Although age is generally measured by the number of years since birth, many factors contribute to the rate at which a person physically ages. In adults, linking these measurements to age gives a measure of overall health and resilience. This ‘biological age’ offers a better prediction of remaining life and disease risk than the number of years lived.

Multiple factors can be used to calculate biological age, such as measuring the length of telomeres – protective caps on the end of chromosomes – which shorten as people age. The rate at which they shorten can give an indication of how quickly someone is ageing. Researchers can also study epigenetic factors: these mechanisms lead to certain genes being switched on or off, and they can be combined into a ‘epigenetic clock’ to assess biological age. However, compared with adults, the relationship between biological age and child health and developmental maturity is less well understood.

Robinson et al. studied 1,173 school-aged children from six European countries, measuring telomere length, epigenetic factors and other biological indicators related to metabolism and the immune system. The relationships between these factors and an array of child developmental measures such as height, weight, behaviour and the age of onset of puberty were established. The findings showed that biological age indicators are only weakly linked to each other in children. Despite this, biological age was related to greater amount of body fat across all tested indicators – which is also associated with biological age in adults and is an important determinant of lifespan.

Among several observed effects on development, analysis found that shorter telomere length and older epigenetic age were associated with greater behavioural problems, suggesting they may be detrimental to child development. On the other hand, a greater age due to metabolic and immune related changes was associated with greater cognitive and behavioural maturity. Environmental factors were also linked to biological ageing, with children exposed to smoking in their homes or while their mother was pregnant displaying an older epigenetic age.

Robinson et al. showed that biological ageing in children is multifaceted and can have both beneficial and harmful impacts on development. This knowledge is important for identifying early life risk factors that might influence healthy ageing in later life. Future work will help researchers to understand these complex interactions and the long-term consequences for health and well-being.

The field of geroscience proposes that biological aging, a set of interrelated molecular and cellular changes associated with aging, drives the physiological deterioration that is the root of multiple age-related health conditions (Sierra, 2016). Understanding the process of biological aging and developing markers to accurately assess biological age in individuals, holds great promise for public health and biomedical research in general to develop interventions, even in childhood and early life, that slow physiological decline and reduce the risk of chronic disease and disability in later life.

Telomere length, which shortens with age, is one of the most widely applied biological age markers primarily as it directly assesses a primary Hallmark of Aging (López-Otín et al., 2013; Jylhävä et al., 2017). More recently, high-throughput ‘omic’ methods, which provide simultaneous quantification of thousands of epigenetic marks, transcripts, proteins, and metabolites, have been used to develop ‘biological clocks’ that provide a global measure of changes with age at the molecular level (Rutledge et al., 2022). While biological clocks have been primarily trained on chronological age, ‘age acceleration,’ commonly defined as the difference between clock-predicted age and chronological age, has been associated with age-related phenotypes and mortality (Hertel et al., 2016; Perna et al., 2016; Marioni et al., 2015; Peters et al., 2015; Lehallier et al., 2019; van den Akker et al., 2020; Macdonald-Dunlop et al., 2022), indicating their utility as biological age markers. DNA methylation-based clocks, such as the clock of Horvath, 2013, have been extensively applied in large-scale studies and remain a research field under active development, with ‘second generation’ clocks further incorporating clinical biomarker and mortality information to improve their clinical utility (Lu et al., 2019; Belsky et al., 2020). Further clocks have been developed using transcriptome (Peters et al., 2015), metabolome (Robinson et al., 2020), and proteome (Lehallier et al., 2019) data, including those that specifically target immune-system-related proteins (Sayed et al., 2021). Generally, clocks have been found to be only weakly correlated with each other, suggesting that each clock captures different facets of biological aging (Belsky et al., 2018; Jansen et al., 2021).

