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Record W4205429050 · doi:10.1002/alz.052600

Disturbed sleep and reduced hippocampal integrity at midlife: Implications for AD risk in women with early surgical menopause

2021· article· en· W4205429050 on OpenAlexaff
Nicole Gervais, Alana Brown, Laura Gravelsins, Gina Nicoll, Dorothy Leqi Sun, Jennifer Xiangning Ge, Kaz Laird, Anne Almey, Rebekah Reuben, Laurice Karkaby, Annie Duchesne, Mateja Perović, Nadia Gosselin, Cheryl L. Grady, Rosanna K. Olsen, Gillian Einstein

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsUniversité de MontréalMcGill UniversityUniversity of TorontoCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of Northern British ColumbiaBaycrest Hospital
Fundersnot available
KeywordsMenopauseSubiculumMedicineHippocampal formationPolysomnographySurgical MenopauseSleep disorderInternal medicinePsychologyAudiologyGynecologyPsychiatryDentate gyrusCognition

Abstract

fetched live from OpenAlex

Abstract Background Women bear the greatest burden of Alzheimer’s disease (AD), and early (<45y) ovarian hormone deprivation via surgical menopause further increases risk (Rocca et al., 2007). Given that sleep disturbance is implicated in AD progression (Lim et al., 2013), an important area of investigation is determining whether younger women with early surgical menopause demonstrate disturbed sleep at midlife, and whether this may confer additional risks by contributing to changes in hippocampal integrity. Thus, we investigated whether middle‐aged women with bilateral salpingo‐oophorectomy not taking hormone therapy demonstrate sleep disturbance, and reduced hippocampal volume and memory. Additionally, we assessed whether sleep and hippocampal volume predict memory performance. Method Women with an early BSO that were either taking estradiol‐based hormone therapy (BSO+ET: age=43y; n=22), or not taking ET (BSO: age=46y; n=18) were recruited to the study, and were compared to age‐matched premenopausal (AMC: n=24) and spontaneously postmenopausal women who were ∼10y older (SM: 55y; n=20). Sleep was assessed via portable polysomnography (Vitaport‐5/REMbo‐234, Temec Technologies) for 1‐3 nights, and sleep staging was acquired using automated scoring (Z3score, Neurobit technologies). High‐resolution T2‐weighted scans were acquired using a Siemens 3T Prisma scanner, and hippocampal subfields (CA1, Subiculum, combined region: DGCA23), were manually segmented (Olsen et al., 2017) in FSLView. The interference match‐to‐sample (IMTS) task was used to asses and recognition memory for scenes (Watson et al., 2013). Result Both hormone‐deprived groups demonstrated increased sleep latency (BSOvsAMC: p<.05, d= 0.74; SMvsAMC: d= 0.69) and decreased sleep efficiency (BSOvsAMC: p<.05, d= 0.79; SMvsAMC: p<.05, d= 0.67). Only the BSO group demonstrated reduced volume in the anterior CA1 and DGCA23 relative to AMC (p<.05, d= 0.96‐1.06) and BSO+ET (p<.05, d= 0.67‐0.84). Additionally, the BSO demonstrated reduced efficiency (accuracy/RTcorrect) on scene recognition memory (p<.05, d=0.70‐0.86). Multiple regression analyses show that DGCA23 volume and group membership predict scene recognition memory performance. Conclusion While both hormone‐deprived groups demonstrated disturbed sleep, women with early BSO also showed reduced hippocampal integrity, despite being ∼10 years younger than SM women. These findings are consistent with the elevated AD risk among women with BSO. It remains to be seen whether sleep disturbance at a younger age exacerbates risk.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.000
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 head0.036
GPT teacher head0.322
Teacher spread0.286 · 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
Published2021
Admission routes1
Has abstractyes

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