Alicia Guillien
email: alicia.guillien@univ-grenoble-alpes.fr

Rémy Slama

Sandra Andrusaityte

Maribel Casas

Leda Chatzi

Montserrat De Castro

Blandine De Lauzon-Guillain

Berit Granum

Regina Grazuleviciene

Jordi Julvez

Blandine De Lauzon-Guillain

Norun Krog

Johanna Lepeule

Léa Maitre

Rosemary Mceachan

Mark Nieuwenhuijsen

Bente Oftedal

Jose Urquiza

Marina Vafeiadi

John Wright

Martine Vrijheid

Xavier Basagaña

Valérie Siroux

Methodology, Resources, Software, Writing -Original Draft. Rémy : Conceptualization

Sandra Slama

Maribel Andrusaityte

Leda Casas

Montserrat Chatzi

Blandine De Castro

Berit De Lauzon-Guillain

Regina Granum

Jordi Grazuleviciene

Norun Julvez

Johanna Krog

Léa Lepeule

Rosemary Maitre

Mark Mceachan

Bente Nieuwenhuijsen

Jose Oftedal

Marina Urquiza

John Vafeiadi

Wright

Associations between combined urban and lifestyle factors and respiratory health in European children

Keywords: environment, exposome, respiratory health, asthma, children

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

A growing body of evidence suggests that prenatal and childhood windows of exposure are a cornerstone in the development of chronic diseases over the life course [START_REF] Mastorci | Environment in Children's Health: A New Challenge for Risk Assessment[END_REF].

Lungs develop until early adulthood, but important structural developments take place in earlylife, including fetal life. During this vulnerable period for the development of organs, harmful factors may influence life-long risks for impaired lung function and respiratory diseases, including asthma and chronic obstructive pulmonary disease [START_REF] Bui | Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life[END_REF][START_REF] Sanna | Characterisation of lung function trajectories and associated early-life predictors in an Australian birth cohort study[END_REF].

The prevalence and incidence of respiratory and allergy-related diseases widely increased in the second half of the last century. As an example, the prevalence of asthma has doubled (or even tripled) in developed countries in less than 50 years [START_REF] Eder | The Asthma Epidemic[END_REF]. While genetic predispositions are known to be involved in allergic diseases, this huge increase in a relatively short period of time points out the role of environmental and lifestyle factors, in particular those that have changed in this period. In recent decades, a large number of studies have demonstrated associations between some environmental factors and respiratory diseases, such as exposure to tobacco [START_REF] Burke | Prenatal and Passive Smoke Exposure and Incidence of Asthma and Wheeze: Systematic Review and Meta-analysis[END_REF][START_REF] Sunde | Prenatal tobacco exposure and risk of asthma and allergy outcomes in childhood[END_REF], air pollution [START_REF] Lu | Intrauterine and early postnatal exposure to air pollution associated with childhood allergic rhinitis[END_REF]W. Zhang et al., 2022;Y. Zhang et al., 2022) and aeroallergens (house-dust mites, molds, …) [START_REF] Wright | Environmental remediation in the treatment of allergy and asthma: latest updates[END_REF]. Other factors such as climate factors [START_REF] Guilbert | Association of Prenatal and Postnatal Exposures to Warm or Cold Air Temperatures With Lung Function in Young Infants[END_REF][START_REF] Li | Ambient temperature and lung function in children with asthma in Australia[END_REF], greenness [START_REF] Cavaleiro Rufo | The neighbourhood natural environment is associated with asthma in children: A birth cohort study[END_REF] and diet [START_REF] Garcia-Marcos | Influence of Mediterranean diet on asthma in children: A systematic review and meta-analysis[END_REF] are suspected to be involved in the development of respiratory diseases and need to be further investigated. Most of the studies conducted to date considered a limited number of exposures, while these exposures are potentially correlated and could have combined effects on health (Robinson et al., 2018;[START_REF] Tamayo-Uria | The early-life exposome: Description and patterns in six European countries[END_REF].

The concept of exposome, defined as all environmental exposures from conception onwards [START_REF] Wild | Complementing the Genome with an "Exposome": The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology[END_REF] emphasizes the need of more holistic methods to characterize environmental exposures to improve our understanding of the etiology of complex multifactorial diseases. In particular, because people are continuously exposed to a wide range of environmental factors, considering simultaneously a large set of exposures in exposure-health association studies is warranted to better reflect real-life exposure patterns. Among existing statistical methods able to simultaneously account for multiple exposures, clustering analyses that identify subgroups of individuals sharing the same exposure profiles have previously been applied to identify environmental and behavioral factors associated with asthma in adults [START_REF] Guillien | Exposome Profiles and Asthma Among French Adults[END_REF] and with blood pressure in children [START_REF] Warembourg | Early-Life Environmental Exposures and Blood Pressure in Children[END_REF]. To the best of our knowledge, no study applied clustering analysis to specifically identify early-life urban and lifestyle environmental profiles associated with child respiratory health. This method could shed more light on respiratory risk factors and may help decision makers to identify potential interventions that jointly address the impact of several exposures on child health.

The aim of the present study was to identify profiles of pregnancy and childhood urban and lifestyle factors and to further assess their association with respiratory conditions (asthma, wheezing, rhinitis) and lung function parameters (forced expiratory volume in 1s (FEV1) and forced vital capacity (FVC)) in a population of European children.

Methods

Study population

This study is based on the European Human Early-Life Exposome (HELIX) project, which is a were recruited and follow-ups are still ongoing. Overall, the six cohorts recruited a total of 31,472 mother-child pairs during pregnancy. In the frame of the Helix project, a new follow-up was organized in 2013-2016 (children were between 6 and 12 years of age), based on a standardized protocol across the 6 cohorts. During this follow-up, 1,301 mother-child pairs were selected to be fully characterized for a broad suite of environmental exposures and to be clinically examined. This subcohort was composed of about 200 children from each of the 6 cohorts, randomly selected from individuals of the entire cohort who met the eligibility criteria, which were described in a previous study [START_REF] Maitre | Human Early Life Exposome (HELIX) study: a European population-based exposome cohort[END_REF]. From this subcohort, only children with acceptable and reproducible spirometry test were selected (n=1,033). Informed consent was obtained from all participants or their legal referee.

