Exposome-wide Association Study for Metabolic Syndrome

Liquid and gas-chromatography-mass spectrometry methods for exposome analysis

Mass spectrometry-based methods have become fundamental to exposome research, providing the capability to explore a broad spectrum of chemical exposures. Liquid and gas chromatography coupled with low/high-resolution mass spectrometry (MS) are among the most frequently employed platforms due to their sensitivity and accuracy. However, these approaches present challenges, such as the inherent complexity of MS data and the expertise of biologists, chemists, clinicians, and data analysts to integrate and interpret MS data with other datasets effectively. The "omics" era advances rapidly, driven by developments of AI-based algorithms and an increase in accessible data; nevertheless, further efforts are necessary to ensure that exposomics outputs are comparable and reproducible, thus enhancing research findings. This review outlines the principles of MS-based methods for the exposome analytical pipeline, from sample collection to data analysis. We summarize and review both standard and cutting-edge strategies in exposome research, covering sample preparation, focusing on MS-based platforms, data acquisition strategies, and data annotation. The ultimate goal of this review is to highlight applications that enable the simultaneous analysis of endogenous metabolites and xenobiotics, which can help enhance our understanding of the impact of human exposure on health and disease and support personalized healthcare.

Machine learning-based health environmental-clinical risk scores in European children

BACKGROUND

Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.

METHODS

In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions.

RESULTS

ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health.

CONCLUSIONS

Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

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.

Examination of the exposome in an animal model: The impact of high fat diet and rat strain on local and systemic immune markers following occupational welding fume exposure

The goal of this study was to investigate the impact of multiple exposomal factors (genetics, lifestyle factors, environmental/occupational exposures) on pulmonary inflammation and corresponding alterations in local/systemic immune parameters. Accordingly, male Sprague-Dawley (SD) and Brown Norway (BN) rats were maintained on either regular (Reg) or high fat (HF) diets for 24wk. Welding fume (WF) exposure (inhalation) occurred between 7 and 12wk. Rats were euthanized at 7, 12, and 24wk to evaluate local and systemic immune markers corresponding to the baseline, exposure, and recovery phases of the study, respectively. At 7wk, HF-fed animals exhibited several immune alterations (blood leukocyte/neutrophil number, lymph node B-cell proportionality)-effects which were more pronounced in SD rats. Indices of lung injury/inflammation were elevated in all WF-exposed animals at 12wk; however, diet appeared to preferentially impact SD rats at this time point, as several inflammatory markers (lymph node cellularity, lung neutrophils) were further elevated in HF over Reg animals. Overall, SD rats exhibited the greatest capacity for recovery by 24wk. In BN rats, resolution of immune alterations was further compromised by HF diet, as many exposure-induced alterations in local/systemic immune markers were still evident in HF/WF animals at 24wk. Collectively, HF diet appeared to have a greater impact on global immune status and exposure-induced lung injury in SD rats, but a more pronounced effect on inflammation resolution in BN rats. These results illustrate the combined impact of genetic, lifestyle, and environmental factors in modulating immunological responsivity and emphasize the importance of the exposome in shaping biological responses.

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.