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Record W4408216599 · doi:10.1126/sciadv.adt0757

The interplay between age at menopause and synaptic integrity on Alzheimer’s disease risk in women

2025· article· en· W4408216599 on OpenAlexaff
Madeline Wood Alexander, William G. Honer, Rowan Saloner, Liisa A.M. Galea, David A. Bennett, Jennifer S. Rabin, Kaitlin B. Casaletto

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsOntario Brain InstituteHealth Sciences CentreMental Health Research CanadaUniversity of British ColumbiaSunnybrook Health Science CentreToronto Rehabilitation InstituteUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute on Aging
KeywordsMenopauseDiseaseAlzheimer's diseaseNeuroscienceMedicineGerontologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Menopause is a major biological transition that may influence women's late-life brain health. Earlier estrogen depletion-via earlier menopause-has been associated with increased risk for Alzheimer's disease (AD). Synaptic dysfunction also incites and exacerbates AD progression. We investigated whether age at menopause and synaptic health together influence AD neuropathology and cognitive trajectories using clinical and autopsy data from 268 female decedents in the Rush Memory and Aging Project. We observed significant interactions between age at menopause and synaptic integrity on cognitive decline and tau tangles, such that earlier menopause strengthened the associations of reduced synaptic integrity with faster cognitive decline and elevated tau. Exploratory analyses showed that these relationships were attenuated in women who took menopausal hormone therapy. These findings suggest that midlife endocrine processes or their sequalae may influence synaptic vulnerability to AD. Interventions addressing both hormonal factors and synaptic health could enhance resilience to dementia in women.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.378
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.028
GPT teacher head0.385
Teacher spread0.357 · 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

Citations20
Published2025
Admission routes1
Has abstractyes

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