Respiratory outcomes

During the follow-up (i.e. 6-12 years of the children), all included children performed a spirometry test (EasyOne spirometer; NDD [New Diagnostic Design], Zurich, Switzerland) according to a common standardized protocol across the six cohorts to assess FEV1 and FVC.

Data quality control was applied to retain only the curves with valid criteria, as described in a previous study [START_REF] Agier | Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort[END_REF]. Finally, we used the Global Lung Initiative 2012 reference equations to compute the FEV1 and FVC in percent of the predicted value (ie, standardized for child age, height, gender and ethnicity) [START_REF] Quanjer | Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations[END_REF].

Regarding respiratory conditions of children, occurrence of asthma, wheezing and rhinitis were assessed through questionnaires adapted from the International Study on Asthma and Allergy in Childhood (ISAAC) and Mechanisms of the Development of ALLergy projects (MeDALL) [START_REF] Bousquet | MeDALL (Mechanisms of the Development of ALLergy): an integrated approach from phenotypes to systems medicine[END_REF] projects during the interview with the mother at the follow-up examination, the same day as the spirometry test. Ever asthma was defined by a positive answer to "Has your child ever been diagnosed by a doctor as having asthma?"; wheezing in the last 12 months was defined by a positive answer to: "Has your child had wheezing or whistling in the chest in the last year?"; and rhinitis in the last 12 months was defined by a positive answer to: "In the last year, has your child had problems with sneezing, or a runny, or blocked nose when he/she did not have a cold or the flu?".

Characterization of the exposome

A wide range of exposure variables were collected in the frame of the HELIX project. For this study, we focused on urban environmental and lifestyle factors assessed twice, first during the pregnancy and secondly in childhood (from the Helix study visit). Briefly, environmental variables were assessed with geographic information system-based models at residential address (during pregnancy and during childhood) and school address (only for childhood exposures) of the children. Pregnancy exposures were average on the entire pregnancy period and childhood exposure were collected during the study visit (children were 6-12 years old, depending of the cohort). Environmental variables covered atmospheric pollutants (nitrogen dioxide (NO2), particulate matter of less than 2.5 μm in aerodynamical diameter (PM2.5), particulate matter of less than 10 μm in aerodynamical diameter (PM10), absorbance of PM2.5 (PMabs) filters; 16 variables), built environment (building density, street connectivity, accessibility, facilities, land use evenness index, walkability index, population density; 26 variables), indoor air (NO2, PM2.5, PMabs, benzene and TEX (toluene, ethylbenzene, xylene); 5 variables), meteorological variables (mean air temperature, humidity, atmospheric pressure; 9 variables), ultraviolet (UV) (vitamin-D UV levels; 3 variables), road traffic (traffic load, traffic density and distance to nearest road; 8 variables), road traffic noise (night-time and 24h road noise levels; 5 variables), surrounding natural space (Normalized Difference Vegetation Index (NDVI), major green space and major blue space; 9 variables) and water disinfection byproduct (DBPs) (Trialometanes, Chloroform and Bromodichloromethane; 3 variables).

Lifestyle (alcohol, diet, physical activity, sleep and pets; 38 variables) were collected through standardized and validated questionnaires. In total, 122 exposures were assessed, 38 during the pregnancy period and 84 during the childhood period, as presented in Table 1. The detailed assessment methods, correlations between exposures and descriptive statistics of all exposures are described in previous studies [START_REF] Maitre | Human Early Life Exposome (HELIX) study: a European population-based exposome cohort[END_REF][START_REF] Robinson | The Urban Exposome during Pregnancy and Its Socioeconomic Determinants[END_REF]Tamayo-Uria et al., 2019) and in Appendices.

Covariates

Information about children and mothers' sociodemographic characteristics as well as pregnancy parameters were collected through questionnaires based and mother's interview. Covariates included center of recruitment, child's sex, age and height, ethnicity defined by both parents native to the cohort country (yes, no), breastfeeding duration, season of conception, presence of older siblings (none, one, two or more), parental education level (primary school, secondary school, university degree or higher), maternal age, maternal pre-pregnancy body mass index (BMI), postnatal passive smoking status and prenatal maternal active and passive smoking status, as identified in a previous study based on exposome-respiratory health associations in the Helix population [START_REF] Agier | Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort[END_REF]. Additionally, socioeconomic factors (family affluence score, family contact, participation in organizations and house crowding) and season of examination were used as covariates in the present study.

Statistical analyses

All continuous variables were transformed to approach normality, or categorized when it was not possible. Exposure variables and covariates were imputed using the method of chained equations and 20 complete datasets were computed [START_REF] White | Multiple imputation using chained equations: Issues and guidance for practice[END_REF]. All continuous exposure variables were centered and standardized by cohort to overcome the different distribution between the cohorts.

The following analyses were performed independently on pregnancy exposures and childhood exposures. First, the kproto method, an extension of the kmeans for mixed (including both continuous and categorical) data, was fitted to identify profiles of pregnancy and childhood exposures [START_REF] Huang | Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values[END_REF]. Then, we used a previously defined framework to combine multiple imputation with cluster analysis [START_REF] Basagaña | A Framework for Multiple Imputation in Cluster Analysis[END_REF]. The framework implemented in the miclust R package for the kmeans algorithm was modified to use kproto [START_REF] Huang | Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values[END_REF] and is detailed in the Appendices. This algorithm also included a variable selection step to reduce the dimension of the exposure dataset. The profiles identified from pregnancy and childhood exposures were named "profile Px" and "profile Cx", respectively. The description of each identified profile was presented through barplots where each bar relates to one exposure factor and represents the difference between the exposure mean level in the corresponding profile with the exposure mean level in the whole population for continuous exposure. Same methodology was used for categorical exposures, using the prevalence instead of the mean. Some exposures (walkability index, index of greenness, presence of blue space and green space, intakes of fish, fruits, legumes, vegetables, folic acid, physical activity, presence of pets, sleep duration and social factors) have been inverted so that a bar going to the left is a priori expected to be associated with a protective effect on respiratory health, and a bar going to the right is a priori expected to be associated with deleterious effect on respiratory health.