While biological age in adults is intuitively understood as an overall indicator of general health and resilience, the conceptual interpretation of biological age acceleration in children is much less clear. Child development and aging may at first be considered opposing processes, representing growth and decay respectively. However, various related theoretical frameworks link the two processes: Under the developmental origin of health and disease hypothesis, the early life environment is a key determinant of ageing trajectories and disease risk in later life. Life-course models of ageing, supported by measures of physical and cognitive capability, view the childhood developmental phase as key to building up ‘biological capital’ and to determining how long capabilities and disease risk remain above critical thresholds in later years following the gradual decline phase of adult life (Kuh, 2007). Horvath’s DNA methylation clock is currently the only clock trained to predict age throughout the lifespan, and many of the clock’s CpGs are in genomic regions known to regulate development and differentiation (Horvath, 2013). However, unlike life-course models of physical function, the level of DNA methylation at the clock’s CpGs changes in a predictable, unidirectional manner throughout the life-course, albeit at a much faster rate during childhood. This continuous molecular readout suggests that processes directing development are at least indirectly related to the detrimental processes in later-life and is consistent with quasi-programmed theories of aging such as antagonistic pleiotropy (Emery Thompson, 2022), whereby molecular functions that promote development, inadvertently lead to aging in later life (Horvath and Raj, 2018). Therefore, some authors have suggested that DNA methylation-based age acceleration may be beneficial during childhood (Horvath and Raj, 2018; McEwen et al., 2020), reflecting greater physical maturity and build-up of biological capital.

Biological aging is conceived as a continuous balance of cellular damage, caused by both extrinsic environmental factors and by normal physiological processes, and resiliency mechanisms that protect against and compensate for this damage (Ferrucci et al., 2020). An alternative ‘wear and tear’ model would view cellular damage to occur continuously from birth and, since the epigenetic clock has been proposed to reflect the epigenomic maintenance system, a resiliency mechanism, DNA methylation age acceleration in children may, as in adults, represent a greater accumulation of epigenetic instability and, therefore, reduced biological capital. However, so far only a handful of studies have examined associations with developmental maturity in children (Binder et al., 2018; Simpkin et al., 2017; Suarez et al., 2018). Telomere length attrition is more rapid in early childhood during rapid somatic growth and more gradual in adulthood, with those with a shorter telomere length in childhood maintaining a lower telomere length into adulthood (Martens et al., 2021). While telomere length may serve as both a mitotic-clock and as a mediator of cellular stress (Coimbra et al., 2017), the associations reported between environmental stress in childhood and shorter telomere length suggest it reflects early-life cellular damage that may be carried into adulthood.

Little is known regarding the interpretation of biological age in children assessed at the transcriptome, proteomic and metabolomic levels, since few biological clocks are available for this age range using these data. To the best of our knowledge, only the study of Giallourou et al., 2020 has applied metabolomic data to provide a multivariate model of age in children, finding that growth-constrained infants lag in their metabolic maturity relative to their healthier peers. It is possible that biological clocks constructed using these data, particularly proteomics and metabolomics, support the life-course aging framework, where age acceleration in children represents a buildup of biological capital, since they are closer to the phenotype than the DNA-based epigenetic clocks and telomere length.

We hypothesized that age acceleration would be associated with child development. To test this and assess whether age acceleration is associated with beneficial or detrimental effects on child development, we have performed a comparative analysis of two established and two candidate assessments of biological age within the pan-European Human Early Life Exposome (HELIX) cohort of children aged between 5 and 12 years. We systematically compared associations with developmental endpoints, including growth and adiposity, cognition, behavior, lung function and pubertal development, and common health risk factors, for telomere length, DNA methylation age, and two newly derived clocks: transcriptome age and immunometabolic age. Through this analysis, we aimed to clarify the interpretation of age acceleration in children, and more broadly develop new biological markers of overall developmental staging in children.

