Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Assessing the impact of specific PM2.5-Bound metallic elements on asthma emergency department visits: A case-crossover study in Taiwan.

OBJECTIVES: This study aims to assess the specific PM2.5-bound metallic elements that contribute to asthma emergency department visits by using a case-crossover study design. METHODS: This study analyzed data from 11,410 asthma emergency department visits as case group and 22,820 non-asthma onset dates occurring one week and two weeks preceding the case day as controls from 2017 to 2020. PM2.5 monitoring data and 35 PM.2.5-bound metallic elements from six different regions in Taiwan were collected. Conditional logistic regression models were used to assess the relationship between asthma and PM2.5-bound metallic elements. RESULTS: Our investigation revealed a statistically significant risk of asthma emergency department visits associated with PM2.5 exposure at lag 0, 1, 2, and 3 during autumn. Additionally, PM2.5-bound hafnium (Hf), thallium (Tl), rubidium (Rb), and aluminum (Al) exhibited a consistently significant positive correlation with asthma emergency department visits at lags 1, 2, and 3. In stratified analyses by area, age, and sex, PM2.5-bound Hf showed a significant and consistent correlation. CONCLUSIONS: This study provides evidence of PM2.5-bound metallic elements effects in asthma exacerbations, particularly for Hf. It emphasizes the importance of understanding the origins of these metallic elements and pursuing emission reductions to mitigate regional health risks.

High-confidence structural annotation of substances via multi-layer molecular network reveals the system-wide constituent alternations in milk interfered with diphenylolpropane.

The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, p < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 µg kg-1 1.7 times) and maltodextrin (original: 6.9 µg kg-1 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics.

Exploring Single-Cell Exposomics by Mass Spectrometry.

Single-cell exposomics, a revolutionary approach that investigates cell-environment interactions at cellular and subcellular levels, stands distinct from conventional bulk exposomics. Leveraging advancements in mass spectrometry, it provides a detailed perspective on cellular dynamics, interactions, and responses to environmental stimuli and their impacts on human health. This work delves into this innovative realm, highlighting the nuanced interplay between environmental stressors and biological responses at cellular and subcellular levels. The application of spatial mass spectrometry in single-cell exposomics is discussed, revealing the intricate spatial organization and molecular composition within individual cells. Cell-type-specific exposomics, shedding light on distinct susceptibilities and adaptive strategies of various cell types to environmental exposures, is also examined. The Perspective further emphasizes the integration with molecular and cellular biology approaches to validate hypotheses derived from single-cell exposomics in a comprehensive biological context. Looking toward the future, we anticipate continued technological advancements and convergence with other -omics approaches and discuss implications for environmental health research, disease progression studies, and precision medicine. The final emphasis is on the need for robust computational tools and interdisciplinary collaboration to fully leverage the potential of single-cell exposomics, acknowledging the complexities inherent to this paradigm.

Toward Advancing Precision Environmental Health: Developing a Customized Exposure Burden Score to PFAS Mixtures to Enable Equitable Comparisons Across Population Subgroups, Using Mixture Item Response Theory.

Quantifying a person's cumulative exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures is important for risk assessment, biomonitoring, and reporting of results to participants. However, different people may be exposed to different sets of PFASs due to heterogeneity in the exposure sources and patterns. Applying a single measurement model for the entire population (e.g., by summing concentrations of all PFAS analytes) assumes that each PFAS analyte is equally informative to PFAS exposure burden for all individuals. This assumption may not hold if PFAS exposure sources systematically differ within the population. However, the sociodemographic, dietary, and behavioral characteristics that underlie systematic exposure differences may not be known, or may be due to a combination of these factors. Therefore, we used mixture item response theory, an unsupervised psychometrics and data science method, to develop a customized PFAS exposure burden scoring algorithm. This scoring algorithm ensures that PFAS burden scores can be equitably compared across population subgroups. We applied our methods to PFAS biomonitoring data from the United States National Health and Nutrition Examination Survey (2013-2018). Using mixture item response theory, we found that participants with higher household incomes had higher PFAS burden scores. Asian Americans had significantly higher PFAS burden compared with non-Hispanic Whites and other race/ethnicity groups. However, some disparities were masked when using summed PFAS concentrations as the exposure metric. This work demonstrates that our summary PFAS burden metric, accounting for sources of exposure variation, may be a more fair and informative estimate of PFAS exposure.