One of the novel concepts to address environment and health concerns is the human exposome concept originally coined by Dr Chris Wild in 2005.⁶ The human exposome concept encompasses the totality of environmental exposures and their endogenous response across the lifespan, and how they shape disease risk and disease development.⁶ However, when people live in urban settings with variable population densities, individuals often share their personal exposomes due to common characteristics, which in turn may be influenced by socioeconomic (such as ethnicity, social class) or physical and infrastructure parameters (such as built environment and neighborhood status).⁷ The urban exposome appears as the natural continuum of the human exposome towards the characterization of small(er) urban areas in the city and their interactions with residents, city infrastructure, built environment and policies. The urban exposome was earlier defined as “the continuous temporal, and spatial surveillance/monitoring of quantitative and qualitative indicators associated with the urban external and internal parameters (belonging to the domains of the urban exposome) that would ultimately shape up the quality of life and the health of the urban population, using small areas of the city, such as neighborhoods, quarters, smaller administrative districts, as the point of reference.”⁷ This definition outlines the importance of the time dimension on one hand, underlining the need for continuous monitoring of the temporal variation in the urban exposome components, and the space dimension on the other, allowing for the geospatial detection and monitoring of exposomic perturbations and health trajectories within the urban setting. Within the framework of the urban exposome, individual-level measurements aggregated within small(er)-areas of the urban setting become the key level of analysis, allowing for an improved understanding of the variability in exposures and urban health disparities. The general external urban exposome domain includes global policies, decisions and factors relating to the urban setting; the specific external urban exposome domain includes climate, migration and demographic changes, etc, while the internal urban exposome includes parameters integral to the urban setting, and extending into the individual-level human exposome domains.⁷ The integration of longitudinal multi-omics platforms presents with opportunities to better understand the complexity of urban exposures as depicted on population health indicator dynamics and associated health outcomes (Figure 1).^8,9

Urban exposome studies require the critical infrastructure and resources of pregnancy birth/population cohorts, registries/survey datasets, as well as the availability of routinely collected environmental (non-genetic) variables. One of the very first urban exposome studies within established cohorts, the Human Early Life Exposome (HELIX) project used exposomic data from six urban EU population-based birth cohorts linking them with -omics signatures and child health outcomes.¹⁰ The EXPANSE study studied the impact of the urban exposome on cardiometabolic and pulmonary diseases over the life course of 55 million European inhabitants across 12 countries.¹¹ The Child Cohort Network brought together several EU child cohorts into one data sharing platform, hence investigating the association of repeated measurements of the pregnancy and early life exposome with multi-omics and health outcomes followed up to 18 years of age.¹²

This methodological manuscript describes the incorporation of the urban exposome methodological framework taking the form of a study protocol describing the main elements (at a minimum) that such exposomic studies nested within existing prospective cohorts would possess, including their exposomics tools. The scientific approach presented hereafter aims to showcase a novel framework to articulate how longitudinal data from ongoing cohorts can be integrated into nested urban exposome study designs to capture and characterize the spatiotemporal dynamics of exposome profiles across urban populations—an area that has remained largely underexplored. The description of this methodological manuscript may operationalize either within an existing longitudinal study or applied to multiple cohort-nested exposome studies. By leveraging the existing CONSTANCES (“Cohorte des consultants des Centres d’examens de santé”) cohort data infrastructure and focusing on the urban exposome of the population of Paris (Paris UrbanX), this approach breaks new ground by laying out how the associations between geospatially dynamic, temporally resolved assessments of environmental exposures and their health impacts can be studied within a cohort infrastructure. As a theoretical illustration, we describe exposomic methods and tools for investigating the association between the urban exposome and body mass index (BMI), using small-area level units of measurement and analysis. This paper follows the “Strengthening The Reporting of OBservational Studies in Epidemiology” (STROBE) guidelines (Table S1).¹³

