Integrative Network Analysis Linking Clinical Outcomes With Environmental Exposures and Molecular Variations in Service Personnel Deployed to Balad and Bagram

A review of statistical strategies to integrate biomarkers of chemical exposure with biomarkers of effect applied in omic-scale environmental epidemiology

Humans are exposed to a growing list of synthetic chemicals, some of them becoming a major public health concern due to their capacity to impact multiple biological endpoints and contribute to a range of chronic diseases. The integration of endogenous (omic) biomarkers of effect in environmental health studies has been growing during the last decade, aiming to gain insight on the potential mechanisms linking the exposures and the clinical conditions. The emergence of high-throughput omic platforms has raised a list of statistical challenges posed by the large dimension and complexity of data generated. Thus, the aim of the present study was to critically review the current state-of-the-science about statistical approaches used to integrate endogenous biomarkers in environmental-health studies linking chemical exposures with health outcomes. The present review specifically focused on internal exposure to environmental chemical pollutants, involving both persistent organic pollutants (POPs), non-persistent pollutants like phthalates or bisphenols, and metals. We identified 42 eligible articles published since 2016, reporting 48 different statistical workflows, mostly focused on POPs and using metabolomic profiling in the intermediate layer. The outcomes were mainly binary and focused on metabolic disorders. A large diversity of statistical strategies were reported to integrate chemical mixtures and endogenous biomarkers to characterize their associations with health conditions. Multivariate regression models were the most predominant statistical method reported in the published workflows, however some studies applied latent based methods or multipollutant models to overcome the specific constraints of omic or exposure of data. A minority of studies used formal mediation analysis to characterize the indirect effects mediated by the endogenous biomarkers. The principles of each specific statistical method and overall workflow set-up are summarized in the light of highlighting their applicability, strengths and weaknesses or interpretability to gain insight into the causal structures underlying the triad: exposure, effect-biomarker and outcome.

Network Dynamics in Elemental Assimilation and Metabolism

Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element's degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.

Traditional Farming Lifestyle in Old Older Mennonites Modulates Human Milk Composition

Background

In addition to farming exposures in childhood, maternal farming exposures provide strong protection against allergic disease in their children; however, the effect of farming lifestyle on human milk (HM) composition is unknown.

Objective

This study aims to characterize the maternal immune effects of Old Order Mennonite (OOM) traditional farming lifestyle when compared with Rochester (ROC) families at higher risk for asthma and allergic diseases using HM as a proxy.

Methods

HM samples collected at median 2 months of lactation from 52 OOM and 29 ROC mothers were assayed for IgA₁ and IgA₂ antibodies, cytokines, endotoxin, HM oligosaccharides (HMOs), and targeted fatty acid (FA) metabolites. Development of early childhood atopic diseases in children by 3 years of age was assessed. In addition to group comparisons, systems level network analysis was performed to identify communities of multiple HM factors in ROC and OOM lifestyle.

Results

HM contains IgA₁ and IgA₂ antibodies broadly recognizing food, inhalant, and bacterial antigens. OOM HM has significantly higher levels of IgA to peanut, ovalbumin, dust mites, and Streptococcus equii as well TGF-β2, and IFN-λ3. A strong correlation occurred between maternal antibiotic use and levels of several HMOs. Path-based analysis of HMOs shows lower activity in the path involving lactoneohexaose (LNH) in the OOM as well as higher levels of lacto-N-neotetraose (LNnT) and two long-chain FAs C-18OH (stearic acid) and C-23OH (tricosanoic acid) compared with Rochester HM. OOM and Rochester milk formed five different clusters, e.g., butyrate production was associated with Prevotellaceae, Veillonellaceae, and Micrococcaceae cluster. Development of atopic disease in early childhood was more common in Rochester and associated with lower levels of total IgA, IgA₂ to dust mite, as well as of TSLP.

Conclusion

Traditional, agrarian lifestyle, and antibiotic use are strong regulators of maternally derived immune and metabolic factors, which may have downstream implications for postnatal developmental programming of infant's gut microbiome and immune system.

Metabolomics as a Truly Translational Tool for Precision Medicine

Metabolomics is unique among omics technologies in being applicable to metabolism and toxicity studies broadly across organisms (e.g., humans, other mammals, model organisms, and even bacteria) and across biological materials (e.g., blood, urine, saliva, biopsy, and stool), including cultured cells and subcellular fractions. Metabolomics can be used to characterize biologic response patterns in humans as well as to support mechanistic studies in model systems and ex vivo studies. A broad range of resources are available, including publicly accessible data repositories (e.g., Metabolomics Workbench), tools for biostatistics and bioinformatics (e.g., MetaboAnalyst), metabolite identification (e.g., Metlin), and pathway analysis (e.g., Kyoto Encyclopedia of Genes and Genomes). Thus, metabolomics is more than a promise of the future; metabolomics is already available as a translational approach to facilitate precision medicine. This ACT Symposium review will contain an introduction to metabolomics in toxicity studies followed by sections on translational metabolic networks, translational metabolite biomarkers of acetaminophen-induced acute liver injury, translational framework using high-resolution metabolomics for integrated pharmacokinetics and pharmacodynamics, and precision medicine applications: extracting actionable targets from untargeted metabolomics data following one year in space.

Use of Biomarkers to Assess Environmental Exposures and Health Outcomes in Deployed Troops

OBJECTIVE

This paper provides an overview of our Military Biomarkers Research Study (MBRS) designed to assess whether biomarkers can be used to retrospectively assess deployment exposures and health impacts related to deployment environmental exposures.

METHODS

The MBRS consists of four phases. Phase I was a feasibility study of stored sera. Phase II looks at associations between exposures and biomarkers. Phase III examines relationships of biomarkers and health outcomes, and Phase IV investigates in vitro biomarker changes associated with exposures to chemicals of interest. This paper briefly summarizes work already published and introduces the new reports contained in this supplement.

RESULTS

Novel biomarkers were identified. These were associated with deployment exposures.

CONCLUSIONS

Significant associations were noted between deployment exposures, microRNA biomarkers and metabolomic biomarkers, and deployment health outcomes.