Associations of four biological age markers with child development: a multi-omic analysis in the European HELIX cohort

  1. Oliver Robinson  Is a corresponding author
  2. Chung-Ho E Lau
  3. Sungyeon Joo
  4. Sandra Andrusaityte
  5. Eva Borras
  6. Paula de Prado-Bert
  7. Lida Chatzi
  8. Hector C Keun
  9. Regina Grazuleviciene
  10. Kristine Bjerve Gutzkow
  11. Lea Maitre
  12. Dries S Martens
  13. Eduard Sabido
  14. Valérie Siroux
  15. Jose Urquiza
  16. Marina Vafeiadi
  17. John Wright
  18. Tim S Nawrot
  19. Mariona Bustamante
  20. Martine Vrijheid
  1. Imperial College London, United Kingdom
  2. Vytautas Magnus University, Lithuania
  3. Barcelona Institute of Science and Technology, Spain
  4. Barcelona Institute for Global Health, Spain
  5. University of Southern California, United States
  6. Norwegian Institute of Public Health, Norway
  7. Hasselt University, Belgium
  8. Université Grenoble Alpes, Inserm U1209, CNRS UMR 5309, France
  9. University of Crete, Greece
  10. Bradford Royal Infirmary, United Kingdom

Abstract

Background: While biological age in adults is often understood as representing general health and resilience, the conceptual interpretation of accelerated biological age in children and its relationship to development remains unclear. We aimed to clarify the relationship of accelerated biological age, assessed through two established biological age indicators, telomere length and DNA methylation age, and two novel candidate biological age indicators , to child developmental outcomes, including growth and adiposity, cognition, behaviour, lung function and onset of puberty, among European school-age children participating in the HELIX exposome cohort.

Methods: The study population included up to 1,173 children, aged between 5 and 12 years, from study centres in the UK, France, Spain, Norway, Lithuania, and Greece. Telomere length was measured through qPCR, blood DNA methylation and gene expression was measured using microarray, and proteins and metabolites were measured by a range of targeted assays. DNA methylation age was assessed using Horvath's skin and blood clock, while novel blood transcriptome and 'immunometabolic' (based on plasma protein and urinary and serum metabolite data) clocks were derived and tested in a subset of children assessed six months after the main follow-up visit. Associations between biological age indicators with child developmental measures as well as health risk factors were estimated using linear regression, adjusted for chronological age, sex, ethnicity and study centre. The clock derived markers were expressed as Δ age (i.e., predicted minus chronological age).

Results: Transcriptome and immunometabolic clocks predicted chronological age well in the test set (r= 0.93 and r= 0.84 respectively). Generally, weak correlations were observed, after adjustment for chronological age, between the biological age indicators. Among associations with health risk factors, higher birthweight was associated with greater immunometabolic Δ age, smoke exposure with greater DNA methylation Δ age and high family affluence with longer telomere length. Among associations with child developmental measures, all biological age markers were associated with greater BMI and fat mass, and all markers except telomere length were associated with greater height, at least at nominal significance (p<0.05). Immunometabolic Δ age was associated with better working memory (p = 4e -3) and reduced inattentiveness (p= 4e -4), while DNA methylation Δ age was associated with greater inattentiveness (p=0.03) and poorer externalizing behaviours (p= 0.01). Shorter telomere length was also associated with poorer externalizing behaviours (p=0.03).

Conclusions: In children, as in adults, biological ageing appears to be a multi-faceted process and adiposity is an important correlate of accelerated biological ageing. Patterns of associations suggested that accelerated immunometabolic age may be beneficial for some aspects of child development while accelerated DNA methylation age and telomere attrition may reflect early detrimental aspects of biological ageing, apparent even in children.

Funding: UK Research and Innovation (MR/S03532X/1); European Commission (grant agreement numbers: 308333; 874583).

Data availability

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.

