The effects of ApoE e4 genotype on plasma p‐tau181 concentrations on clinically diagnosed Alzheimer’s disease (AD) compared to controls
Bibliographic record
Abstract
Abstract Background The association between ApoE e4, a powerful genetic risk factor for AD, and beta‐amyloid (Aβ) pathology has been reported, but the evidence for the correlation with tau pathology is not as clear. The current study examined the relationship between ApoE e4 allele and plasma concentrations of p‐tau181 in patients with clinically diagnosed AD compared with an age‐matched controls with normal cognitive function. Method We examined 302 EDTA plasma samples of patients assessed at the UBC Hospital Clinic for Alzheimer’s disease. The cohort was clinically characterized as controls (n = 59) if no significant cognitive impairment was found, and AD (n = 243) if they fulfilled the NIA‐AA criteria (McKhann 2011). The plasma ptau‐181 levels were measured using an ADx‐developed p‐tau181‐specific Simoa assay. ApoE genotyping was performed on extracted DNA from blood samples. We examined the association of presence of ApoE e4 allele and plasma p‐tau181 concentrations using two‐by‐two ANOVA using SPSS 29. Result The plasma concentrations of p‐tau181 in AD who are ApoE e4 carriers (75.4 pg/ml) were slightly higher than but not significantly different from in AD non‐ ApoE4 carriers (70.1 pg/ml) (p = 0.64). The plasma levels of ptau‐181 in control cases with and without the ApoE allele were 24.8 and 24 pg/ml respectively (p = 1.000). There was no statistical significant interaction between the effects of AD and ApoE e4 carrier status on plasma p‐tau181 (F (1, 298) = 0.21, p = 0.65, ηp2 = 0.001). Conclusion While the plasma p‐tau181 concentrations were significantly higher in AD groups compared to controls (p<0.001) it was no significant effect of ApoE e4 genotype on the levels of plasma p‐tau181 in the control or AD groups (p = 0.94, p = 0.21). This suggests that the ApoE e4 carrier status does not affect plasma p‐tau181 concentrations significantly, independent of the AD clinical status.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".