Secondly, for each health outcome, regression-based models (linear for FEV1 and FVC, logistic for presence of asthma, wheezing and rhinitis) were fitted to assess the association between the mutually-adjusted exposure profiles and the outcome variables. The reference profile was chosen as the one having the lowest prevalence of a priori deleterious exposures. All of these models were adjusted for the covariates defined above. All analyses were performed using the R software (version 3.4) and mice package for multiple imputation and clustMixType for clustering analyses.

Results

Description of the population

Among the 1,301 children examined in the frame of the Helix project, 1,033 had valid spirometry values and were selected for this analysis. Briefly, 53% of children were boys and the mean age ± standard deviation (SD) of children was 8.1 ± 1.6 years old when respiratory outcomes were collected (Table 2). The distribution of children per cohort was overall homogenic with about 15% for most of the cohort and slightly more for INMA (18%) and MOBA (23%). The mean maternal age was 30.9±4.8 years and 15% of mothers were active smokers during pregnancy. The characterization of the exposome was detailed in a previous study [START_REF] Agier | Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort[END_REF] and in Appendices.

Regarding respiratory parameters, children had a mean ± SD of FEV1% at 99 ± 13% (range: 61-139) and a mean ± SD FVC% of 101 ± 14% (range: 60-147). Finally, 123 (12%) children had ever asthma, 124 (12%) reported wheezing in the last 12 months and 246 (24%) reported rhinitis in the last 12 months.

Identification of pregnancy and childhood exposure profiles

Four exposure profiles were identified from the 38 pregnancy exposures, each including from 197 (19%) to 335 (32%) children, and each including children from all cohorts (Table A.1).

The distribution of pregnancy exposures in each profile is presented in Figure 1 andTable A.2.

Briefly, profile P1 (reference profile) was mainly characterized by children with low levels of exposure to all risk factors except for water DBPs and high temperature during the prenatal period. The profile P2 was mainly characterized by children with high levels of exposure to all risk factors, but an overall healthy lifestyle (high practice of physical activity and high intake of fruit, vegetables and fish) of their mothers during pregnancy. Children belonging to profile P3 had overall low levels of exposure to all risk factors but moderate exposure to unhealthy lifestyle habits during the prenatal period. Finally, the profile P4 was mainly characterized by children with highest exposure to almost all exposures during the prenatal period.

Regarding childhood exposures, four exposure profiles were identified from the 84 exposures, each including 191 (18%) to 322 (31%) children, and each including children from all cohorts (Table A.1). The distribution of childhood exposures in each profile is presented in Figure 2 and Table A.3. The profile C1 (reference profile) was mainly characterized by children with lower mean exposure levels to all risk factors during childhood as compared to children from other profiles except for humidity. The profile C2 was also characterized by children with globally low exposure to all exposures during childhood except high exposure to high UV dose and high temperatures. Children belonging to profile C3 were characterized by the most adverse levels of exposure to air pollutants (both indoor and outdoor), unhealthy lifestyle habits and low exposure to high UV doses and high temperatures during childhood. Finally, children from profile C4 had the most deleterious exposure to natural spaces (e.g. lack of green and blue spaces in their surroundings) and highest exposure to the built environment (e.g. high population density and high building density) and road traffic during childhood.

The median [interquartile] probability of attribution of individuals to the same profile in all imputed dataset (n=20) was 80% [65][66][67][68][69] for the pregnancy exposures and 100% [100-100] for the childhood exposures, illustrating the low uncertainty introduced by missing data imputation.

There was an association between pregnancy and childhood exposure profiles (Figure 3; Chisquared test, p<0.001). Briefly, as compared to children belonging to childhood exposure profile P2 and P4, children belonging to pregnancy exposure profile P1 and P3 were less often assigned to childhood exposure profile C4, but more often assigned to childhood exposure profile C1 and C2.

Association between exposure profiles and respiratory health

All profile-outcomes association studies were presented in Tables 3 and4 for the lung function and respiratory conditions, respectively.

No association was found between pregnancy profiles and children lung function. One childhood profile, the profile C4, characterized by children with the most deleterious exposure to natural spaces (e.g. poor number of green and blue spaces in their surrounding) and highest exposure to the built environment and road traffic presented lower levels of FEV1 (beta [95%CI] = -2.55 [-5.04;-0.07], p=0.04), compared to the reference (low exposure) profile. Moreover, children in the childhood profile C2, characterized by globally low exposure to all exposures except high exposure to high UV dose and high temperatures also tended to have lower level of FEV1 (beta [95%CI] = -2.34 [-5.06;0.38], p=0.09).

Regarding respiratory conditions, this last profile, the profile C2, was also associated with increased risk of ever-asthma for children (OR [95%CI] = 2.93 [1.36;6.31], p=0.01). Profile P2, characterized by high levels of exposure to all risk factors, but an overall healthy lifestyle of the mothers (high practice of physical activity and high intakes of fruits, vegetables and fish), during pregnancy, was associated with a lower risk of wheezing and rhinitis in the last 12 months (OR [95%CI] = 0.38 [0.18;0.77], p=0.01 and 0.64 [0.39;1.05], p=0.08, respectively), compared with children from the reference profile. Finally, a non-significant trend for an association was observed between profile C3, characterized by children with the most adverse levels of exposure to air pollutants (both indoor and outdoor), unhealthy lifestyle habits and low exposure to high UV doses and high temperatures, and a lower risk of rhinitis (OR [95%CI] = 0.65 [0.39;1.07], p=0.09).

