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Record W3199520271 · doi:10.1016/j.ynstr.2021.100394

The pediatric buccal epigenetic clock identifies significant ageing acceleration in children with internalizing disorder and maltreatment exposure

2021· article· en· W3199520271 on OpenAlexfundno aff
Felix Dammering, Jade Martins, Katja Dittrich, Darina Czamara, Monika Rex‐Haffner, Judith Overfeld, Karin de Punder, Claudia Buß, Sonja Entringer, Sibylle Winter, Elisabeth B. Binder, Christine Heim

Bibliographic record

VenueNeurobiology of Stress · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersUniversity of British ColumbiaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBundesministerium für Bildung und ForschungBundesministerium für Forschung und TechnologieBC Children's Hospital
KeywordsEpigeneticsDNA methylationBuccal swabModerationMedicineAgeingPsychologyClinical psychologyPsychiatryInternal medicineGeneticsBiologyGeneGene expression

Abstract

fetched live from OpenAlex

Background Studies reporting accelerated ageing in children with affective disorders or maltreatment exposure have relied on algorithms for estimating epigenetic age derived from adult samples. These algorithms have limited validity for epigenetic age estimation during early development. We here use a pediatric buccal epigenetic (PedBE) clock to predict DNA methylation-based ageing deviation in children with and without internalizing disorder and assess the moderating effect of maltreatment exposure. We further conduct a gene set enrichment analysis to assess the contribution of glucocorticoid signaling to PedBE clock-based results. Method DNA was isolated from saliva of 158 children [73 girls, 85 boys; mean age (SD) = 4.25 (0.8) years] including children with internalizing disorder and maltreatment exposure. Epigenetic age was estimated based on DNA methylation across 94 CpGs of the PedBE clock. Residuals of epigenetic age regressed against chronological age were contrasted between children with and without internalizing disorder. Maltreatment was coded in 3 severity levels and entered in a moderation model. Genome-wide dexamethasone-responsive CpGs were derived from an independent sample and enrichment of these CpGs within the PedBE clock was identified. Results Children with internalizing disorder exhibited significant acceleration of epigenetic ageing as compared to children without internalizing disorder (F 1,147 = 6.67, p = .011). This association was significantly moderated by maltreatment severity (b = 0.49, 95% CI [0.073, 0.909], t = 2.322, p = .022). Children with internalizing disorder who had experienced maltreatment exhibited ageing acceleration relative to children with no internalizing disorder (1–2 categories: b = 0.50, 95% CI [0.170, 0.821], t = 3.008, p = .003; 3 or more categories: b = 0.99, 95% CI [0.380, 1.593], t = 3.215, p = .002). Children with internalizing disorder who were not exposed to maltreatment did not show epigenetic ageing acceleration. There was significant enrichment of dexamethasone-responsive CpGs within the PedBE clock (OR = 4.36, p = 1.65*10–6). Among the 94 CpGs of the PedBE clock, 18 (19%) were responsive to dexamethasone. Conclusion Using the novel PedBE clock, we show that internalizing disorder is associated with accelerated epigenetic ageing in early childhood. This association is moderated by maltreatment severity and may, in part, be driven by glucocorticoids. Identifying developmental drivers of accelerated epigenetic ageing after maltreatment will be critical to devise early targeted interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations77
Published2021
Admission routes1
Has abstractyes

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