Socioeconomic Inequalities in the External Exposome in European Cohorts: The EXPANSE Project.

Socioeconomic inequalities in the exposome have been found to be complex and highly context-specific, but studies have not been conducted in large population-wide cohorts from multiple countries. This study aims to examine the external exposome, encompassing individual and environmental factors influencing health over the life course, and to perform dimension reduction to derive interpretable characterization of the external exposome for multicountry epidemiological studies. Analyzing data from over 25 million individuals across seven European countries including 12 administrative and traditional cohorts, we utilized domain-specific principal component analysis (PCA) to define the external exposome, focusing on air pollution, the built environment, and air temperature. We conducted linear regression to estimate the association between individual- and area-level socioeconomic position and each domain of the external exposome. Consistent exposure patterns were observed within countries, indicating the representativeness of traditional cohorts for air pollution and the built environment. However, cohorts with limited geographical coverage and Southern European countries displayed lower temperature variability, especially in the cold season, compared to Northern European countries and cohorts including a wide range of urban and rural areas. The individual- and area-level socioeconomic determinants (i.e., education, income, and unemployment rate) of the urban exposome exhibited significant variability across the European region, with area-level indicators showing stronger associations than individual variables. While the PCA approach facilitated common interpretations of the external exposome for air pollution and the built environment, it was less effective for air temperature. The diverse socioeconomic determinants suggest regional variations in environmental health inequities, emphasizing the need for targeted interventions across European countries.

Associations of night shift work with weight gain among female nurses in The Netherlands: results of a prospective cohort study.

OBJECTIVE: This study aimed to prospectively investigate associations of working night shifts with weight gain in the Nightingale Study, a large cohort of female nurses. METHODS: This study included 36 273 registered nurses, who completed questionnaires in 2011 and 2017. Cumulative number of nights, mean number of nights/month and consecutive number of nights/month in 2007-2011 were assessed. We used Poisson regression to estimate multivariable-adjusted incidence rate ratios (IRR) of >5% weight gain from 2011 to 2017 among all participants and assess risk of development of overweight/obesity (BMI≥25 kg/m2) among women with healthy baseline body mass index. The reference group consisted of women who never worked nights. RESULTS: Overall, working night shifts in 2007-2011 was associated with >5% weight gain [IRR 1.07, 95% confidence interval (CI) 1.01-1.13]. Associations differed by menopausal status in 2011, with an increased risk of gaining >5% weight limited to postmenopausal women who worked nights (IRR 1.23, 95% CI 1.10-1.38). Postmenopausal women had an increased risk of >5% weight gain when they worked on average ≥4 nights/month (4-5: IRR 1.29, 95% CI 1.09-1.52, ≥6: IRR 1.27, 95% CI 1.11-1.47) or ≥4 consecutive nights/month (IRR 1.37, 95% CI 1.19-1.58), compared to postmenopausal women who never worked nights. For postmenopausal women with healthy weight at baseline, night shift work was associated with an increased risk of overweight/obesity at follow-up (IRR 1.24, 95% CI 1.03-1.50). CONCLUSIONS: Working night shifts was associated with a slightly increased risk of weight gain and overweight/obesity development among women who were postmenopausal at study inclusion. Our findings emphasize the importance of health promotion to maintain a healthy weight among (postmenopausal) night workers.

Chronotype in relation to shift work: A cohort study among 37,731 female nurses.