Similar patterns of association between exposure profiles and respiratory health were observed when pregnancy and childhood exposure profiles were considered independently in the regression models (see additional Tables A.4 and A.5).

Finally, similar patterns of associations were found when analyses were stratified by child sex (see additional Tables A.6 and A.7).

Discussion

The present study is the first to comprehensively investigate effects of early-life (prenatal and childhood) urban and lifestyle exposure through exposure profiles on children respiratory health. Results showed that one childhood profile, mainly characterized by low number of natural spaces in the surrounding and high exposure to the built environment and road traffic, was associated with lower levels of FEV1 in children. A second childhood exposure profile, globally characterized by low exposure to all exposures except high exposure to high UV dose and high temperatures, was associated with higher risk of ever-asthma and lower level of FEV1 in children. Finally, one pregnancy profile, mainly characterized by high levels of exposure to all risk factors, but a healthy maternal lifestyle, and one childhood profile, characterized by high exposure to air pollutants, unhealthy lifestyle and lowest exposure to high temperatures and UV doses, was associated with decreased risk of rhinitis in children.

The benefit of combining exposures through exposure profiles

Two previous studies, also based on the Helix population, assessed the associations between pregnancy and childhood exposures and rhinitis [START_REF] Granum | Multiple environmental exposures in early-life and allergy-related outcomes in childhood[END_REF] and FEV1 [START_REF] Agier | Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort[END_REF] by considering independent association of each exposure. The Exposome Wide Association Studies (ExWAS) identified few urban and lifestyle exposures, and none of the association resisted to correction of the p-value for multiple testing. Using variable selection algorithm, the previous Helix study on rhinitis selected two pregnancy urban exposures, PMabs and inverse distance to nearest road, which were respectively associated with an unexpected decreased risk (OR [95%CI] = 0.5 [0.3;0.9]) and increased risk (OR

[95%CI] = 1.3 [1.1;1.6])
of rhinitis. In this study, we go a step further than individual associations by considering the joint effect of exposures on children respiratory health. For that, we identified profiles of exposure able to simultaneously account for multiple exposures, which a priori better reflect human exposures in their daily life. In environmental epidemiology, a few studies have used clustering method to assess the association between urban exposome and health outcomes [START_REF] Guillien | Exposome Profiles and Asthma Among French Adults[END_REF][START_REF] Warembourg | Early-Life Environmental Exposures and Blood Pressure in Children[END_REF][START_REF] Guillien | Profile of exposures and lung function in adults with asthma: An exposome approach in the EGEA study[END_REF][START_REF] De Bont | Urban environment and obesity and weight-related behaviours in primary school children[END_REF]. In all these studies, the authors identified some exposure profiles associated with the health outcome while single-exposure association studies reported only a few or sometimes no association when correcting p-value for the multiple testing hypothesis. This observation supports that clustering analysis, where only k-1 tests (where k is the number of clusters) are performed for each health outcome, is a way to overcome the statistical power issue raised in ExWAS analysis. Moreover, by considering all exposures simultaneously through profiles of exposure, the clustering analysis is able to account for complex synergistic effects between exposures, that cannot be assessed though regression based-model, especially when considering multiple exposome domains such as urban environment and lifestyle.

The choice of the clustering method

There is an increasingly rich literature on clustering methods, divided in two categories. The determinist clustering, which includes the kproto method used here, assign each individual to a unique cluster while probabilistic approaches assign a probability to belong to each cluster for each individual. These approaches also differ in terms of assumptions. Determinist clustering relies on a geometric approach where all individuals are assigned to the nearest cluster center, according to a defined metric, and where the cluster centers are firstly randomly assigned and then recalculated for each new assignment. Probabilistic methods assume that the distribution of the overall population hides several distributions corresponding to subpopulations and aim to estimate the parameters of these distributions, most often considered as Gaussian. In this study, we chose to apply the kproto because most of model-based methods rely on supervised methods, meaning that both the exposures and the outcome are considered to identify clusters, which would mean identifying different exposure profiles for each of the 5 health outcomes considered here. Moreover, we were not able to identify any studies comparing deterministic to probabilistic clustering methods, thus it remains unclear which method outperforms the other.

We acknowledge that other methods can provide different partitions, which can also be informative on the health outcomes, and we do not claim having found the best one.

Interpretation of results

One pregnancy profile, characterized by high levels of exposure to all risk factors, especially air pollutants and road traffic, but an overall healthy lifestyle (high practice of physical activity of mothers during pregnancy and high maternal intakes of fruits, vegetables and fish), was associated with a lower risk of wheezing and rhinitis compared with children from the reference profile. Although direct comparisons with previous single-exposure association studies are not possible, these results seem partly contradictory with the large literature supporting increased risk of respiratory diseases and asthma-related symptoms with exposure to air pollutants [START_REF] Bowatte | The influence of childhood traffic-related air pollution exposure on asthma, allergy and sensitization: a systematic review and a meta-analysis of birth cohort studies[END_REF], especially among children living in areas with high traffic-related air pollution [START_REF] Delfino | Asthma Morbidity and Ambient Air Pollution: Effect Modification by Residential Traffic-Related Air Pollution[END_REF]. This unexpected result could be driven by the fact that this profile is also characterized by the largest proportion of mothers with healthy diet during pregnancy. The literature about maternal diet and respiratory health in children is still scarce [START_REF] Donovan | Maternal Diet during Pregnancy and Lactation and Risk of Child Food Allergies and Atopic Allergic Diseases: A Systematic Review[END_REF], but there are growing evidences to indicate associations. A previous study based on 460 children-mothers pairs in a Mediterranean island in Spain, Menorca, reported a protective effect of healthy diet on asthma-related symptoms on children at age 6.5 years [START_REF] Chatzi | Mediterranean diet in pregnancy is protective for wheeze and atopy in childhood[END_REF]. For example, children with mother having high maternal adherence to a Mediterranean diet (vs. low adherence) during pregnancy had less persistent wheeze at 6.5 years (OR [95% CI] = 0.23 [0.09-0.60]). Indeed, diet composed of antioxidants and antiinflammatory nutrients may help reducing the impact of air pollution on health, that is mediated by oxidative stress [START_REF] Péter | Nutritional Solutions to Reduce Risks of Negative Health Impacts of Air Pollution[END_REF][START_REF] Saenen | Air pollution-induced placental alterations: an interplay of oxidative stress, epigenetics, and the aging phenotype?[END_REF].

