# Global research trends on the human exposome: a bibliometric analysis (2005–2024)

Pascal Petit, Nicolas Vuillerme

► To cite this version:

Pascal Petit, Nicolas Vuillerme. Global research trends on the human exposome: a bibliometric analysis (2005–2024). Environmental Science and Pollution Research, 2025, 10.1007/s11356-025-36197-7. http://hal-04985620

HAL Id: hal-04985620

https://hal.science/hal-04985620v1

Submitted on 11 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et 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.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Global research trends on the human exposome: a bibliometric analysis (2005–2024)

Pascal Petit<fnref n="1" />,<fnref n="3" />☐ · Nicolas Vuillerme<fnref n="1" />,<fnref n="2" />

Received: 21 December 2023 / Accepted: 24 February 2025

© The Author(s) 2025

## Abstract

Exposome represents one of the most pressing issues in the environmental science research field. However, a comprehensive summary of worldwide human exposome research is lacking. We aimed to explore the bibliometric characteristics of scientific publications on the human exposome. A bibliometric analysis of human exposome publications from 2005 to December 2024 was conducted using the Web of Science in accordance with PRISMA guidelines. Trends/hotspots were investigated with keyword frequency, co-occurrence, and thematic map. Sex disparities in terms of publications and citations were examined. From 2005 to 2024, 931 publications were published in 363 journals and written by 4529 authors from 72 countries. The number of publications tripled during the last 5 years. Publications written by females (51% as first authors and 34% as last authors) were cited fewer times (13,674) than publications written by males (22,361). Human exposome studies mainly focused on air pollution, metabolomics, chemicals (e.g., per- and polyfluoroalkyl substances (PFAS), endocrine-disrupting chemicals, pesticides), early-life exposure, biomarkers, microbiome, omics, cancer, and reproductive disorders. Social and built environment factors, occupational exposure, multi-exposure, digital exposure (e.g., screen use), climate change, and late-life exposure received less attention. Our results uncovered high-impact countries, institutions, journals, references, authors, and key human exposome research trends/hotspots. The use of digital exposome technologies (e.g., sensors, and wearables) and data science (e.g., artificial intelligence) has blossomed to overcome challenges and could provide valuable knowledge toward precision prevention. Exposome risk scores represent a promising research avenue.

**Keywords** Exposome · Digital environmental health · Digital public health · Disease · Scientometrics · Bibliometrics

## Abbreviations

<table><tr><td>AD</td><td>Alzheimer's disease</td></tr><tr><td>AGEIS</td><td>Autonomy, Gerontology, E-health, Imaging & Society (Autonomie, Gérontologie, E-santé, Imagerie et Société in French)</td></tr><tr><td>AGR</td><td>Annual growth rate</td></tr><tr><td>AI</td><td>Artificial intelligence</td></tr><tr><td>ALS</td><td>Amyotrophic lateral sclerosis</td></tr><tr><td>ANR</td><td>French National Research Agency (Agence Nationale de la Recherche in French)</td></tr></table>

Responsible Editor: Philippe Garrigues

<figure><img src="image_2.png" /></figure>

Pascal Petit
pascal.petit@univ-grenoble-alpes.fr

<table><tr><td>AOPs</td><td>Adverse outcome pathways</td></tr><tr><td>ATHLETE</td><td>Advancing Tools for Human Early Life-course Exposome Research and Translation</td></tr><tr><td>BKMR</td><td>Bayesian kernel machine regression</td></tr><tr><td>BMI</td><td>Body mass index</td></tr><tr><td>CIBER</td><td>Spanish Biomedical Research Center in Epidemiology and Public Health (Centro de Investigación Biomédica en Red in Spanish)</td></tr><tr><td>CNRS</td><td>Centre National de la Recherche Scientifique (French National Centre for Scientific Research)</td></tr><tr><td>CVD</td><td>Cardiovascular disease</td></tr><tr><td>DF</td><td>Dominance factor</td></tr><tr><td>DNA</td><td>Deoxyribonucleic acid</td></tr><tr><td>EDCs</td><td>Endocrine-disrupting chemicals</td></tr><tr><td>EMF</td><td>Electromagnetic field</td></tr><tr><td>EPHOR</td><td>Exposome project for health and occupational research</td></tr><tr><td>ERS</td><td>Exposome risk score</td></tr></table>

<footnote n="1">¹ AGEIS, Université Grenoble Alpes, 38000 Grenoble, France</footnote>

<footnote n="2">² Institut Universitaire de France, Paris, France</footnote>

<footnote n="3">³ Laboratoire AGEIS, Université Grenoble Alpes, Bureau 315, Bâtiment Jean Roget, UFR de Médecine, Domaine de La Merci, 38706 La Tronche Cedex, France</footnote>
<table><tr><td>EXPANSE</td><td>Exposome-powered tools for healthy living in urban settings</td></tr><tr><td>EWAS</td><td>Exposome-wide association study</td></tr><tr><td>FAIR</td><td>Findable, accessible, interoperable, reusable</td></tr><tr><td>GDP</td><td>Gross domestic product</td></tr><tr><td>GIS</td><td>Geography information system</td></tr><tr><td>GWAS</td><td>Genome-wide association study</td></tr><tr><td>HBM</td><td>Human biomonitoring</td></tr><tr><td>HELIX</td><td>Human early-life exposome</td></tr><tr><td>HRMS</td><td>High-resolution mass spectrometry</td></tr><tr><td>IBD</td><td>Inflammatory bowel diseases</td></tr><tr><td>ICMJE</td><td>International Committee of Medical Journal Editors</td></tr><tr><td>IMIDs</td><td>Immune-mediated inflammatory diseases</td></tr><tr><td>INSERM</td><td>French National Institute of Health and Medical Research (Institut National de la Santé et de la Recherche Médicale in French)</td></tr><tr><td>IR</td><td>Infrared</td></tr><tr><td>MAM</td><td>Methylazoxymethanol</td></tr><tr><td>MIAI</td><td>Multidisciplinary Institute in Artificial Intelligence</td></tr><tr><td>ML</td><td>Machine learning</td></tr><tr><td>MMH</td><td>Monomethylhydrazine</td></tr><tr><td>MS</td><td>Mass spectrometry</td></tr><tr><td>NIEHS</td><td>National Institute of Environmental Health Sciences</td></tr><tr><td>NIH</td><td>National Institutes of Health</td></tr><tr><td>NTA</td><td>Non-targeted analysis</td></tr><tr><td>ORCID</td><td>Open researcher and contributor ID</td></tr><tr><td>OS</td><td>Oxidative stress</td></tr><tr><td>PAHs</td><td>Polycyclic aromatic hydrocarbons</td></tr><tr><td>PCB</td><td>Polychlorinated biphenyls</td></tr><tr><td>PD</td><td>Parkinson's disease</td></tr><tr><td>PDP</td><td>Partial dependence plot</td></tr><tr><td>PFAS</td><td>Per- and polyfluoroalkyl substances</td></tr><tr><td>POPs</td><td>Persistent organic pollutants</td></tr><tr><td>PM</td><td>Particulate matter</td></tr><tr><td>PRFK</td><td>Potential risk factor keyword</td></tr><tr><td>PRISMA</td><td>Preferred reporting items for systematic reviews and meta-analyses</td></tr><tr><td>R&D</td><td>Research and development</td></tr><tr><td>RMD</td><td>Rheumatic and musculoskeletal disease</td></tr><tr><td>SES</td><td>Socioeconomic status</td></tr><tr><td>SHAP</td><td>Shapley additive explanations</td></tr><tr><td>UGA</td><td>Université Grenoble Alpes</td></tr><tr><td>UK</td><td>United Kingdom</td></tr><tr><td>US</td><td>United States</td></tr><tr><td>UV</td><td>Ultraviolet</td></tr><tr><td>WoSCC</td><td>Web of Science core collection</td></tr><tr><td>XGBoost</td><td>Extreme gradient boosting</td></tr></table>

## Introduction

Understanding and identifying the factors that contribute to the onset and development of diseases is crucial for public health in order to determine relevant and effective public health interventions that can promote health and well-being for all. Genetics is believed to account for about 10% of diseases, while environmental factors and gene-environment interactions appear to be the remaining causes (Zhang et al. 2021). Hence, environmental exposures throughout the life course play an important role in the onset and development of diseases. The exposome term/concept, first formulated in 2005 by Dr. Wild (Wild 2005), is a complement of the genome that can be defined as the holistic study of all the lifetime environmental exposures (physical, chemical, biological, psychosocial, social, behavioral, etc.) from conception to death (Barouki et al. 2022; Buck Louis et al. 2017; Dennis et al. 2017; Guillien et al. 2023; Juarez et al. 2014; Kim and Hong 2017; Maitre et al. 2022a; Miller and Jones 2014; Niedzwiecki et al. 2019; Price et al. 2022; Robinson and Vrijheid 2015; Siroux et al. 2016; Vermeulen et al. 2020; Wild 2005, 2012). In 2012, the exposome definition was refined with the classification of the exposome into three overlapping domains that can change over time: the internal exposome (e.g., aging, oxidative stress, metabolism, gut microbiome), the general external exposome (e.g., climate, built environment), and the specific external exposome (e.g., chemical exposure, lifestyle, occupations) (Haddad et al. 2019; Santos et al. 2020; Wild 2012) (Fig. 1).

Exposome is a relatively new, emerging, evolving field and mainstream concept that can be considered one of the hallmarks of environmental and public health research for the last decade (Abdelzaher et al. 2022; Barouki et al. 2022; Siroux et al. 2016). A deep understanding of the exposome is crucial for enabling both population-level and personalized (or precision) prevention efforts (Barouki and Lévi 2024; Bíró et al. 2018). The exposome has inspired numerous researchers worldwide and is expected to influence environmental and public health research, practices, and policies (Barouki et al. 2022). Numerous studies presenting, challenging, and favoring the exposome concept and utility have been published (Haddad et al. 2019). In addition, the number of publications and citations for “exposome” has increased over recent years (Haddad et al. 2019; Kiossoglou et al. 2017; Zhang et al. 2021); from six citations in PubMed in 2005 to 150 citations in 2010 and > 1600 citations in 2015 (Siroux et al. 2016).

Despite its increasing popularity and scientific importance, to our knowledge, no study has attempted to comprehensively summarize worldwide research trends/hot-spots of the entire human exposome research field, with the
<figure><img src="image_3.png" /><figcaption>**Fig. 1** The exposome concept and its domains</figcaption></figure>

exception of one study mainly focusing on the impact of
the exposome concept in the field of dermatology (Molina-
García et al. 2022). It is paramount to improve our under-
standing of the human exposome research field in order to
advance the utility and societal impact of the exposome.
To contribute to this effort, we conducted a bibliometric
analysis that aims to present the state of knowledge and
emerging trends of exposome from 2005 to December
2024 by providing an overview of the interdisciplinarity
and connectedness (structural relationships between dif-
ferent research constituents) of the entire human exposome
research field. Bibliometric analysis is a recognized and
rigorous technique that uses a set of quantitative methods
to measure, track, and analyze scholarly literature (Cobo
et al. 2011; Donthu et al. 2021). Our secondary objectives
were to (i) examine potential sex differences regarding
scholarly impact and academic influence; (ii) investigate

the impact of gross domestic product (GDP), population
size, and the number of researchers in research and devel-
opment (R&D) on scientific productivity; (iii) summarize
and get a complete picture of health outcomes and poten-
tial risk factors investigated within the human exposome
research field; and (iv) investigate trends regarding digital
technologies involved in human exposome research.

**Materials and methods**

A bibliometric approach was conducted to analyze the
research trends on the human exposome by time and num-
ber of publications, countries of publications, most active
journals, institutions, authors, funding bodies, subject areas,
citations of publications, and keywords of publications. This
work was conducted from January 2023 to July 2023 and
updated in January 2025.

## Search strategy and selection criteria

To develop and validate the search strategy, a literature
search was conducted to identify reviews on the human
exposome. From this search, we identified one recent scoping review on human exposome studies (Haddad et al. 2019).
In line with this scoping review, the following retrieval strategy using topic terms was carried out for identifying records
on the human exposome: TS = (exposom*). A pilot run was
conducted to validate the search strategy using the Web of
Science Core Collection (WoSCC) database as a bibliometric data source. The validity of our search results was confirmed by making reference to the publications identified in
the aforementioned scoping review (Haddad et al. 2019).
The final literature search was conducted using WoSCC,
according to the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines (Tricco
et al. 2018), following a single screening approach (i.e., one
author performing the literature search and bibliometric
analysis). Only the WoSCC database was used because for
other bibliographic databases (e.g., PubMed), some metadata of interest for bibliometric analysis cannot be extracted
(Aria and Cuccurullo 2017; Moral-Muñoz et al. 2020).
To reduce the bias induced by daily database changes, all data
collection (literature retrieval and data download) was conducted and completed on the same day, January 16, 2023.
The search was repeated on January 14, 2025, to include
publications from 2023 to 2024. The search was restricted
to original peer-reviewed journal publications (i.e., articles,
reviews, and peer-reviewed conference papers) published
from January 1, 2005, (the year of the first exposome publication by Dr. Wild) (Wild 2005) to December 31, 2024. The
only exception was the publication from Wild (2005), an
editorial material. In order to guarantee the representative
ness of the included publications, the search results were filtered by title and abstract to exclude irrelevant publications.
Publications were included if they pertained to the human
exposome (Table A.1 in the supplemental materials). Publications conducted solely in animals or in vitro, or publications not relevant to the exposome concept, were excluded.
The protocol of this study was not registered.

## Data collection and processing

Several metadata were extracted, including keywords,
authors' names, country, institution, journal, publication
date, funding bodies, and total citations. All of this data was
manually checked, cleaned, and harmonized to lessen potential mistakes that could lead to an incorrect representation of
the research field. For each record, the names of the primary

(or first) and senior (or last) authors were collected, and then
the online Genderize database was used to determine the
author's sex, as previously done in other works (Chatterjee
and Werner 2021). For each author's first name, sex was
assigned based on how frequently (probability) it occurs in
public social media profiles where the sex of the user can
be verified (Wais 2006). For unknown sex or for probability
under 0.9, a manual internet-based search of the author's sex
was conducted. The population size, number of researchers
in R&D, and GDP of each country were obtained from the
World Bank.

## Data analysis

All analyses were performed using R software 4.4.1® (R
Core Team, Vienna, Austria) for Windows 10©. The bibliometric analysis was performed using the bibliometrix package version 4.3.0 (Aria and Cuccurullo 2017; Moral-Muñoz et al. 2020), following published guidelines on bibliometric analyses (Donthu et al. 2021; Montazeri et al. 2023) (supplementary materials). Seven bibliometric metrics were computed (supplementary materials) (Bienert et al. 2015; Bihari et al. 2023; Kumar and Kumar 2008; Shao et al. 2022; Wu et al. 2022a). A two-sided Wilcoxon Mann-Whitney rank sum test was used to examine sex differences in authorship (primary and senior) regarding the number of publications and citations of peer-reviewed publications. Differences in publication and citation counts between different sex combinations of primary and senior author pairs were investigated using a one-way, two-sided Kruskal-Wallis test. The effect size was calculated using Cohen's d. Single-author publications were excluded from analyses of senior authorship and dominance factor calculation. To determine whether sex differences in citation could be associated with an impact journal effect (Chatterjee and Werner 2021), several sensitivity analyses were conducted focusing exclusively on high-impact journals. Three definitions of high-impact journals were used: those with an impact factor ≥ 5, ≥ 10, and journals ranked in the top tier (Q1). Journals' impact factors and ranks were retrieved manually using the Journal Citation Report in January 2025. The gender package version 0.6.0 and the genderizeR package version 2.1.1 were used to assign sex to the authors' first names (Mullen 2021; Wais 2006). Spearman correlations were calculated to examine the association between the number of publications and GDP, population size (Wang and Zhao 2018), as well as the number of researchers in R&D (per million people). Research trends/hotspots were investigated with keyword frequency, co-occurrence (counting of paired keywords for keywords with a frequency > 10), and co-word analysis (study of the strength of association between keywords using a thematic map). To summarize and get a complete picture of health outcomes investigated within the human

<figure><img src="image_4.png" /></figure>
exposome research field, the co-occurrence between poten-
tial risk factor keywords (PRFKs) and specific health out-
come categories (e.g., cancers, reproductive disorders) was
studied. Research trends related to digital tools/techniques
were also investigated by focusing on keywords related to
digital technology (e.g., wearable sensors) and analytics
(e.g., machine learning).

Results

Main characteristics

After the exclusion of 1321 irrelevant records and three
duplicates, 931 publications were included in the analysis
(Fig. A.1 and Table A.2, multimedia appendix). The main
characteristics of the included publications are presented
in Table A.3. Most publications were articles (70%),
followed by reviews (28%), conference papers (1.8%),
and editorial materials (0.11%) (Fig. A.2). Most of the
publications (68%) were mostly open access. The pub-
lication's average age was 4.41 years. Publications were
cited 26,623 times, with an average of 28.6 citations per
publication. There were 4529 authors, with 7815 author
appearances and a mean of eight authors per publication.
There were 45 (4.83%) single-authored publications that
were written by 34 authors. On average, there were 61
references per publication.

Scientific productivity

There was a gap of 4 years between the publication from
Wild (2005) and the second exposome publication accepted
in 2009 and published online in 2010 (Fig. A.3) (Bonneterre
et al. 2010). From 2010, there was a gradual and continuous
increase in exposome publications, reaching a maximum of
187 (20.0%) in 2024 (Fig. 2), with an annual growth rate
(AGR) that fluctuated over time, stabilizing around 20–30%
in the last 5 years. In the last 10 years, the number of publi-
cations was multiplied by 20, while it tripled during the last
5 years. The average number of citations per year was 1331,
with a maximum of 3060 citations in 2022.

The most productive countries

Globally, 72 countries from all continents have contributed to human exposome research (Table A.4), with mostly multi-country publications. Europe was the most represented continent (n=33 countries). Most publications originated from the United States (US) (n=430, 46.2%), and four European countries (France, the United Kingdom (UK), Spain, and the Netherlands) (Fig. A.5–A.6). For these five countries, there was a gradual increase in the published research, reaching a maximum in 2024, with the exception of France reaching a maximum in 2022 (Fig. A.7–A.11). Czech Republic (86.7%), Canada (67.2%), and Spain (63.0%) had the highest mean AGR. There was a positive association between the number of publications and GDP (ρ=0.68, p=6.1e-11), population size (ρ=0.26, p=0.03), and the number of researchers

<figure><img src="image_5.png" /><figcaption>Fig. 2 Scientific productivity—number of publications per year</figcaption></figure>
in R&D ($\rho$=0.59, *p* = 1.4e-7). Lithuania (6.7e-10 publications/GDP), Suriname (5.5e-10 publications/GDP), and Cyprus (4.2e-10 publications/GDP) had the highest scientific production per capita, while Luxembourg (2.0e-5 publications/inhabitants), Lithuania (1.7e-5 publications/inhabitants), and Norway (1.2e-5 publications/inhabitants) had the highest scientific production per population. The US (0.089), Nigeria (0.077), India (0.059), China (0.056), Spain (0.051), Columbia (0.045), Burkina Faso (0.042), and France (0.040) had the highest scientific production per researcher in R&D.

## The most cited publications

The most cited publications were open access, with the exception of reviews (Fig. A.12). The most cited publication was the paper from Wild (2005). The top 30 most-cited publications (Burbank et al. 2017; Celebi Sozener et al. 2022; Davis et al. 2023; Dennis et al. 2017; Gacesa et al. 2022; Go et al. 2015a,b; Janssen et al. 2015; Jones 2015; Jones et al. 2012; Kumar et al. 2020; Krutmann et al. 2017; Lochhead et al. 2015; Maitre et al. 2018; Miller and O'Callaghan 2015; Nikolich-Zugich 2018; Rappaport 2011, 2016; Rappaport et al. 2014; Ugai et al. 2022; Uppal et al. 2016; Vermeulen et al. 2020; Vineis et al. 2017; Vrijens et al. 2015; Vrijheid 2014; Vrijheid et al. 2014; Wild 2005, 2012; Wild et al. 2013; Wishart et al. 2015) were published in 26 different journals and had between 144 (0.54%) and 1457 (5.47%) citations (Table A.5). The most commonly studied topics among the top 30 most-cited publications included early-life exposures (n=5), omics (n=5), cancer (n=4), allergies (n=3), and metabolomics (n=3).

## The most active journals

Publications were published in 363 journals, with 368 (39.5%) that were published in the top 20. Environment International, Environmental Research, Environmental Health Perspectives, and Environmental Science & Technology were the most active journals for publishing human exposome research, with 58 (6.23%), 35 (3.76%), 32 (3.51%), and 32 (3.44%) publications, respectively (Table A.6).

## The most prolific authors

The top 30 of the most prolific authors contributed to at least 16 (1.72%) publications (Table A.7). Half of them were females. Among the top 30, M. Vrijheid was the most productive in terms of total publications (70, 7.52%). D.P. Jones was the most productive in terms of fractionalized frequency (6.93), followed by M. Vrijheid (5.38) and P. Vineis (3.60). D. A. Sarigiannis was the most dominant in terms

of first author among the top 30, with a dominance factor (DF) of 26.7%, followed by P. Vineis (21.4) and D.I. Walker (13.6). B. Warth was the most dominant in terms of last author among the top 30, with a DF of 72.2%, followed by D. P. Jones (62.5) and D. A. Sarigiannis (33.3). M. Vrijheid (30), R. Slama (24), and X. Basagana (24) had the highest h-indexes. M. Vrijheid (53), R. Slama (44), and L. Chatzi (41) had the highest g-indexes. J. Wright (2.75), L. Maitre (2.62), and M. Vafeiadi (2.50) had the highest m-indexes. R. Slama (70.7%), B. Heude (39.5%), and X. Basagana (37.4%) had the highest mean AGR among the top 30. Among the top 30 most prolific authors, there were an equal number of females (n=15) and males (n=15). The most frequently studied exposures by leading authors included chemicals, air pollution, smoking, pesticides, per- and polyfluoroalkyl substances (PFAS), endocrine-disrupting chemicals (EDCs), and phthalates (Fig. A.13). The most common health outcomes were reproductive disorders, neurodevelopmental disorders, and respiratory diseases (Fig. A.14).

## Sex differences in authorship

Among the 931 publications, 471 (50.5%) had females as primary authors (Table A.3). Of 897 multi-authored publications, 318 (35.4%) had females as senior authors. Overall, female primary and senior authors had fewer publications and were cited less compared to males (Table A.3). This pattern was not consistent across each year included in the study period (Table A.8 and Fig. A.15–A.16). Publications with female senior authors were cited the fewest times (6294 citations), whereas publications with males as senior authors (15631 citations) or both primary and senior authors (8554 citations) were cited the most. Publications with males as primary authors and females as senior authors had fewer citations (2404 citations) than publications with females as primary authors and males as senior authors (7077 citations) (Table A.9). Publications with females as both primary and senior authors (n=201, 22.4%) were cited fewer times (3890 citations) than publications with males as both primary and senior authors (n=309, 34.4%, 8554 citations). No evidence of a high-impact journal effect was found, as sex-based citation disparities persisted even when focusing on top-tier journals (Tables A.10–A.15 and Fig. A.17–A.22).

## The most active institutions

The top 20 of the most active institutions contributed to 35 (3.76%) or more publications during the study period (Table A.16). Among them, there were eight countries represented, with six institutions from the US, five from France, and three from Spain and the Netherlands. INSERM (France) was the institution with the highest number of publications (n=132, 14.2%), followed by CIBER (Spain)
(n = 98, 10.5%) and Pompeu Fabra University (Spain) (n = 95, 10.2%). INSERM (n = 5038, 18.9%), CIBER (n = 3096, 11.6%), and CNRS (France) (n = 2720, 10.2%) were cited the most. INSERM (37), CIBER (29), and CNRS (27) had the highest *h*-index. Icahn School of Medicine at Mount Sinai (US) (93%), Utrecht University (The Netherlands) (86%), and INSERM (72%) had the highest mean AGR among the top 20.

