Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR)

The association between trace metals in both cancerous and non-cancerous tissues with the risk of liver and gastric cancer progression in northwest China

Liver cancer and gastric cancer have extremely high morbidity and mortality rates worldwide. It is well known that an increase or decrease in trace metals may be associated with the formation and development of a variety of diseases, including cancer. Therefore, this study aimed to evaluate the contents of aluminium (Al), arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), selenium (Se), and zinc (Zn) in cancerous liver and gastric tissues, compared to adjacent healthy tissues, and to investigate the relationship between trace metals and cancer progression. During surgery, multiple samples were taken from the cancerous and adjacent healthy tissues of patients with liver and gastric cancer, and trace metal levels within these samples were analysed using inductively coupled plasma mass spectrometry (ICP-MS). We found that concentrations of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, and Zn in tissues from patients with liver cancer were significantly lower than those in healthy controls (P < 0.05). Similarly, patients with gastric cancer also showed lower levels of Cd, Co, Cr, Mn, Ni, and Zn-but higher levels of Cu and Se-compared to the controls (P < 0.05). In addition, patients with liver and gastric cancers who had poorly differentiated tumours and positive lymph node metastases showed lower levels of trace metals (P < 0.05), although no significant changes in their concentrations were observed to correlate with sex, age, or body mass index (BMI). Logistic regression, principal component analysis (PCA), Bayesian kernel regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g computing (qgcomp) models were used to analyse the relationships between trace metal concentrations in liver and gastric cancer tissues and the progression of these cancers. We found that single or mixed trace metal levels were negatively associated with poor differentiation and lymph node metastasis in both liver and gastric cancer, and the posterior inclusion probability (PIP) of each metal showed that Cd contributed the most to poor differentiation and lymph node metastasis in both liver and gastric cancer (all PIP = 1.000). These data help to clarify the relationship between changes in trace metal levels in cancerous liver and gastric tissues and the progression of these cancers. Further research is warranted, however, to fully elucidate the mechanisms and causations underlying these findings.

Application of novel AI-based algorithms to biobank data: uncovering of new features and linear relationships

We independently analyzed two large public domain datasets that contain ¹H-NMR spectral data from lung cancer and sex studies. The biobanks were sourced from the Karlsruhe Metabolomics and Nutrition (KarMeN) study and Bayesian Automated Metabolite Analyzer for NMR data (BATMAN) study. Our approach of applying novel artificial intelligence (AI)-based algorithms to NMR is an attempt to globalize metabolomics and demonstrate its clinical applications. The intention of this study was to analyze the resulting spectra in the biobanks via AI application to demonstrate its clinical applications. This technique enables metabolite mapping in areas of localized enrichment as a measure of true activity while also allowing for the accurate categorization of phenotypes.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Prenatal exposure to multiple organochlorine compounds and childhood body mass index

Background:

Prenatal exposure to organochlorine compounds (OCs) has been associated with increased childhood body mass index (BMI); however, only a few studies have focused on longitudinal BMI trajectories, and none of them used multiple exposure mixture approaches.

Aim:

To determine the association between in-utero exposure to eight OCs and childhood BMI measures (BMI and BMI z-score) at 4 years and their yearly change across 4–12 years of age in 279 Rhea child-mother dyads.

Methods:

We applied three approaches: (1) linear mixed-effect regressions (LMR) to associate individual compounds with BMI measures; (2) Bayesian weighted quantile sum regressions (BWQSR) to provide an overall OC mixture association with BMI measures; and (3)Bayesian varying coefficient kernel machine regressions (BVCKMR) to model nonlinear and nonadditive associations.

Results:

In the LMR, yearly change of BMI measures was consistently associated with a quartile increase in hexachlorobenzene (HCB) (estimate [95% Confidence or Credible interval] BMI: 0.10 [0.06, 0.14]; BMI z-score: 0.02 [0.01, 0.04]). BWQSR results showed that a quartile increase in mixture concentrations was associated with yearly increase of BMI measures (BMI: 0.10 [0.01, 0.18]; BMI z-score: 0.03 [0.003, 0.06]). In the BVCKMR, a quartile increase in dichlorodiphenyldichloroethylene concentrations was associated with higher BMI measures at 4 years (BMI: 0.33 [0.24, 0.43]; BMI z-score: 0.19 [0.15, 0.24]); whereas a quartile increase in HCB and polychlorinated biphenyls (PCB)-118 levels was positively associated with BMI measures yearly change (BMI: HCB:0.10 [0.07, 0.13], PCB-118:0.08 [0.04, 012]; BMI z-score: HCB:0.03 [0.02, 0.05], PCB-118:0.02 [0.002,04]). BVCKMR suggested that PCBs had nonlinear relationships with BMI measures, and HCB interacted with other compounds.