Regarding childhood exposures, one profile (C4), characterized by the most deleterious exposure to natural spaces (e.g. low number of green and blue spaces in their surrounding) and highest exposure to the built environment and road traffic, was associated with lower FEV1 level which demonstrates the deleterious impact of combined risk factors. Even if not statistically significant, the childhood profile C2, characterized by globally low exposure to all exposure families but high exposure to high UV dose and high temperatures, also had lower level of FEV1. Children from this profile also had increased risk of ever asthma and wheezing, providing additional support for a deleterious impact of this exposure profile on respiratory health. These results suggest a short and mid-term effects of meteorological factors (assessed during the day, the week and the month prior to clinical examination in this study) on respiratory health, that might be easier to evidence in the absence of other risk factors such as air pollution.

Finally, the profile C3, characterized by the most adverse levels of exposure to air pollutants (both indoor and outdoor), unhealthy lifestyle habits and low exposure to high UV doses and to high temperatures shows a trend for a decreased risk of rhinitis in children. These results, which seems controversial regarding air pollutants and lifestyle, support the potential existing interactions between exposures and the need to use more comprehensive and holistic methods that consider all exposures as a whole to better understand exposome-health associations. Here, this association may be driven by the low exposure to high temperature and UV doses. Even if the impact of these meteorological factors on respiratory health in children is not clearly established, there are growing evidence for a beneficial effect of moderate exposure to UV on asthma [START_REF] Cherrie | Climatic factors are associated with asthma prevalence: An ecological study using English quality outcomes framework general practitioner practice data[END_REF][START_REF] Hart | Modulation of the immune system by UV radiation: more than just the effects of vitamin D?[END_REF] and for deleterious respiratory health effects associated with increased ambient temperature, including lower lung function [START_REF] Li | Ambient temperature and lung function in children with asthma in Australia[END_REF], higher exacerbation rate in children with asthma [START_REF] Schinasi | Associations between high ambient temperatures and asthma exacerbation among children in Philadelphia, PA: a time series analysis[END_REF] and rhinitis [START_REF] Norbäck | Asthma and rhinitis among Chinese children -Indoor and outdoor air pollution and indicators of socioeconomic status (SES)[END_REF].

Strengths and weaknesses

Our study has several strengths. First, the multi-center design of this study encompassing 6 European countries, led to a study population covering a wide range of outdoor exposures and lifestyle habits, which is a strength in terms of the generalizability of the results. Secondly, this study covers a wide range of exposures at several time points. Here, we considered pregnancy and childhood which are critical windows of exposure where lungs are particularly vulnerable with long lasting consequences of noxious exposures [START_REF] Barouki | Developmental origins of non-communicable disease: Implications for research and public health[END_REF][START_REF] Bousquet | Developmental determinants in non-communicable chronic diseases and ageing[END_REF]. By analyzing five respiratory outcomes, including two objective lung function parameters (FEV1 and FVC) and three prevalent respiratory conditions (ever asthma, wheezing and rhinitis), the consistency of associations across the different respiratory health outcomes was used in results interpretation. Finally, as previously discussed, the methodology performed in this study overcame many of the issues associated with exposome studies, especially the unstraightforward issue of multiple comparisons, while providing results on exposures that are more representative of real life.

Nevertheless, this study also suffers from some limitations. First, the number of participants remains limited (n=1,033) and could imply a lack of statistical power. Then, we acknowledge that some measurement errors, possibly different from one cohort to another, is unavoidable in environmental epidemiology studies. The methodological complexity related to the consideration of multiple centers and the potential confounding by center when it comes to assessing associations with health outcomes has already been addressed [START_REF] Basagaña | Analysis of multicentre epidemiological studies: contrasting fixed or random effects modelling and metaanalysis[END_REF].

Here, we standardized all continuous variables within cohort to limit this issue. In that way, we removed the between-center differences and focused on the variation of the levels of exposure within each cohort. Moreover, although the spirometry tests were performed by trained technicians, following the same standardized protocols defined from current guidelines [START_REF] Quanjer | Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations[END_REF], and an extensive quality control has been applied [START_REF] Agier | Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort[END_REF], some measurement errors in FEV1 and FVC could have arose as spirometry, which is a forced expiratory test, is often difficult to perform in children. Then, as demonstrated in a previous study [START_REF] Maitre | Human Early Life Exposome (HELIX) study: a European population-based exposome cohort[END_REF], the excluded participants of this study presented some differences from the included participants which could affect the representativeness of the studied population, but should not affect the association between the identified exposure profiles and respiratory outcomes. Furthermore, avoidance of exposure in the presence of child specific disorder can't be totally excluded and could lead to a reverse causality phenomenon. Here, this potential selection bias is limited because the study is based on population-based cohorts and not on a population of moderate-to-severe patients with asthma where avoidance of environmental triggers is more often implemented. Then, we simultaneously considered a wide range of exposures while avoidance would be specific to a single exposure or family of exposure. In addition, despite a large set of urban exposures collected in this study, some relevant exposures are missing. For example, indoor exposures and aeroallergens such as pollens were not available while it would be of interest in the context of allergy-related outcomes [START_REF] De Roos | Ambient daily pollen levels in association with asthma exacerbation among children in Philadelphia, Pennsylvania[END_REF][START_REF] Lu | Effects of early life exposure to home environmental factors on childhood allergic rhinitis: Modifications by outdoor air pollution and temperature[END_REF][START_REF] Mattila | Scoping Review-The Association between Asthma and Environmental Chemicals[END_REF]. The chemical exposome was not considered in this outdoor-lifestyle exposome study, but because chemical exposures are not related to most of the outdoor exposome [START_REF] Tamayo-Uria | The early-life exposome: Description and patterns in six European countries[END_REF] this should not impact the association estimates of the outdoor exposome profile. Also, genetic factors, that are involved in asthma and allergic diseases susceptibility [START_REF] Stikker | Decoding the genetic and epigenetic basis of asthma[END_REF], were not considered in our study. Indeed, we aimed at identifying environmental and lifestyle factors that might be responsible of the huge increase of asthma and allergic diseases prevalence observed since the second half of the last century. Once environmental and lifestyle factors associated with respiratory health will be well identified, further studies investigating gene*environment interactions might be of great interest. An additional limitation is related to the challenge of interpreting associations with exposure profiles accounting for many exposures and to compare these associations with results of single-exposure association studies where potential confounding by co-exposures and synergistic effects are not considered. Finally, analyses regarding childhood exposure-child health associations presented here are cross-sectional. This issue mainly impacts the associations studied with ever-asthma when the timeline between exposure in childhood and child health outcomes can't be defined. Further projects considering multiple timepoints for exposure and respiratory health are needed to yield more insights into the presumed causal relationships.