## The most active funding bodies

Funding sources were not reported for 177 (19.0%) publications. The top 20 of the most active funding bodies contributed to 22 (2.36%) or more publications (Table A.17). The National Institutes of Health (NIH) (n = 183, 19.7%), National Institute of Environmental Health Sciences (NIEHS) (n = 140, 15.0%), and European Commission (n = 101, 10.8%) funded the highest number of publications, had the highest number of citations, and had the highest citation indexes. The European Union (161%), French National Research Agency (ANR) (116%), and Norwegian Ministry of Health and Care Services (105%) had the highest mean AGR among the top 20.

## Trends in research topics

The research area with the highest number of publications was environmental sciences (n = 284, 30.5%), followed by public, environmental, and occupational health (n = 222, 23.8%), toxicology (n = 93, 9.9%), biochemistry and molecular biology (n = 66, 7.09%), engineering environmental (n = 43, 4.62%), chemistry analytical (n = 42, 4.51%), and multidisciplinary sciences (n = 41, 4.40%) (Fig. A.23).

## Keyword analysis

The human exposome publication keywords reflect the research conducted in the three exposome domains, with most research conducted in the internal and specific external domains (Fig. 3).

## Most frequent keywords

A word cloud of the most frequent keywords is presented in Fig. A.24. The top 40 of the most frequent keywords were mentioned in at least 26 publications (2.79%) (Table A.18). The “exposome” was the most frequent keyword (57.9%), followed by “environment” (20.0%), “chemicals” (15.0%), “exposure” (13.7%), and “metabolomics” (10.8%). The frequency of the top five keywords increased drastically this past decade (Fig. A.25). “Exposome,” “exposure,” “environment,” “biomarkers,” “epidemiology,” “chemicals,” “metabolomics,” “reproductive

<figure><img src="image_6.png" /><figcaption>**Fig. 3** Most frequent keywords by exposome domains. AOP adverse outcome pathway, BMI body mass index, EDCs endocrine-disrupting chemicals, EMF electromagnetic field, IBD inflammatory bowel disease, IR infrared, PAH polycyclic aromatic hydrocarbon, PCB polychlorinated biphenyls PFAS per- and polyfluoroalkyl substances, POP persistent organic pollutant, PM particulate matter, RMD rheumatic and musculoskeletal disease, SES socio-economic status, UV ultraviolet</figcaption></figure>

disorders,” and “children” were the most frequent keywords that were mentioned in at least one publication for 12 years (not necessarily consecutive) (Fig. A.26). “Amyotrophic lateral sclerosis,” “risk factors,” “road traffic,” “extracellular vesicles,” “high-resolution mass spectrometry (HRMS),” and “epigenetics” were the keywords with the highest mean AGR (Fig. A.27).

The most frequently mentioned components of the exposome were the chemical exposome (n = 64, 6.9%), the internal exposome (n = 21), the urban exposome (n = 20), the public health exposome (n = 11), the social exposome (n = 11), the skin exposome (n = 9), the working life exposome (n = 9), the pregnancy exposome (n = 8), the blood exposome (n = 7), the personal exposome (n = 6), the endogenous exposome (n = 5), the farming exposome (n = 4), the eco-exposome (n = 3), the placental exposome (n = 3), the gero-exposome (n = 2), the athlete's exposome (n = 2), the psychoexposome (n = 1), the environmental exposome (n = 1), the space exposome (n = 1), the viral exposome (n = 1), the fungal exposome (n = 1), the drug exposome (n = 1), the in utero exposome (n = 1), and the metal exposome (n = 1). Early-life exposures, including pregnancy, were the most commonly reported life stage (n = 206, 22.1%). In contrast, only 22 publications focused on working life and 12 on late-life exposures. Only one study considered
digital exposure (e.g., length of mobile phone use) (Cama-
cho et al. 2024).

Of the 389 (41.8%) publications reporting only mono-
exposure keywords, most were related to chemicals (n=226,
58.1%) (e.g., PFAS), followed by internal (n=47, 12.1%),
social (n=41, 10.5%), biological (n=21, 5.40%), dietary
(n=18, 4.62%), and lifestyle exposures (n=18, 4.62%).
In contrast, 309 (33.2%) publications addressed multi-
exposure keywords, often combining multiple exposure
domains. The most common combinations were chemical/
chemical (n=78), chemical/internal (n=24), chemical/diet
(n=16), chemical/lifestyle (n=13), and chemical/social
factor (n=13). Other combinations included diet/internal
(n=11), social factor/internal (n=9), social factor/lifestyle
(n=7), chemical/biological agent (n=7), chemical/urban
environment (n=7), chemical/physical agent (n=8), chemi-
cal/pharmaceutical (n=8), and social factor/urban environ-
ment (n=7). Six studies focused on exposure mixtures using
Bayesian kernel machine regression (BKMR) (Brydges et al.
2023; Guillien et al. 2021; Li et al. 2024; Papadopoulou
et al. 2021; Sun et al. 2024; Wu et al. 2022b).

A total of 49 (5.26%) publications mentioned “question-
naire,” with only ten using validated ones (Dypås et al. 2023;
Guo et al. 2024; Jedynak et al. 2021; Li et al. 2024; Prieto-
Botella et al. 2022; Rejek and Misiak 2024a,b; Romera-
Vilchez et al. 2022; Stratakis et al. 2020; van der Sloot et al.
2021).

Among top authors and funding bodies, keywords show-
ing the most important positive growth over time included
chemicals, metabolomics, cancer, air pollution, reproduc-
tive disorders, respiratory diseases, mental health disorders,
omics, exposomics, and AI (Fig. A.28 and 29).

Keyword co-occurrence

Four clusters of keyword co-occurrence were identified using multiple correspondence analyses. One cluster (in green) centered on respiratory diseases, autoimmune diseases, skin diseases, microbiome, and climate change, while another cluster (in blue) focused on air pollution, physical factors, urban exposome, physical activity, stress, and cardiovascular diseases. Another cluster (in purple) centered on the chemical exposome and its associated analytical techniques, while the fourth cluster (in red) focused on various topics such as prenatal and neonatal exposures, reproductive disorders, mental health disorders, and statistical techniques (e.g., artificial intelligence (AI), modeling) (Fig. A.30).

A keyword co-occurrence network was also constructed,
with node size corresponding to the frequency of keywords
(Fig. 4). Five distinct clusters were identified: one (in red)
on microbiome, air pollution, diet, lifestyle, built environ-
ment, epigenetics, aging, respiratory, skin, cardiovascular,
and autoimmune diseases; one (in orange) on prenatal and

neonatal exposure, reproductive disorders, pesticides, EDCs, metals, mixtures, and PFAS; one (in blue) on AI-related topics; one (in green) on the chemical exposome, exposomics, metabolomics, biomarkers, and other analytical techniques; and one (in purple) on cancer, neurodegenerative diseases, mental health disorders, occupation, prevention, epidemiology, and omics.

Thematic mapping—research hotspots

Figure 5 presents a thematic map (results of co-word analy-
sis), where each theme (clusters of keywords) is character-
ized by density (the internal strength of a research theme)
and centrality (the degree of interaction of a theme with
other themes in the map) (Fig. 5).

There were three central and developed themes (motor themes in the upper-right quadrant), with one related to “exposomics” and analytical techniques (e.g., mass spectrometry), one to “reproductive disorders,” “children,” “pregnancy,” “diet” and “air pollution,” and one to “neurodegenerative diseases,” “pharmaceuticals,” “risk factors,” and “physical activity.”

Four transversal and underdeveloped themes were identi-
fied (basic themes in the lower-right quadrant): one focused
on chemicals (e.g., pesticides, mixtures, EDCs, metals,
PFAS); another on metabolomics; a third on microbiome,
respiratory, and autoimmune diseases; and a fourth on men-
tal health disorders, prediction, risk score, gene-environment
interaction, and genetics.

Four peripheral but more developed themes were also
found (niche themes in the upper-left quadrant): one on
adductomics; another on skin diseases, UV radiation, and
personal care products; a third on metabolism, toxicity, and
xenobiotics; and a fourth on schizophrenia and psychosis.

Finally, three emerging or declining themes were identi-
fied (weakly developed and marginal themes in the lower-
left quadrant): one on oxidative stress, one on epigenetics
and DNA methylation, and one on AI and machine learning,
which is close to becoming a basic theme.

Co-occurrence between PRFKs and specific health outcome

There were 12 different types of cancer (e.g., leukemia) that co-occurred in publications with between one and 44 PRFKs (e.g., pesticides, air pollution) (Fig. A.31–A.33). Regarding neurodegenerative diseases (e.g., Alzheimer’s disease), there were seven disorders that co-occurred with between 2 and 30 PRFKs (Figs. 6 and A.34–A.35). Ten keywords related to mental disorders (e.g., depression) co-occurred with between 1 and 38 PRFKs (Fig. A.36–A.38), while nine keywords related to autoimmune diseases (e.g., Crohn’s disease) co-occurred with between 2 and 45 PRFKs (Fig. A.39–A.41). Six keywords related to cardiovascular
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Keyword co-occurrence network. AI artificial intelligence, CVDs cardiovascular diseases, EDCs endocrine-disrupting chemicals, EWAS exposome-wide association study, HBM human biomonitoring, HRMS high-resolution mass spectrometry, IMIDs immune-</figcaption></figure>

mediated inflammatory diseases, ML machine learning, MS mass spectrometry, NTA non-targeted analysis, PFAS per- and polyfluoro-alkyl substances, PM particulate matter, SES socio-economic status

diseases (e.g., stroke) co-occurred with between 2 and 42 PRFKs (Fig. A.42–A.44), while 14 keywords related to gastrointestinal diseases (e.g., enteropathy) co-occurred with between 1 and 18 PRFKs (Fig. A.45–A.47). Twelve keywords related to respiratory diseases (e.g., asthma) co-occurred with between 1 and 50 PRFKs (Fig. A.48–A.50), while 16 keywords related to reproductive disorders (e.g., infertility) co-occurred with between 1 and 52 PRFKs (Fig. A.51–A.53), and 15 keywords related to skin diseases (e.g., skin aging) co-occurred with between 1 and 34 PRFKs (Fig. A.54–A.56). Figure A.57 presents the results for other health events (e.g., hospital admissions, mortality).

## Research trends on exposome digital tools

Figure A.58 presents the top ten of the most frequent keywords related to digital tools by total number of years of appearance. Keywords were mainly related to statistical and computational techniques (e.g., BKMR, regression, spatial

analysis), software (e.g., Python, R), AI (e.g., machine learning, deep learning, prediction, SHAP—Shapley Additive Explanations), and data (e.g., databases, sensors, wearables, smartphone data). Digital tool-related keywords were most commonly associated with studies on reproductive disorders, respiratory diseases, mental health issues, breast cancer, diabetes, and cardiovascular diseases (Fig. A.59).

## Discussion

### Main findings

This study provides a bibliometric analysis of human exposome publications from 2005 to 2024. The number of publications over time reflects the amount of interest that the human exposome research field is receiving and whether this field is growing over time. The exposome represents a critical driver of human health and disease, as shown by the
<figure><img src="image_8.png" /><figcaption>**Fig. 5** Thematic mapping. AD Alzheimer's disease, AI artificial intelligence, DNA deoxyribonucleic acid, EDCs endocrine-disrupting chemicals, G-E gene-environment interaction, HBM human biomonitoring, HRMS high-resolution mass spectrometry, IMIDs immune-</figcaption></figure>

mediated inflammatory diseases, ML machine learning, MS mass spectrometry, NTA non-targeted analysis, OS oxidative stress, PCPs personal care products, PFAS per- and polyfluoroalkyl substances, UV ultraviolet

<figure><img src="image_9.png" /><figcaption>**Fig. 6** Chord diagram of keyword co-occurrence between potential risk factor and neurodegenerative disease keywords. AD Alzheimer's disease, ALS amyotrophic lateral sclerosis, MAM methylazoxymethanol, MMH monomethylhydrazine, PD Parkinson's disease, SES socio-economic status</figcaption></figure>

<figure><img src="image_10.png" /></figure>
increasing scholarly interest in the human exposome and related issues since 2010, which is consistent with published data (Haddad et al. 2019; Kiossoglou et al. 2017; Molina-García et al. 2022; Zhang et al. 2021). Consistent with previous studies, most human exposome studies were conducted in European countries (Haddad et al. 2019). Developed countries represented by the US, France, and the UK have undertaken most of the human exposome research (top tier), with close international cross-institutional collaboration. This is not surprising because most projects are funded by US and European sources (Haddad et al. 2019). However, countries reported in the publications only refer to the countries of affiliation of the authors, which does not necessarily correspond to where the research has been conducted. Research in low-income countries should be promoted and supported, as this could enable a better understanding of the human exposome and improve global health.

The most prolific authors were mostly from institutions in developed countries. Most of these authors were/are involved in one or several large-scale/international projects (Benjdir et al. 2021; Buck Louis et al. 2017; Guillien et al. 2023; Martinez et al. 2021; Ronsmans et al. 2022; Ruiz-Castell et al. 2023), such as the Human Early-Life Exposome (HELIX) (Maitre et al. 2022b; Vrijheid et al. 2014), Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) (Vrijheid et al. 2021), EXPOsOMICS (Canali and Leonelli 2022; Vineis et al. 2017), Exposome Project for Health and Occupational Research (EPHOR) (Pronk et al. 2022; Schlünssen et al. 2023), or EXposome-Powered tools for healthy living in urbAN Settings (EXPANSE) (Gruzieva et al. 2022; Vlaanderen et al. 2021). HELIX is a European longitudinal population-based birth cohort study that aims to characterize the early-life exposome (Maitre et al. 2022b; Vrijheid et al. 2014). ATHLETE is devoted to developing a toolbox of exposome tools and a Europe-wide exposome cohort (Vrijheid et al. 2021). EXPOsOMICS aims to develop a novel approach to the assessment of exposure to high-priority environmental pollutants by characterizing the external and internal components of the exposome (Canali and Leonelli 2022; Vineis et al. 2017). EPHOR aims to advance knowledge on the complex working life exposome (Pronk et al. 2022; Schlünssen et al. 2023). EXPANSE is devoted to studying the impact of the urban exposome on the major contributors to Europe's burden of disease (Gruzieva et al. 2022; Vlaanderen et al. 2021). These projects have led and will continue to lead to numerous publications, including many authors. This could explain why the authors working on these projects were among the most prolific and why some of them had a relatively low dominance factor and fractionalized frequency.

There were sex disparities between publications. As already reported in other fields, females were less likely

to be senior authors (Chatterjee and Werner 2021; Sugimoto et al. 2019; Uribe-Bohorquez et al. 2023). Regarding primary authorship, there was almost equality between both sexes. Publications with female primary and senior authors had, most of the time, fewer citations than those written by males. This observation is consistent with previous works that reported lower levels of visibility and amplification for females in academic science compared with males (Chatterjee and Werner 2021; Sugimoto et al. 2019; Uribe-Bohorquez et al. 2023). These sex disparities were not explained by an impact journal effect. In addition, the human exposome is a broad field, so we cannot exclude that females wrote publications on topics that have smaller audiences and are less frequently cited. Other potential disparities regarding potential sociodemographic imbalances (e.g., race, ethnicity) could warrant further research. Similar to previous studies, the total number of publications was positively associated with GDP and population size (Wang and Zhao 2018).

Given the holistic aspect of the exposome concept, human exposome publications have been published in many journals, mainly in the environmental health, public health, exposure science, epidemiology, toxicology, and medical fields. Journals identified in this work are not dedicated primarily or specifically to the exposome but rather sponsored special issues or sections on this topic, which do not necessarily provide a tailored environment to facilitate dialogue on an international scale. There is one journal, named “Exposome,” that was launched in 2021 to focus specifically on the human exposome field. However, given its recent age, at the time of this work, it did not have an impact factor and was not indexed in WoSCC, which explains why no publication from this journal was identified in this work.

## Exposome genesis

Historically, the exposome term was first formulated in 2005 (Wild 2005). Other terms have sometimes been used to refer to the exposome, in particular “envirome”, and “environome” (Anthony et al. 1995; Eaton 2002; Eaton and Harrison 1998; Kane et al. 2021; Price et al. 2022; Rose 2001). These terms were first coined before 2005. Anthony et al. (1995) defined the “envirome” as a complement to the human genome that refers to the “total ensemble of environments, both current and in earlier life, that affect the occurrence of psychiatric disturbances” (Anthony et al. 1995). Since then, the term “envirome” has been used in other publications, with several that were published before the work from Wild (2005) (Eaton 2002; Eaton and Harrison 1998; Rose 2001; Wild 2005). In 2002, Dr. William W. Eaton gave a broader definition of the “envirome” to include the entire ensemble of environmental factors (air pollution, water pollution, infections, and social life) over
the life span (Eaton 2002). More recently, Price et al. (2022) proposed reattributing the exposome concept to “exclusively represent the totality of contact with external factors that a biological entity experiences.” They also introduced the term “functional exposomics” to describe the systematic study of exposure-phenotype interactions. Safarlou et al. (2024) suggested replacing the term exposome with “exposomics” to clarify the distinction between environment and exposome. They defined exposomics as “a research program in environmental health aimed at enabling a comprehensive and discovery-driven approach to identifying environmental determinants of human health.”

## Exposome concepts/components

According to Haddad et al. (2019), there is a lack of commonly accepted exposome language. Indeed, since 2005, many concepts have emerged within the human exposome framework to explore and focus on some specific components of the exposome (Buck Louis et al. 2017).

The “public health exposome” integrates information about endogenous and exogenous exposure in relation to health outcomes at both the individual and population health levels (Juarez et al. 2014). The “eco-exposome” is defined as the bidirectional influences between ecosystems and human exposures (Barouki et al. 2022; Juarez et al. 2014). The “endo-exposome” is the study of the inward effects arising from exposure to the biological receptor of an individual (Juarez et al. 2014; Nakamura et al. 2014; Valero et al. 2022). The “adductome” encompasses chemical exposures leading to the formation of DNA adducts contributing to carcinogenesis (Ananthakrishnan 2015). The “plasma exposome” provides a comprehensive capture of the systemic inflammatory network (Schisler et al. 2015). The “blood exposome” and “serum exposome” focus on blood and serum to characterize the exposome (Rappaport 2012a; Rappaport et al. 2014).

Some exposome components are restricted to specific exposure stressors. The “chemical exposome” focuses on chemical stressors/exposures in relation to health outcomes (Misra 2020; Petit 2022; Price et al. 2022). The “mineral exposome” encompasses dust exposure from environmental and occupational settings (Catinon et al. 2018). The “infec-tome” is used to measure lifetime exposure to infections (Ananthakrishnan 2015). The “pollutome” is the study of pollution effects on human health (Pini et al. 2023). The “social exposome” or “socio-exposome” studies the role of social environmental health determinants (Deguen et al. 2022; Gudi-Mindermann et al. 2023; Neufcourt et al. 2022). The “digital exposome” refers to the impact of exposure to digital technologies on human health (Hong 2024; Lopez-Campos et al. 2017; Martin-Sanchez et al. 2020; Mumtaz et al. 2023; Pagliaccio et al. 2024).

Other exposome components are restricted to specific exposure environments. The “occupational exposome” or “working life exposome” focuses on occupational stressors/ exposures (Bonneterre et al. 2010; Faisandier et al. 2011; Pronk et al. 2022), along with the “spectrosome” which refers to the network/spectrum of multi-occupational expo-sures related to potential health outcomes (Bosson-Rieutort et al. 2018). The “farming exposome” or “agricultural expo-some” focuses on all farming and related nonoccupational exposures (Petit and Vuillerme 2025; Petit et al. 2024a,b, 2025). The “urbanome” or “urban exposome” focuses on urban stressors (e.g., built environment) that can have an impact on human health (Morawska et al. 2019; Novak et al. 2023). This concept can be extended or generalize beyond urban environments to any location (“locusome,” from the Latin word locus, which means location). The “space expo-some” refers to the effects of spaceflight on human health (Marchal et al. 2024).

Some exposome components are restricted to specific life periods, with, for instance, the “pregnancy exposome,” which refers to the study of environmental contaminants during pregnancy and continuing into early life (Chen et al. 2022; Robinson and Vrijheid 2015), or the “gero-exposome” for late-life exposures (Finch and Haghani 2021; Finch 2023).

Other exposome components focus on specific diseases (Celebi Sozener et al. 2022; Guillien et al. 2023; Hall et al. 2023). The “psychoexposome” considers psychosocial environmental exposures in the context of psychopathol-ogy (Gudi-Mindermann et al. 2023). The “gastrointestinal exposome” considers the integration of all elements (e.g., xenobiotics, host-derived endogenous elements) affecting the host’s health during the lifespan (Moon 2016). The “AD exposome” pertains to endogenous and exogenous environ-mental factors related to Alzheimer’s disease (Finch and Kulminski 2019), while the “neurol exposome” focuses on non-genetic factors that influence neurological disorders, including those that may be reflected in epigenetic changes (Tamiz et al. 2022). Finally, there is an exposome component restricted to skin health (Khmaladze et al. 2020; Krutmann et al. 2017; Molina-García et al. 2022).

The above-mentioned exposome components are not exhaustive but provide a plethora of evidence that there is a need for further refining of the human exposome framework and utility (Haddad et al. 2019) and that the exposome has inspired numerous researchers worldwide and is expected to influence environmental and public health research, prac-tices, and policies (Barouki et al. 2022). The wide range of exposome components reflects the complexity of fully characterizing the exposome, which is complicated by the diversity and temporal and spatial variability of environmen-tal factors (Barouki and Lévi 2024). However, these expo-some divisions may sometimes represent the rebranding of
existing approaches (Safarlou et al. 2024). While dividing
the exposome into components can be valuable for develop-
ing specialized exposomics tools and methods, this approach
must adhere to a discovery-driven methodology or include
plans for integrating these tools into broader strategies for
comprehensive exposure assessment (Safarlou et al. 2024).

Research hotspots/trends

As previously reported, only a subset of the entire human
exposome framework has been studied (Haddad et al. 2019).
Keyword analysis revealed that human exposome studies
mainly focused on air pollution, the environment, biomark-
ers, metabolomics, chemicals (e.g., PFAS, pesticides, EDCs,
metals), early-life exposure, diet, non-targeted analysis (e.g.,
high-resolution mass spectrometry), and the microbiome.
The most popular/frequent health outcomes investigated
were cancer, respiratory diseases, cardiovascular diseases,
reproductive diseases, and mental health disorders. Adduc-
tomics, skin diseases, metabolism, and psychosis were niche
themes, while oxidative stress, epigenetics, DNA methyla-
tion, and AI represented emerging/declining themes, with
AI close to becoming a basic theme. Social factors, physi-
cal agents, biological agents, occupational exposures, digi-
tal exposure (e.g., screen use), multi-exposure, and late-life
exposure have received less attention, representing signifi-
cant gaps in the literature that need to be addressed. How-
ever, recent years have seen growing interest in lifestyle,
social, and built environment factors (Andrianou et al. 2021;
Deguen et al. 2022; Gudi-Mindermann et al. 2023; Haddad
et al. 2022; Morawska et al. 2019; Neufcourt et al. 2022),
as well as in multi-exposure studies (Barbey et al. 2024).
Climate change remains an understudied area in exposome
research (Do et al. 2024). Additionally, there has been
limited investigation into certain health outcomes, such
as autoimmune diseases, neurodegenerative diseases, and
musculoskeletal disorders. Few studies have explored the
impact of policies on the exposome, such as public health
measures during the COVID-19 pandemic (Andrianou
et al. 2021; Matus et al. 2024). Notably, there is a lack of
exposome-based public health intervention studies (Pearson
et al. 2020), although several exposome-related studies have
proposed policy recommendations (Andrianou et al. 2021;
Matus et al. 2024; Sonnenschein et al. 2022; Vineis et al.
2023).