In a large sample of European children, we have analyzed two established and two candidate measures of biological age, derived from molecular features at different levels of biological organization, in relation to developmental outcomes and health risk factors. We assessed two established biological age markers, telomere length and DNA methylation age, and derived two new measures, transcriptome age, and immunometabolic age. Despite finding only null to weak correlations between the measures, we found all measures to be positively associated with greater BMI and adiposity, and both DNA methylation Δ age and immunometabolic Δ age were associated with taller height. While immunometabolic Δ age was associated with greater cognitive maturity including greater working memory and attentiveness, conversely DNA methylation Δ age was nominally associated with greater inattentiveness and both DNA methylation Δ age and shorter telomere length were nominally associated with poorer externalizing behaviors.

BMI has consistently been associated with accelerated aging in adults across a diverse range of biological age markers (Peters et al., 2015; Jansen et al., 2021; Nevalainen et al., 2017; Fiorito et al., 2019) underlining the integral role of metabolism in aging. Indeed, a recent large study of Dutch adults found BMI to be the only health risk factor tested associated with accelerated aging across five biological age clocks, including telomere length, DNA methylation, transcriptome, proteome, and metabolomic age markers (Jansen et al., 2021). Here, we show that the link between BMI and multiple dimensions of accelerated aging is also apparent in children. Energy and nutrient intake influence all Ageing Hallmarks and multiple lines of evidence link increased adiposity to shorter lifespan (López-Otín et al., 2016). These effects appear to be partially mediated through evolutionarily conserved nutrient-sensing systems such as the mTOR signaling pathway, which promote anti-aging cellular repair mechanisms, at the expense of growth and metabolism, in response to lower nutrient availability (López-Otín et al., 2016). Furthermore, excess adiposity increases generalized inflammation and oxidative stress (Suzuki et al., 2003; Minamino et al., 2009), which may have direct effects on age markers, particularly telomere length and DNA methylation age acceleration.

The observed associations between greater height with biological age may indicate developmental maturity. Height is generally considered reflective of a beneficial early-life environment (Davey Smith et al., 2000), however, evidence for an association with lifespan is mixed (Davey Smith et al., 2000; Tanisawa et al., 2018), with a recent meta-analysis suggesting a u-shaped relationship with all-cause mortality (Li et al., 2021). Greater comparative height at 10 years was also inversely associated with longevity in a recent large-scale Medelian randomization study (Huang et al., 2021). Furthermore, there is some evidence that the link between height and longevity may be mediated through the insulin-like growth factor-1 signaling pathway (Tanisawa et al., 2018; He et al., 2014). The associations may also be interpreted as greater rates of growth and anabolism exerting greater ‘wear and tear’ on cellular structures. Two other studies have also observed an association between height and DNA methylation age acceleration in children (Simpkin et al., 2017; Suarez et al., 2018).

Despite similarities in associations with growth and adiposity measures, patterns of association across cognitive and behavioral domains varied across biological age markers, underlying the view of biological aging as a multi-faceted process. Immunometabolic Δ age was associated with greater cognitive maturity, fitting the life-course model of greater accumulation of biological capital during the build-up phase of development. Immunometabolic Δ age may be considered a phenotypic summary measure of metabolic and immune system maturity, and these cognitive developmental associations suggest that it may also be generalizable to the overall developmental stage. On the other hand, DNA methylation Δ age was related to relative immaturity in attentiveness and externalizing behavior. A previous Finish study of children aged between 11 and 13 years also reported associations between DNA methylation age acceleration and behavioral problems (Suarez et al., 2018). Similarly, shorter telomere length was associated here with greater externalizing behaviors, although not with any cognitive domains. Four other studies have examined the link between shorter telomere length and externalizing behaviors (Costa et al., 2015; Daoust et al., 2023; Wojcicki et al., 2015; Kroenke et al., 2011), with all, except one (Kroenke et al., 2011), also reporting an association.