Conclusions

This study is among the firsts to use a holistic method that simultaneously considers a large range of environmental factors through pregnancy and childhood exposure profiles, to further investigate the effect of combined environmental exposures on children respiratory health. The results highlighted two childhood exposure profiles associated with lower levels of FEV1, one of which was also associated with a higher risk of ever-asthma and wheezing. This study also identified a prenatal exposure profile associated with a lower risk of wheezing and rhinitis in children. In terms of public health, these results suggest that a better understanding of combined exposures associated with health might shed light on the etiology of complex multifactorial diseases, which in turn can provide clues for more efficient preventive measures.

List of figures Figure 1. Description of pregnancy profiles

Barplots representing the distribution of continuous (at the top) and categorical (at the bottom) variables of each exposure family. Each bar represents the difference between the mean level of one exposure in the corresponding profile with the mean level of the same exposure in the whole population for continuous exposure. Same methodology was used for categorical exposures, using the prevalence instead of the mean. Exposures highlighted with a * at the end of their label have been inverted so that a bar going to the left is a priori expected to be associated with a protective effect on respiratory health, and a bar going to the right is a priori expected to be associated with deleterious effect on respiratory health. NDVI: normalized difference vegetation index; PM2.5: particulate matter of less than 2.5 μm in aerodynamical diameter; PM10: particulate matter of less than 10 μm in aerodynamical diameter; PMabs: absorbance of particulate matter of less than 2.5 μm in aerodynamical diameter; NO2: nitrogen dioxide.

Figure 2. Description of childhood profiles

Barplots representing the distribution of continuous (at the top) and categorical (at the bottom) variables of each exposure family. Each bar represents the difference between the mean level of one exposure in the corresponding profile with the mean level of the same exposure in the whole population for continuous exposure. Same methodology was used for categorical exposures, using the prevalence instead of the mean. Exposures highlighted with a * at the end of their label have been inverted so that a bar going to the left is a priori expected to be associated with a protective effect on respiratory health, and a bar going to the right is a priori expected to be associated with deleterious effect on respiratory health. KIDMED: Mediterranean Diet Index; NO2: nitrogen dioxide; BTEX: benzene, toluene, ethylbenzene and xylene; NDVI: normalized difference vegetation index; UV: ultraviolet; PM2.5: particulate matter of less than 2.5 μm in aerodynamical diameter; PM10: particulate matter of less than 10 μm in aerodynamical diameter; PMabs: absorbance of particulate matter of less than 2.5 μm in aerodynamical diameter. Children belonging to profile C3 were characterized by the most adverse levels of exposure to air pollutants (both indoor and outdoor), unhealthy lifestyle habits and low exposure to high UV doses and high temperatures during childhood. Finally, children from profile C4 had the most deleterious exposure to natural spaces (e.g. lack of green and blue spaces in their surroundings) and highest exposure to the built environment (e.g. high population density and high building density) and road traffic during childhood. Profile C1 (reference profile) was mainly characterized by children with lower mean exposure levels to all risk factors during childhood as compared to children from other profiles except for humidity. The profile C2 was also characterized by children with globally low exposure to all exposures during childhood except high exposure to high UV dose and high temperatures.

Children belonging to profile C3 were characterized by the most adverse levels of exposure to air pollutants (both indoor and outdoor), unhealthy lifestyle habits and low exposure to high UV doses and high temperatures during childhood. Finally, children from profile C4 had the most deleterious exposure to natural spaces (e.g. lack of green and blue spaces in their surroundings) and highest exposure to the built environment (e.g. high population density and high building density) and road traffic during childhood.

  collaborative project combining six existing longitudinal birth cohorts: BiB (Born in Bradford) in United Kingdom, EDEN (Etude des Déterminants pré et postnatals du développement et de la santé de l'Enfant) in France, INMA (Infancia y Medio Ambiente) in Spain, KANC (Kaunas Cohort) in Lithuania, MoBa (Norwegian Mother, Father and Child Cohort Study) in Norway and RHEA (Mother-Child Cohort in Crete) in Greece. Briefly, children born in 2007-08 for BiB and RHEA, 2003-05 for EDEN, 2005-07 for INMA and MoBa and 2007-09 for KANC

Figure 3 .

 3 Figure 3. Sankey diagram of distribution of children in pregnancy and childhood profiles

Table 1 .

