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Quantifying the impact of exposure to environmental chemical mixtures is important for identifying risk factors for diseases and developing more targeted public health interventions. The National Institute of Environmental Health Sciences (NIEHS) held a workshop in July 2015 to address the need to develop novel statistical approaches for multi-pollutant epidemiology studies. The primary objective of the workshop was to identify and compare different statistical approaches and methods for analyzing complex chemical mixtures data in both simulated and real-world data sets. At the workshop, participants compared approaches and results and speculated as to why they may have differed. Several themes emerged: a) no one statistical approach appeared to outperform the others, b) many methods included some form of variable reduction or summation of the data before statistical analysis, c) the statistical approach should be selected based upon a specific hypothesis or scientific question, and d) related mixtures data should be shared among researchers to more comprehensively and accurately address methodological questions and statistical approaches. Future efforts should continue to design and optimize statistical approaches to address questions about chemical mixtures in epidemiological studies.

Characterization of the epigenome is a primary interest for children's environmental health researchers studying the environmental influences on human populations, particularly those studying the role of pregnancy and early-life exposures on later-in-life health outcomes. Our objective was to consider the state of the science in environmental epigenetics research and to focus on DNA methylation and the collective observations of many studies being conducted within the Children's Environmental Health and Disease Prevention Research Centers, as they relate to the Developmental Origins of Health and Disease (DOHaD) hypothesis. We address the current laboratory and statistical tools available for epigenetic analyses, discuss methods for validation and interpretation of findings, particularly when magnitudes of effect are small, question the functional relevance of findings, and discuss the future for environmental epigenetics research. A common finding in environmental epigenetic studies is the small-magnitude epigenetic effect sizes that result from such exposures. Although it is reasonable and necessary that we question the relevance of such small effects, we present examples in which small effects persist and have been replicated across populations and across time. We encourage a critical discourse on the interpretation of such small changes and further research on their functional relevance for children's health. The dynamic nature of the epigenome will require an emphasis on future longitudinal studies in which the epigenome is profiled over time, over changing environmental exposures, and over generations to better understand the multiple ways in which the epigenome may respond to environmental stimuli.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Maternal smoking during pregnancy, especially when sustained, leads to numerous adverse health outcomes in offspring. Pregnant women disproportionately underreport smoking and smokers tend to have lower follow-up rates to repeat questionnaires. Missing, incomplete, or inaccurate data on presence and duration of smoking in pregnancy impairs identification of novel health effects and limits adjustment for smoking in studies of other pregnancy exposures. An objective biomarker in newborns of maternal smoking during pregnancy would be valuable. We developed a biomarker of sustained maternal smoking in pregnancy using common DNA methylation platforms. Using a dimension reduction method, we developed and tested a numeric score in newborns to reflect sustained maternal smoking in pregnancy from data on cotinine, a short-term smoking biomarker measured mid-pregnancy, and Illumina450K cord blood DNA methylation from newborns in the Norwegian Mother and Child Cohort Study (MoBa). This score reliably predicted smoking status in the training set ( = 1,057; accuracy = 96%, sensitivity = 80%, specificity = 98%). Sensitivity (58%) was predictably lower in the much smaller test set ( = 221), but accuracy (91%) and specificity (97%) remained high. Reduced birth weight, a well-known effect of maternal smoking, was as strongly related to the score as to cotinine. A three-site score had lower, but acceptable, performance (accuracy = 82%, accuracy = 83%). Our smoking methylation score represents a promising novel biomarker of sustained maternal smoking during pregnancy easily calculated with Illumina450K or IlluminaEPIC data. It may help identify novel health impacts and improve adjustment for smoking when studying other risk factors with more subtle effects.

Smoking while pregnant is associated with a myriad of negative health outcomes in the child. Some of the detrimental effects may be due to epigenetic modifications, although few studies have investigated this hypothesis in detail. To characterize site-specific epigenetic modifications conferred by prenatal smoking exposure within asthmatic children. Using Illumina HumanMethylation27 microarrays, we estimated the degree of methylation at 27,578 distinct DNA sequences located primarily in gene promoters using whole blood DNA samples from the Childhood Asthma Management Program (CAMP) subset of Asthma BRIDGE childhood asthmatics (n = 527) ages 5-12 with prenatal smoking exposure data available. Using beta-regression, we screened loci for differential methylation related to prenatal smoke exposure, adjusting for gender, age and clinical site, and accounting for multiple comparisons by FDR. Of 27,578 loci evaluated, 22,131 (80%) passed quality control assessment and were analyzed. Sixty-five children (12%) had a history of prenatal smoke exposure. At an FDR of 0.05, we identified 19 CpG loci significantly associated with prenatal smoke, of which two replicated in two independent populations. Exposure was associated with a 2% increase in mean CpG methylation in FRMD4A (p = 0.01) and Cllorf52 (p = 0.001) compared to no exposure. Four additional genes, XPNPEP1, PPEF2, SMPD3 and CRYGN, were nominally associated in at least one replication group. These data suggest that prenatal exposure to tobacco smoke is associated with reproducible epigenetic changes that persist well into childhood. However, the biological significance of these altered loci remains unknown.

