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Record W6957933040 · doi:10.60692/39q3r-3y521

Reproduction predicts shorter telomeres and epigenetic age acceleration among young adult women

2018· article· en· W6957933040 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsBC Children's HospitalCanadian Institute for Advanced ResearchUniversity of British Columbia
Fundersnot available
KeywordsTelomereReproductionEpigeneticsYoung adultOffspringFertilityBiomarkerSomatic cellCohortCellular Aging

Abstract

fetched live from OpenAlex

Abstract Evolutionary theory predicts that reproduction entails costs that detract from somatic maintenance, accelerating biological aging. Despite support from studies in human and non-human animals, mechanisms linking 'costs of reproduction' (CoR) to aging are poorly understood. Human pregnancy is characterized by major alterations in metabolic regulation, oxidative stress, and immune cell proliferation. We hypothesized that these adaptations could accelerate blood-derived cellular aging. To test this hypothesis, we examined gravidity in relation to telomere length (TL, n = 821) and DNA-methylation age (DNAmAge, n = 397) in a cohort of young (20–22 year-old) Filipino women. Age-corrected TL and accelerated DNAmAge both predict age-related morbidity and mortality, and provide markers of mitotic and non-mitotic cellular aging, respectively. Consistent with theoretical predictions, TL decreased (p = 0.031) and DNAmAge increased (p = 0.007) with gravidity, a relationship that was not contingent upon resource availability. Neither biomarker was associated with subsequent fertility (both p > 0.3), broadly consistent with a causal effect of gravidity on cellular aging. Our findings provide evidence that reproduction in women carries costs in the form of accelerated aging through two independent cellular pathways.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.204
Teacher spread0.185 · 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.

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
Published2018
Admission routes1
Has abstractyes

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