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Record W4400868718 · doi:10.1186/s13195-024-01509-5

Hormone therapy is associated with lower Alzheimer’s disease tau biomarkers in post-menopausal females -evidence from two independent cohorts

2024· article· en· W4400868718 on OpenAlexafffund
Joseph Therriault, Cécile Tissot, Stijn Servaes, Nesrine Rahmouni, Arthur C. Macedo, Jaime Fernandez‐Arias, Sulantha Mathotaarachchi, Jenna Stevenson, Firoza Z Lussier, Andréa Lessa Benedet, Tharick A. Pascoal, Nicholas J. Ashton, Henrik Zetterberg, Kaj Blennow, Serge Gauthier, Pedro Rosa‐Neto

Bibliographic record

VenueAlzheimer s Research & Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsMcGill University Health CentreMcGill Genome CentreMontreal Neurological Institute and HospitalMcGill University
FundersFonds de Recherche du Québec - SantéNational Institutes of HealthUK Dementia Research InstituteVetenskapsrådetHjärnfondenFamiljen Erling-Perssons StiftelseStiftelsen för Gamla TjänarinnorMcGill UniversityFondation Brain CanadaEuropean CommissionAlzheimer's Disease Neuroimaging InitiativeMontreal Neurological Institute and HospitalServierEU Joint Programme – Neurodegenerative Disease ResearchHORIZON EUROPE Framework ProgrammeUniversity of Southern CaliforniaCanadian Institutes of Health ResearchWeston Brain InstituteNorthern California Institute for Research and EducationAlzheimer's Drug Discovery FoundationConsortium canadien en neurodégénérescence associée au vieillissementAlzheimer's Association
KeywordsBiomarkerDementiaMedicinePositron emission tomographyInternal medicineOncologyCognitive declineNeuroimagingMagnetic resonance imagingNeurologyHormone therapyMenopauseStandardized uptake valuePsychologyCohortDiseaseEndocrinologyBreast cancerNuclear medicinePsychiatryCancerRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Females represent approximately 70% of the Alzheimer's disease (AD) cases and the literature has proposed a connection between the decreased estrogen levels during menopause and an increased AD risk. Previous investigations have predominantly focused on assessing how hormone therapy (HT) affects the likelihood of AD development and cognitive deterioration. However, as the research framework has shifted toward a biomarker-defined AD and alterations in specific biomarkers could take place years before cognitive decline becomes discernible, it is crucial to examine how HT influences AD biomarkers. The main goal of this study was to evaluate the impact of HT on AD biomarker-informed pathophysiology in both cognitively unimpaired (CU) and cognitively impaired (CI) post-menopausal females across the aging and AD spectrum. METHODS: This cross-sectional study included post-menopausal females without HT history (HT-) and with HT (HT+) at the time of PET imaging assessment from two cohorts: the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent magnetic resonance imaging (MRI), positron emission tomography (PET) and biofluid collection. Voxel-based t-tests were performed to assess the differences in amyloid-β (Aβ) and tau neurofibrillary tangles (NFTs) loads between HT- and HT + females. Linear regression models with interaction terms were also conducted to examine the interactive effects of HT and Aβ-PET on regional tau-PET. RESULTS: (P < 0.0001) concentrations. Additionally, results from multivariate linear regression models indicated that HT interacts with cortical Aβ and is associated with lower regional NFT load. CONCLUSIONS: Overall, findings from this observational study suggest that HT is associated with lower tau neuroimaging and fluid biomarkers in postmenopausal females. Due to the close link between tau and cognition, this study highlights the need for large randomized controlled trials designed to systemically study the influences of HT on AD biomarkers and disease progression.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.189
GPT teacher head0.434
Teacher spread0.245 · 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.

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

Citations19
Published2024
Admission routes2
Has abstractyes

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