Evidence map›Paper›PMID 37278618›Full record

ArticleeLife2023

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

Oliver Robinson, ChungHo E Lau, Sungyeon Joo, Sandra Andrusaityte, Eva Borras, Paula de Prado-Bert, Lida Chatzi, Hector C Keun, Regina Grazuleviciene, Kristine B Gutzkow and 10 more

Open access · goldAbstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.0field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Placental Pathophysiology and Developmental Programming of Adult Cardiometabolic Risk: A Narrative Review of Pregnancy Exposures.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 9 institutions in 8 countries.

Oliver RobinsonΜedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom.ORCID 0000-0002-4735-0468
ChungHo E LauΜedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom.
Sungyeon JooΜedical Research Council Centre for Environment and Health, Imperial College London, London, United Kingdom.
Sandra AndrusaityteDepartment of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania.
Eva BorrasCenter for Genomics Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
Paula de Prado-BertUniversitat Pompeu Fabra (UPF), Barcelona, Spain.
Lida ChatziDepartment of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States.
Hector C KeunDivision of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom.
Regina GrazulevicieneDepartment of Environmental Science, Vytautas Magnus University, Kaunas, Lithuania.
Kristine B GutzkowDivision of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.ORCID 0000-0002-6716-5921
Lea MaitreUniversitat Pompeu Fabra (UPF), Barcelona, Spain.
Dries S MartensCentre for Environmental Sciences, Hasselt University, Hasselt, Belgium.
Eduard SabidoCenter for Genomics Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.ORCID 0000-0001-6506-7714
Valérie SirouxUniversity 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.
Jose UrquizaUniversitat Pompeu Fabra (UPF), Barcelona, Spain.
Marina VafeiadiDepartment of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece.
John WrightBradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom.
Tim S NawrotCentre for Environmental Sciences, Hasselt University, Hasselt, Belgium.
Mariona BustamanteUniversitat Pompeu Fabra (UPF), Barcelona, Spain.
Martine VrijheidUniversitat Pompeu Fabra (UPF), Barcelona, Spain.
Universitat Pompeu Fabra · ESImperial College London · GBHasselt University · BEVytautas Magnus University · LTBradford Royal Infirmary · GBCentre National de la Recherche Scientifique · FRNorwegian Institute of Public Health · NOUniversity of Crete · GRUniversity of Southern California · US

Funding

Medical Research Council MR/S03532X/1
6 · The paper itself

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 ( 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).

Indexed as

AgingMultiomicsAdultBiomarkersChildChild, PreschoolDNA MethylationEpigenesis, GeneticHumansInfantObesityRisk FactorsBiomarkersbiological agechild developmentcomputational biologyepidemiologyglobal healthhumanomicssystems biology

Identifiers

PMID37278618
PMCPMC10338035
OpenAlexW4379508447

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.