Identifying Subsets of Complex Mixtures Most Associated With Complex Diseases

Chris Gennings, Gennings C, Roy Sabo, Sabo R, Carney E, Ed Carney
Epidemiology (Cambridge, Mass.) · 2010 · vol. 21(4) , pp. S77–S84 · doi:10.1097/ede.0b013e3181ce946c · PMID:21422968 · W1989674032
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An optimization algorithm identified PCB 114 as the primary driver of the association between antiestrogenic PCBs and endometriosis, suggesting potential estrogenicity or alternative mechanisms.

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This study applies a novel optimization algorithm to epidemiologic data to identify specific subsets of polychlorinated biphenyl (PCB) mixtures most strongly associated with endometriosis risk. The analysis revealed that PCB 114 accounted for nearly all the association within the antiestrogenic subgroup, while PCBs 99 and 188 drove the association within the estrogenic subgroup, challenging prior assumptions about estrogen dependency. The authors note that these findings generate testable hypotheses regarding alternative biological mechanisms, although the precise role of PCB mixtures in endometriosis remains unclear due to the exploratory nature of the statistical approach. This paper is centrally about endometriosis — specifically investigating the association between environmental exposures to polychlorinated biphenyls and the risk of developing the condition.

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Abstract

BACKGROUND: Exploratory statistical analyses have been conducted on an epidemiologic data set in which the relationship was examined between exposure to polychlorinated biphenyl (PCB) mixtures and risk of endometriosis in women. In that study, the association between endometriosis and the sum of 4 antiestrogenic PCBs (PCBs 105, 114, 126, and 169) was borderline significant (P = 0.079), whereas an association was not found (P = 0.681) with the sum of 12 estrogenic PCBs. This finding was inconsistent with the widely held notion that endometriosis is an estrogen-dependent disease, prompting further statistical analyses to explore these associations in more detail. METHODS: As an alternative method of data reduction, an optimization algorithm was developed to determine weights in a linear combination of scaled PCB levels that has the strongest possible association with the risk of endometriosis. RESULTS: Application of this method to the antiestrogenic PCB subgroup revealed that PCB 114 was responsible for nearly 100% of the association. The fact that PCB 114 is neither the most potent nor abundant antiestrogen in the mixture suggests that PCB 114 might be estrogenic or that the association may be driven by a different mechanism. Use of this statistical weighting method for further analyses of 12 estrogenic PCBs showed that any association with endometriosis was driven mainly by PCBs 99 and 188 and possibly a few others. CONCLUSION: Although the role of PCB mixtures in endometriosis remains unclear, these results demonstrate how the integration of refined statistical methods coupled with toxicologic and biologic interpretation can generate testable hypotheses that might not otherwise have been generated.
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Background

Exploratory statistical analyses have been conducted on an epidemiologic data set in which the relationship was examined between exposure to polychlorinated biphenyl (PCB) mixtures and risk of endometriosis in women. In that study, the association between endometriosis and the sum of 4 antiestrogenic PCBs (PCBs 105, 114, 126, and 169) was borderline significant (P = 0.079), whereas an association was not found (P = 0.681) with the sum of 12 estrogenic PCBs. This finding was inconsistent with the widely held notion that endometriosis is an estrogen-dependent disease, prompting further statistical analyses to explore these associations in more detail.

Methods

As an alternative method of data reduction, an optimization algorithm was developed to determine weights in a linear combination of scaled PCB levels that has the strongest possible association with the risk of endometriosis.

Results

Application of this method to the antiestrogenic PCB subgroup revealed that PCB 114 was responsible for nearly 100% of the association. The fact that PCB 114 is neither the most potent nor abundant antiestrogen in the mixture suggests that PCB 114 might be estrogenic or that the association may be driven by a different mechanism. Use of this statistical weighting method for further analyses of 12 estrogenic PCBs showed that any association with endometriosis was driven mainly by PCBs 99 and 188 and possibly a few others.

Conclusion

Although the role of PCB mixtures in endometriosis remains unclear, these results demonstrate how the integration of refined statistical methods coupled with toxicologic and biologic interpretation can generate testable hypotheses that might not otherwise have been generated.

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Condition tags

endometriosis

MeSH descriptors

Complex Mixtures Data Interpretation, Statistical Endometriosis Environmental Pollutants Estrogen Receptor Modulators Estrogens Polychlorinated Biphenyls Adolescent Adult Algorithms Complex Mixtures Complex Mixtures Endometriosis Environmental Exposure Environmental Exposure Environmental Pollutants Environmental Pollutants Estrogen Receptor Modulators Estrogen Receptor Modulators Estrogens

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