The following previously published data sets were used

Article and author information

Author details

  1. Oliver Robinson

    Μedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom
    For correspondence
    o.robinson@imperial.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4735-0468
  2. Chung-Ho E Lau

    Μedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Sungyeon Joo

    Μedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Sandra Andrusaityte

    Department of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania
    Competing interests
    The authors declare that no competing interests exist.
  5. Eva Borras

    Center for Genomics Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  6. Paula de Prado-Bert

    Barcelona Institute for Global Health, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  7. Lida Chatzi

    Department of Preventive Medicine, University of Southern California, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Hector C Keun

    Department of Metabolism, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Regina Grazuleviciene

    Department of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania
    Competing interests
    The authors declare that no competing interests exist.
  10. Kristine Bjerve Gutzkow

    Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6716-5921
  11. Lea Maitre

    Barcelona Institute for Global Health, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  12. Dries S Martens

    Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  13. Eduard Sabido

    Center for Genomics Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6506-7714
  14. Valérie Siroux

    Institute for Advanced Biosciences, Université Grenoble Alpes, Inserm U1209, CNRS UMR 5309, Grenoble, France
    Competing interests
    The authors declare that no competing interests exist.
  15. Jose Urquiza

    Barcelona Institute for Global Health, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  16. Marina Vafeiadi

    Department of Social Medicine, University of Crete, Crete, Greece
    Competing interests
    The authors declare that no competing interests exist.
  17. John Wright

    Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  18. Tim S Nawrot

    Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  19. Mariona Bustamante

    Barcelona Institute for Global Health, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  20. Martine Vrijheid

    Barcelona Institute for Global Health, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.

Funding

UK Research and Innovation (MR/S03532X/1)

  • Oliver Robinson
  • Chung-Ho E Lau

European Commission (308333; 874583)

  • Oliver Robinson
  • Chung-Ho E Lau
  • Sandra Andrusaityte
  • Eva Borras
  • Paula de Prado-Bert
  • Lida Chatzi
  • Hector C Keun
  • Regina Grazuleviciene
  • Kristine Bjerve Gutzkow
  • Lea Maitre
  • Dries S Martens
  • Eduard Sabido
  • Valérie Siroux
  • Jose Urquiza
  • Marina Vafeiadi
  • John Wright
  • Tim S Nawrot
  • Mariona Bustamante
  • Martine Vrijheid

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Prior to the start of HELIX, all six cohorts had undergone the required evaluation by national ethics committees and obtained all the required permissions for their cohort recruitment and follow-up visits. Each cohort also confirmed that relevant informed consent and approval were in place for secondary use of data from pre-existing data. The work in HELIX was covered by new ethical approvals in each country and at enrolment in the new follow-up, participants were asked to sign a new informed consent form. Additionally, the current study was approved by the Imperial College Research Ethics Committee (Reference: 19IC5567).

Copyright

© 2023, Robinson et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,496
    views
  • 232
    downloads
  • 3
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Oliver Robinson
  2. Chung-Ho E Lau
  3. Sungyeon Joo
  4. Sandra Andrusaityte
  5. Eva Borras
  6. Paula de Prado-Bert
  7. Lida Chatzi
  8. Hector C Keun
  9. Regina Grazuleviciene
  10. Kristine Bjerve Gutzkow
  11. Lea Maitre
  12. Dries S Martens
  13. Eduard Sabido
  14. Valérie Siroux
  15. Jose Urquiza
  16. Marina Vafeiadi
  17. John Wright
  18. Tim S Nawrot
  19. Mariona Bustamante
  20. Martine Vrijheid
(2023)
Associations of four biological age markers with child development: a multi-omic analysis in the European HELIX cohort
eLife 12:e85104.
https://doi.org/10.7554/eLife.85104

Share this article

https://doi.org/10.7554/eLife.85104

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Pierre Barrat-Charlaix, Richard A Neher
    Research Article

    As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host’s immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.

    1. Computational and Systems Biology
    2. Medicine
    Xin Zhou, Zhinuo Jenny Wang ... Blanca Rodriguez
    Research Article

    Sudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic current remodelling. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with pro-arrhythmic heterogeneities are unknown. We aim to provide mechanistic explanations of clinical phenotypes in acute and chronic MI, from ionic current remodelling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations. A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets, incorporating varying degrees of ionic current remodelling as reported in literature. In acute MI, T-wave inversion and Brugada phenocopy were explained by conduction abnormality and local action potential prolongation in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion between the border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.