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Record W4307982974 · doi:10.1111/psyp.14205

Cumulative risk exposure and child cellular aging in a Dutch low‐risk community sample

2022· article· en· W4307982974 on OpenAlexaff
Roseriet Beijers, Ilse ten Thije, Emma Bolhuis, Kieran J. O’Donnell, Marieke S. Tollenaar, Idan Shalev, Waylon J. Hastings, Julia L. MacIsaac, David Lin, Michael J. Meaney, Michael S. Kobor, Jay Belsky, Carolina de Weerth

Bibliographic record

VenuePsychophysiology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaMcGill UniversityDouglas Mental Health University Institute
FundersJacobs FoundationNederlandse Organisatie voor Wetenschappelijk OnderzoekKoninklijke Nederlandse Akademie van Wetenschappen
KeywordsPsychologyModerationLongitudinal studyStressorCellular AgingDevelopmental psychologyCumulative riskReactivity (psychology)TelomereClinical psychologyDemographyGerontologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

One of the proposed mechanisms linking childhood stressor exposure to negative mental and physical health outcomes in later life is cellular aging. In this prospective, longitudinal, and pre-registered study, we examined the association between a cumulative pattern of childhood risk exposure from age 6 to age 10 (i.e., poor maternal mental health, parental relationship problems, family/friend death, bullying victimization, poor quality friendships) and change in two biomarkers of cellular aging (i.e., telomere length, epigenetic age) from age 6 to age 10 in a Dutch low-risk community sample (n = 193). We further examined the moderating effect of cortisol reactivity at age 6. Ordinary Least Squares regression analyses revealed no significant main effects of childhood risk exposure on change in cellular aging, nor a moderation effect of child cortisol reactivity. Secondary findings showed a positive correlation between telomere length and cortisol reactivity at age 6, warranting further investigation. More research in similar communities is needed before drawing strong conclusions based on the null results.

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 score0.096
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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