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Record W4409408387 · doi:10.1186/s13073-025-01451-7

Epigenetic timing effects on child developmental outcomes: a longitudinal meta-regression of findings from the Pregnancy And Childhood Epigenetics Consortium

2025· review· en· W4409408387 on OpenAlexaff
Alexander Neumann, Sara Sammallahti, Marta Cosín‐Tomás, Sarah E. Reese, Matthew Suderman, Silvia Alemany, Catarina Almqvist, Sandra Andrušaitytė, Syed Hasan Arshad, Marian J. Bakermans‐Kranenburg, Lawrence J. Beilin, Carrie V. Breton, Mariona Bustamante, Darina Czamara, Dana Dabelea, Celeste Eng, Brenda Eskenazi, Bernard F. Fuemmeler, Frank D. Gilliland, Regina Gražulevičienė, Siri E. Håberg, Gunda Herberth, Nina Holland, Amy Hough, Donglei Hu, Karen Huen, Anke Hüls, Marjo‐Riitta Järvelin, Jianping Jin, Jordi Júlvez, Berthold Koletzko, Gerard H. Koppelman, Inger Kull, Xueling Lu, Léa Maître, Dan Mason, Erik Melén, Simon Kebede Merid, Peter L. Molloy, Trevor A. Mori, Rosa H. Mulder, Christian M. Page, Rebecca C. Richmond, Stefan Röder, Jason P. Ross, Laura Schellhas, Sylvain Sebért, Dean Sheppard, Harold Snieder, Anne P. Starling, Dan J. Stein, Gwen Tindula, Marinus H. van IJzendoorn, Judith M. Vonk, Esther Walton, Jonathan Witonsky, Cheng‐Jian Xu, Ivana V. Yang, Paul Yousefi, Heather J. Zar, Ana Claudia Zenclussen, Hongmei Zhang, Henning Tiemeier, Stephanie J. London, Janine F. Felix, Charlotte A. M. Cecil

Bibliographic record

VenueGenome Medicine · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsInstitute of Infection and Immunity
FundersNational Cancer InstituteNational Heart, Lung, and Blood Institute
KeywordsdNaMEpigeneticsMeta-analysisMedicineDNA methylationAllostatic loadPsychopathologyClinical psychologyDemographyBiologyGeneticsInternal medicineGerontology

Abstract

fetched live from OpenAlex

BACKGROUND: DNA methylation (DNAm) is a developmentally dynamic epigenetic process; yet, most epigenome-wide association studies (EWAS) have examined DNAm at only one timepoint or without systematic comparisons between timepoints. Thus, it is unclear whether DNAm alterations during certain developmental periods are more informative than others for health outcomes, how persistent epigenetic signals are across time, and whether epigenetic timing effects differ by outcome. METHODS: We applied longitudinal meta-regression models to published meta-analyses from the PACE consortium that examined DNAm at two timepoints-prospectively at birth and cross-sectionally in childhood-in relation to the same child outcome (ADHD symptoms, general psychopathology, sleep duration, BMI, asthma). These models allowed systematic comparisons of effect sizes and statistical significance between timepoints. Furthermore, we tested correlations between DNAm regression coefficients to assess the consistency of epigenetic signals across time and outcomes. Finally, we performed robustness checks, estimated between-study heterogeneity, and tested pathway enrichment. RESULTS: Our findings reveal three new insights: (i) across outcomes, DNAm effect sizes are consistently larger in childhood cross-sectional analyses compared to prospective analyses at birth; (ii) higher effect sizes do not necessarily translate into more significant findings, as associations also become noisier in childhood for most outcomes (showing larger standard errors in cross-sectional vs prospective analyses); and (iii) DNAm signals are highly time-specific, while also showing evidence of shared associations across health outcomes (ADHD symptoms, general psychopathology, and asthma). Notably, these observations could not be explained by sample size differences and only partly to differential study-heterogeneity. DNAm sites changing associations were enriched for neural pathways. CONCLUSIONS: Our results highlight developmentally-specific associations between DNAm and child health outcomes, when assessing DNAm at birth vs childhood. This implies that EWAS results from one timepoint are unlikely to generalize to another. Longitudinal studies with repeated epigenetic assessments are direly needed to shed light on the dynamic relationship between DNAm, development and health, as well as to enable the creation of more reliable and generalizable epigenetic biomarkers. More broadly, this study underscores the importance of considering the time-varying nature of DNAm in epigenetic research and supports the potential existence of epigenetic "timing effects" on child health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.320
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2025
Admission routes1
Has abstractyes

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