title: Identifying subsets of complex mixtures most associated with complex diseases: polychlorinated biphenyls and endometriosis as a case study.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298-0032, USA. gennings@vcu.edu
authors.forename: Chris
authors.initials: C
authors.lastname: Gennings
authors.forename: Roy
authors.initials: R
authors.lastname: Sabo
authors.forename: Ed
authors.initials: E
authors.lastname: Carney
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D045424:Complex Mixtures / Q000097:blood* / Q000633:toxicity; D003627:Data Interpretation, Statistical; D004715:Endometriosis / Q000453:epidemiology*; D004781:Environmental Exposure / Q000706:statistics & numerical data; D004785:Environmental Pollutants / Q000097:blood* / Q000633:toxicity; D020847:Estrogen Receptor Modulators / Q000097:blood* / Q000633:toxicity; D004967:Estrogens / Q000097:blood* / Q000633:toxicity; D005260:Female; D006801:Humans; D016015:Logistic Models; D011078:Polychlorinated Biphenyls / Q000097:blood* / Q000633:toxicity; D018570:Risk Assessment; D055815:Young Adult
chemical_list: D045424:Complex Mixtures; D004785:Environmental Pollutants; D020847:Estrogen Receptor Modulators; D004967:Estrogens; D011078:Polychlorinated Biphenyls