Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements.

BACKGROUND:Environmental health and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field quality control (QC) data. Reporting QC data along with chemical measurements in biological and environmental samples allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry. OBJECTIVES:We discuss approaches for implementing quality assurance/quality control (QA/QC) in environmental exposure measurement projects and describe our process for interpreting QC results and drawing conclusions about data validity. DISCUSSION:Our methods build upon existing guidance and years of practical experience collecting exposure data and analyzing it in collaboration with contract and university laboratories, as well as the Centers for Disease Control and Prevention. With real examples from our data, we demonstrate problems that would not have come to light had we not engaged with our QC data and incorporated field QC samples in our study design. Our approach focuses on descriptive analyses and data visualizations that have been compatible with diverse exposure studies with sample sizes ranging from tens to hundreds of samples. Future work could incorporate additional statistically grounded methods for larger datasets with more QC samples. CONCLUSIONS:This guidance, along with example table shells, graphics, and some sample R code, provides a useful set of tools for getting the best information from valuable environmental exposure datasets and enabling valid comparison and synthesis of exposure data across studies

Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements

BACKGROUND: Environmental health and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field quality control (QC) data. Reporting QC data along with chemical measurements in biological and environmental samples allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry. OBJECTIVES: We discuss approaches for implementing quality assurance/quality control (QA/QC) in environmental exposure measurement projects and describe our process for interpreting QC results and drawing conclusions about data validity. DISCUSSION: Our methods build upon existing guidance and years of practical experience collecting exposure data and analyzing it in collaboration with contract and university laboratories, as well as the Centers for Disease Control and Prevention. With real examples from our data, we demonstrate problems that would not have come to light had we not engaged with our QC data and incorporated field QC samples in our study design. Our approach focuses on descriptive analyses and data visualizations that have been compatible with diverse exposure studies with sample sizes ranging from tens to hundreds of samples. Future work could incorporate additional statistically grounded methods for larger datasets with more QC samples. CONCLUSIONS: This guidance, along with example table shells, graphics, and some sample R code, provides a useful set of tools for getting the best information from valuable environmental exposure datasets and enabling valid comparison and synthesis of exposure data across studiU.S. Department of Housing and Urban Development (Grant MAHHU0005-12

Environmental chemicals and breast cancer: An updated review of epidemiological literature informed by biological mechanisms.

BackgroundMany common environmental chemicals are mammary gland carcinogens in animal studies, activate relevant hormonal pathways, or enhance mammary gland susceptibility to carcinogenesis. Breast cancer's long latency and multifactorial etiology make evaluation of these chemicals in humans challenging.ObjectiveFor chemicals previously identified as mammary gland toxicants, we evaluated epidemiologic studies published since our 2007 review. We assessed whether study designs captured relevant exposures and disease features suggested by toxicological and biological evidence of genotoxicity, endocrine disruption, tumor promotion, or disruption of mammary gland development.MethodsWe systematically searched the PubMed database for articles with breast cancer outcomes published in 2006-2016 using terms for 134 environmental chemicals, sources, or biomarkers of exposure. We critically reviewed the articles.ResultsWe identified 158 articles. Consistent with experimental evidence, a few key studies suggested higher risk for exposures during breast development to dichlorodiphenyltrichloroethane (DDT), dioxins, perfluorooctane-sulfonamide (PFOSA), and air pollution (risk estimates ranged from 2.14 to 5.0), and for occupational exposure to solvents and other mammary carcinogens, such as gasoline components (risk estimates ranged from 1.42 to 3.31). Notably, one 50-year cohort study captured exposure to DDT during several critical windows for breast development (in utero, adolescence, pregnancy) and when this chemical was still in use. Most other studies did not assess exposure during a biologically relevant window or specify the timing of exposure. Few studies considered genetic variation, but the Long Island Breast Cancer Study Project reported higher breast cancer risk for polycyclic aromatic hydrocarbons (PAHs) in women with certain genetic variations, especially in DNA repair genes.ConclusionsNew studies that targeted toxicologically relevant chemicals and captured biological hypotheses about genetic variants or windows of breast susceptibility added to evidence of links between environmental chemicals and breast cancer. However, many biologically relevant chemicals, including current-use consumer product chemicals, have not been adequately studied in humans. Studies are challenged to reconstruct exposures that occurred decades before diagnosis or access biological samples stored that long. Other problems include measuring rapidly metabolized chemicals and evaluating exposure to mixtures

Environmental chemicals and breast cancer: An updated review of epidemiological literature informed by biological mechanisms.

