Exposome in human health: Utopia or wonderland?

Emerging contaminants: A One Health perspective

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Using Machine Learning to Construct the Blood-Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

Permeabilities of various trace elements (TEs) through the blood-follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood-follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19-4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076-0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was evenly distributed in the two compartments with transfer efficiencies of 0.998-1.056. Multiomics analysis showed that the enrichment of TEs was associated with the synthesis and action of steroid hormones and the glucose metabolism. Random forest model can provide the most accurate predictions of the concentrations of TEs in FF among the concerned ML models. In conclusion, the selective permeability through the BFB for various TEs may be significantly regulated by the steroid hormones and the glucose metabolism. Also, the concentrations of some TEs in FF can be well predicted by their serum levels with a random forest model.

The science of uncertainty guides fetal-neonatal neurology principles and practice: diagnostic-prognostic opportunities and challenges

Fetal-neonatal neurologists (FNNs) consider diagnostic, therapeutic, and prognostic decisions strengthened by interdisciplinary collaborations. Bio-social perspectives of the woman's health influence evaluations of maternal-placental-fetal (MPF) triad, neonate, and child. A dual cognitive process integrates "fast thinking-slow thinking" to reach shared decisions that minimize bias and maintain trust. Assessing the science of uncertainty with uncertainties in science improves diagnostic choices across the developmental-aging continuum. Three case vignettes highlight challenges that illustrate this approach. The first maternal-fetal dyad involved a woman who had been recommended to terminate her pregnancy based on an incorrect diagnosis of an encephalocele. A meningocele was subsequently identified when she sought a second opinion with normal outcome for her child. The second vignette involved two pregnancies during which fetal cardiac rhabdomyoma was identified, suggesting tuberous sclerosis complex (TSC). One woman sought an out-of-state termination without confirmation using fetal brain MRI or postmortem examination. The second woman requested pregnancy care with postnatal evaluations. Her adult child experiences challenges associated with TSC sequelae. The third vignette involved a prenatal diagnosis of an open neural tube defect with arthrogryposis multiplex congenita. The family requested prenatal surgical closure of the defect at another institution at their personal expense despite receiving a grave prognosis. The subsequent Management of Myelomeningocele Study (MOMS) would not have recommended this procedure. Their adult child requires medical care for global developmental delay, intractable epilepsy, and autism. These three evaluations involved uncertainties requiring shared clinical decisions among all stakeholders. Falsely negative or misleading positive interpretation of results reduced chances for optimal outcomes. FNN diagnostic skills require an understanding of dynamic gene-environment interactions affecting reproductive followed by pregnancy exposomes that influence the MPF triad health with fetal neuroplasticity consequences. Toxic stressor interplay can impair the neural exposome, expressed as anomalous and/or destructive fetal brain lesions. Functional improvements or permanent sequelae may be expressed across the lifespan. Equitable and compassionate healthcare for women and families require shared decisions that preserve pregnancy health, guided by person-specific racial-ethnic, religious, and bio-social perspectives. Applying developmental origins theory to neurologic principles and practice supports a brain health capital strategy for all persons across each generation.

Interdisciplinary fetal-neonatal neurology training applies neural exposome perspectives to neurology principles and practice

An interdisciplinary fetal-neonatal neurology (FNN) program over the first 1,000 days teaches perspectives of the neural exposome that are applicable across the life span. This curriculum strengthens neonatal neurocritical care, pediatric, and adult neurology training objectives. Teaching at maternal-pediatric hospital centers optimally merges reproductive, pregnancy, and pediatric approaches to healthcare. Phenotype-genotype expressions of health or disease pathways represent a dynamic neural exposome over developmental time. The science of uncertainty applied to FNN training re-enforces the importance of shared clinical decisions that minimize bias and reduce cognitive errors. Trainees select mentoring committee participants that will maximize their learning experiences. Standardized questions and oral presentations monitor educational progress. Master or doctoral defense preparation and competitive research funding can be goals for specific individuals. FNN principles applied to practice offer an understanding of gene-environment interactions that recognizes the effects of reproductive health on the maternal-placental-fetal triad, neonate, child, and adult. Pre-conception and prenatal adversities potentially diminish life-course brain health. Endogenous and exogenous toxic stressor interplay (TSI) alters the neural exposome through maladaptive developmental neuroplasticity. Developmental disorders and epilepsy are primarily expressed during the first 1,000 days. Communicable and noncommunicable illnesses continue to interact with the neural exposome to express diverse neurologic disorders across the lifespan, particularly during the critical/sensitive time periods of adolescence and reproductive senescence. Anomalous or destructive fetal neuropathologic lesions change clinical expressions across this developmental-aging continuum. An integrated understanding of reproductive, pregnancy, placental, neonatal, childhood, and adult exposome effects offers a life-course perspective of the neural exposome. Exosome research promises improved disease monitoring and drug delivery starting during pregnancy. Developmental origins of health and disease principles applied to FNN practice anticipate neurologic diagnoses with interventions that can benefit successive generations. Addressing health care disparities in the Global South and high-income country medical deserts require constructive dialogue among stakeholders to achieve medical equity. Population health policies require a brain capital strategy that reduces the global burden of neurologic diseases by applying FNN principles and practice. This integrative neurologic care approach will prolong survival with an improved quality of life for persons across the lifespan confronted with neurological disorders.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization

BACKGROUND

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans.

