Urinary metabolic biomarkers of diet quality in European children are associated with metabolic health.

Urinary metabolic profiling is a promising powerful tool to reflect dietary intake and can help understand metabolic alterations in response to diet quality. Here, we used 1H NMR spectroscopy in a multicountry study in European children (1147 children from 6 different cohorts) and identified a common panel of 4 urinary metabolites (hippurate, N-methylnicotinic acid, urea, and sucrose) that was predictive of Mediterranean diet adherence (KIDMED) and ultra-processed food consumption and also had higher capacity in discriminating children's diet quality than that of established sociodemographic determinants. Further, we showed that the identified metabolite panel also reflected the associations of these diet quality indicators with C-peptide, a stable and accurate marker of insulin resistance and future risk of metabolic disease. This methodology enables objective assessment of dietary patterns in European child populations, complementary to traditional questionary methods, and can be used in future studies to evaluate diet quality. Moreover, this knowledge can provide mechanistic evidence of common biological pathways that characterize healthy and unhealthy dietary patterns, and diet-related molecular alterations that could associate to metabolic disease.

Food intake and weight loss of surviving inpatients in the course of COVID-19 infection: A longitudinal study of the multicenter NutriCoviD30 cohort.

OBJECTIVES: NutriCoviD30 is a longitudinal multicenter cohort study that aimed to provide nutritional objective data of inpatients during COVID-19 infection. The aims of this study were to describe the nutritional effects of COVID-19 infection on adult inpatients on the short- to mid-term (≤30 d after hospital discharge), using food intake and weight measurements and to identify factors associated with a decrease in food intake and weight. METHODS: Food intake and weight trajectories, as well as clinical signs of the disease, preexisting chronic diseases, and nutritional strategies were collected and analyzed during the course of the disease. Their association was estimated using mixed-effect regression modeling. Patients were recruited from French university hospitals from May to July 2020. For the 403 included patients (mean 62.2 ± 14.2 y of age; 63% men), median (interquartile range [IQR]) hospital length of stay was 13 d (IQR = 8, 20), and 30% of patients were admitted to the intensive care unit. RESULTS: Patients declared a median 70% food intake decrease in the acute phase, and the disease resulted in an average loss of 8% of predisease weight (corresponding to -6.5 kg). Although most patients recovered their usual food intake 1 month after hospital discharge, they only regained half of their weight loss, such that malnutrition, which affected 67% of patients during hospitalization, persisted in 41%. Patients with overweight, obesity, and diabetes reported an additional weight loss of >1.5% of their initial bodyweight during hospitalization and recovery phase. CONCLUSIONS: To prevent malnutrition and its long-term effects, mainly combined with a rapid weight loss predominantly affecting lean body mass, implementation of nutritional support is needed for COVID-19 inpatients. It should be started early in the course of the infection, and be extended up to the recovery phase.

Dynamic SOFA score assessments to predict outcomes after acute admission of octogenarians to the intensive care unit.

BACKGROUND: Identifying which octogenarians could benefit most from continuing critical care is challenging. We aimed to see if responses to therapies using the sequential organ failure assessment (SOFA) score on day 4 after unplanned admission to the intensive care unit (ICU) could be associated with short-term mortality. METHODS: In this prospective observational cohort study, data from 4 ICUs in a University Hospital included SOFA scores on admission and day 4, along with preadmission measurements of frailty, comorbidities, nutritional status and number of medications. Outcome measures included mortality and loss of autonomy on day 90 after admission. RESULTS: Eighty-seven critically ill patients aged 80 years or older with preadmission functional independence and no missing SOFA score data on day 4 were studied (primary analyses). The mortality rate on day 90 was 30%. In a univariate Cox model, the SOFA score on day 4 was significantly associated with mortality rate: hazard ratio = 1.18 per one-point increase, 95% confidence interval (CI), 1.08 to 1.28 (p<0.001). A SOFA score of 6 or more on day 4 could correctly classify 75% of patients who died on day 90, with a sensitivity of 54% and a specificity of 84%. After adjustment, the SOFA score on day 4, neurological failure on admission and the number of preadmission medications were significantly associated with mortality on day 90, with an area under the receiver operating characteristic curve of 0.81 (95% CI, 0.71 to 0.91). These findings were confirmed in a sensitivity analysis with 109 patients. Preadmission frailty was the only variable independently associated with loss of autonomy in the 49 surviving patients. CONCLUSION: Measuring SOFA score on day 4 and preadmission frailty could help predict mortality and loss of autonomy on day 90 in octogenarians after their acute admission to the ICU.

