Sex influences the effects of APOE genotype and Alzheimer’s diagnosis on neuropathology and memory
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is characterised by severe cognitive decline and pathological changes in the brain (brain atrophy, hyperphosphorylation of tau, and deposition of toxic amyloid-beta protein). Females have greater neuropathology (AD biomarkers and brain atrophy rates) and cognitive decline than males, however these effects can depend on diagnosis (amnestic mild cognitive impairment (aMCI) or AD) and APOE genotype (presence of ε4 alleles). Using the ADNI database (N=630 females, N=830 males), we analysed the effect of sex, APOE genotype (non-carriers or carriers of APOEε4 alleles), and diagnosis (cognitively normal (CN), early aMCI (EMCI), late aMCI (LMCI), probable AD) on cognition (memory and executive function), hippocampal volume, and AD biomarkers (CSF levels of amyloid beta, tau and ptau). Regardless of APOE genotype, memory scores were higher in CN, EMCI, and LMCI females compared to males but this sex difference was absent in probable AD, which may suggest a delay in the onset of cognitive decline or diagnosis and/or a faster trajectory of cognitive decline in females. We found that, regardless of diagnosis, CSF tau-pathology was disproportionately elevated in female carriers of APOEε4 alleles compared to males. In contrast, male carriers of APOEε4 alleles had reduced levels of CSF amyloid beta compared to females, irrespective of diagnosis. We also detected sex differences in hippocampal volume but the direction was dependent on the method of correction. Altogether results suggest that across diagnosis females show greater memory decline compared to males and APOE genotype affects AD neuropathology differently in males and females which may influence sex differences in incidence and progression of aMCI and AD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".