Esther M John1, Jocelyn Koo2, Sue A Ingles3, Theresa H Keegan4, Jenny T Nguyen2, Catherine Thomsen5, Mary Beth Terry6, Regina M Santella7, Khue Nguyen8, Beizhan Yan8. 1. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: emjohn@stanford.edu. 2. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. 3. Department of Population and Public Health Sciences, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA. 4. Division of Hematology and Oncology, UC Davis Comprehensive Cancer Center, University of California, Davis, CA, USA. 5. Zero Breast Cancer, San Rafael, CA, USA. 6. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA. 7. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA. 8. Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA.
Abstract
BACKGROUND: Polycyclic aromatic hydrocarbon (PAH) exposures from tobacco smoke, automobile exhaust, grilled or smoked meat and other sources are widespread and are a public health concern, as many are classified as probable carcinogens and suspected endocrine-disrupting chemicals. PAH exposures can be quantified using urinary biomarkers. METHODS: Seven urinary metabolites of naphthalene, fluorene, phenanthrene, and pyrene were measured in two samples collected from girls aged 6-16 years from the San Francisco Bay Area. We used Spearman correlation coefficients (SCC) to assess correlations among metabolite concentrations (corrected for specific gravity) separately in first (n = 359) and last (N = 349) samples, and to assess consistency of measurements in samples collected up to 72 months apart. Using multivariable linear regression, we assessed variation in mean metabolites across categories of participant characteristics and potential outdoor, indoor, and dietary sources of PAH exposures. RESULTS: The detection rate of PAH metabolites was high (4 metabolites in ≥98% of first samples; 5 metabolites in ≥95% of last samples). Correlations were moderate to strong between fluorene, phenanthrene and pyrene metabolites (SCC 0.43-0.82), but weaker between naphthalene and the other metabolites (SCC 0.18-0.36). SCC between metabolites in first and last samples ranged from 0.15 to 0.49. When classifying metabolite concentrations into tertiles based on single samples (first or last samples) vs. the average of the two samples, agreement was moderate to substantial (weighted kappa statistics 0.52-0.65). For specific metabolites, concentrations varied by age, race/ethnicity, and body mass index percentile, as well as by outdoor sources (season of sample collection, street traffic), indoor sources (heating with gas, cigarette smoke), and dietary sources (frequent use of grill, consumption of smoked meat or fish) of PAH exposures. CONCLUSIONS: Urinary PAH exposure was widespread in girls aged 6-16 years and associated with several sources of exposure. Tertile classification of a single urine sample provides reliable PAH exposure ranking.
BACKGROUND: Polycyclic aromatic hydrocarbon (PAH) exposures from tobacco smoke, automobile exhaust, grilled or smoked meat and other sources are widespread and are a public health concern, as many are classified as probable carcinogens and suspected endocrine-disrupting chemicals. PAH exposures can be quantified using urinary biomarkers. METHODS: Seven urinary metabolites of naphthalene, fluorene, phenanthrene, and pyrene were measured in two samples collected from girls aged 6-16 years from the San Francisco Bay Area. We used Spearman correlation coefficients (SCC) to assess correlations among metabolite concentrations (corrected for specific gravity) separately in first (n = 359) and last (N = 349) samples, and to assess consistency of measurements in samples collected up to 72 months apart. Using multivariable linear regression, we assessed variation in mean metabolites across categories of participant characteristics and potential outdoor, indoor, and dietary sources of PAH exposures. RESULTS: The detection rate of PAH metabolites was high (4 metabolites in ≥98% of first samples; 5 metabolites in ≥95% of last samples). Correlations were moderate to strong between fluorene, phenanthrene and pyrene metabolites (SCC 0.43-0.82), but weaker between naphthalene and the other metabolites (SCC 0.18-0.36). SCC between metabolites in first and last samples ranged from 0.15 to 0.49. When classifying metabolite concentrations into tertiles based on single samples (first or last samples) vs. the average of the two samples, agreement was moderate to substantial (weighted kappa statistics 0.52-0.65). For specific metabolites, concentrations varied by age, race/ethnicity, and body mass index percentile, as well as by outdoor sources (season of sample collection, street traffic), indoor sources (heating with gas, cigarette smoke), and dietary sources (frequent use of grill, consumption of smoked meat or fish) of PAH exposures. CONCLUSIONS: Urinary PAH exposure was widespread in girls aged 6-16 years and associated with several sources of exposure. Tertile classification of a single urine sample provides reliable PAH exposure ranking.
Authors: Zheng Li; Lovisa Romanoff; Scott Bartell; Erin N Pittman; Debra A Trinidad; Michael McClean; Thomas F Webster; Andreas Sjödin Journal: Chem Res Toxicol Date: 2012-06-13 Impact factor: 3.739
Authors: Gideon St Helen; Maciej L Goniewicz; Delia Dempsey; Margaret Wilson; Peyton Jacob; Neal L Benowitz Journal: Chem Res Toxicol Date: 2012-03-29 Impact factor: 3.739
Authors: Teresa Cirillo; Paolo Montuori; Pierangela Mainardi; Imma Russo; Maria Triassi; Renata Amodio-Cocchieri Journal: J Environ Sci Health A Tox Hazard Subst Environ Eng Date: 2006 Impact factor: 2.269
Authors: James Grainger; Wenlin Huang; Donald G Patterson; Wayman E Turner; James Pirkle; Samuel P Caudill; Richard Y Wang; Larry L Needham; Eric J Sampson Journal: Environ Res Date: 2005-10-12 Impact factor: 6.498
Authors: Esther M John; Mary Beth Terry; Theresa H M Keegan; Angela R Bradbury; Julia A Knight; Wendy K Chung; Caren J Frost; Lothar Lilge; Linda Patrick-Miller; Lisa A Schwartz; Alice S Whittemore; Saundra S Buys; Mary B Daly; Irene L Andrulis Journal: Epidemiology Date: 2016-05 Impact factor: 4.822
Authors: Alexandra J White; Susan L Teitelbaum; Steven D Stellman; Jan Beyea; Susan E Steck; Irina Mordukhovich; Kathleen M McCarty; Jiyoung Ahn; Pavel Rossner; Regina M Santella; Marilie D Gammon Journal: Environ Health Date: 2014-12-12 Impact factor: 5.984
Authors: Mandy Goldberg; Aimee A D'Aloisio; Katie M O'Brien; Shanshan Zhao; Dale P Sandler Journal: Breast Cancer Res Date: 2020-10-27 Impact factor: 6.466
Authors: Esther M John; Theresa H Keegan; Mary Beth Terry; Jocelyn Koo; Sue A Ingles; Jenny T Nguyen; Catherine Thomsen; Regina M Santella; Khue Nguyen; Beizhan Yan Journal: Epidemiology Date: 2022-07-27 Impact factor: 4.860