Multiple environmental exposures in early-life and allergy-related outcomes in childhood.

INTRODUCTION: Early onset and high prevalence of allergic diseases result in high individual and socio-economic burdens. Several studies provide evidence for possible effects of environmental factors on allergic diseases, but these are mainly single-exposure studies. The exposome provides a novel holistic approach by simultaneously studying a large set of exposures. The aim of the study was to evaluate the association between a broad range of prenatal and childhood environmental exposures and allergy-related outcomes in children. MATERIAL AND METHODS: Analyses of associations between 90 prenatal and 107 childhood exposures and allergy-related outcomes (last 12 months: rhinitis and itchy rash; ever: doctor-diagnosed eczema and food allergy) in 6-11 years old children (n = 1270) from the European Human Early-Life Exposome cohort were performed. Initially, we used an exposome-wide association study (ExWAS) considering the exposures independently, followed by a deletion-substitution-addition selection (DSA) algorithm considering all exposures simultaneously. All the exposure variables selected in the DSA were included in a final multi-exposure model using binomial general linear model (GLM). RESULTS: In ExWAS, no exposures were associated with the outcomes after correction for multiple comparison. In multi-exposure models for prenatal exposures, lower distance of residence to nearest road and higher di-iso-nonyl phthalate level were associated with increased risk of rhinitis, and particulate matter absorbance (PMabs) was associated with a decreased risk. Furthermore, traffic density on nearest road was associated with increased risk of itchy rash and diethyl phthalate with a reduced risk. DSA selected no associations of childhood exposures, or between prenatal exposures and eczema or food allergy. DISCUSSION: This first comprehensive and systematic analysis of many environmental exposures suggests that prenatal exposure to traffic-related variables, PMabs and phthalates are associated with rhinitis and itchy rash.

Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach.

BACKGROUND: Chemical and nonchemical environmental exposures are increasingly suspected to influence the development of obesity, especially during early life, but studies mostly consider single exposure groups. OBJECTIVES: Our study aimed to systematically assess the association between a wide array of early-life environmental exposures and childhood obesity, using an exposome-wide approach. METHODS: The HELIX (Human Early Life Exposome) study measured child body mass index (BMI), waist circumference, skinfold thickness, and body fat mass in 1,301 children from six European birth cohorts age 6-11 y. We estimated 77 prenatal exposures and 96 childhood exposures (cross-sectionally), including indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants (persistent organic pollutants, metals, phthalates, phenols, and pesticides). We used an exposure-wide association study (ExWAS) to screen all exposure-outcome associations independently and used the deletion-substitution-addition (DSA) variable selection algorithm to build a final multiexposure model. RESULTS: The prevalence of overweight and obesity combined was 28.8%. Maternal smoking was the only prenatal exposure variable associated with higher child BMI (z-score increase of 0.28, 95% confidence interval: 0.09, 0.48, for active vs. no smoking). For childhood exposures, the multiexposure model identified particulate and nitrogen dioxide air pollution inside the home, urine cotinine levels indicative of secondhand smoke exposure, and residence in more densely populated areas and in areas with fewer facilities to be associated with increased child BMI. Child blood levels of copper and cesium were associated with higher BMI, and levels of organochlorine pollutants, cobalt, and molybdenum were associated with lower BMI. Similar results were found for the other adiposity outcomes. DISCUSSION: This first comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. Cross-sectional biomarker results may suffer from reverse causality bias, whereby obesity status influenced the biomarker concentration. https://doi.org/10.1289/EHP5975.