OBJECTIVES

Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern.

METHODS

We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances.

RESULTS

We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07μM, the mean absolute error (MAE) values of 1.28 and 1.56μM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 μM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants.

DISCUSSION

We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

Digital Twins for Predictive, Preventive Personalized, and Participatory Treatment of Immune-Mediated Diseases

Digital twins are computational models of complex systems, which aim to understand and optimize those systems more effectively than would be possible in real life. Ideally, digital twins can be translated to individual patients, to characterize and computationally treat their diseases with thousands of drugs, to select the drug or drugs that cure the patients. The background problem is that many patients do not respond adequately to drug treatment. This problem reflects a wide gap between the complexity of diseases and clinical practice. Each disease may involve altered interactions between thousands of genes that vary between different cell types in different organs. To our knowledge, these altered interactions have not been characterized on a genome-, cellulome-, and organ-wide scale in any disease. Thus, clinical translation of the digital twin ideal for predictive, preventive, personalized and participatory treatment involves formidable challenges, which are close to the limits of, or beyond today's technologies. Here, I discuss recent developments and challenges in relation to that ideal focusing on immune-mediated inflammatory diseases, as well as examples from other diseases.

Urinary PAHs metabolites in Karakoram Highway's heavy traffic vehicle (HTV) drivers: evidence of exposure and health risk

The current study features PAHs exposure on Karakoram Highway, a route of utmost importance in Pakistan. The drivers of heavy traffic vehicles (HTV) on Karakoram Highway spend long hours amid dense traffic and therefore, inevitably inhale huge amount of PAH carcinogens. The urinary metabolites of PAHs in such drivers (meeting selection criteria n = 48) and a control group (n = 49) were comparatively profiled. The higher urinary biomarkers among ninety-six percent HTV drivers were evident of PAHs exposure. We observed elevated concentrations of urinary benzo[a]pyrene metabolites (3-OH-BaP = 3.53 ± 0.62 ng g^-1 creatinine and 9-OH-BaP = 3.69 ± 0.74 ng g^-1 creatinine) in HTV driver's samples compared to controls (0.85 ± 0.08 and 0.31 ± 0.03 ng g^-1 creatinine, respectively). Interestingly, urinary benzo[a]pyrene metabolites were detected in almost similar amount among HTV drivers irrespective of their working hours. A distinct smoking effect was manifested with rising urinary levels of 1-hydroxypyrene, 2-hydroxyphenanthrene, and 3-hydroxybenzo[a]pyrene with corresponding increase in driving hours per day. These metabolites exhibited characteristic exposures to low molecular weight volatile PAHs that are commonly found in vehicular exhaust. The elevated PAH body burden was directly linked to the nature of their job and the route-long environmental pollution on Karakoram Highway. Additionally, the poor economic status and smoking also increased HTV driver's health vulnerability and significantly declined their health capacity. There was conclusive evidence that HTV drivers were exposed to PAHs during a ride on Karakoram Highway, back and forth, an aspect not reported earlier.

Enrichment of boron element in follicular fluid and its potential effect on the immune function

The blood-follicle barrier (BFB) between the blood and follicular fluid (FF) can maintain the microenvironment balance of oocyte. Boron, an exogenous environmental trace element, has been found to possibly play an important role in oocyte maturation. This study aimed to examine the distribution characteristics of boron across the BFB and find the potential effect of boron on FF microenvironment. We analyzed the concentration of boron in paired FF and serum collected from 168 women undergoing in vitro fertilization and embryo transfer in Beijing City and Shandong Province, China. To explore the potential health impact of boron enrichment in oocyte maturation, a global proteomics analysis was conducted to tentatively correlate the protein levels with the boron enrichment. Interestingly, the results showed that the concentration of boron in FF (34.5 ng/mL) was significantly higher than that in serum (22.0 ng/mL), with a median concentration ratio of 1.52. Likewise, the concentrations of boron in FF and serum were positively correlated (r = 0.446), suggesting that boron concentration in serum can represent its concentration in follicular fluid to a large extent.. This is the first time to observe the enrichment of boron in the FF to our knowledge. It is interesting to observe a total of 13 proteins, which mainly belong to immunoglobulin class, were positively correlated with boron concentration in FF. We concluded that boron, as one environmental trace element, was enriched in FF from blood validated by two area in north china, which may be involved in an increased level of immune processes of immunoglobulins.