Chronotype may affect tolerance for circadian disruption induced by shift work. This study examines the association between chronotype, self-reported sleep timing, shift type preference, and sleep problems among nurses, and studies chronotype stability over time. The study included 37,731 Dutch female nurses who completed a baseline (2011) and follow-up questionnaire (2017), with information on shift work (e.g., job history, shift type preference [collected in 2017 only]), and sleep characteristics (e.g., chronotype, preferred sleep-wake time in a work-free period [collected in 2017 only], and sleep problems between working days according to Medical Outcomes Study-Sleep Problem Index II [MOS-SPI-II]). The association between chronotype and sleep timing was examined using (age-adjusted) linear regression. Associations between chronotype and shift type preference and sleep problems (MOS-SPI-II >30) were examined using ordered logistic and Poisson regression, respectively. With later chronotype, midsleep time increased (definite evening vs. intermediate types [reference]: ß = 55 min, 95% confidence interval [95% CI]: 54-55), the odds ratio (OR) for 1-point increase in preference for night (2.68; 95% CI: 2.48-2.90) and evening shifts increased (OR 2.20; 95% CI: 2.03-2.38), while the odds for day (OR 0.17; 95% CI: 0.16-0.18) and morning shifts (OR 0.22; 95% CI: 0.21-0.24) decreased. Intermediate chronotype was associated with fewer sleep problems (median MOS-SPI-II = 27.2, p < 0.01), compared with definite morning (28.9) and evening types (31.7). This study shows that chronotype is associated with sleep-wake times in a work-free period, shift type preference, and sleep problems in nurses. Future studies on the association of shift work-induced circadian disruption and health outcomes should therefore consider chronotype as effect-modifier.

External exposome and all-cause mortality in European cohorts: the EXPANSE project.

Background: Many studies reported associations between long-term exposure to environmental factors and mortality; however, little is known on the combined effects of these factors and health. We aimed to evaluate the association between external exposome and all-cause mortality in large administrative and traditional adult cohorts in Europe. Methods: Data from six administrative cohorts (Catalonia, Greece, Rome, Sweden, Switzerland and the Netherlands, totaling 27,913,545 subjects) and three traditional adult cohorts (CEANS-Sweden, EPIC-NL-the Netherlands, KORA-Germany, totaling 57,653 participants) were included. Multiple exposures were assigned at the residential addresses, and were divided into three a priori defined domains: (1) air pollution [fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and warm-season Ozone (warm-O3)]; (2) land/built environment (Normalized Difference Vegetation Index-NDVI, impervious surfaces, and distance to water); (3) air temperature (cold- and warm-season mean and standard deviation). Each domain was synthesized through Principal Component Analysis (PCA), with the aim of explaining at least 80% of its variability. Cox proportional-hazards regression models were applied and the total risk of the external exposome was estimated through the Cumulative Risk Index (CRI). The estimates were adjusted for individual- and area-level covariates. Results: More than 205 million person-years at risk and more than 3.2 million deaths were analyzed. In single-component models, IQR increases of the first principal component of the air pollution domain were associated with higher mortality [HRs ranging from 1.011 (95% CI: 1.005-1.018) for the Rome cohort to 1.076 (1.071-1.081) for the Swedish cohort]. In contrast, lower levels of the first principal component of the land/built environment domain, pointing to reduced vegetation and higher percentage of impervious surfaces, were associated with higher risks. Finally, the CRI of external exposome increased mortality for almost all cohorts. The associations found in the traditional adult cohorts were generally consistent with the results from the administrative ones, albeit without reaching statistical significance. Discussion: Various components of the external exposome, analyzed individually or in combination, were associated with increased mortality across European cohorts. This sets the stage for future research on the connections between various exposure patterns and human health, aiding in the planning of healthier cities.

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Lung Cancer Risks Associated with Occupational Exposure to Pairs of Five Lung Carcinogens: Results from a Pooled Analysis of Case-Control Studies (SYNERGY).