Relying on repeated biospecimens to reduce the effects of classical-type exposure measurement error in studies linking the exposome to health.

The exposome calls for assessing numerous exposures, typically using biomarkers with varying amounts of measurement error, which can be assumed to be of classical type. We evaluated the impact of classical-type measurement error on the performance of exposome-health studies, and the efficiency of two measurement error correction methods relying on the collection of repeated biospecimens: within-subject biospecimens pooling and regression calibration. In a simulation study, we generated 237 exposures from a realistic correlation matrix, with various amounts of classical-type measurement error, and a continuous health outcome linearly influenced by exposures. Measurement error decreased the sensitivity to identify exposures influencing health from a value of 75% down to 46%, increased false discovery proportion from 26% to 49% and increased attenuation bias in the slope of true predictors from 45% to 66%. Assuming that repeated biospecimens were available, within-subject pooling and regression calibration improved sensitivity (which increased to 63%), false discovery proportion (down to 37%) and bias (down to 49%) compared to an error-prone study with a single biospecimen per subject. Performances were poorer for the exposures with the largest amount of measurement error, and increased with the number of available biospecimens. Relying on repeated biospecimens only for the exposures with the largest amount of measurement error provided similar performance improvement. Exposome studies relying on spot exposure biospecimens suffer from decreased performances if some biomarkers suffer from measurement error due to their temporal variability; performances can be improved by collecting repeated biospecimens per subject, in particular for non persistent chemicals.

Association of Fish Consumption and Mercury Exposure During Pregnancy With Metabolic Health and Inflammatory Biomarkers in Children.

Importance: The balance of mercury risk and nutritional benefit from fish intake during pregnancy for the metabolic health of offspring to date is unknown. Objective: To assess the associations of fish intake and mercury exposure during pregnancy with metabolic syndrome in children and alterations in biomarkers of inflammation in children. Design, Setting, and Participants: This population-based prospective birth cohort study used data from studies performed in 5 European countries (France, Greece, Norway, Spain, and the UK) between April 1, 2003, and February 26, 2016, as part of the Human Early Life Exposome (HELIX) project. Mothers and their singleton offspring were followed up until the children were aged 6 to 12 years. Data were analyzed between March 1 and August 2, 2019. Exposures: Maternal fish intake during pregnancy (measured in times per week) was assessed using validated food frequency questionnaires, and maternal mercury concentration (measured in micrograms per liter) was assessed using maternal whole blood and cord blood samples. Main Outcomes and Measures: An aggregate metabolic syndrome score for children was calculated using the z scores of waist circumference, systolic and diastolic blood pressures, and levels of triglyceride, high-density lipoprotein cholesterol, and insulin. A higher metabolic syndrome score (score range, -4.9 to 7.5) indicated a poorer metabolic profile. Three protein panels were used to measure several cytokines and adipokines in the plasma of children. Results: The study included 805 mothers and their singleton children. Among mothers, the mean (SD) age at cohort inclusion or delivery of their infant was 31.3 (4.6) years. A total of 400 women (49.7%) had a high educational level, and 432 women (53.7%) were multiparous. Among children, the mean (SD) age was 8.4 (1.5) years (age range, 6-12 years). A total of 453 children (56.3%) were boys, and 734 children (91.2%) were of white race/ethnicity. Fish intake consistent with health recommendations (1 to 3 times per week) during pregnancy was associated with a 1-U decrease in metabolic syndrome score in children (ß = -0.96; 95% CI, -1.49 to -0.42) compared with low fish consumption (<1 time per week) after adjusting for maternal mercury levels and other covariates. No further benefit was observed with fish intake of more than 3 times per week. A higher maternal mercury concentration was independently associated with an increase in the metabolic syndrome score of their offspring (ß per 2-fold increase in mercury concentration = 0.18; 95% CI, 0.01-0.34). Compared with low fish intake, moderate and high fish intake during pregnancy were associated with reduced levels of proinflammatory cytokines and adipokines in children. An integrated analysis identified a cluster of children with increased susceptibility to metabolic disease, which was characterized by low fish consumption during pregnancy, high maternal mercury levels, decreased levels of adiponectin in children, and increased levels of leptin, tumor necrosis factor α, and the cytokines interleukin 6 and interleukin 1ß in children. Conclusions and Relevance: Results of this study suggest that moderate fish intake consistent with current health recommendations during pregnancy was associated with improvements in the metabolic health of children, while high maternal mercury exposure was associated with an unfavorable metabolic profile in children.