Conclusions:

All analyses consistently demonstrated detrimental associations between prenatal OC exposures and childhood BMI measures.

Chemical exposures assessed via silicone wristbands and endogenous plasma metabolomics during pregnancy

BACKGROUND

Metabolomics is a promising method to investigate physiological effects of chemical exposures during pregnancy, with the potential to clarify toxicological mechanisms, suggest sensitive endpoints, and identify novel biomarkers of exposures.

OBJECTIVE

Investigate the influence of chemical exposures on the maternal plasma metabolome during pregnancy.

METHODS

Data were obtained from participants (n = 177) in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. Chemical exposures were assessed via silicone wristbands worn for one week at ~13 gestational weeks. Metabolomic features were assessed in plasma samples obtained at ~24-28 gestational weeks via the Biocrates AbsoluteIDQ® p180 kit and nuclear magnetic resonance (NMR) spectroscopy. Associations between chemical exposures and plasma metabolomics were investigated using multivariate modeling.

RESULTS

Chemical exposures predicted 11 (of 226) and 23 (of 125) metabolomic features in Biocrates and NMR, respectively. The joint chemical exposures did not significantly predict pathway enrichment, though some individual chemicals were associated with certain amino acids and related metabolic pathways. For example, N,N-diethyl-m-toluamide was associated with the amino acids glycine, L-glutamic acid, L-asparagine, and L-aspartic acid and enrichment of the ammonia recycling pathway.

SIGNIFICANCE

This study contributes evidence to the potential effects of chemical exposures during pregnancy upon the endogenous maternal plasma metabolome.

Non-linear and non-additive associations between the pregnancy metabolome and birthweight

BACKGROUND

Birthweight is an indicator of fetal growth and environmental-related alterations of birthweight have been linked with multiple disorders and conditions progressing into adulthood. Although a few studies have assessed the association between birthweight and the totality of exogenous exposures and their downstream molecular responses in maternal urine and cord blood; no prior research has considered a) the maternal serum prenatal metabolome, which is enriched for hormones, and b) non-linear and synergistic associations among exposures.

METHODS

We measured the maternal serum metabolome during pregnancy using an untargeted metabolomics approach and birthweight for gestational age (BWGA) z-score in 410 mother-child dyads enrolled in the PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort. We leveraged a Bayesian factor analysis for interaction to select the most important metabolites associated with BWGA z-score and to evaluate their linear, non-linear and non-additive associations. We also assessed the primary biological functions of the identified proteins using the MetaboAnalyst, a centralized repository of curated functional information. We compared our findings with those of a traditional metabolite-wide association study (MWAS) in which metabolites are individually associated with BWGA z-score.

RESULTS

Among 1110 metabolites, 46 showed evidence of U-shape associations with BWGA z-score. Most of the identified metabolites (85%) were lipids primarily enriched for pathways central to energy production, immune function, and androgen and estrogen metabolism, which are essential for pregnancy and parturition processes. Metabolites within the same class, i.e. steroids and phospholipids, showed synergistic relationships with each other.

CONCLUSIONS

Our results support that the aspects of the maternal metabolome during pregnancy contribute linearly, non-linearly and synergistically to variation in newborn birthweight.

Prenatal urinary concentrations of phthalate metabolites and behavioral problems in Mexican children: The Programming Research in Obesity, Growth Environment and Social Stress (PROGRESS) study

BACKGROUND

Phthalate exposure has been associated with increased childhood behavioral problems. Existing studies failed to include phthalate replacements and did not account for high correlations among phthalates. Phthalates' exposure is higher in Mexico than in U.S. locations, making it an ideal target population for this study.

AIM

To examine associations between 15 maternal prenatal phthalate metabolite concentrations and children's behavioral problems.

METHODS

We quantified phthalate metabolites in maternal urine samples from maternal-child dyads (n = 514) enrolled in the Programming Research in Obesity, Growth Environment and Social Stress (PROGRESS) birth cohort in Mexico City. We performed least absolute shrinkage and selection operator (LASSO) regressions to identify associations between specific-gravity adjusted log₂-transformed phthalate metabolites and parent-reported 4-6 year old behavior on the Behavior Assessment System for Children (BASC-2), accounting for metabolite correlations. We adjusted for socio-demographic and birth-related factors, and examined associations stratified by sex.