Digital exposome toolbox

Exposome data is currently dispersed across hard-to-find
and difficult-to-access sources, highlighting the urgent
need for openly accessible resources (Vrijheid et al. 2021).
However, only four studies have specifically referred to
FAIR (findable, accessible, interoperable, reusable) data

principles (Ghosh et al. 2023; Martinez et al. 2021; Petit
2022; Vrijheid et al. 2021), underscoring the importance
of adopting FAIR practices in exposome research (Chung
et al. 2024; Schmitt et al. 2023). As the volume, variety,
and veracity of exposome data increase (Fig. 7), the data
becomes highly heterogeneous and high-dimensional, pos-
ing major challenges for management and analysis (Ghosh
et al. 2023; Haddad et al. 2019; Maitre et al. 2022a; Marín
et al. 2024; Vrijheid et al. 2021). Consequently, the tra-
ditional tools and methods used to integrate, manage,
analyze, and visualize these vast and heterogeneous data
sources may prove insufficient, requiring the adoption and
application of new technologies and innovative statistical
methods (Fig. 7). Therefore, there is a growing need for
advanced statistical methods and data science approaches,
such as BKMR and AI (Chung et al. 2024; Isola et al.
2024; Maitre et al. 2022a; Zhu et al. 2024). The integra-
tion of data science into public health is at the heart of an
emerging field called “digital public health,” which seeks
to use new types of data sources and technologies (Fig. 7)
to provide the right intervention to the right population
at the right time (Dolley 2018; Iyamu et al. 2024; Odone
et al. 2019; Petit and Vuillerme 2025; Wienert et al. 2022).

To address the logistical and analytical challenges posed
by exposome data—such as its heterogeneous nature (Fig. 7
and Fig. A.61) and the development of appropriate statisti-
cal techniques—digital health technologies and analytics
have proliferated. These include sensors, mobile applica-
tions, wearables, electronic health records, databases, omics,
non-targeted analysis, big data, and AI (Abdelzaher et al.
2022; Barouki et al. 2022; Buck Louis et al. 2017; Den-
nis et al. 2017; Descatha and Fadel 2023; Guillien et al.
2023; Haddad et al. 2019; Juarez et al. 2014; Kane et al.
2021; Kuijpers et al. 2021; Maitre et al. 2022a; Marín et al.
2024; Petit 2022; Pronk et al. 2022; Safarlou et al. 2023;
Santos et al. 2020; Schmitt et al. 2023; Vineis et al. 2017;
Vlaanderen et al. 2021; Vrijheid et al. 2021; Zhang et al.
2021). Exposome assessment primarily relies on objective
approaches, such as passive data collection through sensor
technology and non-targeted analyses (Kalia et al. 2022; Lai
et al. 2024; Manz et al. 2023; Nguyen et al. 2022; Turner
et al. 2017). However, many studies rely on self-reported
questionnaire data, which is an inexpensive and effective
approach to collecting data from a large population (Turner
et al. 2017). Only four studies have reported using an expo-
some questionnaire (Akhtari et al. 2023; Claros et al. 2023;
Gabriel et al. 2020; Lee et al. 2022), such as in the Personal-
ized Environment and Genes Study (Lee et al. 2022; Lloyd
et al. 2024a,b), though none are currently validated. The
lack of validated questionnaires may stem from the challenge
of assessing the vast array of potentially causative expo-
sures (Rappaport 2012b). As previously emphasized, there
is a pressing need for improved questionnaire data (Vineis
<figure><img src="image_11.png" /><figcaption>**Fig. 7** Digital exposome toolbox. AHDs administrative health databases, HBM human biomonitoring, HRMS high-resolution mass spectrometry, IoT Internet of Things, NTA non-targeted analysis, SES socio-economic status. Primary data are specifically collected and tailored for a particular public health research purpose and can</figcaption></figure>

be used either once or multiple times to achieve the same objective. They form the foundation of traditional public health policy and decision-making. In contrast, secondary data are not originally gathered for public health purposes, but they can complement primary data to enrich analysis and insights

et al. 2020), such as a validated and standardized exposome questionnaire.

The use and analysis of routinely collected real-world data, including contextual, person-generated, and clinical data (Fig. 7 and Fig. A.61), hold major potential to advance human health. These data can provide real-time insights that support health surveillance and guide future precision prevention and medicine strategies (Kalia et al. 2022; Schmitt et al. 2023; Subramanian et al. 2020; Tagliaferro et al. 2024; Zhang et al. 2021). Several studies have developed and/or used sensors (Doherty et al. 2021; Pronk et al. 2022), such as a personal exposure monitoring kit composed of a smartphone, accelerometer, UV dosimeter, and air pollution monitors (Donaire-Gonzalez et al. 2019), or a flexible individual sensor device combining environmental and biomarker toolkits (Benjdir et al. 2021).

Among the 40 (4.30%) publications utilizing AI, 12 focused on identifying predictors of health outcomes, including type 2 diabetes (Atehortúa et al. 2023; Ohanyan et al. 2022a), mental health (van Kamp et al. 2021), body mass

index (BMI) (Ohanyan et al. 2022b, 2024), physical activity (Lee et al. 2023), amyotrophic lateral sclerosis (Rotem et al. 2024), self-perceived health (Hoekstra et al. 2023; Loef et al. 2022), healthy aging (Loef et al. 2023), dementia (Camacho et al. 2024), and childhood outcomes (Lester et al. 2024). Random forest and XGBoost (extreme gradient boosting) were the most commonly applied machine learning algorithms, with SHAP (Lundberg et al. 2020; Zhu et al. 2024) and partial dependence plots (PDP) (Hoekstra et al. 2023; Zhu et al. 2024) being the preferred methods for model interpretation (explainable AI). As previously reported, most authors applied supervised or unsupervised ML models, while few used deep learning (Isola et al. 2024). The application of AI to identify exposomic risk factors for health outcomes represents a promising and innovative research avenue (Isola et al. 2024). However, major challenges remain and need to be addressed to facilitate AI application and advance its reliability and impact, in particular regarding data integration, standardization, and the appropriate choice of the AI model (Isola et al. 2024). The integration and

<figure><img src="image_12.png" /></figure>
application of AI and data science in exposome research hold great promise and have the potential to support the development of informed, evidence-based, effective, practical, and personalized prevention, decision, and intervention tools and strategies (Isola et al. 2024).

Exposome Risk Scores (ERS), which summarize the effects of multiple exposomic risk factors for a specific health outcome at the individual level, are another emerging area of study (Guimbaud et al. 2024; Hu et al. 2023; Pries et al. 2019, 2021a, b). Inspired by polygenic risk scores (Torkamani et al. 2018), ERS allow for the identification of modifiable risk factors crucial for prevention and healthcare decision-making. Twenty-four studies (2.58%) have reported the calculation or use of ERS, predominantly for mental health disorders such as schizophrenia and psychosis (Cuesta et al. 2023, 2024; Erzin et al. 2021, 2023; Fusar-Poli et al. 2023; Mas et al. 2020; Paquin et al. 2023; Pries et al. 2019, 2020a,b, 2021a,b, 2022; Rejek and Misiak 2024a,b; Smith et al. 2023) or depression (Farré et al. 2024). ERS have also been applied, to a lesser extent, for diabetes (Akhtari et al. 2023), cardiovascular disorders (Atehortúa et al. 2023; Ferrari et al. 2022), well-being (van de Weijer et al. 2024), and childhood outcomes (Guimbaud et al. 2024; Healy et al. 2024; Smith et al. 2023; Visoki et al. 2024). AI-based ERS computation has been explored in two studies on schizophrenia (Pries et al. 2019, 2021a) and one on child outcomes (Guimbaud et al. 2024).

The environmental impact of data-driven research is a growing concern, as computational processes contribute to greenhouse gas emissions and climate change, which in turn affect health (Amini et al. 2024; Barouki 2024; Di Napoli et al. 2022; Filippini et al. 2024; Jabakhanji et al. 2022; Rocque et al. 2021). This calls for a reevaluation of research methodologies to reduce environmental footprints (Attari et al. 2016; Capet and Aumont 2024; Feigin et al. 2023; Gratiot et al. 2023; Mariette et al. 2022; Schrems and Upham 2020). Despite its increasing importance in public health, ecologically responsible research (e.g., data reuse, green AI) in the exposome domain remains largely underdeveloped (Aleixandre-Benavent et al. 2020; Anthony et al. 2020; Capet and Aumont 2024; Durgan et al. 2023; Feigin et al. 2023; Gratiot et al. 2023; Henderson et al. 2020). No studies have adopted environmentally sustainable research approaches, such as green AI, in exposome research. Green AI, which seeks to generate results without increasing—and ideally reducing—computational costs (Chaperon et al. 2024; Moyano-Fernández et al. 2024; Schwartz et al. 2019; Ueda et al. 2024), remains an underdeveloped field.

While advances have been made, ethical concerns exist, and standardization remains lacking in exposome data, health outcome assessments, and analytical methods and tools (e.g., personal sensors, wearables, remote sensors, and harmonized job exposure matrix) (Haddad et al. 2019;

Isola et al. 2024; Pronk et al. 2022; Safarlou et al. 2023; Schmitt et al. 2023; Turner et al. 2018; Zhang et al. 2021). Issues such as geographical, sex, and ethnic disparities further complicate data availability, analysis, and interpretation (Bucher et al. 2023; Fayet et al. 2024; Nwanaji-Enwerem et al. 2021). Addressing these challenges requires efforts to promote data integration and reuse (Martinez et al. 2021; Zhang et al. 2021). Numerous initiatives have emerged to tackle some of these limitations, including data challenges to address high-dimensionality data (Maitre et al. 2022a); R packages for exposome data management, analysis, and visualization (Chung et al. 2024; He and Patel 2023; Hernandez-Ferrer et al. 2019); seminars on data analysis (Grady et al. 2023); the development of bioinformatics tools (Ronsmans et al. 2022; Vrijheid et al. 2021); and the development of a data management plan and platform (Ghosh et al. 2023; Martinez et al. 2021). In addition, the EXPOsOMICS project consultation with stakeholders highlighted the need for standardized methodologies, improved data sharing, and integration to demonstrate the added value of exposome science in addressing key policy questions (Turner et al. 2018). Collaborative efforts involving stakeholders, researchers, and communities are critical to ensure that exposome research aligns with public health priorities (Lebow-Skelley et al. 2022; Pearson et al. 2020; Stingone et al. 2023; van Kamp et al. 2021). For example, the HERCULES Exposome Research Center aimed to engage local communities in Atlanta by incorporating community environmental health and exposome-related concerns into scientific research. This was achieved through the creation of a community grant program (Pearson et al. 2020). The program successfully identified local environmental health issues while delivering tangible benefits to both the grantees and the communities they serve (Pearson et al. 2020). Research that actively involves the community has been shown to build trust, increase the relevance and applicability of findings, address equity and social justice concerns, and enhance both community knowledge and capacity (Pearson et al. 2020). Governments and institutions must also play an active role in supporting evidence-based policy-making (Barouki and Lévi 2024; Whitsel et al. 2019). Ultimately, interdisciplinary collaboration and international cooperation are essential to guide effective public health interventions (Cui et al. 2016; Fayet et al. 2024; Pearson et al. 2020). For instance, the Human Exposome Assessment Platform (HEAP) seeks to “enable global collaborative research on exposure to cost-effective health interventions” (Martinez et al. 2021). In addition, there is a need to integrate the concept of the exposome into the daily practices of healthcare professionals, as well as to raise awareness among patients and citizens to encourage both local and individualized prevention, especially since the concept of the exposome remains largely unknown to the general public and many healthcare professionals (Barouki
and Lévi 2024; Bíró et al. 2018). Integrating the exposome into clinical practice aims to equip healthcare professionals with the necessary knowledge to enhance primary and secondary prevention through “6P” medicine: personalized, preventive, predictive, participatory, evidence-based, and patient-centered (Barouki and Lévi 2024; Eigenmann 2023). In this context, the development of technological innovations, such as decision-support tools, is crucial (Muhiyaddin et al. 2020). The goal of such technological innovations is to promote early interventions and guide toward exposomes that are most favorable to health and well-being.

The digital exposome toolbox (Fig. 7) holds great potential to inform precision decision-making and underpin evidence-based, cost-effective public health policies, ensuring the right interventions for the right populations at the right time (Bosward et al. 2022; Canfell et al. 2022a, b, c; Dolley 2018; Iyamu et al. 2021; Kalia et al. 2022; Khoury et al. 2016; Odone et al. 2019; Schmitt et al. 2023; Velmovitsky et al. 2021; Wienert et al. 2022). These technologies could also foster green (i.e., environmentally sustainable) innovations, such as the secondary use (or reuse) of data, to scale up effective interventions and ensure long-term population health benefits.

## Strengths and limitations

To our knowledge, no bibliometric analysis has been conducted to comprehensively describe the entire human exposome body of the literature so far. Only one bibliometric analysis was conducted on the exposome, focusing mainly on the skin exposome (Molina-García et al. 2022). However, the exposome research field was only briefly described based on 910 documents published from 2005 to September 30, 2021 (Molina-García et al. 2022). In our analysis, we included almost the same number of publications as Molina-García et al. (2022). However, compared to the work from Molina-García et al. (2022), we considered three additional years, and we screened publication titles and abstracts following PRISMA guidelines to ensure search accuracy. There should be fewer potential errors inherent to the extraction and nature of bibliometric data than in traditional bibliometric analyses because all terms (e.g., authors, keywords) were checked, cleaned, and harmonized (e.g., use of one unique term for a given entity) before conducting the analysis. While this approach is time-consuming, it allows for a more accurate analysis. Indeed, if the same entity had several denominations, it could lead to possible underestimates (e.g., number of publications).

Several limitations of the study warrant careful consideration. One of the main limitations is the use of a single bibliographic database (WoSCC). However, WoSCC has a broader coverage than other databases, with more consistent, reliable, high-quality, and standardized metadata, and is

the most widely used database for bibliometric analysis (Lu and Ni 2022; Yin et al. 2022). Because of the existence of a broad range of exposome concepts/components, along with the non-systematic use of the exposome term in exposome studies (Haddad et al. 2019), we cannot exclude the possibility that publications with a methodology that abides by the exposome definition have been excluded, as previously acknowledged (Haddad et al. 2019). However, our search strategy was similar to other reviews on the human exposome (Haddad et al. 2019; Neufcourt et al. 2022; Safarlou et al. 2023). Only one author conducted the bibliometric data search, retrieval, management, and analysis. Therefore, it is possible that some human exposome studies may have been missed. Two independent reviewers may have decreased the chance of systemic error and bias. Sex was assigned using an external database, which may have resulted in misclassification. To limit this bias, sex was assigned manually for a low probability of accuracy. Last authorship may not perfectly match senior authorship. Shortcomings inherent to bibliometric analysis cannot be excluded. Indeed, a potential bias regarding authorship may occur if different authors have the same name or if authors use different names over time in their publications. A solution to overcome this limitation would be to use a unique author identifier (e.g., ORCID number), which was not available. Co-authorship (e.g., primary and senior) could not be identified, which is a limitation. Self-citation also represents a blind spot for citation analysis. We cannot exclude a potential Matthew effect that could affect publication citation (Uribe-Bohorquez et al. 2023). Despite the aforementioned limitations, we are confident that our findings can provide an effective perspective on the human exposome from a global insight, with a complete picture of what has been published until now.

## Conclusions

Exposome research is a pivotal component of digital public health, offering a pathway to developing accessible, affordable, integrated, scalable, and sustainable solutions that promote health and well-being across populations, regardless of geographic location or socio-economic status. Our findings provide a framework for scientists, policymakers, administrators, funding bodies, and other stakeholders to enhance their understanding and chart the future course of exposome research. The exposome will continue to inspire research across disciplines, shaping environmental and public health policies and practices as they transition from curative approaches to precision prevention and 6P medicine.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s11356-025-36197-7.

<figure><img src="image_13.png" /></figure>
**Author contribution** P. P.: conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, visualization, and project administration. N. V.: conceptualization, methodology, investigation, validation, and writing—review and editing. All authors meet all four criteria for authorship in the ICMJE Recommendations. All authors affirm that the manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted and that any potential discrepancies from the study as originally planned (and, if relevant, registered) have been explained.

**Funding** This work was partly supported by funding from the French government, managed by the French National Research Agency (ANR) under the France 2030 program [ANR-23-IACL-006, 2025] and under the "Investissements d'avenir" program [ANR-10-AIRT-05, 2012; ANR-15-IDEX-02, 2016]. The funding sources had no role in the study design and conduct of the study; collection, management, analysis, and interpretation of data; writing of the report; preparation, review, or approval of the article; or the decision to submit the paper for publication. The authors were not precluded from accessing data in the study, and all authors took final responsibility for the decision to submit for publication.

**Data availability** The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Abdelzaher H, Tawfik SM, Nour A, Abdelkader S, Elbalkiny ST, Abdelkader M, Abbas WA, Abdelnaser A (2022) Climate change, human health, and the exposome: utilizing OMIC technologies to navigate an era of uncertainty. Front Public Health 10:973000. https://doi.org/10.3389/fpubh.2022.973000

Akhtari FS, Lloyd D, Burkholder A, Tong X, House JS, Lee EY, Buse J, Schurman SH, Fargo DC, Schmitt CP, Hall J, Motsinger-Reif AA (2023) Questionnaire-based polyexposure assessment outperforms polygenic scores for classification of type 2 diabetes in a multiancestry cohort. Diabetes Care 46(5):929–937. https://doi.org/10.2337/dc22-0295

Aleixandre-Benavent R, Vidal-Infer A, Alonso-Arroyo A, Peset F, Ferrer Sapena A (2020) Research data sharing in spain: exploring determinants, practices, and perceptions. Data 5(2):29. https://doi.org/10.3390/data5020029

Amini H, Amini M, Wright RO (2024) Climate change, exposome change, and allergy: a review. Immunol Allergy Clin North Am 44(1):1–13. https://doi.org/10.1016/j.iac.2023.09.003

Ananthakrishnan AN (2015) Epidemiology and risk factors for IBD. Nat Rev Gastroenterol Hepatol 12(4):205–217. https://doi.org/10.1038/nrgastro.2015.34

Andrianou XD, Pronk A, Galea KS, Stierum R, Loh M, Riccardo F, Pezzotti P, Makris KC (2021) Exposome-based public health interventions for infectious diseases in urban settings. Environ Int 146:106246. https://doi.org/10.1016/j.envint.2020.106246

Anthony JC, Eaton WW, Henderson AS (1995) Looking to the future in psychiatric epidemiology. Epidemiol Rev 17(1):240–242. https://doi.org/10.1093/oxfordjournals.epirev.a036182

Anthony LFW, Kanding B, Selvan R (2020) Carbontracker: tracking and predicting the carbon footprint of training deep learning models. arXiv. https://doi.org/10.48550/arXiv.2007.03051

Aria M, Cuccurullo C (2017) Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetrics 11(4):959–975. https://doi.org/10.1016/j.joi.2017.08.007

Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix JF, Sebert S, Lekadir K (2023) Cardiometabolic risk estimation using exposome data and machine learning. Int J Med Inform 179:105209. https://doi.org/10.1016/j.ijmedinf.2023.105209

Attari SZ, Krantz DH, Weber EU (2016) Statements about climate researchers' carbon footprints affect their credibility and the impact of their advice. Clim Chang 138:325–338. https://doi.org/10.1007/s10584-016-1713-2

Barbey C, Bonvallot N, Clerc F (2024) Health outcomes related to multiple exposures in occupational settings: a review. Saf Health Work 15(4):382–395. https://doi.org/10.1016/j.shaw.2024.10.004

Barouki R (2024) A toxicological perspective on climate change and the exposome. Front Public Health 12:1361274. https://doi.org/10.3389/fpubh.2024.1361274

Barouki R, Lévi Y (2024) Rapport 24–02. L'exposome, une contribution majeure pour la prévention. Bull Acad Natl Med 208:523–535. https://doi.org/10.1016/j.banm.2024.03.004

Barouki R, Audouze K, Becker C, Blaha L, Coumoul X, Karakitsios S, Klanova J, Miller GW, Price EJ, Sarigiannis D (2022) The exposome and toxicology: a win-win collaboration. Toxicol Sci 186(1):1–11. https://doi.org/10.1093/toxsci/kfab149

Benjdir M, Audureau É, Beresniak A, Coll P, Epaud R, Fiedler K, Jacquemin B, Niddam L, Pandis SN, Pohlmann G, Sandanger TM, Simmons K, Sørensen M, Wagner P, Lanone S (2021) Assessing the impact of exposome on the course of chronic obstructive pulmonary disease and cystic fibrosis: the REMEDIA European project approach. Environ Epidemiol 5(4):e165. https://doi.org/10.1097/EE9.0000000000000165

Bienert IR, Oliveira RC, Andrade PB, Caramori CA (2015) Bibliometric indexes, databases and impact factors in cardiology. Rev Bras Cir Cardiovasc 30(2):254–259. https://doi.org/10.5935/1678-9741.20150019

Bihari A, Tripathi S, Deepak A (2023) A review on h-index and its alternative indices. J Inf Sci 49(3):624–665. https://doi.org/10.1177/0165551521101447

Bíró K, Dombrádi V, Jani A, Boruzs K, Gray M (2018) Creating a common language: defining individualized, personalized and precision prevention in public health. J Public Health (Oxf) 40(4):e552–e559. https://doi.org/10.1093/pubmed/fdy066

Bonneterre V, Faisandier L, Bicout D, Bernardet C, Piollat J, Ameille J, de Clavière C, Aptel M, Lasfargues G, de Gaudemaris R, RNV3P (2010) Programmed health surveillance and detection of emerging diseases in occupational health: contribution of the French national occupational disease surveillance and prevention network (RNV3P). Occup Environ Med 67(3):178–186. https://doi.org/10.1136/oem.2008.044610
Bosson-Rieutort D, De Gaudemaris R, Bicout DJ (2018) The spectrosome of occupational health problems. PLoS One 13(1):e0190196. https://doi.org/10.1371/journal.pone.0190196

Bosward R, Braunack-Mayer A, Frost E, Carter S (2022) Mapping precision public health definitions, terminology and applications: a scoping review protocol. BMJ Open 12(2):e058069. https://doi.org/10.1136/bmjopen-2021-058069

Brydges C, Che X, Lipkin WI, Fiehn O (2023) Bayesian statistics improves biological interpretability of metabolomics data from human cohorts. Metabolites 13(9):984. https://doi.org/10.3390/metabo13090984

Bucher ML, Anderson FL, Lai Y, Dicent J, Miller GW, Zota AR (2023) Exposomics as a tool to investigate differences in health and disease by sex and gender. Exposome 3(1):osad003. https://doi.org/10.1093/exposome/osad003

Buck Louis GM, Smarr MM, Patel CJ (2017) The exposome research paradigm: an opportunity to understand the environmental basis for human health and disease. Curr Environ Health Rep 4(1):89-98. https://doi.org/10.1007/s40572-017-0126-3

Burbank AJ, Sood AK, Kesic MJ, Peden DB, Hernandez ML (2017) Environmental determinants of allergy and asthma in early life. J Allergy Clin Immunol 140(1):1-12. https://doi.org/10.1016/j.jaci.2017.05.010

Camacho M, Atehortúa A, Wilkinson T, Gkontra P, Lekadir K (2024) Low-cost predictive models of dementia risk using machine learning and exposome predictors. Health Technol. https://doi.org/10.1007/s12553-024-00937-5

Canali S, Leonelli S (2022) Reframing the environment in data-intensive health sciences. Stud Hist Philos Sci 93:203-214. https://doi.org/10.1016/j.shpsa.2022.04.006

Canfell OJ, Davidson K, Woods L, Sullivan C, Cocoros NM, Klompas M, Zambarano B, Eakin E, Littlewood R, Burton-Jones A (2022a) Precision public health for non-communicable diseases: an emerging strategic roadmap and multinational use cases. Front Public Health 10:854525. https://doi.org/10.3389/fpubh.2022.854525

Canfell OJ, Davidson K, Sullivan C, Eakin E, Burton-Jones A (2022c) Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia. BMC Public Health 22(1):584. https://doi.org/10.1186/s12889-022-12939-x

Canfell OJ, Kodiyattu Z, Eakin E, Burton-Jones A, Wong I, Macaulay C, Sullivan C (2022b) Precision public health for non-communicable diseases: BMC public health 22(1):2166. https://doi.org/10.1186/s12889-022-14452-7