BackgroundMany common environmental chemicals are mammary gland carcinogens in animal studies, activate relevant hormonal pathways, or enhance mammary gland susceptibility to carcinogenesis. Breast cancer's long latency and multifactorial etiology make evaluation of these chemicals in humans challenging.ObjectiveFor chemicals previously identified as mammary gland toxicants, we evaluated epidemiologic studies published since our 2007 review. We assessed whether study designs captured relevant exposures and disease features suggested by toxicological and biological evidence of genotoxicity, endocrine disruption, tumor promotion, or disruption of mammary gland development.MethodsWe systematically searched the PubMed database for articles with breast cancer outcomes published in 2006-2016 using terms for 134 environmental chemicals, sources, or biomarkers of exposure. We critically reviewed the articles.ResultsWe identified 158 articles. Consistent with experimental evidence, a few key studies suggested higher risk for exposures during breast development to dichlorodiphenyltrichloroethane (DDT), dioxins, perfluorooctane-sulfonamide (PFOSA), and air pollution (risk estimates ranged from 2.14 to 5.0), and for occupational exposure to solvents and other mammary carcinogens, such as gasoline components (risk estimates ranged from 1.42 to 3.31). Notably, one 50-year cohort study captured exposure to DDT during several critical windows for breast development (in utero, adolescence, pregnancy) and when this chemical was still in use. Most other studies did not assess exposure during a biologically relevant window or specify the timing of exposure. Few studies considered genetic variation, but the Long Island Breast Cancer Study Project reported higher breast cancer risk for polycyclic aromatic hydrocarbons (PAHs) in women with certain genetic variations, especially in DNA repair genes.ConclusionsNew studies that targeted toxicologically relevant chemicals and captured biological hypotheses about genetic variants or windows of breast susceptibility added to evidence of links between environmental chemicals and breast cancer. However, many biologically relevant chemicals, including current-use consumer product chemicals, have not been adequately studied in humans. Studies are challenged to reconstruct exposures that occurred decades before diagnosis or access biological samples stored that long. Other problems include measuring rapidly metabolized chemicals and evaluating exposure to mixtures

Application of High-Resolution Skeletal Imaging to Measurements of Volumetric BMD and Skeletal Microarchitecture in Chinese-American and White Women: Explanation of a Paradox

Asian women have lower rates of hip and forearm fractures despite lower areal BMD (aBMD) by DXA compared with white women and other racial groups. We hypothesized that the lower fracture rates may be explained by more favorable measurements of volumetric BMD (vBMD) and microarchitectural properties, despite lower areal BMD. To address this hypothesis, we used high-resolution pQCT (HRpQCT), a new method that can provide this information noninvasively. We studied 63 premenopausal Chinese-American (n = 31) and white (n = 32) women with DXA and HRpQCT. aBMD by DXA did not differ between groups for the lumbar spine (1.017 ± 0.108 versus 1.028 ± 0.152 g/cm2; p = 0.7), total hip (0.910 ± 0.093 versus 0.932 ± 0.134 g/cm2; p = 0.5), femoral neck (0.788 ± 0.083 versus 0.809 ± 0.129 g/cm2; p = 0.4), or one-third radius (0.691 ± 0.052 versus 0.708 ± 0.047 g/cm2; p = 0.2). HRpQCT at the radius indicated greater trabecular (168 ± 41 versus 137 ± 33 mg HA/cm3; p = <0.01) and cortical (963 ± 46 versus 915 ± 42 mg HA/cm3; p < 0.0001) density; trabecular bone to tissue volume (0.140 ± 0.034 versus 0.114 ± 0.028; p = <0.01); trabecular (0.075 ± 0.013 versus 0.062 ± 0.009 mm; p < 0.0001) and cortical thickness (0.98 ± 0.16 versus 0.80 ± 0.14 mm; p < 0.0001); and lower total bone area (197 ± 34 versus 232 ± 33 mm2; p = <0.001) in the Chinese versus white women and no difference in trabecular number, spacing, or inhomogeneity before adjustment for covariates. Similar results were observed at the weight-bearing tibia. At the radius, adjustment for covariates did not change the direction or significance of differences except for bone, which became similar between the groups. However, at the tibia, adjustment for covariates attenuated differences in cortical BMD and bone area and accentuated differences in trabecular microarchitecture such that Chinese women additionally had higher trabecular number and lower trabecular spacing, as well as inhomogeneity after adjustment. Using the high-resolution technology, the results provide a mechanistic explanation for why Chinese women have fewer hip and forearm fractures than white women