Applying the exposome concept in birth cohort research: a review of statistical approaches.

The exposome represents the totality of life course environmental exposures (including lifestyle and other non-genetic factors), from the prenatal period onwards. This holistic concept of exposure provides a new framework to advance the understanding of complex and multifactorial diseases. Prospective pregnancy and birth cohort studies provide a unique opportunity for exposome research as they are able to capture, from prenatal life onwards, both the external (including lifestyle, chemical, social and wider community-level exposures) and the internal (including inflammation, metabolism, epigenetics, and gut microbiota) domains of the exposome. In this paper, we describe the steps required for applying an exposome approach, describe the main strengths and limitations of different statistical approaches and discuss their challenges, with the aim to provide guidance for methodological choices in the analysis of exposome data in birth cohort studies. An exposome approach implies selecting, pre-processing, describing and analyzing a large set of exposures. Several statistical methods are currently available to assess exposome-health associations, which differ in terms of research question that can be answered, of balance between sensitivity and false discovery proportion, and between computational complexity and simplicity (parsimony). Assessing the association between many exposures and health still raises many exposure assessment issues and statistical challenges. The exposome favors a holistic approach of environmental influences on health, which is likely to allow a more complete understanding of disease etiology.

Using methylome data to inform exposome-health association studies: An application to the identification of environmental drivers of child body mass index.

BACKGROUND: The exposome is defined as encompassing all environmental exposures one undergoes from conception onwards. Challenges of the application of this concept to environmental-health association studies include a possibly high false-positive rate. OBJECTIVES: We aimed to reduce the dimension of the exposome using information from DNA methylation as a way to more efficiently characterize the relation between exposome and child body mass index (BMI). METHODS: Among 1,173 mother-child pairs from HELIX cohort, 216 exposures ("whole exposome") were characterized. BMI and DNA methylation from immune cells of peripheral blood were assessed in children at age 6-10 years. A priori reduction of the methylome to preselect BMI-relevant CpGs was performed using biological pathways. We then implemented a tailored Meet-in-the-Middle approach to identify from these CpGs candidate mediators in the exposome-BMI association, using univariate linear regression models corrected for multiple testing: this allowed to point out exposures most likely to be associated with BMI ("reduced exposome"). Associations of this reduced exposome with BMI were finally tested. The approach was compared to an agnostic exposome-wide association study (ExWAS) ignoring the methylome. RESULTS: Among the 2284 preselected CpGs (0.6% of the assessed CpGs), 62 were associated with BMI. Four factors (3 postnatal and 1 prenatal) of the exposome were associated with at least one of these CpGs, among which postnatal blood level of copper and PFOS were directly associated with BMI, with respectively positive and negative estimated effects. The agnostic ExWAS identified 18 additional postnatal exposures, including many persistent pollutants, generally unexpectedly associated with decreased BMI. DISCUSSION: Our approach incorporating a priori information identified fewer significant associations than an agnostic approach. We hypothesize that this smaller number corresponds to a higher specificity (and possibly lower sensitivity), compared to the agnostic approach. Indeed, the latter cannot distinguish causal relations from reverse causation, e.g. for persistent compounds stored in fat, whose circulating level is influenced by BMI.