Capet X, Aumont O (2024) Decarbonization of academic laboratories: on the trade-offs between CO2 emissions, spending, and research output. Clean Environ Systems 12:1000168. https://doi.org/10.1016/j.cesys.2024.100168

Catinon M, Cavalin C, Chemarin C, Rio S, Roux E, Pecquet M, Blanchet AS, Vuillermoz S, Pison C, Arbib F, Bonneterre V, Valeyre D, Freynet O, Mornex JF, Pacheco Y, Freymond N, Thivolet F, Kambouchner M, Bernaudin JF, Nathalizio A, Pradat P, Rosental PA, Vincent M (2018) Sarcoidosis, inorganic dust exposure and content of bronchoalveolar lavage fluid: the MINASARC pilot study. Sarcoidosis Vasc Diffuse Lung Dis 35(4):327-332. https://doi.org/10.36141/svdld.v35i4.7058

Celebi Sozener Z, Özbey Yücel Ü, Altiner S, Ozdel Oztürk B, Cerci P, Türk M, Gorgülü Akin B, Akdis M, Yilmaz I, Ozdemir C, Mungan D, Akdis CA (2022) The external exposome and allergies: from the perspective of the epithelial barrier hypothesis. Front Allergy 3:887672. https://doi.org/10.3389/falgy.2022.887672

Chaperon L, Deplanche M, Grignon P, Haroutunian L, Stempfelet S (2024) L'IA: synthèse des connaissances et perspectives pour la santé environnementale à Santé publique France. Saint-Maurice: Santé publique France. 38p. https://www.santepubliquefrance.fr/docs/l-ia-synthèse-des-connaissances-et-perspectives-pour-la-sante-environnementale-a-sante-publique-france. Accessed 17 Jan 2025

Chatterjee P, Werner RM (2021) Gender disparity in citations in high-impact journal articles. JAMA Netw Open 4(7):e2114509. https://doi.org/10.1001/jamanetworkopen.2021.14509

Chen M, Guan Y, Huang R, Duan J, Zhou J, Chen T, Wang X, Xia Y, London SJ (2022) Associations between the maternal exposome and metabolome during pregnancy. Environ Health Perspect 130(3):37003. https://doi.org/10.1289/EHP9745

Chung MK, House JS, Akhtari FS, Makris KC, Langston MA, Islam KT, Holmes P, Chadeau-Hyam M, Smirnov AI, Du X, Thesen AE, Cui Y, Zhang K, Manrai AK, Motsinger-Reif A, Patel CJ, Members of the Exposomics Consortium (2024) Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs). Exposome 4(1):osae001. https://doi.org/10.1093/exposome/osae001

Claros MG, Lequio M, Cheli S, Garlatti AB, Cecilia N, Juarez L, Bitar M, Leiva MJ, Hernandez ML, Márquez JM, Badaracco G, Leclerc-Mercier S, Leal MIA (2023) A cross-sectional epidemiological study conducted in Argentina to evaluate the impact of the exposome on skin aging. J Cosmet Dermatol 22(12):3459-3469. https://doi.org/10.1111/jocd.15854

Cobo MJ, Lopez-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the Fuzzy Sets Theory field. J Inform 5(1):146-166. https://doi.org/10.1016/j.joi.2010.10.002

Cuesta MJ, Papiol S, Ibañez B, García de Jalón E, Sánchez-Torres AM, Gil-Berrozpe GJ, Moreno-Izco L, Zarzuela A, Fañanás L, Peralta V, SEGPEPs Group (2023) Effect of polygenic risk score, family load of schizophrenia and exposome risk score, and their interactions, on the long-term outcome of first-episode psychosis. Psychol Med 53(14):6838-6847. https://doi.org/10.1017/S0033291723000351

Cuesta MJ, Gil-Berrozpe GJ, Sánchez-Torres AM, Moreno-Izco L, García de Jalón E, Peralta V, SEGPEPs group (2024) 20-year trajectories of six psychopathological dimensions in patients with first-episode psychosis: could they be predicted? Psychiatry Res 331:115614. https://doi.org/10.1016/j.psychres.2023.115614

Cui Y, Balshaw DM, Kwok RK, Thompson CL, Collman GW, Birnbaum LS (2016) The exposome: embracing the complexity for discovery in environmental health. Environ Health Perspect 124(8):A137-A140. https://doi.org/10.1289/EHP412

Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ (2023) Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Res 51(D1):D1257-D1262. https://doi.org/10.1093/nar/gkac833

Deguen S, Amuzu M, Simoncic V, Kihal-Talantikite W (2022) Exposome and social vulnerability: an overview of the literature review. Int J Environ Res Public Health 19(6):3534. https://doi.org/10.3390/ijerph19063534

Dennis KK, Marder E, Balshaw DM, Cui Y, Lynes MA, Patti GJ, Rapaport SM, Shaughnessy DT, Vrijheid M, Barr DB (2017) Biomonitoring in the era of the exposome. Environ Health Perspect 125(4):502-510. https://doi.org/10.1289/EHP474

Descatha A, Fadel M (2023) Mental health of people in the agricultural sector: insights from massive database in occupational health. Lancet Reg Health Eur 31:100691. https://doi.org/10.1016/j.lanepe.2023.100691

Di Napoli C, McGushin A, Romanello M, Ayeb-Karlsson S, Cai W, Chambers J, Dasgupta S, Escobar LE, Kelman I, Kjellstrom T, Kniveton D, Liu Y, Liu Z, Lowe R, Martinez-Urtaza J, McMichael C, Moradi-Lakeh M, Murray KA, Rabbaniha M, Semenza JC, Shi L, Tabatabaei M, Trinanes JA, Vu BN, Brimicombe C, Robinson EJ (2022) Tracking the impacts of climate change on
human health via indicators: lessons from the Lancet count-
down. BMC Public Health 22(1):663. https://doi.org/10.1186/
s12889-022-13055-6

Do V, Parks RM, Casey JA, Goin DE, Kioumoutzoglou MA (2024)
Use, limitations, and future directions of mixtures approaches to
understand the health impacts of weather- and climate change-
related exposures, an under-studied aspect of the exposome.
Exposome 4(1):osae007. https://doi.org/10.1093/exposome/
osae007

Doherty BT, Koelmel JP, Lin EZ, Romano ME, Godri Pollitt KJ (2021) Use of exposomic methods incorporating sensors in environmental epidemiology. Curr Environ Health Rep 8(1):34–41. https://doi.org/10.1007/s40572-021-00306-8

Dolley S (2018) Big data’s role in precision public health. Front Public Health 6:68. https://doi.org/10.3389/fpubh.2018.00068

Donaire-Gonzalez D, Curto A, Valentín A, Andrusaityte S, Basa-
gaña X, Casas M, Chatzi L, de Bont J, de Castro M, Dedele
A, Granum B, Grazuleviciene R, Kampouri M, Lyon-Caen S,
Manzano-Salgado CB, Aasvang GM, McEachan R, Meinhard-
Kjellstad CH, Michalaki E, Pañella P, Petrviciene I, Schwarze
PE, Slama R, Robinson O, Tamayo-Uria I, Vafeiadi M, Waib-
linger D, Wright J, Vrijheid M, Nieuwenhuijsen MJ (2019) Per-
sonal assessment of the external exposome during pregnancy
and childhood in Europe. Environ Res 174:95–104. https://doi.
org/10.1016/j.envres.2019.04.015

Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Durgan J, Rodríguez-Martínez M, Rouse B (2023) Green labs: a guide to developing sustainable science in your organization. Immunol Cell Biol 101(4):289–301. https://doi.org/10.1111/imbcb.12624

Dypás LB, Duale N, Olsen AK, Bustamante M, Maitre L, Escaramis G, Julvez J, Aguilar-Lacasaña S, Andrusaityte S, Casas M, Vafeiadi M, Grazuleviciene R, Heude B, Lepeule J, Urquiza J, Wright J, Yang TC, Vrijheid M, Gützkow KB (2023) Blood miRNA levels associated with ADHD traits in children across six European birth cohorts. BMC Psychiatry 23(1):696. https://doi.org/10.1186/s12888-023-05199-5

Eaton WW (2002) The logic for a conception-to-death cohort study. Ann Epidemiol 12(7):445-451. https://doi.org/10.1016/S1047-2797(01)00314-3

Eaton WW, Harrison G (1998) Epidemiology and social aspects of the human envirome. Curr Opin Psychiatry 11(2):165-168

Eigenmann P (2023) Stratégies thérapeutiques à l'heure de la médecine 6P. Rev Fr Allergol 63:103283. https://doi.org/10.1016/j.reval.2023.103283

Erzin G, Prachason T, Erzin G, Pries LK, Brondino N, Politi P, Deles-
paul P, Kenis G, Luykx JJ, Lin BD, Richards AL, Akdede B,
Binbay T, Altntyazar V, Yalinçetin B, Gümüş-Akay G, Cihan
B, Soygür H, Ulaş H, Cankurtaran EŞ, Kaymak SU, Mihalje-
vic MM, Andric-Petrovic S, Mirjanic T, Bernardo M, Mezquida
G, Amoretti S, Bobes J, Saiz PA, García-Portilla MP, Sanjuan
J, Escarti MJ, Santos JL, Jiménez-López E, Arrojo M, Car-
racedo A, López G, González-Peñas J, Parellada M, Maric
NP, Atbaşoğlu C, Üçok A, Alptekin K, Saka MC, Arango C,
O'Donovan M, van Os J, Rutten BP, Guloksuz S, Genetic Risk
and Outcome of Psychosis (GROUP) investigators (2021) Exam-
ining the association between exposome score for schizophrenia
and functioning in schizophrenia, siblings, and healthy controls:
results from the EUGEI study. Eur Psychiatry 64(1):e25. https://
doi.org/10.1192/j.eurпы.2021.19

Erzin G, Pries LK, Dimitrakopoulos S, Ralli I, Xenaki LA, Soldatos RF, Vlachos I, Selakovic M, Foteli S, Kosteletos I, Nianiakas N, Mantonakis L, Rizos E, Kollias K, Van Os J, Guloksuz S,

Stefanis N (2023) Association between exposome score for schiz-
ophrenia and functioning in first-episode psychosis: results from
the Athens first-episode psychosis research study. Psychol Med
53(6):2609-2618. https://doi.org/10.1017/S0033291721004542

Faisandier L, Bonneterre V, De Gaudemaris R, Bicout DJ (2011) Occupational exposome: a network-based approach for characterizing occupational health problems. J Biomed Inform 44(4):545-552. https://doi.org/10.1016/j.jbi.2011.02.010

Farré X, Blay N, Espinosa A, Castaño-Vinyals G, Carreras A, García-Aymerich J, Cardis E, Kogevinas M, Goldberg X, de Cid R (2024) Decoding depression by exploring the exposome-genome edge amidst COVID-19 lockdown. Sci Rep 14(1):13562. https://doi.org/10.1038/s41598-024-64200-7

Fayet Y, Bonnin T, Canali S, Giroux E (2024) Putting the exposome into practice: an analysis of the promises, methods and outcomes of the European human exposome network. Soc Sci Med 354:117056. https://doi.org/10.1016/j.socscimed.2024.117056

Feigin SV, Wiebers DO, Lueddeke G, Morand S, Lee K, Knight A, Brainin M, Feigin VL, Whitfort A, Marcum J, Shackelford TK, Skerratt LF, Winkler AS (2023) Proposed solutions to anthropogenic climate change: a systematic literature review and a new way forward. Heliyon 9(10):e20544. https://doi.org/10.1016/j.heliyon.2023.e20544

Ferrari R, Cimaglia P, Rapezzi C, Tavazzi L, Guardigli G (2022) Cardiovascular prevention: sometimes dreams can come true. Eur Heart J Suppl 24(Suppl H):H3–H7. https://doi.org/10.1093/eurheartsupp/suac057

Filippini T, Paduano S, Veneri F, Barbolini G, Fiore G, Vinceti M (2024) Adverse human health effects of climate change: an update. Ann Ig 36(3):281–291. https://doi.org/10.7416/ai.2024.2595

Finch CE (2023) Air pollution, dementia, and lifespan in the socio-economic gradient of aging: perspective on human aging for planning future experimental studies. Front Aging 4:1273303. https://doi.org/10.3389/fragi.2023.1273303

Finch CE, Haghani A (2021) Gene-environment interactions and stochastic variations in the gero-exposome. J Gerontol A Biol Sci Med Sci 76(10):1740-1747. https://doi.org/10.1093/gerona/glab045

Finch CE, Kulminski AM (2019) The Alzheimer’s disease exposome. Alzheimers Dement 15(9):1123–1132. https://doi.org/10.1016/j.jalz.2019.06.3914

Fusar-Poli L, Prachason T, Erzin G, Pries LK, Brondino N, Politi P, Delespaul P, Kenis G, Luykx JJ, Lin BD, Richards AL, Akdede B, Binbay T, Altntyazar V, Yalinçetin B, Gümüş-Akay G, Cihan B, Soygür H, Ulaş H, Cankurtaran EŞ, Kaymak SU, Mihaljevic MM, Andric-Petrovic S, Mirjanic T, Bernardo M, Mezquida G, Amoretti S, Bobes J, Saiz PA, García-Portilla MP, Sanjuan J, Escarti MJ, Santos JL, Jiménez-López E, Arrojo M, Carracedo A, López G, González-Peñas J, Parellada M, Maric NP, Atbaşoğlu C, Üçok A, Alptekin K, Saka MC, Arango C, O'Donovan M, van Os J, Rutten BP, Guloksuz S, Genetic Risk and Outcome of Psychosis (GROUP) investigators (2023) Examining the association between exposome score for schizophrenia and cognition in schizophrenia, siblings, and healthy controls: results from the EUGEI study. Psychiatry Res 323:115184. https://doi.org/10.1016/j.psychres.2023.115184

Gabriel MF, Felgueiras F, Fernandes M, Ribeiro C, Ramos E, Mourão Z, de Oliveira Fernandes E (2020) Assessment of indoor air conditions in households of Portuguese families with newborn children. Implementation of the HEALS IAQ checklist. Environ Res 182:108966. https://doi.org/10.1016/j.envres.2019.108966

Gacesa R, Kurilshikov A, Vich Vila A, Sinha T, Klaassen MA, Bolte LA, Andreu-Sánchez S, Chen L, Collij V, Hu S, Dekens JAM, Lenters VC, Björk JR, Swarte JC, Swertz MA, Jansen BH, Gelderloos-Arends J, Jankipersadsing S, Hofker M, Vermeulen
RCH, Sanna S, Harmsen HJM, Wijmenga C, Fu J, Zhernakova A, Weersma RK (2022) Environmental factors shaping the gut microbiome in a Dutch population. Nature 604(7907):732–739. https://doi.org/10.1038/s41586-022-04567-7

Ghosh M, Broothaerts K, Ronsmans S, Roig IB, Scheepers J, Dikmen M, Ciscato ER, Blanch C, Plusquin M, Nygaard UC, Sejbæk CS, Hougaard KS, Hoet PH, EXIMIOUS consortium (2023) Data management and protection in occupational and environmental exposome research – a case study from the EU-funded EXIMIOUS project. Environ Res 237(Pt 1):116886. https://doi.org/10.1016/j.envres.2023.116886

Go YM, Walker DI, Liang Y, Uppal K, Soltow QA, Tran V, Strobel F, Quyyumi AA, Ziegler TR, Pennell KD, Miller GW, Jones DP (2015a) Reference standardization for mass spectrometry and high-resolution metabolomics applications to exposome research. Toxicol Sci 148(2):531–543. https://doi.org/10.1093/toxsci/kfv198

Go YM, Chandler JD, Jones DP (2015b) The cysteine proteome. Free Radic Biol Med 84:227–245. https://doi.org/10.1016/j.freeradbio med.2015.03.022

Grady SK, Dojcsak L, Harville EW, Wallace ME, Vilda D, Donneyong MM, Hood DB, Valdez RB, Ramesh A, Im W, Matthews-Juarez P, Juarez PD, Langston MA (2023) Seminar: scalable preprocessing tools for exposomic data analysis. Environ Health Perspect 131(12):124201. https://doi.org/10.1289/EHP12901

Gratiot N, Klein K, Challet M, Dangles O, Janicot S, Candelas M, Sarret G, Panthou G, Hingray B, Champollon N, Montillaud J, Bellemain P, Marc O, Bationo CS, Monnier L, Laffont L, Fougols MA, Riffault V, Tinel L, Mignot E, Philippon N, Dezetter A, Caron A, Piton G, Verney-Carron A, Delaballe A, Bardet N, Nozay-Maurice F, Loison AS, Delbart F, Anquetin S, Immel F, Baehr C, Malbet F, Berni C, Delattre L, Echevin V, Petitdidier E, Aumont O, Michau F, Bijon N, Vidal JP, Pinel S, Biabiany O, Grevesse C, Mimeau L, Biarnès A, Récapet C, Costes-Thiré M, Poupaud M, Barret M, Bonnin M, Mournetas V, Tourancheau B, Goldman B, Bonnet MP, Michaud Soret I (2023) A transition support system to build decarbonization scenarios in the academic community. PLOS Sustain Transform 2(4):e0000049. https://doi.org/10.1371/journal.pstr.0000049

Gruzieva O, Jeong A, He S, Yu Z, de Bont J, Pinho MG, Eze IC, Kress S, Wheelock CE, Peters A, Vlaanderen J, de Hoogh K, Scalbert A, Chadeau-Hyam M, Vermeulen RCH, Gehring U, Probst-Hensch N, Melén E (2022) Air pollution, metabolites and respiratory health across the life-course. Eur Respir Rev 31(165):220038. https://doi.org/10.1183/16000617.0038-2022

Gudi-Mindermann H, White M, Roczen J, Riedel N, Dreger S, Bolte G (2023) Integrating the social environment with an equity perspective into the exposome paradigm: a new conceptual framework of the social exposome. Environ Res 233:116485. https://doi.org/10.1016/j.envres.2023.116485

Guillien A, Lepeule J, Seyve E, Le Moual N, Pin I, Degano B, Garcia-Aymerich J, Pépin JL, Pison C, Dumas O, Varraso R, Siroux V (2021) Profile of exposures and lung function in adults with asthma: an exposome approach in the EGEA study. Environ Res 196:110422. https://doi.org/10.1016/j.envres.2020.110422

Guillien A, Ghosh M, Gille T, Dumas O (2023) The exposome concept: how has it changed our understanding of environmental causes of chronic respiratory diseases? Breathe (Sheff) 19(2):230044. https://doi.org/10.1183/20734735.0044-2023

Guimbaud JB, Siskos AP, Sakhi AK, Heude B, Sabidó E, Borràs E, Keun H, Wright J, Julvez J, Urquiza J, Gützkow KB, Chatzi L, Casas M, Bustamante M, Nieuwenhuijsen M, Vrijheid M, López-Vicente M, de Castro PM, Stratakis N, Robinson O, Grazuleviciene R, Slama R, Alemany S, Basagaña X, Plantevit M, Cazabet R, Maitre L (2024) Machine learning-based

health environmental-clinical risk scores in European children. Commun Med (Lond) 4(1):98. https://doi.org/10.1038/s43856-024-00513-y

Guo J, Garshick E, Si F, Tang Z, Lian X, Wang Y, Li J, Koutrakis P (2024) Environmental toxicant exposure and depressive symptoms. JAMA Netw Open 7(7):e2420259. https://doi.org/10.1001/jamanetworkopen.2024.20259

Haddad N, Andrianou XD, Makris KC (2019) A scoping review on the characteristics of human exposome studies. Curr Pollut Rep 5(4):378–393. https://doi.org/10.1007/s40726-019-00130-7

Haddad N, Andrianou X, Parrish C, Oikonomou S, Makris KC (2022) An exposome-wide association study on body mass index in adolescents using the national health and nutrition examination survey (NHANES) 2003–2004 and 2013–2014 data. Sci Rep 12(1):8856. https://doi.org/10.1038/s41598-022-12459-z

Hall J, Chawla M, Watson D, Jacob CM, Schoenaker D, Connolly A, Barrett G, Stephenson J (2023) Addressing reproductive health needs across the life course: an integrated, community-based model combining contraception and preconception care. Lancet Public Health 8(1):e76–e84. https://doi.org/10.1016/S2468-2667(22)00254-7

He Y, Patel CJ (2023) Software application profile: PXStools—an R package of tools for conducting exposure-wide analysis and deriving polyexposure risk scores. Int J Epidemiol 52(2):633–640. https://doi.org/10.1093/ije/dyac216

Healy DR, Zarei I, Mikkonen S, Soininen S, Viitasalo A, Haapala EA, Auriola S, Hanhineva K, Kolehmainen M, Lakka TA (2024) Longitudinal associations of an exposome score with serum metabolites from childhood to adolescence. Commun Biol 7(1):890. https://doi.org/10.1038/s42003-024-06146-0

Henderson P, Hu J, Romoff J, Brunskill E, Jurafsky D, Pineau J (2020) Towards the systematic reporting of the energy and carbon footprints of machine learning. J Mach Learn Res 21(248):1–43. https://doi.org/10.48550/arXiv.2002.05651

Hernandez-Ferrer C, Wellenius GA, Tamayo I, Basagaña X, Sunyer J, Vrijheid M, Gonzalez JR (2019) Comprehensive study of the exposome and omic data using rexposome bioconductor packages. Bioinformatics 35(24):5344–5345. https://doi.org/10.1093/bioinformatics/btz526

Hoekstra J, Lenssen ES, Wong A, Loef B, Herber GM, Boshuizen HC, Strak M, Verschuren WMM, Janssen NAH (2023) Predicting self-perceived general health status using machine learning: an external exposome study. BMC Public Health 23(1):1027. https://doi.org/10.1186/s12889-023-15962-8

Hong W (2024) Advances and opportunities of mobile health in the postpandemic era: smartphonization of wearable devices and wearable deviceization of smartphones. JMIR Mhealth Uhealth 12:e48803. https://doi.org/10.2196/48803

Hu H, Laden F, Hart J, James P, Fishe J, Hogan W, Shenkman E, Bian J (2023) A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization. Exposome 3(1):osad005. https://doi.org/10.1093/exposome/osad005

Isola S, Murdaca G, Brunetto S, Zumbo E, Tonacci A, Gangemi S (2024) The use of artificial intelligence to analyze the exposome in the development of chronic diseases: a review of the current literature. Informatics 11(4):86. https://doi.org/10.3390/informatics11040086

Iyamu I, Xu AXT, Gómez-Ramírez O, Ablona A, Chang HJ, Mckee G, Gilbert M (2021) Defining digital public health and the role of digitization, digitalization, and digital transformation: scoping review. JMIR Public Health Surveill 7(11):e30399. https://doi.org/10.2196/30399

Iyamu I, McKee G, Haag D, Gilbert M (2024) Defining the role of digital public health in the evolving digital health landscape: policy
and practice implications in Canada. Health Promot Chronic Dis Prev Can 44:66-69

Jabakhanji SB, Arnold SR, Aunan K, Chersich MF, Jakobsson K, McGushin A, Kelly I, Roche N, Stauffer A, Stanistreet D (2022) Public health measures to address the impact of climate change on population health—proceedings from a stakeholder workshop. Int J Environ Res Public Health 19(20):13665. https://doi.org/10.3390/ijerph192013665

Janssen BG, Byun HM, Gyselaers W, Lefebvre W, Baccarelli AA, Nawrot TS (2015) Placental mitochondrial methylation and exposure to airborne particulate matter in the early life environment: an ENVIRONAGE birth cohort study. Epigenetics 10(6):536-544. https://doi.org/10.1080/15592294.2015.1048412

Jedynak P, Maitre L, Guxens M, Gützkow KB, Julvez J, López-Vicente M, Sunyer J, Casas M, Chatzi L, Gražulevičienė R, Kampouri M, McEachan R, Mon-Williams M, Tamayo I, Thomsen C, Urquiza J, Vafeiadi M, Wright J, Basagaña X, Vrijheid M, Philippat C (2021) Prenatal exposure to a wide range of environmental chemicals and child behaviour between 3 and 7 years of age—an exposome-based approach in 5 European cohorts. Sci Total Environment 763:144115. https://doi.org/10.1016/j.scitotenv.2020.144115