The French general population-based cohort CONSTANCES was designed according to a random sampling scheme stratified on age (aged 18–69 years at inception), sex, socioeconomic status (SES) from 21 selected health screening centers (HSCs) located in 20 “départements” of different regions of France, affiliated to the National Health Insurance Fund (Cnam: Caisse nationale d’ assurance maladie) that covers salaried workers, professionally active or retired and their dependents (more than 85% of the French population), thus, excluding agricultural and self-employed workers which are affiliated to other health insurance funds.¹⁴ Those who agreed to participate to CONSTANCES received questionnaires to complete and attended one of the HSCs for a medical examination and completing specific questionnaires. After enrollment, participants have been followed-up by annual self-administered paper-based or web-based questionnaires collecting various information, and they have been invited for a new medical examination every 4 years.¹⁴ The recruitment was initiated in late 2012 and included >200 000 volunteers recruited up to 2020 and followed-up to date.¹⁵ The CONSTANCES design and methodological framework for data and biospecimen collection have been extensively described elsewhere.^14,16,17 CONSTANCES Cohort project has obtained the authorization related to confidentiality, safety, and security procedures from the relevant French legal authorities. Written informed consent was provided by participants to participate in this study.^14,16 De-identified data are used throughout the process of data transfer, management, and analysis. A summary of the CONSTANCES data collection tools is provided in Table S2.

There are 20 municipal arrondissements in the city of Paris, each divided into several IRIS (Îlots Regroupés pour l‘Information Statistique), the fundamental intra-municipal unit.^18,19 As per the French National Institute of Statistics and Economic Studies (Institut National de la Statistique et des Etudes Economiques, INSEE), a residential IRIS is a geographical unit area with 1800–5000 inhabitants; there are 992 IRIS in Paris city¹⁸ during the study period (2012–2020).²⁰ The degree of urbanization of the provided Paris IRIS was verified using the DEGURBA database of France and cross-validated with the INSEE database of urban units.^21,22 In the CONSTANCES cohort, each participant’s home address was recorded at inclusion and updated throughout the follow-up period and geocoded with a precision indicator ranging from the exact address to the centroid of the zip code, then linked to residential IRIS version of 2016, which corresponds to the middle of the inclusion period.

To allow temporal analysis of exposome variables, we suggest limiting the sample to CONSTANCES volunteers enrolled during 2012–2020 for whom follow-up information between 2013 and 2020 is available. To limit classification bias, we propose limiting the sample to volunteers residing in geocoded IRIS classified as “residential,” and those providing only one residential address throughout their study period. For the sake of this example, we also propose limiting the sample to those being alive during the complete study period of interest.

Variables from self-administered questionnaires and health screening examinations at inclusion and during follow-up²³ of the CONSTANCES population can be classified into the urban and human exposome domains, their components, and their sub-components (groups of variables)^7,24 (Table S3). The temporal analysis of the urban exposome would require the inclusion of exposomic variables assessed at three or more time points of follow-ups (either at inclusion and twice or more during follow-up years; or collected at three or more follow-up years during the study period). Nevertheless, other variables (collecting socio-demographic information, personal medical history, physical limitations, family medical history) need to be selected even if available once at baseline (Table S4).

The availability of the participants’ geocoded residential (eg, IRIS) data in a cohort infrastructure enables their linkage to contextual indicators (ie, deprivation index, localized accessibility indicators, etc.) and environmental databases,²⁵ such as annual mean concentrations of fine particulate matter (PM_2.5, with a diameter <2.5 μm), black carbon, and nitrogen dioxide (NO₂) at the residential addresses which can be estimated with (100 × 100 m) land-use regression models incorporating ground-based measurements, satellite-derived and chemical transport-modeled estimates, road density, land-use variables, and altitude.^26,27 Satellite-based normalized difference vegetation index (NDVI) can be used to estimate residential greenness within 300 m of the participants’ residential addresses.²⁸

As additional auxiliary data sources, further enrichment of CONSTANCES database with extra urban exposome variables can be achieved by screening national French governmental websites (e.g., www.info.gouv.fr, www.paris.fr, www.insee.fr) for articles, datasets and maps on urban exposome components covering any administrative level of Paris during the study period of interest (2012–2020).