RESULTS

Higher prenatal mono-2-ethyl-5-carboxypentyl terephthalate (MECPTP) urinary concentrations were associated with increased hyperactivity scores in the overall sample (β = 0.57, 95% CI = 0.17, 1.13) and in girls (β = 0.54, 95% CI = 0.16, 1.08), overall behavioral problems in boys (β = 0.58, 95% CI = 0.20, 1.15), and depression scores in boys (β = 0.44, 95% CI = 0.06, 0.88). Higher prenatal monobenzyl phthalate (MBzP) concentrations were associated with reduced hyperactivity scores in girls (ß = -0.54, 95% CI = -1.08, -0.21).

DISCUSSION

Our findings suggested that prenatal concentrations of phthalates and their replacements altered child neurodevelopment and those associations may be influenced sex.

Human Health Exposure Analysis Resource (HHEAR): A model for incorporating the exposome into health studies

BACKGROUND

Characterizing the complexity of environmental exposures in relation to human health is critical to advancing our understanding of health and disease throughout the life span. Extant cohort studies open the door for such investigations more rapidly and inexpensively than launching new cohort studies and the Human Health Exposure Analysis Resource (HHEAR) provides a resource for implementing life-stage exposure studies within existing study populations. Primary challenges to incorporation of environmental exposure assessment in health studies include: (1) lack of widespread knowledge of biospecimen and environmental sampling and storage requirements for environmental exposure assessment among investigators; (2) lack of availability of and access to laboratories capable of analyzing multiple environmental exposures throughout the life-course; and (3) studies lacking sufficient power to assess associations across life-stages. HHEAR includes a consortium of researchers with expertise in laboratory analyses, statistics and logistics to overcome these limitations and enable inclusion of exposomics in human health studies.

OBJECTIVE

This manuscript describes the structure and strengths of implementing the harmonized HHEAR resource model, and our approaches to addressing challenges. We describe how HHEAR incorporates analyses of biospecimens and environmental samples and human health studies across the life span - serving as a model for incorporating environmental exposures into national and international research. We also present program successes to date.

DISCUSSION

HHEAR provides a full-service laboratory and data analysis exposure assessment resource, linking scientific, life span, and toxicological consultation with both laboratory and data analysis expertise. HHEAR services are provided without cost but require NIH, NCI, NHLBI, or ECHO funding of the original cohort; internal HHEAR scientific review and approval of a brief application; and adherence to data sharing and publication policies. We describe the benefits of HHEAR's structure, collaborative framework and coordination across project investigators, analytical laboratories, biostatisticians and bioinformatics specialists; quality assurance/quality control (QA/QC) including integrated sample management; and tools that have been developed to support the research (exposure information pages, ontology, new analytical methods, common QA/QC approach across laboratories, etc.). This foundation supports HHEAR's inclusion of new laboratory and statistical analysis methods and studies that are enhanced by including targeted analysis of specific exposures and untargeted analysis of chemicals associated with phenotypic endpoints in biological and environmental samples.

CONCLUSION

HHEAR is an interdisciplinary team of toxicologists, epidemiologists, laboratory scientists, and data scientists across multiple institutions to address broad and complex questions that benefit from integrated laboratory and data analyses. HHEAR's processes, features, and tools include all life stages and analysis of biospecimens and environmental samples. They are available to the wider scientific community to augment studies by adding state of the art environmental analyses to be linked to human health outcomes.

Environmental mixtures and children's health: identifying appropriate statistical approaches

PURPOSE OF REVIEW

Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context.

RECENT FINDINGS

New approaches allow pediatric researchers to answer increasingly complex questions related to environmental mixtures. We present methods to identify the most relevant exposures among a high-multitude of variables, via shrinkage and variable selection techniques, and identify the overall mixture effect, via Weighted Quantile Sum and Bayesian Kernel Machine regressions. We then describe novel extensions that handle high-dimensional exposure data and allow identification of critical exposure windows.

SUMMARY

Recent advances in statistics and machine learning enable researchers to identify important mixture components, estimate joint mixture effects and pinpoint critical windows of exposure. Despite many advantages over single chemical approaches, measurement error and biases may be amplified in mixtures research, requiring careful study planning and design. Future research requires increased collaboration between epidemiologists, statisticians and data scientists, and further integration with causal inference methods.