Jones DP (2015) Redox theory of aging. Redox Biol 5:71-79. https://doi.org/10.1016/j.redox.2015.03.004

Jones DP, Park Y, Ziegler TR (2012) Nutritional metabolomics: progress in addressing complexity in diet and health. Annu Rev Nutr 32:183-202. https://doi.org/10.1146/annur ev-nutr-072610-145159

Juarez PD, Matthews-Juarez P, Hood DB, Im W, Levine RS, Kilbourne BJ, Langston MA, Al-Hamdan MZ, Crosson WL, Estes MG, Estes SM, Agboto VK, Robinson P, Wilson S, Lichtveld MY (2014) The public health exposome: a population-based, exposure science approach to health disparities research. Int J Environ Res Public Health 11(12):12866-12895. https://doi.org/10.3390/ijerph111212866

Kalia V, Belsky DW, Baccarelli AA, Miller GW (2022) An exposomic framework to uncover environmental drivers of aging. Exposome 2(1):osac002. https://doi.org/10.1093/exposome/osac002

Kane NJ, Wang X, Gerkovich MM, Breitkreutz M, Rivera B, Kunchithapatham H, Hoffman MA (2021) The envirome web service: patient context at the point of care. J Biomed Inform 119:103817. https://doi.org/10.1016/j.jbi.2021.103817

Khmaladze I, Leonardi M, Fabre S, Messaraa C, Mavon A (2020) The skin interactome: a holistic "genome-microbiome-exposome" approach to understand and modulate skin health and aging. Clin Cosmet Investig Dermatol 13:1021-1040. https://doi.org/10.2147/ccid.s239367

Khoury MJ, Iademarco MF, Riley WT (2016) Precision public health for the era of precision medicine. Am J Prev Med 50(3):398-401. https://doi.org/10.1016/j.amepre.2015.08.031

Kim KN, Hong YC (2017) The exposome and the future of epidemiology: a vision and prospect. Environ Health Toxicol 32:e2017009. https://doi.org/10.5620/cht.e2017009

Kiossoglou P, Borda A, Gray K, Martin-Sanchez F, Verspoor K, Lopez-Campos G (2017) Characterising the scope of exposome research: a generalisable approach. Stud Health Technol Inform 245:457-461

Krutmann J, Bouloc A, Sore G, Bernard BA, Passeron T (2017) The skin aging exposome. J Dermatol Sci 85(3):152-161. https://doi.org/10.1016/j.jdermsci.2016.09.015

Kuijpers E, van Wel L, Loh MS, Galea KS, Makris KC, Stierum R, Fransman W, Pronk A (2021) A scoping review of technologies and their applicability for exposome-based risk assessment in the oil and gas industry. Ann Work Expo Health 65(9):1011-1028. https://doi.org/10.1093/annweh/wxab039

Kumar S, Kumar S (2008) Trends of collaborative research in journals of oilseeds research (India), 1993–2004. Indian J Agric Library Inf Services 24:80-90

Kumar M, Sarma DK, Shubham S, Kumawat M, Verma V, Prakash A, Tiwari R (2020) Environmental endocrine-disrupting chemical exposure: role in non-communicable diseases. Front Public Health 8:553850. https://doi.org/10.3389/fpubh.2020.553850

Lai Y, Koelmel JP, Walker DI, Price EJ, Papazian S, Manz KE, Castilla-Fernández D, Bowden JA, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin EZ, Jbebli A, McNeil BR, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani HH, Restituito S, Tchou-Wong KM, Lu K, Martin JW, Warth B, Godri Pollitt KJ, Klánová J, Fiehn O, Metz TO, Pennell KD, Jones DP, Miller GW (2024) High-resolution mass spectrometry for human exposomics: expanding chemical space coverage. Environ Sci Technol 58(29):12784-12822. https://doi.org/10.1021/acs.est.4c01156

Lebow-Skelley E, Young L, Noibi Y, Blaginin K, Hooker M, Williamson D, Tomlinson MS, Kegler MC, Pearson MA (2022) Defining the exposome using popular education and concept mapping with communities in Atlanta, Georgia. Front Public Health 10:842539. https://doi.org/10.3389/fpubh.2022.842539

Lee EY, Akhtari F, House JS, Simpson RJ Jr, Schmitt CP, Fargo DC, Schurman SH, Hall JE, Motsinger-Reif AA (2022) Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the personalized environment and genes study. Environ Res 212(Pt D):113463. https://doi.org/10.1016/j.envres.2022.113463

Lee K, Wang J, Heo J (2023) How the physical inactivity is affected by social-, economic- and physical-environmental factors: an exploratory study using the machine learning approach. Int J Digit Earth 16(1):2503-2521. https://doi.org/10.1080/17538947.2023.2230944

Lester BM, Camerota M, Everson TM, Shuster CL, Marsit CJ (2024) Toward a more holistic approach to the study of exposures and child outcomes. Epigenomics 16(9):635-651. https://doi.org/10.2217/epi-2023-0424

Li S, Chen J, Zhang Y, Huang S, Pan Q, Tang D, Lan T, Bu S, Wang Y (2024) Identifying the most critical behavioral lifestyles associated with MAFLD: evidence from the NHANES 2017-2020. Front Endocrinol (Lausanne) 15:1375374. https://doi.org/10.3389/fendo.2024.1375374

Lloyd D, House JS, Akhtari FS, Schmitt CP, Fargo DC, Scholl EH, Phillips J, Choksi S, Shah R, Hall JE, Motsinger-Reif AA (2024a) Questionnaire-based exposome-wide association studies for common diseases in the personalized environment and genes study. Exposome 4(1):osae002. https://doi.org/10.1093/exposome/osae002

Lloyd D, House JS, Akhtari FS, Schmitt CP, Fargo DC, Scholl EH, Phillips J, Choksi S, Shah R, Hall JE, Motsinger-Reif AA (2024b) Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the personalized environment and genes study. Exposome 4(1):osae003. https://doi.org/10.1093/exposome/osae003

Lochhead P, Chan AT, Nishihara R, Fuchs CS, Beck AH, Giovannucci E, Ogino S (2015) Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression. Mod Pathol 28(1):14-29. https://doi.org/10.1038/modpathol.2014.81

Loef B, Wong A, Janssen NAH, Strak M, Hoekstra J, Picavet HSJ, Boshuizen HCH, Verschuren WMM, Herber GM (2022) Using random forest to identify longitudinal predictors of health in a 30-year cohort study. Sci Rep 12(1):10372. https://doi.org/10.1038/s41598-022-14632-w
Loef B, Herber GM, Wong A, Janssen NAH, Hoekstra J, Picavet HSJ, Verschuren WMM (2023) Predictors of healthy physiological aging across generations in a 30-year population-based cohort study: the Doetinchem cohort study. BMC Geriatr 23(1):107. https://doi.org/10.1186/s12877-023-03789-2

Lopez-Campos G, Merolli M, Martin-Sanchez F (2017) Biomedical informatics and the digital component of the exposome. Stud Health Technol Inform 245:496–500

Lu L, Ni R (2022) Bibliometric analysis of global research on poly-cyclic aromatic hydrocarbons and health risk between 2002 and 2021. Environ Sci Pollut Res Int 29(56):84034–84048. https://doi.org/10.1007/s11356-022-23047-z

Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56–67. https://doi.org/10.1038/s42256-019-0138-9

Maitre L, de Bont J, Casas M, Robinson O, Aasvang GM, Agier L, Andrušaitytė S, Ballester F, Basagaña X, Borràs E, Brochot C, Bustamante M, Carracedo A, de Castro M, Dedele A, Donaire-Gonzalez D, Estivill X, Evandt J, Fossati S, Giorgis-Allemand L, Gonzalez JR, Granum B, Grazuleviciene R, Gutzkow KB, Haug LS, Hernandez-Ferrer C, Heude B, Ibarluzea J, Julvez J, Karachaliou M, Keun HC, Krog NH, Lau CHE, Leventakou V, Lyon-Caen S, Manzano C, Mason D, McEachan R, Meltzer HM, Petraviciene I, Quentin J, Roumeliotaki T, Sabido E, Saulnier PJ, Siskos AP, Siroux V, Sunyer J, Tamayo I, Urquiza J, Vafeiadi M, van Gent D, Vives-Usano M, Waiblinger D, Warembourg C, Chatzi L, Coen M, van der Hazel P, Nieuwenhuijsen MJ, Slama R, Thomsen C, Wright J, Vrijheid M (2018) Human early life exposome (HELIX) study: a European population-based exposome cohort. BMJ Open 8(9):e021311. https://doi.org/10.1136/bmjopen-2017-021311

Maitre L, Guimbaud JB, Warembourg C, Güil-Oumrait N, Petrone PM, Chadeau-Hyam M, Vrijheid M, Basagaña X, Gonzalez JR, Exposome Data Challenge Participant Consortium (2022a) State-of-the-art methods for exposure-health studies: results from the exposome data challenge event. Environ Int 168:107422. https://doi.org/10.1016/j.envint.2022.107422

Maitre L, Bustamante M, Hernández-Ferrer C, Thiel D, Lau CE, Siskos AP, Vives-Usano M, Ruiz-Arenas C, Pelegrí-Sisó D, Robinson O, Mason D, Wright J, Cadiou S, Slama R, Heude B, Casas M, Sunyer J, Papadopoulou EZ, Gutzkow KB, Andrusaityte S, Grazuleviciene R, Vafeiadi M, Chatzi L, Sakhi AK, Thomsen C, Tamayo I, Nieuwenhuijsen M, Urquiza J, Borràs E, Sabidó E, Quintela I, Carracedo À, Estivill X, Coen M, González JR, Keun HC, Vrijheid M (2022b) Multi-omics signatures of the human early life exposome. Nat Commun 13(1):7024. https://doi.org/10.1038/s41467-022-34422-2

Manz KE, Feerick A, Braun JM, Feng YL, Hall A, Koelmel J, Manzano C, Newton SR, Pennell KD, Place BJ, Godri Pollitt KJ, Prasse C, Young JA (2023) Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome. J Expo Sci Environ Epidemiol 33(4):524–536. https://doi.org/10.1038/s41370-023-00574-6

Marchal S, Choukér A, Bereiter-Hahn J, Kraus A, Grimm D, Krüger M (2024) Challenges for the human immune system after leaving Earth. NPJ Microgravity 10(1):106. https://doi.org/10.1038/s41526-024-00446-9

Mariette J, Blanchard O, Berné O, Aumont O, Carrey J, Ligozat AL, Lellouch E, Roche PE, Guennebaud G, Thanwerdas J, Bardou P, Salin G, Maigne E, Servan S, Ben-Ari T (2022) An open-source tool to assess the carbon footprint of research. Environ Res Infrastruct Sustain 2:035008. https://doi.org/10.1088/2634-4505/ac84a4

Marín D, Basagaña X, Amaya F, Aristizábal LM, Muñoz DA, Domínguez A, Molina F, Ramos CD, Morales-Betancourt R, Hincapié R, Rodríguez-Villamizar L, Rojas Y, Morales O, Cuel-lar M, Corredor A, Villamil-Osorio M, Bejarano MA, Vidal D, Narváez DM, Groot H, Builes JJ, López L, Henao EA, Lopera V, Hernández LJ, Bangdiwala SI, Marín-Ochoa B, Oviedo AI, Sánchez-García OE, Toro MV, Riaño W, Rueda ZV (2024) Early-life external exposome in children 2–5 years old in Colombia. Environ Res 252(Pt 3):118913. https://doi.org/10.1016/j.envres.2024.118913

Martinez RM, Muller H, Negru S, Ormenisan A, Muhr LS, Zhang X, Moller FT, Clements MS, Kozlakidis Z, Pimenoff VN (2021) Human exposome assessment platform. Environ Epidemiol 5:e182. https://doi.org/10.1097/EE9.0000000000000182

Martin-Sanchez F, Bellazzi R, Casella V, Dixon W, Lopez-Campos G, Peek N (2020) Progress in characterizing the human exposome: a key step for precision medicine. Yearb Med Inform 29:115–120. https://doi.org/10.1055/s-0040-1701975

Mas S, Bocol D, Rodríguez N, Mezquida G, Amoretti S, Cuesta MJ, González-Peñas J, García-Alcón A, Lobo A, González-Pinto A, Corripio I, Vieta E, Castro-Fornieles J, Mané A, Saiz-Ruiz J, Gassó P, Bioque M, Bernardo M, PEPs Group (2020) Examining gene-environment interactions using aggregate scores in a first-episode psychosis cohort. Schizophren Bull 46(4):1019–1025. https://doi.org/10.1093/schbul/sbaa012

Matus P, Urquidi C, Cárcamo M, Vidal V (2024) Integrating the exposome and one health approach to national health surveillance: an opportunity for Latin American countries in health preventive management. Front Public Health 12:1376609. https://doi.org/10.3389/fpubh.2024.1376609

Miller GW, Jones DP (2014) The nature of nurture: refining the definition of the exposome. Toxicol Sci 137(1):1–2. https://doi.org/10.1093/toxsci/kft251

Miller DB, O'Callaghan JP (2015) Biomarkers of Parkinson's disease: present and future. Metabolism 64(3 Suppl 1):S40–S46. https://doi.org/10.1016/j.metabol.2014.10.030

Misra BB (2020) The chemical exposome of human aging. Front Genet 11:574936. https://doi.org/10.3389/fgene.2020.574936

Molina-García M, Granger C, Trullàs C, Puig S (2022) Exposome and skin: part 1. Bibliometric analysis and review of the impact of exposome approaches on dermatology. Derma-tol Ther (Heidelb) 12(2):345–359. https://doi.org/10.1007/s13555-021-00680-z

Montazeri A, Mohammadi S, Hesari PM, Ghaemi M, Riazi H, Sheikhi-Mobarakeh Z (2023) Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Syst Rev 12:239. https://doi.org/10.1186/s13643-023-02410-2

Moon Y (2016) Microbiome-linked crosstalk in the gastrointestinal exposome towards host health and disease. Pediatr Gastroenterol Hepatol Nutr 19(4):221–228. https://doi.org/10.5223/pghn.2016.19.4.221

Moral-Muñoz JA, Herrera-Viedma E, Santisteban-Espejo A, Cobo MJ (2020) Software tools for conducting bibliometric analysis in science: an up-to-date review. Profesional De La Información 29(1). https://doi.org/10.3145/epi.2020.ene.03.

Morawska L, Miller W, Riley M, Vardoulakis S, Zhu YG, Marks GB, Garnawat P, Kumar P, Thynell M (2019) Towards Urbanome the genome of the city to enhance the form and function of future cities. Nat Commun 10(1):4014. https://doi.org/10.1038/s41467-019-11972-6

Moyano-Fernández C, Rueda J, Delgado J, Ausín T (2024) May Artificial Intelligence take health and sustainability on a honeymoon? Towards green technologies for multidimensional health and environmental justice. Glob Bioeth 35(1):2322208. https://doi.org/10.1080/11287462.2024.2322208
Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z (2020) The impact of CDSS on physicians: a scoping review. Stud Health Technol Inform 272:470–473. https://doi.org/10.3233/SHTI200597

Mullen L (2021) Gender: predict gender from names using historical data. R package version 0.6.0. https://cran.r-project.org/web/packages/gender/gender.pdf. Accessed 05 Mar 2025

Mumtaz H, Riaz MH, Wajid H, Saqib M, Zeeshan MH, Khan SE, Chauhan YR, Sohail H, Vohra LI (2023) Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front Digit Health 5:1203945. https://doi.org/10.3389/fdgh.2023.1203945

Nakamura J, Mutlu E, Sharma V, Collins L, Bodnar W, Yu R, Lai Y, Moeller B, Lu K, Swenberg J (2014) The endogenous exposome. DNA Repair (Amst) 19:3–13. https://doi.org/10.1016/j.dnarep.2014.03.031

Neufcourt L, Castagné R, Mabile L, Khalatbari-Soltani S, Delpierre C, Kelly-Irving M (2022) Assessing how social exposures are integrated in exposome research: a scoping review. Environ Health Perspect 130(11):116001. https://doi.org/10.1289/EHP11015

Nguyen VK, Colacino J, Patel CJ, Sartor M, Jolliet O (2022) Identification of occupations susceptible to high exposure and risk associated with multiple toxicants in an observational study: national health and nutrition examination survey 1999–2014. Exposome 2(1):osac004. https://doi.org/10.1093/exposome/osac004

Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW (2019) The exposome: molecules to populations. Annu Rev Pharmacol Toxicol 59:107–127. https://doi.org/10.1146/annurev-pharmtox-010818-021315

Nikolich-Zugich J (2018) The twilight of immunity: emerging concepts in aging of the immune system. Nat Immunol 19(1):10–19. https://doi.org/10.1038/s41590-017-0006-x

Novak R, Robinson JA, Frantzidis C, Sejdullahu I, Persico MG, Kontić D, Sarigiannis D, Kocman D (2023) Integrated assessment of personal monitor applications for evaluating exposure to urban stressors: a scoping review. Environ Res 226:115685. https://doi.org/10.1016/j.envres.2023.115685

Nwanaji-Enwerem JC, Jackson CL, Ottinger MA, Cardenas A, James KA, Malecki KMC, Chen JC, Geller AM, Mitchell UA (2021) Adopting a “compound” exposome approach in environmental aging biomarker research: a call to action for advancing racial health equity. Environ Health Perspect 129(4):45001. https://doi.org/10.1289/EHP8392

Odone A, Buttigieg S, Ricciardi W, Azzopardi-Muscat N, Staines A (2019) Public health digitalization in Europe. Eur J Public Health 29(Supplement_3):28–35. https://doi.org/10.1093/eurpub/ckz161

Ohanyan H, Portengen L, Kaplan I, Huss A, Hoek G, Beulens JWJ, Lakerveld J, Vermeulen R (2022a) Associations between the urban exposome and type 2 diabetes: results from penalised regression by least absolute shrinkage and selection operator and random forest models. Environ Int 170:107592. https://doi.org/10.1016/j.envint.2022.107592

Ohanyan H, Portengen L, Huss A, Traini E, Beulens JWJ, Hoek G, Lakerveld J, Vermeulen R (2022b) Machine learning approaches to characterize the obesogenic urban exposome. Environ Int 158:107015. https://doi.org/10.1016/j.envint.2021.107015

Ohanyan H, van de Wiel M, Portengen L, Wagtendonk A, den Braver NR, de Jong TR, Verschuren M, van den Hurk K, Stronks K, Moll van Charante E, van Schoor NM, Stehouwer CDA, Weselius A, Koster A, Ten Have M, Penninx BWJH, van Wier MF, Motoc I, Oldehinkel AJ, Willemsen G, Boomsma DI, Beenackers MA, Huss A, van Boxtel M, Hoek G, Beulens JWJ, Vermeulen R, Lakerveld J (2024) Exposome-wide association study of body mass index using a novel meta-analytical approach for random forest models. Environ Health Perspect 132(6):67007. https://doi.org/10.1289/EHP13393

Pagliaccio D, Tran KT, Visoki E, DiDomenico GE, Auerbach RP, Barzilay R (2024) Probing the digital exposome: associations of social media use patterns with youth mental health. NPP Digit Psychiatry Neurosci 2:5. https://doi.org/10.1038/s44277-024-00006-9

Papadopoulou E, Stratakis N, Basagaña X, Brantsæter AL, Casas M, Fossati S, Gražulevičienė R, Småstuen Haug J, Heude B, Maitre L, McEachan RRC, Robinson O, Roumeliotaki T, Sabidó E, Boràs E, Urquiza J, Vafeiadi M, Zhao Y, Slama R, Wright J, Conti DV, Vrijheid M, Chatzi L (2021) Prenatal and postnatal exposure to PFAS and cardiometabolic factors and inflammation status in children from six European cohorts. Environ Int 157:106853. https://doi.org/10.1016/j.envint.2021.106853

Paquin V, Pries LK, Ten Have M, Bak M, Gunther N, de Graaf R, van Dorsselaer S, Lin BD, van Eijk KR, Kenis G, Richards A, O'Donovan MC, Luykx JJ, Rutten BPF, van Os J, Shah JL, Guloksuz S (2023) Age- and sex-specific associations between risk scores for schizophrenia and self-reported health in the general population. Soc Psychiatry Psychiatr Epidemiol 58(1):43–52. https://doi.org/10.1007/s00127-022-02346-3

Pearson M, Lebow-Skelley E, Whitaker L, Young L, Warren CB, Williamson D, Kegler MC (2020) Implementation of a community grant program to address community-driven environmental health concerns. Local Environ 25(11–12):830–848. https://doi.org/10.1080/13549839.2020.1843419

Petit P (2022) Toxicological and exposure database inventory: a review. Int J Hyg Environ Health 246:114055. https://doi.org/10.1016/j.ijheh.2022.114055

Petit P, Vuillerme N (2025) Leveraging administrative health databases to address health challenges in farming populations: a scoping review and bibliometric analysis (1975–2024). JMIR Public Health Surveill 11:e62939. https://doi.org/10.2196/62939

Petit P, Chamot S, Al-Salameh A, Cancé C, Desailloud R, Bonneterre V (2024a) Farming activity and risk of treated thyroid disorders: insights from the TRACTOR project, a nationwide cohort study. Environ Res 249:118458. https://doi.org/10.1016/j.envres.2024.118458

Petit P, Berger F, Bonneterre V, Vuillerme N (2025) Investigating Parkinson's disease risk across farming activities using data mining and large-scale administrative health data. NPJ Parkinsons Dis 11(1):13. https://doi.org/10.1038/s41531-024-00864-2

Petit P, Leroyer A, Chamot S, Fumery M, Bonneterre V (2024b) Farming activities and risk of inflammatory bowel disease: a French nationwide population-based cohort study. J Crohns Colitis jjae050. https://doi.org/10.1093/ecco-jcc/jjae050

Pini L, Salvalaggio A, Wennberg AM, Dimakou A, Matteoli M, Corbetta M (2023) The pollutome-connectome axis: a putative mechanism to explain pollution effects on neurodegeneration. Ageing Res Rev 86:101867. https://doi.org/10.1016/j.arr.2023.101867

Price EJ, Vitale CM, Miller GW, David A, Barouki R, Audouze K, Walker DI, Antignac JP, Coumoul X, Bessonneau V, Klánová J (2022) Merging the exposome into an integrated framework for “omics” sciences. iScience 25(3):103976. https://doi.org/10.1016/j.isci.2022.103976

Pries LK, Lage-Castellanos A, Delespaul P, Kenis G, Luykx JJ, Lin BD, Richards AL, Akdede B, Binbay T, Altinyazar V, Yalinçetin B, Gümüş-Akay G, Cihan B, Soygür H, Ulaş H, Cankurtaran EŞ, Kaymak SU, Mihaljevic MM, Petrovic SA, Mirjanic T, Bernardo M, Cabrera B, Bobes J, Saiz PA, García-Portilla MP, Sanjuan J, Aguilar EJ, Santos JL, Jiménez-López E, Arrojo M, Carracedo A, López G, González-Peñas J, Parellada M, Maric NP, Atbaşoğlu C, Ucok A, Alptekin K, Saka MC, Arango C, O'Donovan M, Rutten BPF, van Os J, Guloksuz S (2019) Estimating exposome score for schizophrenia using predictive modeling approach in
two independent samples: the results from the EUGEI study. Schizophr Bull 45(5):960–965. https://doi.org/10.1093/schbul/ sbz054

Pries LK, Dal Ferro GA, van Os J, Delespaul P, Kenis G, Lin BD, Luykx JJ, Richards AL, Akdede B, Binbay T, Altındyazar V, Yalınçetin B, Gümüş-Akay G, Cihan B, Soygür H, Ulaş H, Şahin Cankurtaran E, Ulusoy Kaymak S, Mihaljevic MM, Andric Petrovic S, Mirjanic T, Bernardo M, Mezquida G, Amoretti S, Bobes J, Saiz PA, García-Portilla MP, Sanjuan J, Aguilar EJ, Santos JL, Jiménez-López E, Arrojo M, Carracedo A, López G, González-Peñas J, Parellada M, Maric NP, Atbaşoğlu C, Ucok A, Alptekin K, Can Saka M, Arango C, O'Donovan M, Tosato S, Rutten BPF, Guloksuz S (2020a) Examining the independent and joint effects of genomic and exposomic liabilities for schizo- phrenia across the psychosis spectrum. Epidemiol Psychiatr Sci 29:e182. https://doi.org/10.1017/S2045796020000943

Pries LK, van Os J, Ten Have M, de Graaf R, van Dorsselaer S, Bak M, Lin BD, van Eijk KR, Kenis G, Richards A, O'Donovan MC, Luykx JJ, Rutten BPF, Guloksuz S (2020b) Association of recent stressful life events with mental and physical health in the context of genomic and exposomic liability for schizophrenia. JAMA Psychiatr 77(12):1296–1304. https://doi.org/10.1001/jamapsychi atry.2020.2304

Pries LK, Erzin G, van Os J, Ten Have M, de Graaf R, van Dors- selaer S, Bak M, Rutten BPF, Guloksuz S (2021a) Predictive performance of exposome score for schizophrenia in the general population. Schizophr Bull 47(2):277–283. https://doi.org/10. 1093/schbul/sbaa170

Pries LK, Erzin G, Rutten BPF, van Os J, Guloksuz S (2021b) Esti- mating aggregate environmental risk score in psychiatry: the exposome score for schizophrenia. Front Psychiatry 12:671334. https://doi.org/10.3389/fpsyt.2021.671334

Pries LK, Moore TM, Visoki E, Sotelo I, Barzilay R, Guloksuz S (2022) Estimating the association between exposome and psy- chosis as well as general psychopathology: results from the ABCD study. Biol Psychiatry Glob Open Sci 2(3):283–291. https://doi.org/10.1016/j.bpsgos.2022.05.005

Prieto-Botella D, Valera-Gran D, Santa-Marina L, Babarro I, Subiza- Pérez M, Casas M, Guxens M, Cárdenas-Fuentes G, Heude B, Bernard JY, McEachan RRC, García-Aymerich J, Vrijheid M, Navarrete-Muñoz EM (2022) Validation of a parent-reported physical activity questionnaire by accelerometry in European children aged from 6 to 12 years old. Int J Environ Res Public Health 19(15):9178. https://doi.org/10.3390/ijerph19159178

Pronk A, Loh M, Kuijpers E, Albin M, Selander J, Godderis L, Ghosh M, Vermeulen R, Peters S, Mehlum IS, Turner MC, Schlünssen V, Goldberg M, Kogevinas M, Harding BN, Solovieva S, Garani- Papadatos T, van Tongeren M, Stierum R, EPHOR Consortium (2022) Applying the exposome concept to working life health: the EU EPHOR project. Environ Epidemiol 6(2):e185. https:// doi.org/10.1097/ee9.0000000000000185

Rappaport SM (2011) Implications of the exposome for exposure sci- ence. J Expo Sci Environ Epidemiol 21(1):5–9. https://doi.org/ 10.1038/jes.2010.50

Rappaport SM (2012a) Biomarkers intersect with the exposome. Bio- markers 17(6):483–489. https://doi.org/10.3109/1354750X.2012. 691553

Rappaport SM (2012b) Discovering environmental causes of disease. J Epidemiol Commun Health 66(2):99–102. https://doi.org/10. 1136/jech-2011-200726

Rappaport SM (2016) Genetic factors are not the major causes of chronic diseases. PLoS One 11(4):e0154387. https://doi.org/10. 1371/journal.pone.0154387

Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A (2014) The blood exposome and its role in discovering causes of disease.