All information obtained from the various auxiliary sources require harmonization and linkage to the CONSTANCES dataset as per the smallest geographic level for which each variable is available. To this, we propose statistical methods that can be conducted in separate packages using R version 4.3.2 and R studio 2024.04.1 and beyond.^29,30 These statistical approaches can be further implemented using the hands-on ATHLETE tutorial, which describes their application across key exposome analysis steps: descriptive analysis, visualization, and association analysis.³¹ “ExposomeShiny” is another R-shiny web-based analytical tool that provides an interactive interface for exploring, visualizing and analyzing exposome datasets.³²

A total of 214 exposome variables were selected from CONSTANCES at inclusion and classified into (n, %): the general external exposome domain (30, 14%), the specific external exposome domain (56, 26%) and the internal exposome domain (128, 60%). The UrbanX Paris population matching our selection criteria comprised 20 889 participants; most were women (54%) with a mean age of 46.6 years (Table 1). Descriptive statistics were applied to describe the human exposome profile by domain at inclusion. The distribution of selected variables by human exposome domain and component are provided in Tables S5–S8.

Participant residential locations were distributed over 849 (out of the 992) IRIS units of Paris (Figure 2). Around 1% of the IRIS (n = 10) included ≤5 participants, and 7% (n = 56) included <10 participants. Depending on the research question, it might be necessary to group participants into higher administrative units, such as per quarter or arrondissement (French administratve unit). The various selected exposome components and subcomponents of each human exposome domain at both inclusion and follow-ups of the CONSTANCES population can be depicted in Table 2.

Several challenges come along with the implementation of this complex urban in cohort studies, as illustrated in the current methodological study. First, the movement of the study population—whether within or outside Paris to the rest of France might not be reported timely or accurately by the participants. This becomes further challenging when attempting to study the duration of exposures at small-area residential level in association with the onset of health outcome. This issue of “migration analysis” is common to urban health studies that assume a long-term residence in the provided address. In our study, we have selected participants that provided only one residential address, assuming their address remained the same during the study period. A sensitivity analysis on the duration of residence including participants that provided two or more residential addresses can as well be suggested.⁶⁹ Also, as we limited our analysis to participants for which an IRIS identifier was available following linkage with geocoded address with an exact-address or street-match precision; an additional sensitivity analysis excluding street-match precision can be conducted.

Selecting variables available at three or more time points in CONSTANCES dataset for performing the repeated measures based longitudinal analysis resulted in the exclusion of certain exposome variables, and thus, of exposome components, particularly those relevant to occupational exposures (collected only once at inclusion), characteristics of housing and household products and exposure to pesticides (collected only once at follow-up). Additionally, the exposure to infectious contaminants is a missing exposure component, which is often understudied in human exposome research.^24,70

The lack of repeated measures for urban exposures is also another challenge for urban exposome studies; current strategy is to rely on two time points in cohort studies, or relying on mean values of survey or surveillance schemes in urban centers instead of time-varying exposomic variables, or relying on the sole available information.^{33,35,36,68,70} This underpins the importance of having routinely collected fine-scale information on urban environmental parameters such as air and water pollutants, noise, traffic, UV radiation and other indicators to feed into urban exposomics.

On one hand, in the proposed prospective associations of the Paris urban exposome and BMI, we suggested selecting participants with anthropometrics being assessed during the medical examination at inclusion ad follow-up. On the other hand, we suggested excluding participants with a personal medical history of five medical conditions (cardiovascular, respiratory, osteoarticular fractures and endocrine diseases). A comparative analysis can be suggested also, by excluding participants presenting with any personal medical history.

Future urban exposomics studies would benefit from the proposed methodological framework by providing a guided pipeline of steps to implement a longitudinal exposomics study in urban settings. Notably, this framework represents one of the first comprehensive approaches to operationalize the urban exposome concept within a longitudinal, spatially explicit design, integrating diverse exposome domains over time and space.⁷¹