BACKGROUND: While much research has been done to identify individual workplace lung carcinogens, little is known about joint effects on risk when workers are exposed to multiple agents. OBJECTIVES: We investigated the pairwise joint effects of occupational exposures to asbestos, respirable crystalline silica, metals (i.e., nickel, chromium-VI), and polycyclic aromatic hydrocarbons (PAH) on lung cancer risk, overall and by major histologic subtype, while accounting for cigarette smoking. METHODS: In the international 14-center SYNERGY project, occupational exposures were assigned to 16,901 lung cancer cases and 20,965 control subjects using a quantitative job-exposure matrix (SYN-JEM). Odds ratios (ORs) and 95% confidence intervals (CIs) were computed for ever vs. never exposure using logistic regression models stratified by sex and adjusted for study center, age, and smoking habits. Joint effects among pairs of agents were assessed on multiplicative and additive scales, the latter by calculating the relative excess risk due to interaction (RERI). RESULTS: All pairwise joint effects of lung carcinogens in men were associated with an increased risk of lung cancer. However, asbestos/metals and metals/PAH resulted in less than additive effects; while the chromium-VI/silica pair showed marginally synergistic effect in relation to adenocarcinoma (RERI: 0.24; CI: 0.02, 0.46; p = 0.05). In women, several pairwise joint effects were observed for small cell lung cancer including exposure to PAH/silica (OR = 5.12; CI: 1.77, 8.48), and to asbestos/silica (OR = 4.32; CI: 1.35, 7.29), where exposure to PAH/silica resulted in a synergistic effect (RERI: 3.45; CI: 0.10, 6.8). DISCUSSION: Small or no deviation from additive or multiplicative effects was observed, but co-exposure to the selected lung carcinogens resulted generally in higher risk than exposure to individual agents, highlighting the importance to reduce and control exposure to carcinogens in workplaces and the general environment. https://doi.org/10.1289/EHP13380.

Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses.

Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.

Associating night-shift work with lifetime use of sleep medication and sleep quality in a cohort of female nurses.

Night-shift workers often sleep at moments, not in sync with their circadian rhythm. Though the acute effects of night-shift work on sleep quality directly after a night shift are well described, less is known about the chronic effects of night-shift work on sleep. We associated ever-working night shifts and recently working night shifts (<4 wk) with lifetime use of sleep medication and melatonin, self-reported average sleep duration and sleep quality over the 4 wk preceding inclusion (measured using the Medical Outcomes Study Sleep scale). We explored trends in sleep outcomes with average frequency of night shifts per week, tenure of night-shift works in years, and time since last performed night work. This research was conducted within the Nightingale study which is a Dutch cohort study of 59,947 female registered nurses aged 18 to 65. Working night shifts was not associated with self-reported nonoptimal sleep length and sleep quality. However, we observed higher odds of lifetime use of sleep medication for nurses who ever-worked night shifts (OR 1.24; 95% CI 1.13, 1.35) and who recently worked night shifts (OR 1.13; 95% CI 1.05, 1.22); with night-shift work frequency and tenure being associated with lifetime use of sleep medication (P-value for trend < 0.001 for both). Odds for melatonin use were elevated for nurses who ever worked night shifts (OR 1.55; 95% CI 1.40, 1.71) and recently worked night shifts (OR 1.72; 95% CI 1.59, 1,86). The findings of this study have practical implications for healthcare organizations that employ nurses working night shifts. The observed associations between night-shift work and increased lifetime use of prescribed sleep medication and melatonin highlight the need for targeted support and interventions to address the potential long-term sleep problems faced by these nurses.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.

Mixtures of long-term exposure to ambient air pollution, built environment and temperature and stroke incidence across Europe.

INTRODUCTION: The complex interplay of multiple environmental factors and cardiovascular has scarcely been studied. Within the EXPANSE project, we evaluated the association between long-term exposure to multiple environmental indices and stroke incidence across Europe. METHODS: Participants from three traditional adult cohorts (Germany, Netherlands and Sweden) and four administrative cohorts (Catalonia [region Spain], Rome [city-wide], Greece and Sweden [nationwide]) were followed until incident stroke, death, migration, loss of follow-up or study end. We estimated exposures at residential addresses from different exposure domains: air pollution (nitrogen dioxide (NO2), particulate matter < 2.5 µm (PM2.5), black carbon (BC), ozone), built environment (green/blue spaces, impervious surfaces) and meteorology (seasonal mean and standard deviation of temperatures). Associations between environmental exposures and stroke were estimated in single and multiple-exposure Cox proportional hazard models, and Principal Component (PC) Analyses derived prototypes for specific exposures domains. We carried out random effects meta-analyses by cohort type. RESULTS: In over 15 million participants, increased levels of NO2 and BC were associated with increased higher stroke incidence in both cohort types. Increased Normalized Difference Vegetation Index (NDVI) was associated with a lower stroke incidence in both cohort types, whereas an increase in impervious surface was associated with an increase in stroke incidence. The first PC of the air pollution domain (PM2.5, NO2 and BC) was associated with an increase in stroke incidence. For the built environment, higher levels of NDVI and lower levels of impervious surfaces were associated with a protective effect [%change in HR per 1 unit = -2.0 (95 %CI, -5.9;2.0) and -1.1(95 %CI, -2.0; -0.3) for traditional adult and administrative cohorts, respectively]. No clear patterns were observed for distance to blue spaces or temperature parameters. CONCLUSIONS: We observed increased HRs for stroke with exposure to PM2.5, NO2 and BC, lower levels of greenness and higher impervious surface in single and combined exposure models.