Environ Health Perspect 122(8):769–774. https://doi.org/10. 1289/ehp.1308015

Rejem K, Misiak B (2024a) The associations of exposome score with various domains of psychopathology: a network analysis in a non-clinical sample. Brain Sci 14(3):242. https://doi.org/10. 3390/brainsci14030242

Rejem K, Misiak B (2024b) Modelling the effects of the exposome score within the extended psychosis phenotype. J Psychiatr Res 169:22–30. https://doi.org/10.1016/j.jpsychires.2023.11.022

Robinson O, Vrijheid M (2015) The pregnancy exposome. Curr Environ Health Rep 2(2):204–213. https://doi.org/10.1007/ s40572-015-0043-2

Rocque RJ, Beaudoin C, Ndjaboue R, Cameron L, Poirier-Bergeron L, Poulin-Rheault RA, Fallon C, Tricco AC, Witteman HO (2021) Health effects of climate change: an overview of systematic reviews. BMJ Open 11(6):e046333. https://doi.org/10.1136/ bmjopen-2020-046333

Romera-Vilchez M, Montero-Vilchez T, Herrero-Fernandez M, Rodriguez-Pozo JA, Jimenez-Galvez G, Morales-Garcia C, Buendia-Eisman A, Arias-Santiago S (2022) Impact of expo- some factors on epidermal barrier function in patients with obstructive sleep apnea syndrome. Int J Environ Res Public Health 19(2):659. https://doi.org/10.3390/ijerph19020659

Ronsmans S, Sørig Hougaard K, Nawrot TS, Plusquin M, Huaux F, Jesús Cruz M, Moldovan H, Verpaele S, Jayapala M, Tunney M, Humblet-Baron S, Dirven H, Cecilie Nygaard U, Lindeman B, Duale N, Liston A, Meulengracht Flachs E, Kastaniegaard K, Ketzel M, Goetz J, Vanoirbeek J, Ghosh M, Hoet PHM (2022) The EXIMIOUS project—mapping exposure-induced immune effects: connecting the exposome and the immunome. Environ Epidemiol 6(1):e193. https://doi.org/10.1097/ee9. 0000000000000193

Rose S (2001) Moving on from old dichotomies: beyond nature- nurture towards a lifeline perspective. Br J Psychiatry Suppl 40:s3–s7. https://doi.org/10.1192/bjp.178.40.s3

Rotem RS, Bellavia A, Paganoni S, Weisskopf MG (2024) Medi- cation use and risk of amyotrophic lateral sclerosis: using machine learning for an exposome-wide screen of a large clinical database. Amyotroph Lateral Scler Frontotemporal Degener 25(3–4):367–375. https://doi.org/10.1080/21678421. 2024.2320878

Ruiz-Castell M, Le Coroller G, Pexaras A, Ciprián GM, Fagherazzi G, Bohn T, Maitre L, Sunyer J, Appenzeller BMR (2023) Char- acterizing the adult exposome in men and women from the gen- eral population: results from the EHES-LUX study. Environ Int 173:107780. https://doi.org/10.1016/j.envint.2023.107780

Safarlou CW, Jongsma KR, Vermeulen R, Bredenoord AL (2023) The ethical aspects of exposome research: a systematic review. Expo- some 3(1):osad004. https://doi.org/10.1093/exposome/osad004

Safarlou CW, Jongsma KR, Vermeulen R (2024) Reconceptualizing and defining exposomics within environmental health: expand- ing the scope of health research. Environ Health Perspect 132(9):95001. https://doi.org/10.1289/EHP14509

Santos S, Maitre L, Warembourg C, Agier L, Richiardi L, Basa- gaña X, Vrijheid M (2020) Applying the exposome concept in birth cohort research: a review of statistical approaches. Eur J Epidemiol 35(3):193–204. https://doi.org/10.1007/ s10654-020-00625-4

Schisler JC, Ronnebaum SM, Madden M, Channell M, Campen M, Willis MS (2015) Endothelial inflammatory transcriptional responses to an altered plasma exposome following inhalation of diesel emissions. Inhal Toxicol 27(5):272–280. https://doi. org/10.3109/08958378.2015.1030481

Schlünssen V, du Prel JB, van Tongeren M, Godderis L, Turner MC, McElvenny D (2023) Development of harmonized COVID-19
occupational questionnaires. Ann Work Expo Health 67(1):4–8.
https://doi.org/10.1093/annweh/wxac044

Schmitt CP, Stingone JA, Rajasekar A, Cui Y, Du X, Duncan C, Hea-
cock M, Hu H, Gonzalez JR, Juarez PD, Smirnov AI (2023) A
roadmap to advance exposomics through federation of data. Expo-
some 3(1):osad010. https://doi.org/10.1093/exposome/osad010

Schrems I, Upham P (2020) Cognitive dissonance in sustainability scientists regarding air travel for academic purposes: a qualitative study. Sustainability 12(5):1837. https://doi.org/10.3390/su12051837

Schwartz R, Dodge J, Smith NA, Etzioni O (2019) Green AI. arXiv.
https://doi.org/10.48550/arXiv.1907.10597

Shao Y, Chien TW, Jang FL (2022) The use of radar plots with the Yk-index to identify which authors contributed the most to the Journal of Medicine in 2020 and 2021: a bibliometric analysis. Medicine (Baltimore) 101(45):e31033. https://doi.org/10.1097/MD.00000000000031033

Siroux V, Agier L, Slama R (2016) The exposome concept: a chal-
lenge and a potential driver for environmental health research.
Eur Respir Rev 25(140):124–129. https://doi.org/10.1183/
16000617.0034-2016

Smith WR, Appelbaum PS, Lebowitz MS, Gülöksüz S, Calkins ME, Kohler CG, Gur RE, Barzilay R (2023) The ethics of risk prediction for psychosis and suicide attempt in youth mental health. J Pediatr 263:113583. https://doi.org/10.1016/j.jpeds.2023.113583

Sonnenschein T, Scheider S, de Wit GA, Tonne CC, Vermeulen R (2022) Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges. Exposome 2(1):osac009. https://doi.org/10.1093/exposome/osac009

Stingone JA, Geller AM, Hood DB, Makris KC, Mouton CP, States JC, Sumner SJ, Wu KL, Rajasekar AK, Members of the Exposomics Consortium (2023) Community-level exposomics: a population-centered approach to address public health concerns. Exposome 3(1):osac009. https://doi.org/10.1093/exposome/osad009

Stratakis N, Conti DV, Borras E, Sabido E, Roumeliotaki T, Papado-
poulou E, Agier L, Basagana X, Bustamante M, Casas M, Farzan
SF, Fossati S, Gonzalez JR, Grazuleviciene R, Heude B, Maitre
L, McEachan RRC, Theologidis I, Urquiza J, Vafeiadi M, West
J, Wright J, McConnell R, Brantsaeter AL, Meltzer HM, Vrijheid
M, Chatzi L (2020) Association of fish consumption and mercury
exposure during pregnancy with metabolic health and inflamma-
tory biomarkers in children. JAMA Netw Open 3(3):e201007.
https://doi.org/10.1001/jamanetworkopen.2020.1007

Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, Pitteloud N, Chouchane L (2020) Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med 18(1):472. https://doi.org/10.1186/s12967-020-02658-5

Sugimoto CR, Ahn YY, Smith E, Macaluso B, Larivière V (2019) Factors affecting sex-related reporting in medical research: a cross-disciplinary bibliometric analysis. Lancet 393(10171):550–559.
https://doi.org/10.1016/S0140-6736(18)32995-7

Sun P, Guo X, Ding E, Li C, Ren H, Xu Y, Qian J, Deng F, Shi W, Dong H, Lin EZ, Guo P, Fang J, Zhang Q, Zhao W, Tong S, Lu X, Pollitt KJG, Shi X, Tang S (2024) Association between personal abiotic airborne exposures and body composition changes among healthy adults (60–69 years old): a combined exposome-wide and lipidome mediation approach from the China BAPE study. Environ Health Perspect 132(7):77005. https://doi.org/10.1289/EHP13865

Tagliaferro S, Maio S, Pirona F, Stanisci I, Sarno G, Silvi P, Ker-
menidou M, Papaioannou N, Perchard R, Prpic I, Polanska K,
Jerzynska J, Ramos E, Rovira J, Belmonte J, Snoj Tratnik JS,
Horvat M, Kocman D, Spiric Z, Zickella J, Fasola S, La Grutta
S, Malizia V, Montalbano L, EarlyFOOD, HEALS EXHES,

Baldacci S, Annesi-Maesano I (2024) Assessing external expo-
some by implementing an environmental data management sys-
tem using open data. Sci Rep 14(1):17142. https://doi.org/10.
1038/s41598-024-62924-0

Tamiz AP, Koroshetz WJ, Dhruv NT, Jett DA (2022) A focus on the neural exposome. Neuron 110(8):1286–1289. https://doi.org/10.1016/j.neuron.2022.03.019

Torkamani A, Wineinger NE, Topol EJ (2018) The personal and clinical utility of polygenic risk scores. Nat Rev Genet 19(9):581–590. https://doi.org/10.1038/s41576-018-0018-x

Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, Lewin S, Godfrey CM, Macdonald MT, Langlois EV, Soares-Weiser K, Moriarty J, Clifford T, Tunçalp Ö, Straus SE (2018) PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 169(7):467–473. https://doi.org/10.7326/M18-0850

Turner MC, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y, Dun-
ton G, Hoppin JA, Koutrakis P, Jerrett M (2017) Assessing the
exposome with external measures: commentary on the state of
the science and research recommendations. Annu Rev Public
Health 38:215–239. https://doi.org/10.1146/annurev-publhl
ealth-082516-012802

Turner MC, Vineis P, Seleiro E, Dijmarescu M, Balshaw D, Bertollini R, Chadeau-Hyam M, Gant T, Gulliver J, Jeong A, Kyrtopoulos S, Martuzzi M, Miller GW, Nawrot T, Nieuwenhuijsen M, Phillips DH, Probst-Hensch N, Samet J, Vermeulen R, Vlaanderen J, Vrijheid M, Wild C, Kogevinas M, EXPOsOMICS consortium (2018) EXPOsOMICS: final policy workshop and stakeholder consultation. BMC Public Health 18(1):260. https://doi.org/10.1186/s12889-018-5160-z

Ueda D, Walston SL, Fujita S, Fushimi Y, Tsuboyama T, Kamagata K, Yamada A, Yanagawa M, Ito R, Fujima N, Kawamura M, Nakaura T, Matsui Y, Tatsugami F, Fujioka T, Nozaki T, Hirata K, Naganawa S (2024) Climate change and artificial intelligence in healthcare: review and recommendations towards a sustainable future. Diagn Interv Imaging. https://doi.org/10.1016/j.diii.2024.06.002

Ugai T, Sasamoto N, Lee HY, Ando M, Song M, Tamimi RM, Kawachi I, Campbell PT, Giovannucci EL, Weiderpass E, Rebbeck TR, Ogino S (2022) Is early-onset cancer an emerging global epidemic? Current evidence and future implications. Nat Rev Clin Oncol 19(10):656–673. https://doi.org/10.1038/s41571-022-00672-8

Uppal K, Walker DI, Liu K, Li S, Go YM, Jones DP (2016) Computa-
tional metabolomics: a framework for the million metabolome.
Chem Res Toxicol 29(12):1956–1975. https://doi.org/10.1021/
acs.chemrestox.6b00179

Uribe-Bohorquez MV, Rivera-Ordóñez JC, García-Sánchez IM (2023) Gender disparities in accounting academia: analysis from the lens of publications. Scientometrics 128:3827–3865. https://doi.org/10.1007/s11192-023-04718-1

Valero P, Fuentes G, Cornejo M, Vega S, Grismaldo A, Pardo F, García-Rivas G, Hillebrands JL, Faas MM, Casanello P, van der Beek EM, van Goor H, Sobrevia L (2022) Exposome and foetoplacental vascular dysfunction in gestational diabetes mellitus. Mol Aspects Med 87:101019. https://doi.org/10.1016/j.mam.2021.101019

van de Weijer MP, Pelt DHM, Baselmans BML, Ligthart L, Huider F, Hottenga JJ, Pool R, Bartels M (2024) Capturing the well-being exposome in poly-environmental scores. J Environ Psychol 93:102208. https://doi.org/10.1016/j.jenvp.2023.102208

van der Sloot KWJ, Geertsema P, Rijkmans HC, Voskuil MD, van Dul-
lemen HM, Visschedijk MC, Festen EAM, Weersma RK, Ali-
zadeh BZ, Dijkstra G (2021) Environmental factors associated
with biological use and surgery in inflammatory bowel disease. J
Gastroenterol Hepatol 36(4):1022-1034. https://doi.org/10.1111/jgh.15223

van Kamp I, Persson Waye K, Kanninen K, Gulliver J, Bozzon A, Psyl-lidis A, Boshuizen H, Selander J, van den Hazel P, Brambilla M, Foraster M, Julvez J, Klatte M, Jeram S, Lercher P, Botteldooren D, Ristovska G, Kaprio J, Schreckenberg D, Hornikx M, Fels J, Weber M, Braat-Eggen E, Hartmann J, Clark C, Vrijkotte T, Brown L, Bolte G (2021) Early environmental quality and life-course mental health effects: the equal-life project. Environ Epidemiol 6(1):e183. https://doi.org/10.1097/EE9.0000000000000183

Velmovitsky PE, Bevilacqua T, Alencar P, Cowan D, Morita PP (2021) Convergence of precision medicine and public health into precision public health: toward a big data perspective. Front Public Health 9:561873. https://doi.org/10.3389/fpubh.2021.561873

Vermeulen R, Schymanski EL, Barabási AL, Miller GW (2020) The exposome and health: where chemistry meets biology. Science 367(6476):392-396. https://doi.org/10.1126/science.aay3164

Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z, Kleingans J, Kogevinas M, Kyrtopoulos S, Nieuwenhuijsen M, Phillips DH, Probst-Hensch N, Scalbert A, Vermeulen R, Wild CP, EXPOsOMICS Consortium (2017) The exposome in practice: design of the EXPOsOMICS project. Int J Hyg Environ Health 220(2 Pt A):142-151. https://doi.org/10.1016/j.ijheh.2016.08.001

Vineis P, Robinson O, Chadeau-Hyam M, Dehghan A, Mudway I, Dagnino S (2020) What is new in the exposome? Environ Int 143:105887. https://doi.org/10.1016/j.envint.2020.105887

Vineis P, Handakas E, Alfano R, Millett C, Fecht D, Chatzi L, Plusquin M, Nawrot T, Richiardi L, Barros H, Vrijheid M, Sassi F, Robinson O (2023) The contribution to policies of an exposome-based approach to childhood obesity. Exposome 3(1):osad006. https://doi.org/10.1093/exposome/osad006

Visoki E, Moore TM, Zhang X, Tran KT, Ly C, Gatavinš MM, DiDommenico GE, Brogan L, Fein JA, Warrier V, Guloksuz S, Barzilay R (2024) Classification of suicide attempt risk using environmental and lifestyle factors in 3 large youth cohorts. JAMA Psychiat 81(10):1020-1029. https://doi.org/10.1001/jamapsychiatry.2024.1887

Vlaanderen J, de Hoogh K, Hoek G, Peters A, Probst-Hensch N, Scalbert A, Melén K, Tonne C, de Wit GA, Chadeau-Hyam M, Katsouyanni E, Esko T, Jongsma KR, Vermeulen R (2021) Developing the building blocks to elucidate the impact of the urban exposome on cardiometabolic-pulmonary disease: the EU EXPANSE project. Environ Epidemiol 5(4):e162. https://doi.org/10.1097/EE9.0000000000000162

Vrijens K, Bollati V, Nawrot TS (2015) MicroRNAs as potential signatures of environmental exposure or effect: a systematic review. Environ Health Perspect 123(5):399-411. https://doi.org/10.1289/ehp.1408459

Vrijheid M (2014) The exposome: a new paradigm to study the impact of environment on health. Thorax 69(9):876-878. https://doi.org/10.1136/thoraxjnl-2013-204949

Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagaña X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Grazuleviciene R, Gutzkow KB, Julvez J, Keun HC, Kogevinas M, McEachan RRC, Meltzer HM, Sabido E, Schwarze PE, Siroux V, Sunyer J, Want EJ, Zeman F, Nieuwenhuijsen MJ (2014) The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect 122(6):535-544. https://doi.org/10.1289/ehp.1307204

Vrijheid M, Basagaña X, Gonzalez JR, Jaddoe VWV, Jensen G, Keun HC, McEachan RRC, Porcel J, Siroux V, Swertz MA, Thomsen C, Aasvang GM, Andrušaitytė S, Angeli K, Avraam D, Ballester F, Burton P, Bustamante M, Casas M, Chatzi L, Chevrier C, Cin-gotti N, Conti D, Crépet A, Dadvand P, Duijts L, van Enckevort

E, Esplugues A, Fossati S, Garlantezec R, Gómez Roig MD, Gr-zuleviciene R, Gützkow KB, Guxens M, Haakma S, Hessel EVS, Hoyles L, Hyde E, Klanova J, van Klaveren JD, Kortenkamp A, Le Brusquet L, Leenen I, Lertxundi A, Lertxundi N, Lionis C, Llop S, Lopez-Espinosa MJ, Lyon-Caen S, Maitre L, Mason D, Mathy S, Mazarico E, Nawrot T, Nieuwenhuijsen M, Ortiz R, Pedersen M, Perelló J, Pérez-Cruz M, Philippat C, Piler P, Pizzi C, Quentin J, Richiardi L, Rodriguez A, Roumeliotaki T, Sabin Capote JM, Santiago L, Santos S, Siskos AP, Strandberg-Larsen K, Stratakis N, Sunyer J, Tenenhaus A, Vafeiadi M, Wilson RC, Wright J, Yang T, Slama R (2021) Advancing tools for human early lifecourse exposome research and translation (ATHLETE): project overview. Environ Epidemiol 5(5):e166. https://doi.org/10.1097/ee9.0000000000000166

Wais K (2006) Gender prediction methods based on first names with genderizeR. R J 8(1):17-37. https://doi.org/10.32614/RJ-2016-002.

