Caroline Dilworth’s research while affiliated with National Institute of Environmental Health Sciences, National Institutes of Health and other places

The National Institute of Environmental Health Sciences (NIEHS) introduces a new translational research framework that builds upon previous biomedical models to create a more comprehensive and integrated environmental health paradigm. The framework was developed as a graphical construct that illustrates the complexity of designing, implementing, and tracking translational research in environmental health. We conceptualize translational research as a series of concentric rings and nodes, defining "translation" as movement either from one ring to another or between nodes on a ring. A "Fundamental Questions" ring expands upon the research described in other frameworks as "basic" to include three interrelated concepts critical to basic science research: research questions, experimental settings, and organisms. This feature enables us to capture more granularity and thus facilitates an approach for categorizing translational research and its growth over time. We anticipate that the framework will help researchers develop compelling long-term translational research stories and accelerate public health impacts by clearly mapping out opportunities for collaborations. By using this paradigm, researchers everywhere will be better positioned to design research programs, identify research partners based on cross-disciplinary research needs, identify stakeholders who are likely to use the research for environmental decision-making and intervention, and track progress toward common goals. https://doi.org/10.1289/EHP3657.

The rise of advanced technologies for characterizing human populations at the molecular level, from sequence to function, is shifting disease prevention paradigms toward personalized strategies. Because minimization of adverse outcomes is a key driver for treatment decisions for diseased populations, developing personalized therapy strategies represents an important dimension of both precision medicine and personalized prevention. In this commentary, we highlight recently developed enabling technologies in the field of DNA damage, DNA repair, and mutagenesis. We propose that omics approaches and functional assays can be integrated into population studies that fuse basic, translational and clinical research with commercial expertise in order to accelerate personalized prevention and treatment of cancer and other diseases linked to aberrant responses to DNA damage. This collaborative approach is generally applicable to efforts to develop data-driven, individualized prevention and treatment strategies for other diseases. We also recommend strategies for maximizing the use of biological samples for epidemiological studies, and for applying emerging technologies to clinical applications.

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.