Association between the pregnancy exposome and fetal growth.

BACKGROUND: Several environmental contaminants were shown to possibly influence fetal growth, generally from single exposure family studies, which are prone to publication bias and confounding by co-exposures. The exposome paradigm offers perspectives to avoid selective reporting of findings and to control for confounding by co-exposures. We aimed to characterize associations of fetal growth with the pregnancy chemical and external exposomes. METHODS: Within the Human Early-Life Exposome project, 131 prenatal exposures were assessed using biomarkers and environmental models in 1287 mother-child pairs from six European cohorts. We investigated their associations with fetal growth using a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering each exposure independently. We corrected for exposure measurement error and tested for exposure-exposure and sex-exposure interactions. RESULTS: The DSA model identified lead blood level, which was associated with a 97 g birth weight decrease for each doubling in lead concentration. No exposure passed the multiple testing-corrected significance threshold of ExWAS; without multiple testing correction, this model was in favour of negative associations of lead, fine particulate matter concentration and absorbance with birth weight, and of a positive sex-specific association of parabens with birth weight in boys. No two-way interaction between exposure variables was identified. CONCLUSIONS: This first large-scale exposome study of fetal growth simultaneously considered >100 environmental exposures. Compared with single exposure studies, our approach allowed making all tests (usually reported in successive publications) explicit. Lead exposure is still a health concern in Europe and parabens health effects warrant further investigation.

An Empirical Validation of the Within-subject Biospecimens Pooling Approach to Minimize Exposure Misclassification in Biomarker-based Studies.

BACKGROUND: Within-subject biospecimens pooling can theoretically reduce bias in dose-response functions from biomarker-based studies when exposure assessment suffers from classical-type error. However, collecting many urine voids each day is cumbersome. We evaluated the empirical validity of a within-subject pooling approach and compared several options to avoid sampling each void. METHODS: In 16 pregnant women who collected a spot of each urine void over several nonconsecutive weeks, we compared concentrations of 10 phenols in daily, weekly, and pregnancy within-subject pools. We pooled either three or all daily samples. In a simulation study using these data, we quantified bias in dose-response functions when using one to 20 urine samples per subject to assess methylparaben (a compound with moderate within-subject variability) and bisphenol A (high variability) exposures. RESULTS: Correlations between exposure estimates from pools of all and of only three voids per day were above 0.80 for all time windows and compounds, except for benzophenone-3 and triclosan in the daily time window (correlations, 0.57-0.68). With one spot sample to assess pregnancy exposure, correlations were all below 0.74. Using only one biospecimen led to attenuation bias in the dose-response functions of 29% (methylparaben) and 69% (bisphenol A); four samples for methylparaben and 18 for bisphenol A decreased bias to 10%. CONCLUSIONS: For nonpersistent chemicals, collecting and pooling three samples per day instead of all daily samples efficiently estimates exposures over a week or more. Collecting around 20 biospecimens can strongly limit attenuation bias for nonpersistent chemicals such as bisphenol A.

Influence of the Urban Exposome on Birth Weight.

BACKGROUND: The exposome is defined as the totality of environmental exposures from conception onwards. It calls for providing a holistic view of environmental exposures and their effects on human health by evaluating multiple environmental exposures simultaneously during critical periods of life. OBJECTIVE: We evaluated the association of the urban exposome with birth weight. METHODS: We estimated exposure to the urban exposome, including the built environment, air pollution, road traffic noise, meteorology, natural space, and road traffic (corresponding to 24 environmental indicators and 60 exposures) for nearly 32,000 pregnant women from six European birth cohorts. To evaluate associations with either continuous birth weight or term low birth weight (TLBW) risk, we primarily relied on the Deletion-Substitution-Addition (DSA) algorithm, which is an extension of the stepwise variable selection method. Second, we used an exposure-by-exposure exposome-wide association studies (ExWAS) method accounting for multiple hypotheses testing to report associations not adjusted for coexposures. RESULTS: The most consistent statistically significant associations were observed between increasing green space exposure estimated as Normalized Difference Vegetation Index (NDVI) and increased birth weight and decreased TLBW risk. Furthermore, we observed statistically significant associations among presence of public bus line, land use Shannon's Evenness Index, and traffic density and birth weight in our DSA analysis. CONCLUSION: This investigation is the first large urban exposome study of birth weight that tests many environmental urban exposures. It confirmed previously reported associations for NDVI and generated new hypotheses for a number of built-environment exposures. https://doi.org/10.1289/EHP3971.

Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort.

BACKGROUND: Several single-exposure studies have documented possible effects of environmental factors on lung function, but none has relied on an exposome approach. We aimed to evaluate the association between a broad range of prenatal and postnatal lifestyle and environmental exposures and lung function in children. METHODS: In this analysis, we used data from 1033 mother-child pairs from the European Human Early-Life Exposome (HELIX) cohort (consisting of six existing longitudinal birth cohorts in France, Greece, Lithuania, Norway, Spain, and the UK of children born between 2003 and 2009) for whom a valid spirometry test was recorded for the child. 85 prenatal and 125 postnatal exposures relating to outdoor, indoor, chemical, and lifestyle factors were assessed, and lung function was measured by spirometry in children at age 6-12 years. Two agnostic linear regression methods, a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering exposures independently, were applied to test the association with forced expiratory volume in 1 s percent predicted values (FEV1%). We tested for two-way interaction between exposures and corrected for confounding by co-exposures. FINDINGS: In the 1033 children (median age 8·1 years, IQR 6·5-9·0), mean FEV1% was 98·8% (SD 13·2). In the ExWAS, prenatal perfluorononanoate (p=0·034) and perfluorooctanoate (p=0·030) exposures were associated with lower FEV1%, and inverse distance to nearest road during pregnancy (p=0·030) was associated with higher FEV1%. Nine postnatal exposures were associated with lower FEV1%: copper (p=0·041), ethyl-paraben (p=0·029), five phthalate metabolites (mono-2-ethyl 5-carboxypentyl phthalate [p=0·016], mono-2-ethyl-5-hydroxyhexyl phthalate [p=0·023], mono-2-ethyl-5-oxohexyl phthalate [p=0·0085], mono-4-methyl-7-oxooctyl phthalate [p=0·040], and the sum of di-ethylhexyl phthalate metabolites [p=0·014]), house crowding (p=0·015), and facility density around schools (p=0·027). However, no exposure passed the significance threshold when corrected for multiple testing in ExWAS, and none was selected with the DSA algorithm, including when testing for exposure interactions. INTERPRETATION: Our systematic exposome approach identified several environmental exposures, mainly chemicals, that might be associated with lung function. Reducing exposure to these ubiquitous chemicals could help to prevent the development of chronic respiratory disease. FUNDING: European Community's Seventh Framework Programme (HELIX project).

The early-life exposome: Description and patterns in six European countries.

Characterization of the "exposome", the set of all environmental factors that one is exposed to from conception onwards, has been advocated to better understand the role of environmental factors on chronic diseases. Here, we aimed to describe the early-life exposome. Specifically, we focused on the correlations between multiple environmental exposures, their patterns and their variability across European regions and across time (pregnancy and childhood periods). We relied on the Human Early-Life Exposome (HELIX) project, in which 87 environmental exposures during pregnancy and 122 during the childhood period (grouped in 19 exposure groups) were assessed in 1301 pregnant mothers and their children at 6-11 years in 6 European birth cohorts. Some correlations between exposures in the same exposure group reached high values above 0.8. The median correlation within exposure groups was >0.3 for many exposure groups, reaching 0.69 for water disinfection by products in pregnancy and 0.67 for the meteorological group in childhood. Median correlations between different exposure groups rarely reached 0.3. Some correlations were driven by cohort-level associations (e.g. air pollution and chemicals). Ten principal components explained 45% and 39% of the total variance in the pregnancy and childhood exposome, respectively, while 65 and 90 components were required to explain 95% of the exposome variability. Correlations between maternal (pregnancy) and childhood exposures were high (>0.6) for most exposures modeled at the residential address (e.g. air pollution), but were much lower and even close to zero for some chemical exposures. In conclusion, the early life exposome was high dimensional, meaning that it cannot easily be measured by or reduced to fewer components. Correlations between exposures from different exposure groups were much lower than within exposure groups, which have important implications for co-exposure confounding in multiple exposure studies. Also, we observed the early life exposome to be variable over time and to vary by cohort, so measurements at one time point or one place will not capture its complexities.