Overall patterns of associations between risk factors and biological age measures also suggest the detrimental nature of accelerated aging in children assessed through telomere length and DNA methylation Δ age, and the potentially beneficial nature of advanced immunometabolic Δ age. Both prenatal maternal active smoking and child passive smoking were associated with DNA methylation Δ age, while greater birthweight was associated with immunometabolic Δ age. We examined maternal education level, family affluence, and social capital which broadly represent the three forms of interconvertible capital (cultural, economic, and social) proposed by Bourdieu, 1986. It has been theorized that biological capital represents a fourth type of human capital, and that the conversion across these forms of capital underlies inequalities in aging trajectories (Vineis and Kelly-Irving, 2019). Nominally significant associations between higher family affluence with longer telomere length and high social capital with a younger DNA methylation age indicate that age acceleration assessed through these measures does not represent an accumulation of biological capital. Generally, directions of effect for immunometabolic Δ age were in the opposite direction which may suggest it represents greater biological capital.

Girls were found to have longer telomere lengths than boys. Women have been consistently found to have longer telomere lengths (Gardner et al., 2014) although the few generally smaller studies in children have been inconsistent (Wojcicki et al., 2015; Buxton et al., 2011; Ly et al., 2019; Okuda et al., 2002). No other biological age markers were associated with sex, which contrasts with the study of Jansen et al., 2021 in adults which reported accelerated biological age in men across all measures tested except for proteomic age. Indeed, the phenomenon of accelerated DNA methylation age in men is well-established (Horvath and Raj, 2018), consistent with lower life-expectancies for men. Although it is not known if these biological age differences are due to biological mechanisms or greater prevalence of disease risk factors among men, our data in children before the divergence of risk factor prevalence could indicate a biological mechanism for telomere sex differences and a risk factor-mediated mechanism for other biological age markers. Interestingly, we observed differences in associations between biological age and development between boys and girls, with some consistency across markers: Both shorter telomere length and transcriptome Δ age and were more strongly associated with adiposity in boys, DNA methylation and transcriptome Δ age showed stronger associations among boys with poorer behavior, while in girls both transcriptome and immunometabolic Δ age showed stronger associations with improved attentiveness. Given observed sexual dimorphism in both developmental rates (León et al., 2014) and biological age measures through a variety of proposed mechanisms (Hägg and Jylhävä, 2021), it may be unsurprising that relationship between biological age and development also differs between the sexes.

Furthermore, we observed that immunometabolic Δ age was associated with greater odds of puberty onset, driven by effects observed among girls only. We did not observe any further significant associations with the onset of puberty, however, the sample size in the subset of children was small compared to the other developmental measures. There was also suggestive evidence for associations between DNA methylation Δ age with the onset of puberty with associations close to the nominal significance threshold. Three previous studies have reported associations between DNA methylation age acceleration and puberty onset and stage (Binder et al., 2018; Simpkin et al., 2017; Suarez et al., 2018), and one study has reported associations between shorter telomere length and puberty onset (Koss et al., 2020). However, the directions of effect for telomere length in our study were in the opposite direction. While earlier age at puberty is representative of more advanced physical maturation, it has been associated with metabolic diseases in later life, including cancers (Cancer", 2012) and all-cause mortality (Charalampopoulos et al., 2014).

We found transcriptome data to be highly accurate in predicting chronological age, including in a test set of children assessed six months later, demonstrating that gene expression tracks closely with age in children, even over this relatively short period. We analyzed biological pathways and processes enriched among transcript clusters contributing to the transcriptome clock, observing the integral role of ribosome and ribosome biogenesis pathways, central to protein synthesis, and biological processes including the immunity-related processes leukocyte migration and activation, and cell movement, activation, and secretion. Strikingly, gene expression in adults is similarly characterized by downregulation of ribosomal genes and enrichment of expression in immune-related genes (Peters et al., 2015). This indicates that similar to DNA methylation changes (Horvath and Raj, 2018), there is some overlap in gene expression related to both development in children and aging in adults. Although formal testing of enrichment of genes contributing to the transcriptome clock presented here among age-associated genes in adults showed enrichment at only borderline statistical significance, the transcriptome clock predictors are an underrepresentation of the full profile of gene expression associated with age in children, due to the sparsity enforced during the variable selection training process.