Time to Consider the “Exposome Hypothesis” in the Development of the Obesity Pandemic

The obesity epidemic shows no signs of abatement. Genetics and overnutrition together with a dramatic decline in physical activity are the alleged main causes for this pandemic. While they undoubtedly represent the main contributors to the obesity problem, they are not able to fully explain all cases and current trends. In this context, a body of knowledge related to exposure to as yet underappreciated obesogenic factors, which can be referred to as the “exposome”, merits detailed analysis. Contrarily to the genome, the “exposome” is subject to a great dynamism and variability, which unfolds throughout the individual’s lifetime. The development of precise ways of capturing the full exposure spectrum of a person is extraordinarily demanding. Data derived from epidemiological studies linking excess weight with elevated ambient temperatures, in utero, and intergenerational effects as well as epigenetics, microorganisms, microbiota, sleep curtailment, and endocrine disruptors, among others, suggests the possibility that they may work alone or synergistically as several alternative putative contributors to this global epidemic. This narrative review reports the available evidence on as yet underappreciated drivers of the obesity epidemic. Broadly based interventions are needed to better identify these drivers at the same time as stimulating reflection on the potential relevance of the “exposome” in the development and perpetuation of the obesity epidemic.

Associations between hair levels of trace elements and the risk of preterm birth among pregnant Wwomen: A prospective nested case-control study in Beijing Birth Cohort (BBC), China

Trace elements have various physiochemical functions in humans and are associated with the occurrence of preterm birth (PB). However, their mixed effects on PB risk have rarely been studied. We aimed to investigate the associations between hair levels of trace elements and PB risk among pregnant women. A nested case-control study with a prospective cohort was conducted in Beijing City, China. We included 82 women who had a PB [total PB (tPB)] as cases [including 40 with a spontaneous PB (SPB)] and 415 who had a term delivery as controls. Hair levels of the concerned trace elements were measured including endocrine disrupting metal(loid)s [EDMs; lead, mercury (Hg), arsenic, and cadmium] and nutritional trace metal(loid)s [NTMs; zinc (Zn), iron (Fe), copper, and selenium]. Logistic regression analysis was performed to estimate the odds ratios (ORs) for PB. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixed exposure to the trace elements and PB risk. Significantly lower maternal hair concentrations of Zn and Fe were observed in the SPB cases than in the controls, whereas no differences for the other trace elements. Single-element modeling results suggested second-quartile Hg maternal hair concentrations, third-quartile Zn concentrations, and fourth-quartile Fe concentrations were associated with a reduced risk of tPB with adjusted ORs of 0.43 [95% confidence interval (CI): 0.21-0.87], 0.38 (95% CI: 0.18-0.82), and 0.48 (95% CI: 0.24-0.98), respectively, compared to first-quartile values. Similar results were obtained for SPB. According to the BKMR models, hair NTMs were significantly, monotonously, and inversely associated with the risk of SPB, after controlling for covariates and levels of the four EDMs. Fe and Zn contributed the most strongly to the association. We concluded that maternal higher levels of NTMs, especially Fe and Zn, may reduce the risk of PB.

Thorium and fetal neural tube defects: an epidemiological evidence from large case-control study

Background

Thorium is ubiquitous in the environment and its relationship with birth defects is still under discussion. This study aimed to investigate the associations of maternal exposure to thorium with risk of neural tube defects (NTDs) by using a case–control study, as well as the relationship between thorium exposure and the indoor air pollution from coal combustion.

Methods

This study was conducted in 11 local healthcare hospitals during 2003–2007 in Shanxi and Hebei provinces, China. A total of 774 mothers were included as participants who delivering 263 fetuses with NTDs including 123 with anencephaly, 115 with spina bifida, 18 with encephalocele, and 7 other NTD subtypes (cases), and 511 health fetuses without NTDs (controls). Their hair samples were collected as close as to the occipital posterior scalp, of which those grew from 3 months before to 3 months after conception was cut to measure the thorium concentration by inductively coupled plasma-mass spectrometry.

Results

We found a higher hair thorium concentration in the total NTD cases with 0.901 (0.588–1.382) ng/g hair [median (inter-quartile range)] than that in the controls with a value of 0.621 (0.334–1.058) ng/g hair. Similar results were found for the three concerned NTD subtypes. Maternal hair thorium concentration above its median of the controls was associated with an increased risk of the total NTDs with an adjusted odds ratio of 1.80 [95% confidence interval (CI), 1.23–2.63)] by adjusting for all confounders. There was obvious dose-response relationship between maternal hair thorium concentration and the risk of total NTDs, as well as their two subtypes (i.e. anencephaly and spina bifida). Maternal hair thorium concentration was positive associated with their exposure level to indoor air pollution from coal combustion during cooking.

Conclusions

Overall, our findings revealed that maternal periconceptional thorium exposure was associated with the risk of NTDs in North China. Reducing the coal usage in the household cooking activities may decrease maternal thorium exposure level.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s41021-021-00227-w.