Disentangling the effects of sex and gender on <i>APOE</i> ɛ4–related neurocognitive impairment
Bibliographic record
Abstract
Abstract INTRODUCTION The apolipoprotein E ( APOE ) ɛ4 allele is a well‐established risk factor for neurocognitive impairment (NCI), with varying impacts between men and women. This study investigates the distinct roles of sex and gender in modifying APOE ɛ4–related NCI. METHODS Biological sex was inferred from sex chromosomes, and a femininity score (FS) was used as a proxy for gender. We analyzed 276,596 UK Biobank participants without prior NCI to assess whether sex and FS modified the effect of APOE ɛ4 on NCI. RESULTS NCI risk was higher in APOE ɛ4 carriers compared to non‐carriers (hazard ratio [HR] = 2.48 in females; HR = 1.96 in males) with significant interaction by sex ( P < 0.0001). FS was associated with an increased NCI risk after accounting for sex (HR = 1.07, 95% confidence interval: 1.04–1.10, P < 0.0001) with no significant differences by sex or APOE ɛ4 carrier status. DISCUSSION Our findings show that APOE ɛ4 increases NCI risk more in females, while FS independently elevates risk across sexes. Highlights Apolipoprotein E ( APOE ) ɛ4 increases neurocognitive impairment (NCI) risk, with a greater impact in females (hazard ratio [HR] = 2.48) than males (HR = 1.96). Sex significantly modifies the effect of APOE ɛ4 on NCI ( P < 0.0001f). Femininity score increases NCI risk (HR = 1.07) independently of sex and APOE ɛ4. Understanding the distinct sex and gender contributions to APOE ɛ4–related NCI can improve interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".