Despite associations with growth and adiposity measures, transcriptome age generally showed weaker associations with other developmental outcomes than for the other biological age markers. While this in part can be attributed to the slightly smaller sample size for children with transcriptome data, it is also likely due to the high accuracy in predicting the chronological age of the transcriptome clock, resulting in lower variation in the portion of transcriptome age that is not explained by chronological age, further reducing statistical power. This makes it challenging to judge the relevance of transcriptome age, if any, to developmental endpoints, which may be mixed since a non-significant direction of effects were observed with both maturity in attention and lung function, yet relative immaturity in behavior. In fact, training clocks using chronological age, which while providing an accessible route to understanding molecular changes associated with age, does pose limitations generally for inference regarding biological aging. Particularly for high-dimensional data such as DNA methylation, it has been shown that it is possible to predict chronological age near-perfectly (Zhang et al., 2019), thereby limiting information on biological age and its variation. For this reason, newer epigenetic clocks have included clinical and mortality data, to improve clinical relevance and sensitivity to risk factors (Lu et al., 2019; Belsky et al., 2020), which should be considered in future studies developing clocks in children.

Other limitations include the cross-sectional design of the main analysis, which limits inference regarding the directionality of associations and allows the possibility of age-associated environmental factors to influence the clock development. Furthermore, there were differences in age by study centre. Children from the EDEN cohort study centre were generally older, which likely introduced a degree of cohort bias into the age modeling. For this reason, we adjusted all associations by study centre and additionally assessed age correlation within each study centre. Although cohorts were recruited from the general population, certain ethnicities or socio-economically disadvantaged groups may have been under-represented, limiting generalizability somewhat. A bias towards the over-representation of White ethnic groups is an issue generally with the development of biological clocks, which means associations observed with ethnicity should be interpreted cautiously. While the DNA methylation and transcriptome data were representative of the full genome, our coverage of the metabolome and proteome was limited to targeted assays. For analysis of age at the gene expression and protein/metabolite levels we developed new clocks, and these remain to be validated as true biological age indicators, through testing association with age-related disease and mortality in adults. While biological age markers exist for these data types, they are not appropriate for applying in our dataset, since the same model predictors (i.e. metabolites and proteins) were not included in the assays used here and/or the markers were not trained in pediatric populations, which will drastically reduce their accuracy as molecular changes within in childhood cannot be assumed to follow the same relation with age as in adulthood: For instance, DNA methylation shows a logarithmic dependence with age during childhood and a linear dependence in adulthood (Horvath, 2013).

However, the strengths of this study include the large population sample, drawing from six countries from around Europe, increasing generalizability, and the integration of rich molecular data and a broad range of developmental outcomes into a single systematic analysis. Although our age range was somewhat limited, missing the infancy and adolescent periods, the age range covered a key childhood period, where energy expenditure (an indicator of the level of overall physiology) has entered a period of steady increase following more rapid increases during infancy and before stabilization during adolescence (Pontzer et al., 2021).

In conclusion, in this large Pan-European study we have found that four indicators of biological age, representing complementary molecular processes, were all associated with BMI after controlling for chronological age, indicating that adiposity is an important correlate of accelerated biological aging in children. We developed a highly accurate ‘transcriptome age’ clock although it was found to be relatively insensitive to other development phenotypes. We found that immunometabolic Δ age was associated with cognitive maturity fitting a buildup of the biological capital model of aging in children, while shorter telomere length and DNA methylation Δ age was associated with greater behavioral problems suggesting a ‘wear and tear’ model of aging in children. Our findings contribute to the interpretation and understanding of biological age measures in children, crucial for clinical and epidemiological research into early life risk factors for adverse aging trajectories. Future long-term studies should investigate associations between age acceleration in children and adults to further test the antagonistic pleiotropy hypothesis.

Due to data protection regulations in each participating country and participant data use agreements, human subject data used in this project cannot be freely shared. The raw data supporting the current study are available on request subject to ethical and legislative review. The "HELIX Data External Data Request Procedures" are available with the data inventory in this website: http://www.projecthelix.eu/data-inventory. The document describes who can apply to the data and how, the timings for approval and the conditions to data access and publication. Researchers who have an interest in using data from this project for reproducibility or in using data held in general in the HELIX data warehouse for research purposes can apply for access to data. Interested researchers should fill in the application protocol found in ANNEX I at https://www.projecthelix.eu/files/helix_external_data_request_procedures_final.pdf and send this protocol to helixdata@isglobal.org. The applications are received by the HELIX Coordinator, and are processed and approved by the HELIX Project Executive Committee. All code used for data analysis has been provided as supplementary material. Deidentified dataset for generation of figures 1 and 2 has been provided as a supplementary dataset.

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Author details

1. Oliver Robinson

   1. Μedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom
   2. Mohn Centre for Children's Health and Well-being, School of Public Health, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   o.robinson@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4735-0468

2. ChungHo E Lau

   Μedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Sungyeon Joo

   Μedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

4. Sandra Andrusaityte

   Department of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania

   Contribution

   Resources, Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Eva Borras

   1. Center for Genomics Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

   Contribution

   Resources, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Paula de Prado-Bert

   1. Universitat Pompeu Fabra (UPF), Barcelona, Spain
   2. Institute for Global Health (ISGlobal), Barcelona, Spain
   3. CIBER Epidemiologa y Salud Pública (CIBERESP), Madrid, Spain

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Lida Chatzi

   Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States

   Contribution

   Resources, Funding acquisition, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Hector C Keun

   1. Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
   2. Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer; Imperial College London, London, United Kingdom

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Regina Grazuleviciene

   Department of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania

   Contribution

   Resources, Data curation, Funding acquisition, Investigation

   Competing interests

   No competing interests declared

10. Kristine B Gutzkow

    Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway

    Contribution

    Resources, Data curation, Funding acquisition, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6716-5921

11. Lea Maitre

    1. Universitat Pompeu Fabra (UPF), Barcelona, Spain
    2. Institute for Global Health (ISGlobal), Barcelona, Spain
    3. CIBER Epidemiologa y Salud Pública (CIBERESP), Madrid, Spain

    Contribution

    Resources, Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Dries S Martens

    Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium

    Contribution

    Resources, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Eduard Sabido

    1. Center for Genomics Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
    2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Contribution

    Resources, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6506-7714

14. Valérie Siroux

    University Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of environmental epidemiology applied to the development and respiratory health, Institute for Advanced Biosciences, 38000 Grenoble, France

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. Jose Urquiza

    1. Universitat Pompeu Fabra (UPF), Barcelona, Spain
    2. Institute for Global Health (ISGlobal), Barcelona, Spain
    3. CIBER Epidemiologa y Salud Pública (CIBERESP), Madrid, Spain

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Marina Vafeiadi

    Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece

    Contribution

    Resources, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

17. John Wright

    Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom

    Contribution

    Resources, Data curation, Funding acquisition, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

18. Tim S Nawrot

    Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium

    Contribution

    Resources, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

19. Mariona Bustamante

    1. Universitat Pompeu Fabra (UPF), Barcelona, Spain
    2. Institute for Global Health (ISGlobal), Barcelona, Spain
    3. CIBER Epidemiologa y Salud Pública (CIBERESP), Madrid, Spain

    Contribution

    Data curation, Formal analysis, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

20. Martine Vrijheid

    1. Universitat Pompeu Fabra (UPF), Barcelona, Spain
    2. Institute for Global Health (ISGlobal), Barcelona, Spain
    3. CIBER Epidemiologa y Salud Pública (CIBERESP), Madrid, Spain

    Contribution

    Conceptualization, Resources, Data curation, Funding acquisition, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

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