Sex and ApoE‐4 interactions on development and severity of psychosis in AD
Bibliographic record
Abstract
Abstract Background Nearly half of all Alzheimer’s Disease (AD) patients experiences psychosis at some point in their disease trajectory. Kim et al. (2017) found a sexual dimorphism in AD psychosis where the ε4 allele may play a larger role in influencing psychosis development in females with Lewy body (LB) pathology. Method We investigated the effect of sex and ApoE‐4 status on age, presence and severity of psychotic symptoms on a clinical cohort using data from the National Alzheimer’s Disease Coordinating Centre (Figure 1). The non‐psychotic group (AD‐P) was compared against each of the overlapping diagnostic groups – psychotic (AD+P), delusion (AD+D) and hallucination (AD+H). Result We found a significant association between ε4 homozygotes and AD+P (OR = 1.08, p =. 017) and AD+D (OR = 1.09, p = .007). There was also a significant association between ε4 heterozygotes and AD+P (OR = 1.09, p = .004) and AD+D (OR = 1.08, p = .012). In males, the likelihood of having two copies of ε4 was increased in AD+P (OR = 1.10, p = .034) and AD+D (OR = 1.13, p = .006). The likelihood of having one copy of ε4 was also increased in AD+P (OR = 1.10, p = .030) and AD+D (OR = 1.10, p = .028). In females, the likelihood of having one copy of ε4 was increased in AD+H (OR = 1.10, p = .033). Participants who were female and homozygous for ε4 had a predicted increase of 0.388 in the log‐odds of being in a more severe category for delusions (95% CI: 0.016 ‐ 0.759, p = .041) and hallucinations (95% CI: 0.011 ‐ 1.264, p = .046). Conclusion Our findings show that the presence of ApoE‐4 increases the risk of psychosis in clinically diagnosed AD patients. Moreover, there are differential associations between sex, ApoE‐4 status and psychotic manifestations. The fact that we did not find an increase risk of psychosis among female homozygous ApoE‐4 carriers may be explained by the fact that we did not stratify by Lewy body pathology (Tsuang et al., 2005).
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".