Human Early Life Exposome (HELIX) study: a European population-based exposome cohort.

PURPOSE: Essential to exposome research is the collection of data on many environmental exposures from different domains in the same subjects. The aim of the Human Early Life Exposome (HELIX) study was to measure and describe multiple environmental exposures during early life (pregnancy and childhood) in a prospective cohort and associate these exposures with molecular omics signatures and child health outcomes. Here, we describe recruitment, measurements available and baseline data of the HELIX study populations. PARTICIPANTS: The HELIX study represents a collaborative project across six established and ongoing longitudinal population-based birth cohort studies in six European countries (France, Greece, Lithuania, Norway, Spain and the UK). HELIX used a multilevel study design with the entire study population totalling 31 472 mother-child pairs, recruited during pregnancy, in the six existing cohorts (first level); a subcohort of 1301 mother-child pairs where biomarkers, omics signatures and child health outcomes were measured at age 6-11 years (second level) and repeat-sampling panel studies with around 150 children and 150 pregnant women aimed at collecting personal exposure data (third level). FINDINGS TO DATE: Cohort data include urban environment, hazardous substances and lifestyle-related exposures for women during pregnancy and their offspring from birth until 6-11 years. Common, standardised protocols were used to collect biological samples, measure exposure biomarkers and omics signatures and assess child health across the six cohorts. Baseline data of the cohort show substantial variation in health outcomes and determinants between the six countries, for example, in family affluence levels, tobacco smoking, physical activity, dietary habits and prevalence of childhood obesity, asthma, allergies and attention deficit hyperactivity disorder. FUTURE PLANS: HELIX study results will inform on the early life exposome and its association with molecular omics signatures and child health outcomes. Cohort data are accessible for future research involving researchers external to the project.

Pregnancy exposure to atmospheric pollution and meteorological conditions and placental DNA methylation.

BACKGROUND: Air pollution exposure represents a major health threat to the developing foetus. DNA methylation is one of the most well-known molecular determinants of the epigenetic status of cells. Blood DNA methylation has been proven sensitive to air pollutants, but the molecular impact of air pollution on new-borns has so far received little attention. OBJECTIVES: We investigated whether nitrogen dioxide (NO2), particulate matter (PM10), temperature and humidity during pregnancy are associated with differences in placental DNA methylation levels. METHODS: Whole-genome DNA-methylation was measured using the Illumina's Infinium HumanMethylation450 BeadChip in the placenta of 668 newborns from the EDEN cohort. We designed an original strategy using a priori biological information to focus on candidate genes with a specific expression pattern in placenta (active or silent) combined with an agnostic epigenome-wide association study (EWAS). We used robust linear regression to identify CpGs and differentially methylated regions (DMR) associated with each exposure during short- and long-term time-windows. RESULTS: The candidate genes approach identified nine CpGs mapping to 9 genes associated with prenatal NO2 and PM10 exposure [false discovery rate (FDR) p < 0.05]. Among these, the methylation level of 2 CpGs located in ADORA2B remained significantly associated with NO2 exposure during the 2nd trimester and whole pregnancy in the EWAS (FDR p < 0.05). EWAS further revealed associations between the environmental exposures under study and variations of DNA methylation of 4 other CpGs. We further identified 27 DMRs significantly (FDR p < 0.05) associated with air pollutants exposure and 13 DMRs with meteorological conditions. CONCLUSIONS: The methylation of ADORA2B, a gene whose expression was previously associated with hypoxia and pre-eclampsia, was consistently found here sensitive to atmospheric pollutants. In addition, air pollutants were associated to DMRs pointing towards genes previously implicated in preeclampsia, hypertensive and metabolic disorders. These findings demonstrate that air pollutants exposure at levels commonly experienced in the European population are associated with placental gene methylation and provide some mechanistic insight into some of the reported effects of air pollutants on preeclampsia.