Wang B, Zhao P (2018) Worldwide research productivity in the field of back pain: a bibliometric analysis. Medicine 97(40):e11566. https://doi.org/10.1097/MD.00000000000011566

Whitsel LP, Wilbanks J, Huffman MD, Hall JL (2019) The role of government in precision medicine, precision public health and the intersection with healthy living. Prog Cardiovasc Dis 62(1):50-54. https://doi.org/10.1016/j.pcad.2018.12.002

Wienert J, Jahnel T, Maaß L (2022) What are digital public health interventions? First steps toward a definition and an intervention classification framework. J Med Internet Res 24(6):e31921. https://doi.org/10.2196/31921

Wild CP (2005) Complementing the genome with an "exposome": the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev 14(8):1847-1850. https://doi.org/10.1158/1055-9965. EPI-05-0456

Wild CP (2012) The exposome: from concept to utility. Int J Epidemiol 41(1):24-32. https://doi.org/10.1093/ije/dyr236

Wild CP, Scalbert A, Herceg Z (2013) Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk. Environ Mol Mutagen 54(7):480-499. https://doi.org/10.1002/em.21777

Wishart D, Arndt D, Pon A, Sajed T, Guo AC, Djoumbou Y, Knox C, Wilson M, Liang Y, Grant J, Liu Y, Goldansaz SA, Rappaport SM (2015) T3DB: the toxic exposome database. Nucleic Acids Res 43 part 1:1847-1850. https://doi.org/10.1093/nar/gku1004

Wu T, Duan Y, Zhang T, Tian W, Liu H, Deng Y (2022a) Research trends in the application of artificial intelligence in oncology: a bibliometric and network visualization study. Front Biosci (Landmark Ed) 27(9):254. https://doi.org/10.31083/j.fbl2709254

Wu L, Cui F, Ma J, Huang Z, Zhang S, Xiao Z, Li J, Ding X, Niu P (2022b) Associations of multiple metals with lung function in welders by four statistical models. Chemosphere 298:134202. https://doi.org/10.1016/j.chemosphere.2022.134202

Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z (2022) Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 12:973999. https://doi.org/10.3389/fonc.2022.973999

Zhang H, Hu H, Diller M, Hogan WR, Prosperi M, Guo Y, Bian J (2021) Semantic standards of external exposome data. Environ Res 197:111185. https://doi.org/10.1016/j.envres.2021.111185

Zhu G, Wen Y, Cao K, He S, Wang T (2024) A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures. Front Public Health 12:1377685. https://doi.org/10.3389/fpubh.2024.1377685

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
<table>
  <tbody>
    <tr>
      <td>60</td>
      <td>Fig.A.32: Heatmap of keyword co-occurrence between potential risk factor and cancer keywords</td>
      <td>37</td>
    </tr>
    <tr>
      <td>61</td>
      <td>Fig.A.33: Keyword co-occurrence network between potential risk factor and cancer keywords</td>
      <td>38</td>
    </tr>
    <tr>
      <td>62</td>
      <td>Fig.A.34: Heatmap of keyword co-occurrence between potential risk factor and neurodegenerative disease keywords</td>
      <td>39</td>
    </tr>
    <tr>
      <td>63</td>
      <td>Fig.A.35: Keyword co-occurrence network between potential risk factor and neurodegenerative disease keywords</td>
      <td>40</td>
    </tr>
    <tr>
      <td>64</td>
      <td>Fig.A.36: Chord diagram of keyword co-occurrence between potential risk factor and mental disorder keywords</td>
      <td>41</td>
    </tr>
    <tr>
      <td>65</td>
      <td>Fig.A.37: Heatmap of keyword co-occurrence between potential risk factor and mental disorder keywords</td>
      <td>42</td>
    </tr>
    <tr>
      <td>66</td>
      <td>Fig.A.38: Keyword co-occurrence network between potential risk factor and mental disorder keywords</td>
      <td>43</td>
    </tr>
    <tr>
      <td>67</td>
      <td>Fig.A.39: Chord diagram of keyword co-occurrence between potential risk factor and autoimmune disease keywords</td>
      <td>44</td>
    </tr>
    <tr>
      <td>68</td>
      <td>Fig.A.40: Heatmap of keyword co-occurrence between potential risk factor and autoimmune disease keywords</td>
      <td>45</td>
    </tr>
    <tr>
      <td>69</td>
      <td>Fig.A.41: Keyword co-occurrence network between potential risk factor and autoimmune disease keywords</td>
      <td>46</td>
    </tr>
    <tr>
      <td>70</td>
      <td>Fig.A.42: Chord diagram of keyword co-occurrence between potential risk factor and cardiovascular disease keywords</td>
      <td>47</td>
    </tr>
    <tr>
      <td>71</td>
      <td>Fig.A.43: Heatmap of keyword co-occurrence between risk potential factor and cardiovascular disease keywords</td>
      <td>48</td>
    </tr>
    <tr>
      <td>72</td>
      <td>Fig.A.44: Keyword co-occurrence network between potential risk factor and cardiovascular disease keywords</td>
      <td>49</td>
    </tr>
    <tr>
      <td>73</td>
      <td>Fig.A.45: Chord diagram of keyword co-occurrence between potential risk factor and gastrointestinal disease keywords</td>
      <td>50</td>
    </tr>
    <tr>
      <td>74</td>
      <td>Fig.A.46: Heatmap of keyword co-occurrence between potential risk factor and gastrointestinal disease keywords</td>
      <td>51</td>
    </tr>
    <tr>
      <td>75</td>
      <td>Fig.A.47: Keyword co-occurrence network between potential risk factor and gastrointestinal disease keywords</td>
      <td>52</td>
    </tr>
    <tr>
      <td>76</td>
      <td>Fig.A.48: Chord diagram of keyword co-occurrence between potential risk factor and respiratory disease keywords</td>
      <td>53</td>
    </tr>
    <tr>
      <td>77</td>
      <td>Fig.A.49: Heatmap of keyword co-occurrence between potential risk factor and respiratory disease keywords</td>
      <td>54</td>
    </tr>
    <tr>
      <td>78</td>
      <td>Fig.A.50: Keyword co-occurrence network between potential risk factor and respiratory disease keywords</td>
      <td>55</td>
    </tr>
    <tr>
      <td>79</td>
      <td>Fig.A.51: Chord diagram of keyword co-occurrence between potential risk factor and reproductive disease keywords</td>
      <td>56</td>
    </tr>
    <tr>
      <td>80</td>
      <td>Fig.A.52: Heatmap of keyword co-occurrence between potential risk factor and reproductive disease keywords</td>
      <td>57</td>
    </tr>
    <tr>
      <td>81</td>
      <td>Fig.A.53: Keyword co-occurrence network between potential risk factor and reproductive disease keywords</td>
      <td>58</td>
    </tr>
    <tr>
      <td>82</td>
      <td>Fig.A.54: Chord diagram of keyword co-occurrence between potential risk factor and skin disease keywords</td>
      <td>59</td>
    </tr>
    <tr>
      <td>83</td>
      <td>Fig.A.55: Heatmap of keyword co-occurrence between potential risk factor and skin disease keywords</td>
      <td>60</td>
    </tr>
    <tr>
      <td>84</td>
      <td>Fig.A.56: Keyword co-occurrence network between potential risk factor and skin disease keywords</td>
      <td>61</td>
    </tr>
    <tr>
      <td>85</td>
      <td>Fig.A.57: Chord diagram of keyword co-occurrence between potential risk factor and several health event keywords</td>
      <td>62</td>
    </tr>
    <tr>
      <td>86</td>
      <td>Fig.A.58: Top 10 of the most frequent keyword by periods of time</td>
      <td>63</td>
    </tr>
    <tr>
      <td>87</td>
      <td>Fig.A.59: Most frequent health outcome-related keywords that co-occurred with digital tool-related keywords</td>
      <td>64</td>
    </tr>
    <tr>
      <td>88</td>
      <td>Fig.A.60: Strengths and limitations of exposome data</td>
      <td>65</td>
    </tr>
    <tr>
      <td>89</td>
      <td>Table A.1: Criteria used for the publication selection</td>
      <td>66</td>
    </tr>
    <tr>
      <td>90</td>
      <td>Table A.2: List of all publications included</td>
      <td>67</td>
    </tr>
    <tr>
      <td>91</td>
      <td>Table A.3: Main characteristics of the included publications</td>
      <td>68</td>
    </tr>
    <tr>
      <td>92</td>
      <td>Table A.4: The most productive countries in the human exposome research field</td>
      <td>69</td>
    </tr>
    <tr>
      <td>93</td>
      <td>Table A.5: Top 30 of the most cited publications in the human exposome research field</td>
      <td>71</td>
    </tr>
    <tr>
      <td>94</td>
      <td>Table A.6: Top 20 of the most active journals in the human exposome research field</td>
      <td>72</td>
    </tr>
    <tr>
      <td>95</td>
      <td>Table A.7: Top 30 of the most prolific authors in the human exposome research field</td>
      <td>73</td>
    </tr>
    <tr>
      <td>96</td>
      <td>Table A.8: Number of publications and citations over time, stratified by authorship and sex</td>
      <td>74</td>
    </tr>
    <tr>
      <td>97</td>
      <td>Table A.9: Total number of publications and citations by primary and senior authorship pairs</td>
      <td>74</td>
    </tr>
    <tr>
      <td>98</td>
      <td>Table A.10: Number of publications and citations over time, stratified by authorship and sex, considering only rank 1 journals</td>
      <td>75</td>
    </tr>
    <tr>
      <td>99</td>
      <td>Table A.11: Total number of publications and citations by primary and senior authorship pairs, considering only rank 1 journals</td>
      <td>75</td>
    </tr>
    <tr>
      <td>100</td>
      <td>Table A.12: Number of publications and citations over time, stratified by authorship and sex, considering only journals with impact factor ≥ 5</td>
      <td>76</td>
    </tr>
    <tr>
      <td>101</td>
      <td>Table A.13: Total number of publications and citations by primary and senior authorship pairs, considering only journals with impact factor ≥ 5</td>
      <td>76</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>119</td>
      <td><b>Table A.14:</b> Number of publications and citations over time, stratified by authorship and sex, considering only journals with impact factor ≥ 10</td>
      <td>77</td>
    </tr>
    <tr>
      <td>120</td>
      <td><b>Table A.15:</b> Total number of publications and citations by primary and senior authorship pairs, considering only journals with impact factor ≥ 10</td>
      <td>77</td>
    </tr>
    <tr>
      <td>121</td>
      <td><b>Table A.16:</b> Top 20 of the most active institutions in the human exposome research field</td>
      <td>78</td>
    </tr>
    <tr>
      <td>122</td>
      <td><b>Table A.17:</b> Top 20 of the most active funding bodies in the human exposome research field</td>
      <td>79</td>
    </tr>
    <tr>
      <td>123</td>
      <td><b>Table A.18:</b> Top 40 of the most frequent keywords in the human exposome publications</td>
      <td>80</td>
    </tr>
    <tr>
      <td>124</td>
      <td><b>References</b></td>
      <td>81</td>
    </tr>
    <tr>
      <td>125</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>126</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>127</td>
      <td></td>
      <td></td>
    </tr>
  </tbody>
</table>
<table><thead><tr><th>SECTION</th><th>ITEM</th><th>PRISMA-ScR CHECKLIST ITEM</th><th>REPORTED ON<br>PAGE #</th></tr></thead><tbody><tr><td><strong>TITLE</strong></td><td></td><td></td><td></td></tr><tr><td>Title</td><td>1</td><td>Identify the report as a scoping review.</td><td>1</td></tr><tr><td><strong>ABSTRACT</strong></td><td></td><td></td><td></td></tr><tr><td>Structured summary</td><td>2</td><td>Provide a structured summary that includes (as applicable): background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review questions and objectives.</td><td>2</td></tr><tr><td><strong>INTRODUCTION</strong></td><td></td><td></td><td></td></tr><tr><td>Rationale</td><td>3</td><td>Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.</td><td>6-8</td></tr><tr><td>Objectives</td><td>4</td><td>Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.</td><td>8</td></tr><tr><td><strong>METHODS</strong></td><td></td><td></td><td></td></tr><tr><td>Protocol and<br>registration</td><td>5</td><td>Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number.</td><td>9</td></tr><tr><td>Eligibility criteria</td><td>6</td><td>Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale.</td><td>8-9</td></tr><tr><td>Information sources*</td><td>7</td><td>Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed.</td><td>8-11</td></tr><tr><td>Search</td><td>8</td><td>Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated.</td><td>8</td></tr><tr><td>Selection of sources of<br>evidence†</td><td>9</td><td>State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review.</td><td>8-11</td></tr><tr><td>Data charting process‡</td><td>10</td><td>Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.</td><td>9-11</td></tr><tr><td>Data items</td><td>11</td><td>List and define all variables for which data were sought and any assumptions and simplifications made.</td><td>9-11</td></tr><tr><td>Critical appraisal of<br>individual sources of<br>evidence§</td><td>12</td><td>If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).</td><td>NA</td></tr><tr><td>Synthesis of results</td><td>13</td><td>Describe the methods of handling and summarizing the data that were charted.</td><td>10, 11</td></tr><tr><td><strong>RESULTS</strong></td><td></td><td></td><td></td></tr><tr><td>Selection of sources of<br>evidence</td><td>14</td><td>Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.</td><td>11, supplementary<br>materials</td></tr><tr><td>Characteristics of<br>sources of evidence</td><td>15</td><td>For each source of evidence, present characteristics for which data were charted and provide the citations.</td><td>11-24,<br>supplementary<br>materials</td></tr><tr><td>Critical appraisal within<br>sources of evidence</td><td>16</td><td>If done, present data on critical appraisal of included sources of evidence (see item 12).</td><td>NA</td></tr><tr><td>Results of individual<br>sources of evidence</td><td>17</td><td>For each included source of evidence, present the relevant data that were charted that relate to the review questions and objectives.</td><td>11-24,<br>supplementary<br>materials</td></tr><tr><td>Synthesis of results</td><td>18</td><td>Summarize and/or present the charting results as they relate to the review questions and objectives.</td><td>11-24,<br>supplementary<br>materials</td></tr><tr><td><strong>DISCUSSION</strong></td><td></td><td></td><td></td></tr><tr><td>Summary of evidence</td><td>19</td><td>Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups.</td><td>25-27</td></tr><tr><td>Limitations</td><td>20</td><td>Discuss the limitations of the scoping review process.</td><td>34, 35</td></tr><tr><td>Conclusions</td><td>21</td><td>Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications and/or next steps.</td><td>35</td></tr><tr><td><strong>FUNDING</strong></td><td></td><td></td><td></td></tr><tr><td>Funding</td><td>22</td><td>Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.</td><td>1, 36</td></tr></tbody></table>

<footnote n="131">131 JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.</footnote>

<footnote n="132">132 * Where *sources of evidence* (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and Web sites.</footnote>

<footnote n="134">134 † A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with *information sources* (see first footnote).</footnote>

<footnote n="137">137 ‡ The frameworks by Arksey and O'Malley (6) and Levac and colleagues (7) and the JBI guidance (4, 5) refer to the process of data extraction in a scoping review as data charting.</footnote>

<footnote n="138">138 § The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision.</footnote>

<footnote n="140">140 This term is used for items 12 and 19 instead of "risk of bias" (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).</footnote>

<footnote>143</footnote>

<footnote n="144">144 From: Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA ScR):</footnote>

<footnote n="145">145 Checklist and Explanation. Ann Intern Med. 2018;169:467-473. doi: 10.7326/M18-0850.</footnote>
## A.2. Bibliometric index metrics

### h index

The h index attempts to measure both the productivity and citation impact of the published body of work of an entity (e.g., author, institution, journal) [1,2]. It refers to the total number of publications by a particular entity with at least the same number of citations.

### m index

The m index is calculated by dividing the h index by the number of years of an entity's productive life (e.g., researcher) [1].

### g index

The g index of an entity corresponds to the largest number g such that the top g publications have at least g² or more citations together [2].

### Y index

The Y index refers to the sum of both the total number of first-authored publications and the total number of corresponding-author publications [3].

### Dominance factor

The dominance factor (DF) refers, for a particular researcher, to the proportion of multi-authored publications as specific author's rank to the total number of multi-authored publications [4].

The dominance factor for being a first/primary author (DF<sub>first</sub>) refers to the proportion of multi-authored publications as a first author to the total number of multi-authored publications. It is calculated as follows:

$$DF_{first} = \frac{\text{number of multi - authored papers as first author}}{\text{total number of multi - authored papers}} \times 100$$

The dominance factor for being a last/senior author (DF<sub>last</sub>) refers to the proportion of multi-authored publications as a last author to the total number of multi-authored publications. It is calculated as follows:

$$DF_{last} = \frac{\text{number of multi - authored papers as last author}}{\text{total number of multi - authored papers}} \times 100$$

### Fractionalized frequency

The fractional frequency (FF) intends to reflect/measure an author's contribution. For one publication, it is calculated as follows:

$$FF_i = \frac{1}{\text{total number of authors in the paper}_i}$$

For a given researcher, the FF corresponds to:

$$FF_{author} = \sum_{i=1}^{n} FF_i, \text{ where n refers to the total number of publications for which a researcher was an author.}$$

### Annual growth rate

The annual growth rate refers to the variable's change in percentage as a year-over-year statistic [5], and is calculated as follows:

$$Annual Growth Rate = \frac{(end value - first value)}{first value} \times 100$$
201 A.3. The BIBLIO checklist for reporting the bibliometric reviews of the biomedical literature

202

<table><thead><tr><th>Section/Topic</th><th>Item No.</th><th>Checklist item</th><th>Reported on page No.</th></tr></thead><tbody><tr><td><strong>Title</strong></td><td></td><td></td><td></td></tr><tr><td>Identification</td><td>1</td><td>Identify the report as a bibliometric review in the title.</td><td>1</td></tr><tr><td>Issues/topics</td><td>2</td><td>Indicate the key issues/topics under investigation and coverage of time period.</td><td>1</td></tr><tr><td><strong>Abstract</strong></td><td></td><td></td><td></td></tr><tr><td>Structured summary</td><td>3</td><td>Structured summary including (as applicable): background, methods, results (key findings) and conclusions.</td><td>2</td></tr><tr><td><strong>Introduction/Background</strong></td><td></td><td></td><td></td></tr><tr><td>Justification/ Rationale/ Explanation</td><td>4</td><td>Present review of existing knowledge and epidemiological information.</td><td>6-8</td></tr><tr><td>Objectives</td><td>5</td><td>Statement of the objective (s) or question (s).</td><td>8</td></tr><tr><td><strong>Methods</strong></td><td></td><td></td><td></td></tr><tr><td>Search engines (data sources)</td><td>6</td><td>Describe all information sources (such as electronic databases, contact with study authors, trial registers or other grey literature sources).</td><td>8, 9</td></tr><tr><td>Search strategy</td><td>7</td><td>Keywords and systematization criteria (date of search, language, type of document) for the search.</td><td>8, 9</td></tr><tr><td>Time period</td><td>8</td><td>The period that the review covers and the justification.</td><td>8, 9</td></tr><tr><td>Eligibility criteria</td><td>9</td><td>Describe all inclusion and exclusion criteria; languages; study design, type of publication and time period.</td><td>8, 9</td></tr><tr><td>Data refinement (data selection procedure)</td><td>10</td><td>Remove the irrelevant articles; inspection to eliminate duplicate and unrelated articles (after evaluation of the title, abstract and content).</td><td>8, 9</td></tr><tr><td>Quality assessment (optional)</td><td>11</td><td>Assessment of papers by three authors and the use of assessing checklists.</td><td>NA</td></tr><tr><td>Data synthesis</td><td>12</td><td>Describe the methods used for summarizing, handling, synthesis, tabulations or schematic displays. Describe how the data were analyzed.</td><td>9-11</td></tr><tr><td><strong>Results</strong></td><td></td><td></td><td></td></tr><tr><td>Descriptive findings (statistics)</td><td>13</td><td>- Provide details of the search and selection process in a flow diagram.<br/>- Number of citations retrieved (number of publication, year of publication, type of documents, country of publication, articles with the highest impact, most impactful authors, most impactful articles, authors with the highest production, top journals, top institutions, ...)</td><td>11-16, supplementary materials</td></tr><tr><td>Schematic map and trend</td><td>14</td><td>Summarize and/or present the schematic maps and trends using an appropriate software to present citations, journals, authors, top journals, time trends, emerging literature, and any relevant indicators (as applicable) [1-5].</td><td>16-24, supplementary materials</td></tr><tr><td>Tabulation and summarizing the findings</td><td>15</td><td>General recommendation: Studies under consideration could be summarized and organized by different subtitles and different scenarios. Regardless, results need to be presented in separate tables covering each subtitle. The followings are some options that could help to summarize the findings.<br/>Option 1:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table. Also, specify the review type (scoping review, narrative review, systematic review, and meta-analysis).<br/>- Summarize the findings according to the study designs and main study types.<br/>Option 2:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table. Also, indicate the review type (scoping review, narrative review, systematic review, and meta-analysis) should be specified.<br/>- Summarize the findings according to outcome measures or populations. For example, see [6].<br/>Option 3:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table. Also, specify the review type (scoping review, narrative review, systematic review, and meta-analysis).<br/>- Summarize the findings according to concept [7].<br/>Option 4:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table, and also specify the review type (scoping review, narrative review, systematic review, and meta-analysis).<br/>- Summarize the findings according to different subtitles relevant to the main topic [8].</td><td>11-24, supplementary materials</td></tr><tr><td>Synthesis of findings</td><td>16</td><td>Synthesize the findings as much as possible, find the gap, and propose a model, hypothesis, etc. (if applicable).</td><td>11-24, supplementary materials</td></tr><tr><td><strong>Discussion</strong></td><td></td><td></td><td></td></tr><tr><td>Summary of evidence</td><td>17</td><td>Summarize the main findings. The findings should be presented in more "general" or "accessible" terms.</td><td>25-27</td></tr><tr><td>Interpretation</td><td>18</td><td>Include interpretation consistent with results. Explanations for observed outcomes, similarities, and differences reported would be essential.</td><td>27-34</td></tr><tr><td>Strengths and limitations</td><td>19</td><td>Discuss the strengths and limitations.</td><td>34-35</td></tr><tr><td>Conclusion(s)</td><td>20</td><td>Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications.</td><td>35</td></tr></tbody></table>

203 **Rights and permissions:** The original source of the checklist is: Montazeri A, Mohammadi S, M.Hesari P, Ghaemi M, Riazi H, Sheikhi-Mobarakeh Z. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Systematic Reviews 2023; 12: 239. doi.org/10.1186/s13643-023-02410-2 The article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). A changes was made to the original checklist to add in full references to the cited sources.

<footnote n="1">1. McDougal L, Dehingia N, Cheung WW, Dixit A, Raj A. COVID-19 burden, author affiliation and women's well-being: A bibliometric analysis of COVID-19 related publications including focus on low-and middle-income countries. eClinicalMedicine 2022; 52: 101606.</footnote>

<footnote n="2">2. Henstock L, Wong R, Tsuchiya A, Spencer A. Behavioral theories that have influenced the way health state preferences are elicited and interpreted: A bibliometric mapping analysis of the ttime trade-off method with VOSviewer visualization. Front Health Serv 2022; 2: 848087.</footnote>

<footnote n="3">3. Bodea F, Bungau SG, Negru AP, Radu A, Tarce AG, Tit DM, et al. Exploring new therapeutic avenues for ophthalmic disorders: Glaucoma-related molecular docking evaluation and bibliometric analysis for improved management of ocular diseases. Bioengineering 2023; 10(8): 983.</footnote>

<footnote n="4">4. Sang XZ, Wang CQ, Chen W, Rong H, Hou LJ. An exhaustive analysis of post-traumatic brain injury dementia using bibliometric methodologies. Front Neurol 2023; 14: 1165059.</footnote>

<footnote n="5">5. Ramli MI, Hamzaid NA, Engkasan JP, Usman J. Respiratory muscle training: a bibliometric analysis of 60 years' multidisciplinary journey. Biomed Eng Online 2023; 22(1): 50.</footnote>

<footnote n="6">6. Akosman I, Kumar N, Mortenson R, Lans A, De La Garza Ramos R, Eleswarapu A, et al. Racial differences in perioperative complications, readmissions, and mortalities after elective spine surgery in the United States: A systematic review using AI-assisted bibliometric analysis. Glob Spine J 2023; 21925682231186759.</footnote>

<footnote n="7">7. Tavousi M, Mohammadi S, Sadighi J, Zarei F, Kermani RM, Rostami R, Montazeri A. Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021. Plos One 2022; 17(7): e0271524.</footnote>

<footnote n="8">8. Montazeri A. Health-related quality of life in breast cancer patients: A bibliographic review of the literature from 1974 to 2007. J Exp Clin Cancer Res 2008; 27: 32.</footnote>
<figure><img src="image_14.png" /><figcaption>Fig.A.1: PRISMA flow chart depicting the literature search and the evaluation process for finding relevant records</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Fig.A.2: Number of publications per document type</figcaption></figure>

213

214

215
<figure><img src="image_16.png" /><figcaption>Fig.A.3: Timeline of the first 10 publications published on the human exposome</figcaption></figure>

216

217

218

219
<figure><img src="image_17.png" /><figcaption>Fig.A.5: Map of the number of publications per country</figcaption></figure>

220
221
222
<figure><img src="image_18.png" /><figcaption>Fig.A.6: The most productive countries</figcaption></figure>

SCP: Single Country Publications, MCP: Multiple Country Publications
<figure><img src="image_19.png" /><figcaption>Fig.A.7: Scientific productivity – number of publications per year for the US</figcaption></figure>

228
229
230
231
232
<figure><img src="image_20.png" /><figcaption>Fig.A.8: Scientific productivity – number of publications per year for France</figcaption></figure>

233
234
235
<figure><img src="image_21.png" /><figcaption>Fig.A.9: Scientific productivity – number of publications per year for the UK</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>Fig.A.10: Scientific productivity – number of publications per year for Spain</figcaption></figure>

239
240
241
<figure><img src="image_23.png" /><figcaption>Fig.A.11: Scientific productivity – number of publications per year for the Netherlands</figcaption></figure>

242
243
244
245
<figure><img src="image_24.png" /><figcaption>Fig.A.12: Number of average citations per document and subscription types</figcaption></figure>

246
247
248
<figure><img src="image_25.png" /><figcaption>Fig.A.13: Most investigated exposures by the top 20 most prolific authors</figcaption></figure>

249
250
251
<figure><img src="image_26.png" /><figcaption>Fig.A.14: Most investigated outcomes by the top 20 most prolific authors</figcaption></figure>

252

253

254

255
<figure><img src="image_27.png" /><figcaption>Fig.A.15: Total number of publications per year, authorship, and sex</figcaption></figure>

256
257
258
259
260
261
<figure><img src="image_28.png" /><figcaption>Fig.A.16: Total number of citations per year, authorship, and sex</figcaption></figure>

262
263
264
265
<figure><img src="image_29.png" /><figcaption>Fig.A.17: Total number of publications per year, authorship, and sex (considering only rank 1 journals)</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>Fig.A.18: Total number of citations per year, authorship, and sex (considering only rank 1 journals)</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>Fig.A.19: Total number of publications per year, authorship, and sex (considering only journals with impact factor ≥ 5)</figcaption></figure>

275
276
277
278
279
280
<figure><img src="image_32.png" /><figcaption>Fig.A.20: Total number of citations per year, authorship, and sex (considering only journals with impact factor ≥ 5)</figcaption></figure>
<figure><img src="image_33.png" /><figcaption>Fig.A.21: Total number of publications per year, authorship, and sex (considering only journals with impact factor ≥ 10)</figcaption></figure>
<figure><img src="image_34.png" /><figcaption>Fig.A.22: Total number of citations per year, authorship, and sex (considering only journals with impact factor ≥ 10)</figcaption></figure>
<figure><img src="image_35.png" /><figcaption>Fig.A.23: Tree map of the top 20 research topics/areas</figcaption></figure>

293
294
295
296
297
<figure><img src="image_36.png" /><figcaption>Fig.A.24: Word cloud of the most frequent keywords</figcaption></figure>
<figure><img src="image_37.png" /><figcaption>Fig.A.25: Top 5 of the most frequent keywords – growth trends</figcaption></figure>
<figure><img src="image_38.png" /><figcaption>Fig.A.26: Top ten of the most popular keywords by total number of years of appearance</figcaption></figure>

AD: Alzheimer's disease, AHDs: administrative health databases, AI: artificial intelligence, ALS: amyotrophic lateral sclerosis, BMI: body mass index, CVDs: cardiovascular diseases, DNA: deoxyribonucleic acid, EDCs: endocrine-disrupting chemicals, EWAS: Exposome-wide association study, GC-MS: gas chromatography – mass spectrometry, GIS: geographic information system, HBM: human biological monitoring, HRMS: high-resolution mass spectrometry, IMIDs: immune-mediated inflammatory diseases, LC: liquid chromatography, ML: machine learning, MS: mass spectrometry, NMR: nuclear magnetic resonance, NTA: non-targeted analysis, PD: Parkinson's disease, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, RNA: ribonucleic acid, RMDs: rheumatic and musculoskeletal disorders, SES: socio-economic status, VOCs: volatile organic compounds.

