BACKGROUND: Despite humans being exposed to complex chemical mixtures, much of the available research continues to focus on a single compound or metabolite or a select subgroup of compounds inconsistent with the nature of human exposure. Uncertainty regarding how best to model chemical mixtures coupled with few analytic approaches remains a formidable challenge and served as the impetus for study. OBJECTIVES: To identify the polychlorinated biphenyl (PCB) congener(s) within a chemical mixture that was most associated with an endometriosis diagnosis using novel graphical modeling techniques. METHODS: Bayesian Belief Network (BBN) models were developed and empirically assessed in a cohort comprising 84 women aged 18-40 years who underwent a laparoscopy or laparotomy between 1999 and 2000; 79 (94%) women had serum concentrations for 68 PCB congeners quantified. Adjusted odds ratios (AOR) for endometriosis were estimated for individual PCB congeners using BBN models. RESULTS: PCB congeners #114 (AOR = 3.01; 95% CI = 2.25, 3.77) and #136 (AOR = 1.79; 95% CI = 1.03, 2.55) were associated with an endometriosis diagnosis. Combinations of mixtures inclusive of PCB #114 were all associated with higher odds of endometriosis, underscoring its potential relation with endometriosis. CONCLUSIONS: BBN models identified PCB congener 114 as the most influential congener for the odds of an endometriosis diagnosis in the context of a 68 congener chemical mixture. BBN models offer investigators the opportunity to assess which compounds within a mixture may drive a human health effect.
BACKGROUND: Despite humans being exposed to complex chemical mixtures, much of the available research continues to focus on a single compound or metabolite or a select subgroup of compounds inconsistent with the nature of human exposure. Uncertainty regarding how best to model chemical mixtures coupled with few analytic approaches remains a formidable challenge and served as the impetus for study. OBJECTIVES: To identify the polychlorinated biphenyl (PCB) congener(s) within a chemical mixture that was most associated with an endometriosis diagnosis using novel graphical modeling techniques. METHODS: Bayesian Belief Network (BBN) models were developed and empirically assessed in a cohort comprising 84 women aged 18-40 years who underwent a laparoscopy or laparotomy between 1999 and 2000; 79 (94%) women had serum concentrations for 68 PCB congeners quantified. Adjusted odds ratios (AOR) for endometriosis were estimated for individual PCB congeners using BBN models. RESULTS:PCB congeners #114 (AOR = 3.01; 95% CI = 2.25, 3.77) and #136 (AOR = 1.79; 95% CI = 1.03, 2.55) were associated with an endometriosis diagnosis. Combinations of mixtures inclusive of PCB #114 were all associated with higher odds of endometriosis, underscoring its potential relation with endometriosis. CONCLUSIONS:BBN models identified PCB congener 114 as the most influential congener for the odds of an endometriosis diagnosis in the context of a 68 congener chemical mixture. BBN models offer investigators the opportunity to assess which compounds within a mixture may drive a human health effect.
Authors: D L Phillips; J L Pirkle; V W Burse; J T Bernert; L O Henderson; L L Needham Journal: Arch Environ Contam Toxicol Date: 1989 Jul-Aug Impact factor: 2.804
Authors: G M Buck Louis; J M Weiner; B W Whitcomb; R Sperrazza; E F Schisterman; D T Lobdell; K Crickard; H Greizerstein; P J Kostyniak Journal: Hum Reprod Date: 2004-10-28 Impact factor: 6.918
Authors: Britton Trabert; Anneclaire J De Roos; Stephen M Schwartz; Ulrike Peters; Delia Scholes; Dana B Barr; Victoria L Holt Journal: Environ Health Perspect Date: 2010-04-27 Impact factor: 9.031
Authors: Michael S Bloom; Germaine M Buck Louis; Enrique F Schisterman; Aiyi Liu; Paul J Kostyniak Journal: Environ Health Perspect Date: 2007-09 Impact factor: 9.031
Authors: Soňa Wimmerová; Martin van den Berg; Jana Chovancová; Henrieta Patayová; Todd A Jusko; Majorie B M van Duursen; Ľubica Palkovičová Murínová; Rocio F Canton; Karin I van Ede; Tomáš Trnovec Journal: Environ Int Date: 2016-08-31 Impact factor: 9.621
Authors: Mujtaba Baqar; Yumna Sadef; Sajid Rashid Ahmad; Adeel Mahmood; Abdul Qadir; Iqra Aslam; Jun Li; Gan Zhang Journal: Environ Sci Pollut Res Int Date: 2017-10-07 Impact factor: 4.223