Offspring of older mothers are at increased risk of adverse birth outcomes, childhood cancers, type 1 diabetes, and neurodevelopmental disorders. The underlying biologic mechanisms for most of these associations remain obscure. One possibility is that maternal aging may produce lasting changes in the epigenetic features of a child's DNA. To test this, we explored the association of mothers' age at pregnancy with methylation in her offspring, using blood samples from 890 Norwegian newborns and measuring DNA methylation at more than 450,000 CpG sites across the genome. We examined replication of a maternal-age finding in an independent group of 1062 Norwegian newborns, and then in 200 US middle-aged women. Older maternal age was significantly associated with reduced methylation at four adjacent CpGs near the 2nd exon of KLHL35 in newborns (p-values ranging from 3x10-6 to 8x10-7). These associations were replicated in the independent set of newborns, and replicated again in women 40 to 60 years after their birth. This study provides the first example of parental age permanently affecting the epigenetic profile of offspring. While the specific functions of the affected gene are unknown, this finding opens the possibility that a mother's age at pregnancy could affect her child's health through epigenetic mechanisms.

Background Biorepositories archive and distribute well-characterized biospecimens for research to support the development of medical diagnostics and therapeutics. Knowledge of biobanking and associated practices is incomplete in low- and middle-income countries where disease burden is disproportionately high. In 2011, the African Society of Human Genetics (AfSHG), the National Institutes of Health (NIH), and the Wellcome Trust founded the Human Heredity and Health in Africa (H3Africa) consortium to promote genomic research in Africa and established a network of three biorepositories regionally located in East, West, and Southern Africa to support biomedical research. This manuscript describes the processes established by H3Africa biorepositories to prepare research sites to collect high-quality biospecimens for deposit at H3Africa biorepositories. Methods The biorepositories harmonized practices between the biorepositories and the research sites. The biorepositories developed guidelines to establish best practices and define biospecimen requirements; standard operating procedures (SOPs) for common processes such as biospecimen collection, processing, storage, transportation, and documentation as references; requirements for minimal associated datasets and formats; and a template material transfer agreements (MTA) to govern biospecimen exchange. The biorepositories also trained and mentored collection sites in relevant biobanking processes and procedures and verified biospecimen deposit processes. Throughout these procedures, the biorepositories followed ethical and legal requirements. Results The 20 research projects deposited 107,982 biospecimens (76% DNA, 81,067), in accordance with the ethical and legal requirements and established best practices. The biorepositories developed and customized resources and human capacity building to support the projects. [The biorepositories developed 34 guidelines, SOPs, and documents; trained 176 clinicians and scientists in over 30 topics; sensitized ethical bodies; established MTAs and reviewed consent forms for all projects; attained import permits; and evaluated pilot exercises and provided feedback. Conclusions Biobanking in low- and middle-income countries by local skilled staff is critical to advance biobanking and genomic research and requires human capacity and resources for global partnerships. Biorepositories can help build human capacity and resources to support biobanking by partnering with researchers. Partnerships can be structured and customized to incorporate document development, ethics, training, mentorship, and pilots to prepare sites to collect, process, store, and transport biospecimens of high quality for future research.

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There has been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixtures data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. This article focuses on simplifying the task of identifying which methods are most appropriate to a particular study design, data type, and scientific focus. We present an organized workflow for statistical analysis considerations in environmental mixtures data and two example applications implementing the workflow. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures' context. We also present an accompanying methods repository to increase awareness of and inform application of existing methods and new methods as they are developed. We note several methods may be equally appropriate for a specific context. This article does not present a comparison or contrast of methods or recommend one method over another. Rather, the presented workflow can be used to identify a set of methods that are appropriate for a given application. Accordingly, this effort will inform application, educate researchers (e.g., new researchers or trainees), and identify research gaps in statistical methods for environmental mixtures that warrant further development. https://doi.org/10.1289/EHP16791.

BACKGROUND: Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There has been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixture data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. OBJECTIVES: This article focuses on simplifying the task of identifying which methods are most appropriate to a particular study design, data type, and scientific focus. METHODS: We present an organized workflow for statistical analysis considerations in environmental mixtures data and two example applications implementing the workflow. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures’ context. We also present an accompanying methods repository to increase awareness of and inform application of existing methods and new methods as they are developed. DISCUSSION: We note several methods may be equally appropriate for a specific context. This article does not present a comparison or contrast of methods or recommend one method over another. Rather, the presented workflow can be used to identify a set of methods that are appropriate for a given application. Accordingly, this effort will inform application, educate researchers (e.g., new researchers or trainees), and identify research gaps in statistical methods for environmental mixtures that warrant further development.