304
305
306
307
308
309
310
<figure><img src="image_39.png" /><figcaption>Fig.A.27: The top 40 keywords with the highest mean annual growth rate and their total number of citations</figcaption></figure>

AI: artificial intelligence, ALS: amyotrophic lateral sclerosis, AOPs: adverse outcome pathways, CVDs: cardiovascular diseases, EDCs: endocrine-disrupting chemicals, EWAS: exposome-wide association, HBM: human biological monitoring, HRMS: high-resolution mass spectrometry, ML: machine learning, SES: socio-economic status.
<figure><img src="image_40.png" /><figcaption>Fig.A.28: Keywords with the most important positive growth over time among top authors, countries, funding bodies, institutions, and journals</figcaption></figure>

AI: artificial intelligence, BMI: body mass index, CVDs: cardiovascular diseases, EDCs: endocrine-disrupting chemicals, IMIDs: immune-mediated inflammatory diseases,

ML: machine learning, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, SES: socio-economic status.
<figure><img src="image_41.png" /><figcaption>Fig.A.29: Exposure and health outcome-related keywords with the most important positive growth over time among top funding bodies</figcaption></figure>

AD: Alzheimer's disease, BMI: body mass index, CD: Crohn's disease, CVDs: cardiovascular diseases, EDCs: endocrine-disrupting chemicals, IBD: inflammatory bowel

disease, IMIDs: immune-mediated inflammatory diseases, OS: oxidative stress, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, SES: socio-economic status,

UC: ulcerative colitis.

320

321

322

323

324

325
<figure><img src="image_42.png" /><figcaption>Fig.A.30: Conceptual structure map of the most frequent keywords made with multiple correspondence analysis</figcaption></figure>

AI: artificial intelligence, AOPs: adverse outcome pathways, BMI: body mass index, CVDs: cardiovascular diseases, DNA: deoxyribonucleic acid, EDCs: endocrine-disrupting chemicals, EWAS: exposome-wide association study, G.E: gene-environment interaction, GIS: geographic information system, HBM: human biomonitoring, HRMS: high-resolution mass spectrometry, IBD: inflammatory bowel disease, IMIDs: immune-mediated inflammatory diseases, ML: machine learning, MS: mass spectrometry, NTA: non-targeted analysis, OS: oxidative stress, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic status, UV: ultraviolet.
<figure><img src="image_43.png" /><figcaption>Fig.A.31: Chord diagram of keyword co-occurrence between potential risk factor and cancer keywords</figcaption></figure>

BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EDCs: endocrine-disrupting chemicals, NHL: non-Hodgkin lymphoma, PAHs: polycyclic aromatic hydrocarbons, PCBs: polychlorinated biphenyls, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic status, UV: ultraviolet.

333
334
335
336
337
338
<figure><img src="image_44.png" /><figcaption>Fig.A.32: Heatmap of keyword co-occurrence between potential risk factor and cancer keywords</figcaption></figure>

339

340

341

342

343

BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EDCs: endocrine-disrupting chemicals, NHL: non-Hodgkin lymphoma, PAHs: polycyclic aromatic

hydrocarbons, PCBs: polychlorinated biphenyls, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic

status, UV: ultraviolet.
<figure><img src="image_45.png" /><figcaption>Fig.A.33: Keyword co-occurrence network between potential risk factor and cancer keywords</figcaption></figure>

344

345

346 BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EDCs: endocrine-disrupting chemicals, NHL: non-Hodgkin lymphoma, PAHs: polycyclic aromatic

347 hydrocarbons, PCBs: polychlorinated biphenyls, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic

348 status, UV: ultraviolet.
<figure><img src="image_46.png" /><figcaption>Fig.A.34: Heatmap of keyword co-occurrence between potential risk factor and neurodegenerative disease keywords</figcaption></figure>

349
350
351
352

AD: Alzheimer's disease, ALS: amyotrophic lateral sclerosis, MAM: methylazoxymethanol, MMH: monomethylhydrazine, PD: Parkinson's disease, SES: socio-economic status.
<figure><img src="image_47.png" /><figcaption>Fig.A.35: Keyword co-occurrence network between potential risk factor and neurodegenerative disease keywords</figcaption></figure>

353
354
355
356

AD: Alzheimer's disease, ALS: amyotrophic lateral sclerosis, MAM: methylazoxymethanol, MMH: monomethylhydrazine, PD: Parkinson's disease, SES: socio-economic status.
<figure><img src="image_48.png" /><figcaption>Fig.A.36: Chord diagram of keyword co-occurrence between potential risk factor and mental disorder keywords</figcaption></figure>

357
358
359
360

ADHD: attention deficit hyperactivity disorder, CBCL: child behavior checklist, EDCs: endocrine-disrupting chemicals, OS: oxidative stress, PM: particulate matter, SES: socioeconomic status, VOCs: volatile organic compounds.
<figure><img src="image_49.png" /><figcaption>Fig.A.37: Heatmap of keyword co-occurrence between potential risk factor and mental disorder keywords</figcaption></figure>

ADHD: attention deficit hyperactivity disorder, CBCL: child behavior checklist, EDCs: endocrine-disrupting chemicals, OS: oxidative stress, PM: particulate matter, SES: socioeconomic status, VOCs: volatile organic compounds. The number in each cell corresponds to the number of co-occurrences.

361
362
363
364
<figure><img src="image_50.png" /><figcaption>Fig.A.38: Keyword co-occurrence network between potential risk factor and mental disorder keywords</figcaption></figure>

ADHD: attention deficit hyperactivity disorder, CBCL: child behavior checklist, EDCs: endocrine-disrupting chemicals, OS: oxidative stress, PM: particulate matter, SES: socioeconomic status, VOCs: volatile organic compounds.

365

366

367

368
<figure><img src="image_51.png" /><figcaption>Fig.A.39: Chord diagram of keyword co-occurrence between potential risk factor and autoimmune disease keywords</figcaption></figure>

369
370
371
372
373

CD: Crohn's disease, DBPs: disinfection by-products, EDCs: endocrine-disrupting chemicals, IBDs: inflammatory bowel diseases, IMIDs: immune-mediated inflammatory diseases, PFAS: per- and polyfluoroalkyl substances, POPs: persistent organic pollutants, RA: rheumatoid arthritis, RMDs: rheumatic and musculoskeletal diseases, SLE: systemic lupus erythematosus, SES: socio-economic status, UC: ulcerative colitis, UV: ultraviolet.
<figure><img src="image_52.png" /><figcaption>Fig.A.40: Heatmap of keyword co-occurrence between potential risk factor and autoimmune disease keywords</figcaption></figure>

CD: Crohn's disease, DBP: disinfection by-products, EDCs: endocrine-disrupting chemicals, IBDs: inflammatory bowel diseases, IMIDs: immune-mediated inflammatory diseases, PFAS: per- and polyfluoroalkyl substances, POPs: persistent organic pollutants, RA: rheumatoid arthritis, RMDs: rheumatic and musculoskeletal diseases, SLE: systemic lupus erythematosus, SES: socio-economic status, UC: ulcerative colitis, UV: ultraviolet. The number in each cell corresponds to the number of co-occurrences.
<figure><img src="image_53.png" /><figcaption>Fig.A.41: Keyword co-occurrence network between potential risk factor and autoimmune disease keywords</figcaption></figure>

379 CD: Crohn's disease, DBP: disinfection by-products, EDCs: endocrine-disrupting chemicals, IBDs: inflammatory bowel diseases, IMIDs: immune-mediated inflammatory
380 diseases, PFAS: per- and polyfluoroalkyl substances, POPs: persistent organic pollutants, RA: rheumatoid arthritis, RMDs: rheumatic and musculoskeletal diseases, SLE:
381 systemic lupus erythematosus, SES: socio-economic status, UC: ulcerative colitis, UV: ultraviolet.
382
383
<figure><img src="image_54.png" /><figcaption>Fig.A.42: Chord diagram of keyword co-occurrence between potential risk factor and cardiovascular disease keywords</figcaption></figure>

384
385
386
387

BP: blood pressure, BMI: body mass index, CVDs: cardiovascular diseases, OS: oxidative stress, PAHs: polycyclic aromatic hydrocarbons, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic status, VOCs: volatile organic compounds.
<figure><img src="image_55.png" /><figcaption>Fig.A.43: Heatmap of keyword co-occurrence between risk potential factor and cardiovascular disease keywords</figcaption></figure>

388
389
390
391
392

BP: blood pressure, BMI: body mass index, CVDs: cardiovascular diseases, OS: oxidative stress, PAHs: polycyclic aromatic hydrocarbons, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic status, VOCs: volatile organic compounds. The number in each cell corresponds to the number of co-occurrences.
<figure><img src="image_56.png" /><figcaption>Fig.A.44: Keyword co-occurrence network between potential risk factor and cardiovascular disease keywords</figcaption></figure>

393
394
395
396

BP: blood pressure, BMI: body mass index, CVDs: cardiovascular diseases, OS: oxidative stress, PAHs: polycyclic aromatic hydrocarbons, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic status, VOCs: volatile organic compounds.
<figure><img src="image_57.png" /><figcaption>Fig.A.45: Chord diagram of keyword co-occurrence between potential risk factor and gastrointestinal disease keywords</figcaption></figure>

397
398
399
400
401

BMI: body mass index, CKD: chronic kidney disease, EDCs: endocrine-disrupting chemicals, MAFLD: metabolic associated fatty liver disease, NAFLD: metabolic dysfunction-associated steatotic liver disease, PCBs: polychlorinated biphenyls, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants.
<figure><img src="image_58.png" /><figcaption>Fig.A.46: Heatmap of keyword co-occurrence between potential risk factor and gastrointestinal disease keywords</figcaption></figure>

BMI: body mass index, CKD: chronic kidney disease, EDCs: endocrine-disrupting chemicals, MAFLD: metabolic associated fatty liver disease, NAFLD: metabolic dysfunction-associated steatotic liver disease, PCBs: polychlorinated biphenyls, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants. The number in each cell corresponds to the number of co-occurrences.

402
403
404
405
406
<figure><img src="image_59.png" /><figcaption>Fig.A.47: Keyword co-occurrence network between potential risk factor and gastrointestinal disease keywords</figcaption></figure>

407
408
409
410
411

BMI: body mass index, CKD: chronic kidney disease, EDCs: endocrine-disrupting chemicals, MAFLD: metabolic associated fatty liver disease, NAFLD: metabolic dysfunction-associated steatotic liver disease, PCBs: polychlorinated biphenyls, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants.
<figure><img src="image_60.png" /><figcaption>Fig.A.48: Chord diagram of keyword co-occurrence between potential risk factor and respiratory disease keywords</figcaption></figure>

412
413
414
415

COPD: chronic obstructive pulmonary disease, EDCs: endocrine-disrupting chemicals, PM: particulate matter, OS: oxidative stress, OSA: obstructive sleep apnea, PFAS: per- and polyfluoroalkyl substances, PFOA: perfluorooctanoic acid, SES: socio-economic status.
<figure><img src="image_61.png" /><figcaption>Fig.A.49: Heatmap of keyword co-occurrence between potential risk factor and respiratory disease keywords</figcaption></figure>

416
417
418
419

COPD: chronic obstructive pulmonary disease, EDCs: endocrine-disrupting chemicals, PM: particulate matter, OS: oxidative stress, OSA: obstructive sleep apnea, PFAS: per- and polyfluoroalkyl substances, PFOA: perfluorooctanoic acid, SES: socio-economic status. The number in each cell corresponds to the number of co-occurrences.
<figure><img src="image_62.png" /><figcaption>Fig.A.50: Keyword co-occurrence network between potential risk factor and respiratory disease keywords</figcaption></figure>

420
421
422
423

COPD: chronic obstructive pulmonary disease, EDCs: endocrine-disrupting chemicals, PM: particulate matter, OS: oxidative stress, OSA: obstructive sleep apnea, PFAS: per- and polyfluoroalkyl substances, PFOA: perfluorooctanoic acid, SES: socio-economic status.
<figure><img src="image_63.png" /><figcaption>Fig.A.51: Chord diagram of keyword co-occurrence between potential risk factor and reproductive disease keywords</figcaption></figure>

424
425
426
427

BMI: body mass index, EDCs: endocrine-disrupting chemicals, OS: oxidative stress, PCOS: polycystic ovary syndrome, POPs: persistent organic pollutants, PROs: person reported outcomes, PAHs: polycyclic aromatic hydrocarbons, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, SES: socio-economic status.
<figure><img src="image_64.png" /><figcaption>Fig.A.52: Heatmap of keyword co-occurrence between potential risk factor and reproductive disease keywords</figcaption></figure>

BMI: body mass index, EDCs: endocrine-disrupting chemicals, OS: oxidative stress, PCOS: polycystic ovary syndrome, POPs: persistent organic pollutants, PROs: person reported outcomes, PAHs: polycyclic aromatic hydrocarbons, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, SES: socio-economic status. The number in each cell corresponds to the number of co-occurrences.

428

429

430

431

432
<figure><img src="image_65.png" /><figcaption>Fig.A.53: Keyword co-occurrence network between potential risk factor and reproductive disease keywords</figcaption></figure>

433
434
435
436

BMI: body mass index, EDCs: endocrine-disrupting chemicals, OS: oxidative stress, PCOS: polycystic ovary syndrome, POPs: persistent organic pollutants, PROs: person reported outcomes, PAHs: polycyclic aromatic hydrocarbons, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, SES: socio-economic status.
<figure><img src="image_66.png" /><figcaption>Fig.A.54: Chord diagram of keyword co-occurrence between potential risk factor and skin disease keywords</figcaption></figure>

437
438
439

OS: oxidative stress, PAHs: polycyclic aromatic hydrocarbons, PCP: personal care product, PM: particulate matter, UV: ultraviolet.
<figure><img src="image_67.png" /><figcaption>Fig.A.55: Heatmap of keyword co-occurrence between potential risk factor and skin disease keywords</figcaption></figure>

440
441
442
443

OS: oxidative stress, PAHs: polycyclic aromatic hydrocarbons, PCP: personal care product, PM: particulate matter, UV: ultraviolet. The number in each cell corresponds to the number of co-occurrences.
<figure><img src="image_68.png" /><figcaption>Fig.A.56: Keyword co-occurrence network between potential risk factor and skin disease keywords</figcaption></figure>

444
445
446
447

OS: oxidative stress, PAHs: polycyclic aromatic hydrocarbons, PCP: personal care product, PM: particulate matter, UV: ultraviolet.
<figure><img src="image_69.png" /><figcaption>Fig.A.57: Chord diagram of keyword co-occurrence between potential risk factor and several health event keywords</figcaption></figure>

BMI: body mass index, EDCs: endocrine-disrupting chemicals, DBP: disinfection by-product, PAHs: polycyclic aromatic hydrocarbons, PBDEs: polybrominated diphenyl ethers, PFAS: per- and polyfluoroalkyl substances, PM: particulate matter, POPs: persistent organic pollutants, SES: socio-economic status.

448
449
450
451
<figure><img src="image_70.png" /><figcaption>Fig.A.58: Top 10 of the most frequent keyword by periods of time</figcaption></figure>

AHD: administrative health database, AI: artificial intelligence, BKMR: Bayesian kernel machine regression, GIS: geographic information system, GPS: global positioning system, ICC: intra-class correlation, ML: machine learning, NoSql: not only SQL, PBBK: physiologically-based kinetic, SHAP: SHAPley Additive exPlanations.
<figure><img src="image_71.png" /><figcaption>Fig.A.59: Most frequent health outcome-related keywords that co-occurred with digital tool-related keywords</figcaption></figure>

457
458
459
460
461
462

AHD: administrative health database, AD: Alzheimer's disease, AI: artificial intelligence, ALS: amyotrophic lateral sclerosis, BKMR: Bayesian kernel machine regression, CD: Crohn's disease, CVDs: cardiovascular diseases, IBDs: inflammatory bowel diseases, IMIDs: immune-mediated inflammatory diseases, GIS: geographic information system, LSDA: least squares discriminant analysis, ML: machine learning, NHL: non-Hodgkin lymphoma, PBBK: physiologically-based kinetic, PCOS: polycystic ovary syndrome, PD: Parkinson's disease, UC: ulcerative colitis, XGBoost: extreme gradient boosting.
<table><caption>Fig.A.60: Strengths and limitations of exposome data</caption><thead><tr><th></th><th>Strengths</th><th>Limitations</th></tr></thead><tbody><tr><td>Secondary data</td><td><ul><li>Designed to answer research questions</li><li>Control of certain biases</li><li>Availability of data</li><li>Data quality</li><li>Accurate individual data</li><li>Possibility to link with health data</li><li>Existence of numerous indicators</li><li>Reliable data: often collected and compiled by government agencies or accredited organizations</li><li>Cost reduction</li><li>Allow real-time monitoring of a topic of interest</li><li>Allow the study of individual behaviors</li><li>Cover a wide range of topics and themes</li><li>Data accessible quickly</li><li>Individual data</li><li>Large sample → representativeness of the population, high statistical power</li><li>Allow longitudinal tracking</li><li>Cost reduction</li><li>Accessibility and availability of data</li><li>Reliable, structured, and standardized coded medical data</li><li>Allow the study of rare events and traditionally underrepresented populations</li><li>Possibility to create historical cohorts</li><li>Lower participation and reporting bias</li><li>Cost reduction</li><li>Help address the lack of real data</li><li>Help address privacy issues of administrative health databases</li><li>Structure and granularity similar to real data</li></ul></td><td><ul><li>Expensive and difficult to implement</li><li>Requires qualified and stable staff</li><li>Energy- and time-consuming</li><li>Difficulty maintaining representativeness over time</li><li>Representativeness → limited in sample size, spatial-temporal scope</li><li>Limited statistical power</li><li>Possible presence of biases</li><li>Initial objective ≠ research objective</li><li>Aggregated data at the territorial level</li><li>Spatial-temporal aggregation limitations</li><li>Data not always updated in real-time</li><li>Complex, heterogeneous, and incomplete</li><li>Not standardized</li><li>Interoperability issue</li><li>Initial objective ≠ research objective</li><li>Measurement accuracy (e.g., GPS)</li><li>Data quality (noisy data)</li><li>Data representativeness (selection bias)</li><li>Privacy issues</li><li>Complex and heterogeneous data</li><li>Initial objective ≠ research objective</li><li>Data access and governance</li><li>Complex and heterogeneous data</li><li>Variability in data quality and structure</li><li>Data not available in real-time</li><li>Limited granularity of information</li><li>Absence of certain confounding factors</li><li>Heterogeneity and evolution of data collection methods, coding practices</li><li>Inaccuracy of diagnoses and lack of completeness in reported cases</li><li>Data cleaning and preparation needed</li><li>Privacy issues</li><li>Risk of bias amplification</li><li>Risk of re-identification</li><li>Acquisition, availability, and access to real data</li><li>Data quality and accuracy → depend on real data</li><li>Algorithm/methodology choice</li><li>Simulation accuracy and interpretability</li><li>Malicious use (e.g., deep fakes)</li></ul></td></tr></tbody></table>

AHDs: administrative health databases, HBM: human biomonitoring, HRMS: high-resolution mass spectrometry, IoT: Internet of Things, NTA: non-targeted analysis, SES: socio-economic status. Primary data are specifically collected and tailored for a particular public health research purpose, and can be used either once or multiple times to achieve the same objective. They form the foundation of traditional public health policy and decision-making. In contrast, secondary data are not originally gathered for public health purposes, but they can complement primary data to enrich analysis and insights.
<table><caption>**Table A.1:** Criteria used for the publication selection</caption><thead><tr><th rowspan="2">Question</th><th rowspan="2">Description</th><th colspan="2">Answer</th></tr><tr><th>no</th><th>yes/can't tell</th></tr></thead><tbody><tr><td colspan="4"><strong>Stage 1: Publication language</strong></td></tr><tr><td>Q<sub>11</sub></td><td>Is the publication written in English or French?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>1</sub> = Q<sub>11</sub>; Publication eligible for stage 2 if S<sub>1</sub> &gt; 0</td></tr><tr><td colspan="4"><strong>Stage 2: Screening publication title</strong></td></tr><tr><td>Q<sub>21</sub></td><td>Does the title mention terms related to exposome?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>22</sub></td><td>Does the title mention terms related to humans?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>2</sub> = Q<sub>21</sub> &times; Q<sub>22</sub>; Publication eligible for stage 3 if S<sub>2</sub> = 1</td></tr><tr><td colspan="4"><strong>Stage 3: Screening publication abstract</strong></td></tr><tr><td>Q<sub>31</sub></td><td>Does the abstract describe an analysis related to exposome?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>32</sub></td><td>Does the abstract describe an analysis conducted/related to humans?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>3</sub> = Q<sub>31</sub> &times; Q<sub>32</sub>; Publication eligible for stage 4 if S<sub>3</sub> = 1</td></tr><tr><td colspan="4"><strong>Score = S<sub>1</sub> &times; S<sub>2</sub> &times; S<sub>3</sub>; Publication selected for review/analysis if score &ge; 1</strong></td></tr></tbody></table>

Note: Q: question, S: score.

The final records were copied in Bibtex file format and transferred into the R software for bibliometric data processing.
473 **Table A.2:** List of all publications included

474

475 Please refer to the MS Excel file entitled "Table A.2".