 1 Description of pregnancy and childhood exposuresPregnancy exposures were averaged during the whole pregnancy, and childhood exposures were averaged during the year before study visit (where children were 8-11, depending on the cohort), unless otherwise stated. NO2: nitrogen dioxide; PMabs: absorbance of PM2.5 filters, PM10: particulate matter with an aerodynamic diameter of less than 10μm, PM2.5: particulate matter with an aerodynamic diameter of less than 2.5 μm; BTEX: benzene, toluene, ethylbenzene, xylene; NDVI: Normalized Difference Vegetation Index. Categorical variables are presented as n (%) and continuous variables are presented as mean (standard deviation). BIB: Born In Bradford; EDEN: Étude des Déterminants Pré et Postnatals du Développement et de la Santé de l'Enfant; INMA: Infancia y Medio Ambiente; KANC: Kaunas Cohort; MOBA: Norwegian Mother, Father and Child Cohort Study; RHEA: Mother-Child Cohort in Crete; BMI: body mass index.

	Pregnancy

Acknowledgments

The authors are grateful to all the participating families in the six countries who took part in this study and to all the field workers for their dedication and efficiency. French-speaking Association for the Study of Diabetes and Metabolism (ALFEDIAM), National Agency for Research (ANR non-thematic programme), and National Institute for Research in Public Health (IRESP: TGIR 2008 cohort in health programme).

Funding

The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP7/2007(FP7/ -2013) ) under grant agreement no 308333the HELIX project and by the European Commission's Horizon 2020 Framework Program for Research and Innovation, under grant 874583the ATHLETE project. This project benefited from a funding from FRM, the Foundation for medical research (EPIMEX project, grant #ENV202004011870). Jose Urquiza is supported by Catalan program PERIS (Ref.: SLT017/20/000119), granted by Departament de Salut de la Generalitat de Catalunya (Spain).

RM and JW receive funding from the NIHR Applied Research Collaboration for Yorkshire and Humber (NIHR200166). The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. The EDEN study was supported by the Fondation pour la Recherche Médicale (FRM), French Ministry of Research: Federative Research Institutes and Cohort Program, INSERM Human Nutrition National Research Program, and Diabetes National Research Program (by a collaboration with the French Association of Diabetic Patients [AFD]), French Ministry of Health, French Agency for Environment Security (AFSSET), French National Institute for Population Health Surveillance (InVS), Paris-Sud University, French National Institute for Health Education (INPES), Nestlé, Mutuelle Générale de l'Education Nationale (MGEN),

Profile P1 (reference profile) was mainly characterized by children with low levels of exposure to all risk factors except for water DBPs and high temperature during the prenatal period. The profile P2 was mainly characterized by children with high levels of exposure to all risk factors, but an overall healthy lifestyle (high practice of physical activity and high intake of fruit, vegetables and fish) of their mothers during pregnancy. Children belonging to profile P3 had overall low levels of exposure to all risk factors but moderate exposure to unhealthy lifestyle habits during the prenatal period. Finally, the profile P4 was mainly characterized by children with highest exposure to almost all exposures during the prenatal period. Profile C1 (reference profile) was mainly characterized by children with lower mean exposure levels to all risk factors during childhood as compared to children from other profiles except for humidity. The profile C2 was also characterized by children with globally low exposure to all exposures during childhood except high exposure to high UV dose and high temperatures.

Children belonging to profile C3 were characterized by the most adverse levels of exposure to air pollutants (both indoor and outdoor), unhealthy lifestyle habits and low exposure to high UV doses and high temperatures during childhood. Finally, children from profile C4 had the most deleterious exposure to natural spaces (e.g. lack of green and blue spaces in their surroundings) and highest exposure to the built environment (e.g. high population density and high building density) and road traffic during childhood. Profile P1 (reference profile) was mainly characterized by children with low levels of exposure to all risk factors except for water DBPs and high temperature during the prenatal period. The profile P2 was mainly characterized by children with high levels of exposure to all risk factors, but an overall healthy lifestyle (high practice of physical activity and high intake of fruit, vegetables and fish) of their mothers during pregnancy. Children belonging to profile P3 had overall low levels of exposure to all risk factors but moderate exposure to unhealthy lifestyle habits during the prenatal period. Finally, the profile P4 was mainly characterized by children with highest exposure to almost all exposures during the prenatal period.


Anthony Lourdiane

Laurent Bourdieu

Jean-François Léger

teaching and research institutions in France or abroad, or from public or private research centers.

Evolutions of pure tone representations and off-stimulus

neuronal activity in layer II/III of the mouse primary auditory cortex following active avoidance learning

Evolutions of pure tone representations and off-stimulus neuronal activity in layer II/III of the mouse primary auditory cortex following active avoidance learning Anthony Lourdiane, Laurent Bourdieu, Jean-François Léger IBENS, Ecole Normale Supérieure -PSL University

It is well accepted that stimulus responses in the primary auditory cortex (A1) can be transformed when an animal learns to associate a sound with an appetitive or aversive stimulus. For many decades, it has been suggested and observed that some types of learning can be reflected as a change in the receptive fields of cells within A1 and/or a modification at the scale of tonotopic maps. However, it questionable at the moment whether becoming expert at an auditory task could not only affect sound representation in A1, but would also reorganize the efficacy of functional interactions between neurons independently of the stimuli.

Here, we characterize the evolution of responses to pure tones after learning a negative reinforcement task, followed by a description of the change in correlation between cells of A1 during periods of activity outside stimulus times.

MOTIVATIONS

HIT

MISS BEHAVIOURAL TASK

Animals have to escape an electric shock temporally associated with a pure tone. Mice learn to reach the safe side of the box, opposit to where they stand when a « target » tone starts (HIT). Not crossing in time equals receiving a shock (MISS).

STIMULI

We use amplitude modulated pure tones.

RECORDING BRAIN ACTIVITY

We perform 2-photon imaging on awake head-fixed animals, using GCaMP7f as a reporter for neural activity.

Importantly, in this configuration we record the brain in a radically different environment from the training box.