A systematic comparison of statistical methods to detect interactions in exposome-health associations.

BACKGROUND: There is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions. METHODS: Simulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS2), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate. RESULTS: GLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects. CONCLUSIONS: GLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.

Towards understanding the epidemiology of Neisseria meningitidis in the African meningitis belt: a multi-disciplinary overview.

OBJECTIVES: Neisseria meningitidis is the major cause of seasonal meningitis epidemics in the African meningitis belt. In the changing context of a reduction in incidence of serogroup A and an increase in incidence of serogroups W and C and of Streptococcus pneumoniae, a better understanding of the determinants driving the disease transmission dynamics remains crucial to improving bacterial meningitis control. METHODS: The literature was searched to provide a multi-disciplinary overview of the determinants of meningitis transmission dynamics in the African meningitis belt. RESULTS: Seasonal hyperendemicity is likely predominantly caused by increased invasion rates, sporadic localized epidemics by increased transmission rates, and larger pluri-annual epidemic waves by changing population immunity. Carriage likely involves competition for colonization and cross-immunity. The duration of immunity likely depends on the acquisition type. Major risk factors include dust and low humidity, and presumably human contact rates and co-infections; social studies highlighted environmental and dietary factors, with supernatural explanations. CONCLUSIONS: Efforts should focus on implementing multi-country, longitudinal seroprevalence and epidemiological studies, validating immune markers of protection, and improving surveillance, including more systematic molecular characterizations of the bacteria. Integrating climate and social factors into disease control strategies represents a high priority for optimizing the public health response and anticipating the geographic evolution of the African meningitis belt.

Maternal exposure to diluted diesel engine exhaust alters placental function and induces intergenerational effects in rabbits.

BACKGROUND: Airborne pollution is a rising concern in urban areas. Epidemiological studies in humans and animal experiments using rodent models indicate that gestational exposure to airborne pollution, in particular diesel engine exhaust (DE), reduces birth weight, but effects depend on exposure duration, gestational window and nanoparticle (NP) concentration. Our aim was to evaluate the effects of gestational exposure to diluted DE on feto-placental development in a rabbit model. Pregnant females were exposed to diluted (1 mg/m(3)), filtered DE (NP diameter ≈ 69 nm) or clean air (controls) for 2 h/day, 5 days/week by nose-only exposure (total exposure: 20 days in a 31-day gestation). RESULTS: DE exposure induced early signs of growth retardation at mid gestation with decreased head length (p = 0.04) and umbilical pulse (p = 0.018). Near term, fetal head length (p = 0.029) and plasma insulin and IGF1 concentrations (p = 0.05 and p = 0.019) were reduced. Placental function was also affected, with reduced placental efficiency (fetal/placental weight) (p = 0.049), decreased placental blood flow (p = 0.009) and fetal vessel volume (p = 0.002). Non-aggregated and "fingerprint" NP were observed at various locations, in maternal blood space, in trophoblastic cells and in the fetal blood, demonstrating transplacental transfer. Adult female offspring were bred with control males. Although fetoplacental biometry was not affected near term, second generation fetal metabolism was modified by grand-dam exposure with decreased plasma cholesterol (p = 0.008) and increased triglyceride concentrations (p = 0.015). CONCLUSIONS: Repeated daily gestational exposure to DE at levels close to urban pollution can affect feto-placental development in the first and second generation.