectives, Vol. 105, No. 1, 1997, pp. 13-14. doi:10.1289/ehp.9710513
  7. A. Pauwels, P. J. C. Schepens, T. D’Hooghe, L. Delbeke, M. Dhont, A. Brouwer, et al., “The Risk of Endometriosis and Exposure to Dioxins and Polychlorinated Biphenyls: A Case-Control Study of Infertile Women,” Human Reproduction, Vol. 16, No. 10, 2001, pp. 2050-2055. doi:10.1093/humrep/16.10.2050
  8. P. S. Cooke, T. Sato and D. L. Buchanan, “Disruption of Steroid Hormone Signaling by PCBs,” In: L. W. Robertson and L. G. Hansen, Eds., PCBs: Recent Advances in Environmental Toxicology and Health Effects, The University Press of Kentucky, Lexington, 2001, pp. 257-263.
  9. G. M. Buck Louis, J. M. Weiner, B. W. Whitcomb, R. Sperrazza, E. F. Schisterman, D. T. Lobdell, et al., “Environmental PCB Exposure and Risk of Endometriosis,” Human Reproduction, Vol. 20, No. 1, 2005, pp. 279-285. doi:10.1093/humrep/deh575
  10. C. Gennings, R. Sabo and E. Carney, “Identifying Subsets of Complex Mixtures Most Associated with Complex Diseases: Polychlorinated Biphenyls and Endometriosis as a Case Study,” Epidemiology, Vol. 21, 2010, pp. 77-84. doi:10.1097/EDE.0b013e3181ce946c
  11. G. S. DiZerega, D. L. Barber and G. D. Hodgen, “Endometriosis: Role of Ovarian Steroids in Initiation, Maintenance, and Suppression,” Fertility and Sterility, Vol. 33, 1980, pp. 649-653.
  12. S. Kennedy, A. Bergqqvist, C. Chapron, T. D’Hooghe, G. Dunselmans, R. Greb, et al., “ESHRE Guidelines for the Diagnosis and Treatment of Endometriosis,” Human Reproduction, Vol. 20, No. 10, 2005, pp. 2698-2704. doi:10.1093/humrep/dei135
  13. American Society for Reproductive Medicine, Practice Committee, “Endometriosis and Infertility,” Fertility and Sterility, Vol. 86, No. 5, 2006, pp. S156-S160. doi:10.1016/j.fertnstert.2006.08.014
  14. M. S. Bloom, G. M. Buck Louis, E. F. Schisterman and A. Liu, “Maternal Serum Polychlorinated Biphenyl Concentrations across Critical Windows of Human Development,” Environmental Health Perspectives, Vol. 115, 2007, pp. 1320-1324. doi:10.1289/ehp.10086
  15. D. J. Phillips, J. L. Pirkle, V. W. Burse, J. T. Pernert, L. O. Henderson and L. L. Needham, “Chlorinated Hydrocarbon Levels in Human Serum: Effects of Fasting and Feeding,” Archives of Environmental Contamination and Toxicology, Vol. 18, No. 4, 1989, pp. 495-500. doi:10.1007/BF01055015
  16. T. J. Bedford and R. M. Cooke, “Vines—A New Graphical Model for Dependent Random Variables,” Annals of Statistics, Vol. 30, No. 4, 2002, pp. 1031-1068. doi:10.1214/aos/1031689016
  17. A. M. Hanea, D. Kurowicka and R. M. Cooke, “Hybrid Method for Quantifying and Analyzing Bayesian Belief Nets,” Quality and Reliability Engineering International, Vol. 22, No. 6, 2006, pp. 709-729. doi:10.1002/qre.808
  18. A. M. Hanea, D. Kurowicka, R. M. Cook and D. M. Ababei, “Mining and Visualizing Ordinal Data with NonParametric Continuous BBNs,” Computational Statistics & Data Analysis, Vol. 54, No. 3, 2010, pp. 668-687. doi:10.1016/j.csda.2008.09.032
  19. D. Kurowicka and R. M. Cooke, “Uncertainty Analysis and High Dimensional Dependence Modeling,” Wiley, New York, 2006. doi:10.1002/0470863072
  20. S. Greenland, J. Pearl and J. M. Robins, “Causal Diagrams for Epidemiologic Research,” Epidemiology, Vol. 10, No. 1, 1999, pp. 37-48. doi:10.1097/00001648-199901000-00008
  21. Risk and Environmental Modeling Group, “Uninet 2.74 Software,” Delft University of Technology, 2008. http://dutiosc.twi.tudelft.nl/~risk/

Journal Menu >>