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Record W2951477911 · doi:10.11575/prism/36377

Maternal Adverse Childhood Experiences and Infant DNA Methylation: Examining an Epigenetic Biomarker of Intergenerational Risk

2019· dissertation· en· W2951477911 on OpenAlexaboutno aff
Bikramjit Sekhon

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsEpigeneticsDNA methylationBiomarkerAdverse Childhood ExperiencesTransgenerational epigeneticsDevelopmental psychologyMedicineGeneticsPsychologyBiologyPsychiatryGene

Abstract

fetched live from OpenAlex

While “nature" and "nurture" are often viewed as opposing influences on human development, epigenetics is one area of study investigating how these influences work together. Until recently, transmission of intergenerational risk to human development has centred on claims of genetic inheritance, or the persistence of poor social environments such as adverse childhood experiences (ACEs), across generations. Stress occurring during gestation, that affects both the fetus and mother, has also been proposed as a method of transmitting intergenerational risk to offspring. New evidence in animal models suggests that “preconception stress” may also predict DNA methylation (DNAm; one component of epigenetics) in offspring, potentially impacting developmental health outcomes. Thus, the purpose of this study was to investigate the association between human mothers’ preconception stress and differential DNAm patterns in their biological infants. A secondary analysis was conducted, utilizing data obtained from the Fetal Programming (FetalPro) cohort study, a sub-set of participants in the Alberta Pregnancy Outcomes and Nutrition (APrON) study. APrON study participants were voluntary, and all pregnant women were over 16-years-old and before 22 weeks of gestation at enrolment. Measures included mothers’ scores on the Adverse Childhood Experiences (ACEs) questionnaire, mental health during pregnancy including the Edinburgh Postnatal Depression Scale and the Symptom Checklist 90 Revised, as well as demographics. Epigenetic data were obtained from buccal epithelial cell (BEC) samples collected from mothers’ 3-month-old infants. Cellular DNA were processed with the Illumina Infinium HumanMethylation450 Bead Chip to investigate DNAm. Relationships were investigated using regression modelling with the Limma function in R-package. Results showed a strong relationship between mothers’ total ACE score and differential DNAm patterning in their infants at eight epigenetic sites out of over 450,000 sites investigated. These findings have implications for the study of DNAm patterning as a biomarker for the transfer of preconception stress in humans and suggest a role for epigenetics in the transfer of intergenerational trauma.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.345
Teacher spread0.303 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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