Predictors of Alzheimer’s disease risk in women with bilateral oophorectomy from the UK Biobank
Bibliographic record
Abstract
Abstract Background Two‐thirds of individuals with Alzheimer’s disease (AD) are women and at age 45, the lifetime risk for AD is twice as high in women compared to men (Alzheimer’s Association, 2019). Early life events affecting 17β‐estradiol (E2) may be a mechanism behind this sex difference. Bilateral oophorectomy (BO) predicts cognitive decline, and neurodegeneration, and increased risk of AD in later life—adverse outcomes that are mitigated with E2 therapy (Gervais et al., 2020; Kantarci et al., 2018; Rocca et al., 2007; Zeydan et al., 2019). Our aim was to investigate compounding risk factors for AD in women with BO using UK Biobank data. Method We studied women aged 60 or older who had BO prior to age 49 with an AD diagnosis (n = 53) and without an AD diagnosis (n = 5418) from the UK Biobank (Sudlow et al. 2015). Data were analyzed using Firth’s bias‐reduced logistic regression in R 3.6.1 (R Core Team, 2019). Result Participants were on average 63.7 years old and 43.4 years old at BO. Women with BO who developed AD: (a) were significantly older, (b) had fewer years of education, (c) had an earlier age at BO, (d) less frequently used any form of hormone therapy (HT), and (e) were more frequently apolipoprotein E4 (APOE4) carriers. As age increased, AD risk increased by 25%; it deceased by 10% with greater years of education. Women with BO and an APOE4 allele were 5.2 times as likely to develop AD than those without an APOE4 allele. Finally, ever using any form of HT decreased AD risk by 54%. Conclusion This research fills a gap in understanding predictors of AD in a high‐risk group of women with early E2 loss. The heightened AD risk for women with BO and APOE4 is of note, as prior work suggests women with APOE4 may benefit less from HT (MacLusky, 2004). Further, the reduction in AD risk with any HT provides added support for HT in mitigating cognitive decline. Although details on HT formulation were unavailable, the specific use of E2‐based therapy may further decrease AD risk (Sherwin & Henry, 2008).
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".