Nonlinear low dose hematotoxicity of benzene; a pooled analyses of two studies among Chinese exposed workers.

BACKGROUND: Impairment of the hematopoietic system is one of the primary adverse health effects from exposure to benzene. We previously have shown that exposure to benzene at low levels (<1 ppm) affects the blood forming system and that these effects were proportionally stronger at lower versus higher levels of benzene exposure. This observation is potentially explained by saturation of enzymatic systems. METHODS: Here we extend these analyses by detailed modeling of the exposure response association of benzene and its major metabolites (i.e. catechol, muconic acid, phenol, and hydroquinone) on peripheral white blood cell (WBC) counts and its major cell-subtypes (i.e. granulocytes, lymphocytes, and monocytes) using two previously published cross-sectional studies among occupationally exposed Chinese workers. RESULTS: Supra-linear exposure response associations were observed between air benzene concentrations (range âˆ¼ 0.1 - 100 ppm) and WBC counts and its cell-subtypes, with a larger than proportional decrease in cell counts at lower than at higher levels of benzene exposure. The hematotoxicity associations were largely similar in shape when the analyses were repeated with benzene urinary metabolites suggesting that enzymatic saturation is not a full explanation of the observed non-linearity with WBC endpoints. DISCUSSION: We hypothesize that the flattening of the exposure response curve especially at higher benzene exposure levels may reflect a response by the bone marrow to maintain hematopoietic homeostasis. Toxicity to the bone marrow and an induced hyper-proliferative response could both contribute to risk of subsequently developing a hematopoietic malignancy. Additional work is needed to explore this hypothesis.

A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction.

BACKGROUND: Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms. OBJECTIVES: We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research. METHODS: We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer. RESULTS: We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in >70% of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings. CONCLUSIONS: Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.

Effects of Nightshift Work on Blood Metabolites in Female Nurses and Paramedic Staff: A Cross-sectional Study.

Nightshift work disturbs the circadian rhythm, which might contribute to the development of cardio-metabolic disorders. In this cross-sectional study, we aimed to gain insight into perturbations of disease relevant metabolic pathways due to nightshift work. We characterized the metabolic profiles of 237 female nurses and paramedic staff participating in the Klokwerk study using the Nightingale Health platform. We performed analyses on plasma levels of 225 metabolites, including cholesterol, triglycerides, fatty acids, and amino acids. Using both principal component- and univariate-regression, we compared metabolic profiles of nightshift workers to metabolic profiles from workers that did not work night shifts (defined as day workers). We also assessed whether differential effects were observed between recently started versus more experienced workers. Within the group of nightshift workers, we compared metabolic profiles measured right after a nightshift with metabolic profiles measured on a day when no nightshift work was conducted. We observed evidence for an impact of nightshift work on the presence of unfavorable fatty acid profiles in blood. Amongst the fatty acids, effects were most prominent for PUFA/FA ratios (consistently decreased) and SFA/FA ratios (consistently elevated). This pattern of less favorable fatty acid profiles was also observed in samples collected directly after a night shift. Amino acid levels (histidine, glutamine, isoleucine, and leucine) and lipoproteins (especially HDL-cholesterol, VLDL-cholesterol, and triglycerides) were elevated when comparing nightshift workers with day workers. Amino acid levels were decreased in the samples that were collected directly after working a nightshift (compared to levels in samples that were collected during a non-nightshift period). The observed effects were generally more pronounced in samples collected directly after the nightshift and among recently started compared to more experienced nightshift workers. Our finding of a suggested impact of shift work on impaired lipid metabolism is in line with evidence that links disruption of circadian rhythmicity to obesity and metabolic disorders.

Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept.

Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as 'communities', reflecting co-occurrence patterns of chemicals) using a network analysis approach (n = 515 individuals) on 51 chemical substances in urine. The underlying question is whether the combined body burden of multiple chemicals is of potential health concern. If so, subsequent questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this, a biomonitoring hazard index was developed by summing over hazard quotients, where each biomarker concentration was weighted (divided) by the associated HBM health-based guidance value (HBM-HBGV, HBM value or equivalent). Altogether, for 17 out of the 51 substances, health-based guidance values were available. If the hazard index was higher than 1, then the community was considered of potential health concern and should be evaluated further. Overall, seven communities were identified in the GerES V data. Of the five mixture communities where a hazard index was calculated, the highest hazard community contained N-Acetyl-S-(2-carbamoyl-ethyl)cysteine (AAMA), but this was the only biomarker for which a guidance value was available. Of the other four communities, one included the phthalate metabolites mono-isobutyl phthalate (MiBP) and mono-n-butyl phthalate (MnBP) with high hazard quotients, which led to hazard indices that exceed the value of one in 5.8% of the participants included in the GerES V study. This biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies. Future mixture risk assessment using HBM data will benefit from additional HBM health-based guidance values based on population studies. Additionally, accounting for different biomonitoring matrices would provide a wider range of exposures. Future hazard index analyses could also take a common mode of action approach, rather than the more agnostic and non-specific approach we have taken in this proof of concept.

Impact of PFAS exposure on prevalence of immune-mediated diseases in adults in the Czech Republic.

BACKGROUND: Per- and polyfluoroalkyl substances (PFASs) are emerging environmental contaminants with multiple hazardous properties including immunomodulation potency. Human exposure to PFASs has been associated with various immune-mediated diseases and outcomes. This study aimed to investigate the association between PFAS exposure and immune-mediated diseases such as allergies, eczemas, and autoimmune diseases in a population of adults in the Czech Republic. METHODS: This study included 309 adults from the Central European Longitudinal Study of Parents and Children: Young Adults (CELSPAC: YA). 12 PFASs were measured in participants' serum by HPLC-MS/MS, 3 PFASs were removed from the subsequent analyses due to low detection frequency. The associations of 9 PFASs with 9 immune-mediated diseases were assessed by logistic regression. Furthermore, Bayesian kernel machine regression (BKMR) was used to estimate the effect of the PFAS mixture on immune-mediated diseases. All analyses were adjusted for sex, age, BMI, smoking, education, and family history of immune-mediated diseases. In cases of a statistically significant interaction of PFASs and sex, stratified analyses were performed for men and women. RESULTS: Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) were negatively associated with both atopic eczema (OR per IQR increase 0.58 (95% CI 0.37-0.90) for PFOA and 0.56 (0.32-0.95) for PFOS) and contact dermatitis (0.37 (0.16-0.85) for PFOA and 0.33 (0.11-0.94) for PFOS). Perfluoroundecanoate (PFUnDA) was negatively associated with pollen, dust, and mite allergy (0.62 (0.43-0.89)). BKMR modelling showed a negative tendency in the overall effect of PFAS mixture on immune-health outcomes. Based on the stratified analysis, sex was suggested to be an effect modifier in the association of PFOS and atopic eczema. CONCLUSION: Our results contribute to the body of literature that observes the immunosuppressive effect of PFAS exposure during eczemas and allergies, both for PFASs individually and as a mixture.

Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study.

Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called "communities", within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments.

Effect of residential relocation on environmental exposures in European cohorts: An exposome-wide approach.

Residential relocation is increasingly used as a natural experiment in epidemiological studies to assess the health impact of changes in environmental exposures. Since the likelihood of relocation can be influenced by individual characteristics that also influence health, studies may be biased if the predictors of relocation are not appropriately accounted for. Using data from Swedish and Dutch adults (SDPP, AMIGO), and birth cohorts (BAMSE, PIAMA), we investigated factors associated with relocation and changes in multiple environmental exposures across life stages. We used logistic regression to identify baseline predictors of moving, including sociodemographic and household characteristics, health behaviors and health. We identified exposure clusters reflecting three domains of the urban exposome (air pollution, grey surface, and socioeconomic deprivation) and conducted multinomial logistic regression to identify predictors of exposome trajectories among movers. On average, 7 % of the participants relocated each year. Before relocating, movers were consistently exposed to higher levels of air pollution than non-movers. Predictors of moving differed between the adult and birth cohorts, highlighting the importance of life stages. In the adult cohorts, moving was associated with younger age, smoking, and lower education and was independent of cardio-respiratory health indicators (hypertension, BMI, asthma, COPD). Contrary to adult cohorts, higher parental education and household socioeconomic position were associated with a higher probability of relocation in birth cohorts, alongside being the first child and living in a multi-unit dwelling. Among movers in all cohorts, those with a higher socioeconomic position at baseline were more likely to move towards healthier levels of the urban exposome. We provide new insights into predictors of relocation and subsequent changes in multiple aspects of the urban exposome in four cohorts covering different life stages in Sweden and the Netherlands. These results inform strategies to limit bias due to residential self-selection in epidemiological studies using relocation as a natural experiment.

Mixture risk assessment and human biomonitoring: Lessons learnt from HBM4EU.

Unintentional chemical mixtures that are present in the environment are of societal concern as the (environmental) chemicals contained therein, either singly or in combination, may possess properties that are hazardous (toxic) for human health. The current regulatory practice, however, is still largely based on evaluating single chemical substances one-by-one. Over the years various research efforts have delivered tools and approaches for risk assessment of chemical mixtures, but many of these were not considered sufficiently mature for regulatory implementation. This is (partly) due to mixture risk assessment (MRA) being very complex because of the large number of chemicals present in the environment. A key element in risk assessment is information on actual exposures in the population of interest. To date, information on actual personal (internal) mixture exposures is largely absent, severely limiting MRA. The use of human biomonitoring data may improve this situation. Therefore, we investigated within the European Human Biomonitoring Initiative (HBM4EU) various approaches to assess combined exposures and MRA. Based on the insights and lessons learnt in the context of the HBM4EU project, conclusions as well as recommendations for policy development regarding chemical mixtures and for further research were drafted. These conclusions and recommendations relate to both exposure and adverse health effects in humans. The recommendations were discussed with stakeholders in a workshop held in October 2021. There was considerable support and agreement with the spirit, scope and intention of the draft recommendations. Here we describe the lessons learnt on mixture risk assessment through the HBM4EU project and present the final recommendations. Overall, HBM4EU results demonstrated the potential of human biomonitoring as an instrument to obtain insight into the real-life mixtures the human population is exposed to. Also, HBM4EU results demonstrated that chemical mixtures are of public health concern. In the majority of the cases, it was possible to identify risk drivers, i.e. chemicals that contribute more strongly than others to the health risk. The novel approaches to identify co-occurrence patterns demonstrated clusters of co-occurring chemicals; chemicals in these mixture clusters are regulated independently under different legislative frameworks. Moreover, HBM4EU data and expertise can support a science-based derivation of a Mixture Assessment Factor and gauge potential impacts on the population's exposure to chemicals. While further expansion is needed on various aspects of the mixture activities carried out in the context of HBM4EU, application of available methodologies for mixture risk assessment should already be implemented to the degree possible.