Evidence map›Paper›PMID 35551445›Full record

ArticleInternational journal of legal medicine2022

Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?

F Mayer, J Becker, C Reinauer, P Böhme, S B Eickhoff, B Koop, T Gündüz, J Blum, W Wagner, S Ritz-Timme

Open access · hybridAbstract read
In one paragraph

Article in International journal of legal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 36% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Diagnostics (Basel, Switzerland) · 2024
    Article
  3. Review
  4. Article
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

10 authors at 3 institutions in 1 country.

F MayerInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany. Felix.mayer@med.uni-duesseldorf.de.
J BeckerInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
C ReinauerDepartment of General Paediatrics, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
P BöhmeInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
S B EickhoffInstitute for Systems Neuroscience, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
B KoopInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
T GündüzInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
J BlumInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
W WagnerHelmholtz Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany.
S Ritz-TimmeInstitute of Legal Medicine, University Hospital Düsseldorf, 40225, Düsseldorf, Germany.
Heinrich Heine University Düsseldorf · DEDüsseldorf University Hospital · DERWTH Aachen University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Age estimation based on DNA methylation (DNAm) can be applied to children, adolescents and adults, but many CG dinucleotides (CpGs) exhibit different kinetics of age-associated DNAm across these age ranges. Furthermore, it is still unclear how growth disorders impact epigenetic age predictions, and this may be particularly relevant for a forensic application. In this study, we analyzed buccal mucosa samples from 95 healthy children and 104 children with different growth disorders. DNAm was analysed by pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2, EDARADD and DDO. The relationship between DNAm and age in healthy children was tested by Spearman's rank correlation. Differences in DNAm between the groups "healthy children" and the (sub-)groups of children with growth disorders were tested by ANCOVA. Models for age estimation were trained (1) based on the data from 11 CpGs with a close correlation between DNAm and age (R ≥ 0.75) and (2) on five CpGs that also did not present significant differences in DNAm between healthy and diseased children. Statistical analysis revealed significant differences between the healthy group and the group with growth disorders (11 CpGs), the subgroup with a short stature (12 CpGs) and the non-short stature subgroup (three CpGs). The results are in line with the assumption of an epigenetic regulation of height-influencing genes. Age predictors trained on 11 CpGs with high correlations between DNAm and age revealed higher mean absolute errors (MAEs) in the group of growth disorders (mean MAE 2.21 years versus MAE 1.79 in the healthy group) as well as in the short stature (sub-)groups; furthermore, there was a clear tendency for overestimation of ages in all growth disorder groups (mean age deviations: total growth disorder group 1.85 years, short stature group 1.99 years). Age estimates on samples from children with growth disorders were more precise when using a model containing only the five CpGs that did not present significant differences in DNAm between healthy and diseased children (mean age deviations: total growth disorder group 1.45 years, short stature group 1.66 years). The results suggest that CpGs in genes involved in processes relevant for growth and development should be avoided in age prediction models for children since they may be sensitive for alterations in the DNAm pattern in cases of growth disorders.

Indexed as

DNA MethylationEpigenesis, GeneticAdolescentAdultChildChild, PreschoolCpG IslandsGrowth DisordersHumansInfantMouth MucosaChildren with growth disordersDNA methylationEpigenetic age estimationForensic age estimation

Identifiers

PMID35551445
PMCPMC9170667
OpenAlexW4280630374

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LicenceCC BY
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Registered trials

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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.