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. 2023 Jun 6:12:e85104.
doi: 10.7554/eLife.85104.

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

Oliver Robinson  1   2 , ChungHo E Lau  1 , Sungyeon Joo  1 , Sandra Andrusaityte  3 , Eva Borras  4   5 , Paula de Prado-Bert  5   6   7 , Lida Chatzi  8 , Hector C Keun  9   10 , Regina Grazuleviciene  3 , Kristine B Gutzkow  11 , Lea Maitre  5   6   7 , Dries S Martens  12 , Eduard Sabido  4   5 , Valérie Siroux  13 , Jose Urquiza  5   6   7 , Marina Vafeiadi  14 , John Wright  15 , Tim S Nawrot  12 , Mariona Bustamante  5   6   7 , Martine Vrijheid  5   6   7
Affiliations

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

Oliver Robinson et al. Elife. .

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, behavior, lung function and the onset of puberty, among European school-age children participating in the HELIX exposome cohort.

Methods: The study population included up to 1173 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 proteins and urinary and serum metabolites) 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=4 e-3) and reduced inattentiveness (p=4 e-4), while DNA methylation Δ age was associated with greater inattentiveness (p=0.03) and poorer externalizing behaviors (p=0.01). Shorter telomere length was also associated with poorer externalizing behaviors (p=0.03).

Conclusions: In children, as in adults, biological aging appears to be a multi-faceted process and adiposity is an important correlate of accelerated biological aging. 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 aging, apparent even in children.

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

Keywords: biological age; child development; computational biology; epidemiology; global health; human; omics; systems biology.

Plain language summary

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.

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Conflict of interest statement

OR, CL, SJ, SA, EB, Pd, LC, HK, RG, KG, LM, DM, ES, VS, JU, MV, JW, TN, MB, MV No competing interests declared

Figures

Figure 1.
Figure 1.. Participant flowchart.
See Supplementary file 1 for details on quality control of molecular data at sample and feature levels.
Figure 2.
Figure 2.. Study design schematic.
Source data for reproducing correlation plots are provided in Figure 2—source data 1.
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Comparison between immunometabolic and transcriptome age between first and second study visits.
Box plots (showing minimum, maximum, median, first quartile, and third quartile) of biological age measures at each panel study visit (approximately 6 months apart). Panel clinic 1 was part of the main Helix subcohort examination. p-values were calculated from paired t-tests.
Figure 2—figure supplement 2.
Figure 2—figure supplement 2.. Age Prediction by study centre of transcriptome age.
MAE = mean absolute error. R and p values from Pearson’s correlation.
Figure 2—figure supplement 3.
Figure 2—figure supplement 3.. Age Prediction by study centre of immunometabolic age.
MAE = mean absolute error. R and p values from Pearson’s correlation.
Figure 3.
Figure 3.. Correlations between biological age indicators.
Heatmap shows partial Pearson’s correlations, adjusted for chronological age and study centre. * indicates p<0.05. Source data for reproducing plots is provided in Figure 3—source data 1.
Figure 4.
Figure 4.. Associations between biological age measures and developmental measures.
Estimates were calculated using linear regression, adjusted for chronological age, sex, ethnicity, and study centre. *indicates FDR <5%. Telomere length is expressed as a standard deviation (SD) decrease in length (multiplied by –1) to provide estimates indicative of accelerated biological age, as the other biological age indicators. Error bars show 95% confidence intervals. See Table 3 for numbers included in each analysis and exact point estimates and confidence intervals.
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Associations between biological age measures and developmental measures, stratified by sex.
Estimates were calculated using linear regression, adjusted for chronological age, sex, ethnicity, and study centre. Telomere length is expressed as a % decrease in length (multiplied by –1) to provide estimates indicative of accelerated biological age, as for the other biological age indicators. Error bars show 95% confidence intervals.
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Associations between telomere length and developmental measures adjusted for (A) chronological age, sex, ethnicity, and study centre; (B) as for A plus estimated cell counts; (C) as for A plus family affluence and social capital, birthweight, maternal active smoking, and child passive smoking; (D) as for C plus estimated cell counts.
Error bars show 95% confidence intervals. Telomere length is expressed as a standard deviation decrease in length (multiplied by –1) to provide estimates indicative of accelerated biological age, as for the other biological age indicators.
Figure 4—figure supplement 3.
Figure 4—figure supplement 3.. Associations between DNA methylation Δ age and developmental measures adjusted for (A) chronological age, sex, ethnicity, and study centre; (B) as for A plus estimated cell counts; (C) as for A plus family affluence and social capital, birthweight, maternal active smoking, and child passive smoking; (D) as for C plus estimated cell counts.
Error bars show 95% confidence intervals.
Figure 4—figure supplement 4.
Figure 4—figure supplement 4.. Associations between transcriptome Δ age and developmental measures adjusted for (A) chronological age, sex, ethnicity, and study centre; (B) as for A plus estimated cell counts; (C) as for A plus family affluence and social capital, birthweight, maternal active smoking, and child passive smoking; (D) as for C plus estimated cell counts.
Error bars show 95% confidence intervals.
Figure 4—figure supplement 5.
Figure 4—figure supplement 5.. Associations between immunometabolic Δ age and developmental measures adjusted for (A) chronological age, sex, ethnicity, and study centre; (B) as for A plus estimated cell counts; (C) as for A plus family affluence and social capital, birthweight, maternal active smoking, and child passive smoking; (D) as for C plus estimated cell counts.
Error bars show 95% confidence intervals.
Figure 4—figure supplement 6.
Figure 4—figure supplement 6.. Associations between biological age measures and developmental measures, stratified by study centre (adjusted for chronological age, sex, and ethnicity).
Error bars show 95% confidence intervals. Associations at least at p<0.05 in the pooled analysis are shown for (A) telomere length (TL), (B) DNA methylation (DNAm) age, and (C) Immunometabolic (IM) age.
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  • doi: 10.1101/2023.01.23.23284901

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