« target » is conditioned with the shock, while « foil » serves as an internal control.

FENS stand 109

We record brain activity at the level of the primary auditory cortex (A1) before and after the animals learn the task in order to characterize learningrelated evolutions at the single cell level. Data from 4 CBA mice which successfully completed the learning paradigm.

We also recorded the brain of 4 control mice that were only habitued to sound in the training box, without any unconditioned stimulus.

ANIMAL COHORTS AND BEHAVIOUR ROBUSTNESS OF RESPONSES TO SOUNDS

For a given sound repetition r and a cycle θ of amplitude modulation (5Hz) during a pure tone, we take the integral of the fluorescence F(r, ) θ over this cycle.

We compare F(r, ) θ to the standard deviation of similar integrals computed outside stimulus times, and assign bin(r, ) θ = 1 if F(r, ) θ > σ and bin(r, ) θ = 0 otherwise.

By averaging bin(r, ) θ over all repetitions for each , θ we define how reliably a neuron responds to a given sound (robustness).

sound

EVOLUTION OF POPULATION ROBUSTNESS AFTER LEARNING

Average population robustnesses of target, foil and control (i.e. habituated) sounds show specific evolutions in time.

Δ ROBUSTNESS OF EARLY RESPONSES

robustness is Δ defined as the difference in response reliability between the naive and expert recordings for each neuron.

We look at offstimulus activity, and ask whether learning has an effect on pairwise correlations in this part of the recording.

Δ ROBUSTNESS OF EARLY RESPONSES

We compare the evolution of correlations between naive and expert animals by taking their differences.

Structure of « spontaneous » activity is impacted differently in trained animals from control animals.

TIMELINE OF THE EXPERIMENT ASSESSING ACTIVITY OUTSIDE STIMULUS TIMES

• We show that neurons in layer II/III of the mouse primary auditory cortex modify their representation of pure tones differently between simple habituation and association with an operant negative reinforcement task.

• Moreover, we observe that neuronal activities outside stimulus times is also modified differently between trained and control animal when we look at pairwise correlations, suggesting a learning rewiring of neurons inside the A1 network.

• We are now clustering the matrices of correlations to test whether there is a link between the change in responses to sounds and the stimulus-free evolutions.

• To characterize better the nature of the changes in the « spontaneous » activity, we resort to maximum entropy models.

In particular, by setting the hamiltonian in the form of an Ising model, we perform maximum likelihood estimation of the probability distribution :

where σ i represents binarised activity of neuron i.

CONCLUSIONS AND PERSPECTIVES


Le Fe est concentré dans les horizons oxydés. La Nouvelle-Calédonie possède un climat tropical à l'origine de la formation de sols latéritiques par altération graduelle des roches du socle (dunites), avec un pro l de type silicate de magnésium hydraté [1]. A n d'étudier la mobilité des éléments le long de ce pro l, di érents éléments majeurs (Ni, Si, Fe, Mg) et traces (terres rares) ont été analysés. Les résultats géochimiques pour les di érents horizons d'un tel pro l latéritique montrent une répartition des éléments caractéristique de leur mobilité dans ce contexte ultrama que.

Fig 2 :

 2 Fig 2: spectres des terres rares le long du pro l latéritique de Nouvelle-Calédonie

Fig 1 :

 1 Fig 1: Carte géologique de la Nouvelle-Calédonie et localisation de la zone d'étude (encadré rouge)

Fig 4 : 2 . 1 .Fig 3 :

 4213 Fig 4: Colonne lithostratigraphique du pro le latéritique avec les proportions en Ni, SiO , Fe O et MgO pour chaque horizon.


Olivier Pourret

Gilles Colinet

Bastien Lange

Michel-Pierre Faucon

Comportement des terres rares sur une toposéquence sur sol latéritique

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

La distribution et le fractionnement des terres rares ont été étudiés le long d'une toposéquence situé sur un sol latéritique voisin du monastère de Kiswishi (Rep. Dem. Congo). La toposéquence est caractérisée par trois unités. La première unité se révèle être un haut de versant forestier occupé par une forêt claire ou plus communément appelée Miombo. Elle est localisée sur un plateau avec une pente inférieur à 5 %. La deuxième unité est un versant avec pente de 12,5% occupé par des jachères et des cultures de soja ou de maïs. Ces jachères ont une durée importante pour permettre le renouvellement des parcelles. En n, le bas-fond est occupé par de l'horticulture avec la présence d'une pépinière sur une pente de 10%. Ces trois unités ont été échantillonnées en surface et selon 3 pro ls fonction de la profondeur.

Bien que nous ayons observé un large éventail de variabilité naturelle des concentrations en terres rares, dans l'ensemble, les concentrations moyennes en terres rares sont relativement fortes (plus de dix fois supérieure à la croûte continentale) par rapport à celles signalées dans les sols d'autres parties du monde. En e et, la distribution des terres rares dans les sols dépend essentiellement de la roche source altérée, tandis que le fractionnement des terres rares individuelles dépend des caractéristiques du sol qui sont en partie in uencées par les processus pédogénétiques. Ainsi, nous observons un fort enrichissement en terres rares légères sur le plateau. Cet enrichissement disparait progressivement le long de la toposéquence. Nous pouvons aussi observé un fractionnement important le long du pro l sur le plateau alors qu'il n'y a plus de fractionnement le long du pro l en bas de la toposéquence.

Figure 1 -

 1 Figure 1 -Localisation des points d'échantillonnage

  La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu

		100
			KIS3 Ah
	Sample/Upper Continental Crust	1 10	KIS3 AB KIS3 B1 KIS3 B2
		0,1
		100
			KIS2 A1
	Sample/Upper Continental Crust	1 10	KIS2 B1 KIS2 Bw1
		0,1	La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
		100
			KIS1 A1
			KIS1 A2
	Sample/Upper Continental Crust	1 10	KIS1 A3 KIS1 Bg1
		0,1	La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu