Associations of four biological age markers with child development: a multi-omic analysis in the European HELIX cohort
- Imperial College London, United Kingdom
- Vytautas Magnus University, Lithuania
- Barcelona Institute of Science and Technology, Spain
- Barcelona Institute for Global Health, Spain
- University of Southern California, United States
- Norwegian Institute of Public Health, Norway
- Hasselt University, Belgium
- Université Grenoble Alpes, Inserm U1209, CNRS UMR 5309, France
- University of Crete, Greece
- 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.
Article and author information
Author details
Kristine Bjerve Gutzkow
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
-
- 1,550
- views
-
- 241
- downloads
-
- 5
- 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)
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
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.
-
- Computational and Systems Biology
- Neuroscience
Audiovisual information reaches the brain via both sustained and transient input channels, representing signals’ intensity over time or changes thereof, respectively. To date, it is unclear to what extent transient and sustained input channels contribute to the combined percept obtained through multisensory integration. Based on the results of two novel psychophysical experiments, here we demonstrate the importance of the transient (instead of the sustained) channel for the integration of audiovisual signals. To account for the present results, we developed a biologically inspired, general-purpose model for multisensory integration, the multisensory correlation detectors, which combines correlated input from unimodal transient channels. Besides accounting for the results of our psychophysical experiments, this model could quantitatively replicate several recent findings in multisensory research, as tested against a large collection of published datasets. In particular, the model could simultaneously account for the perceived timing of audiovisual events, multisensory facilitation in detection tasks, causality judgments, and optimal integration. This study demonstrates that several phenomena in multisensory research that were previously considered unrelated, all stem from the integration of correlated input from unimodal transient channels.
