Population enrichment strategy using <i>APOE</i> ε4 genotype in tau‐targeting trials for preclinical Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Trials in preclinical Alzheimer's disease (AD) are becoming increasingly important, as AD pathological changes appear decades before dementia onset. Amyloid‐beta (Aβ) pathology and the apolipoprotein E ε4 ( APOE ε4) carriership jointly accelerate tau tangle accumulation. However, the utility of assessing both variables to enhance participant selection for AD trials using tau positron emission tomography (PET) as outcome has not yet been explored. Here, we investigated the implications of considering APOE ε4 status for participant selection in tau‐targeting trials for preclinical AD. Method We analyzed 96 cognitively unimpaired (CU) individuals (aged 57‐90 years) from the ADNI cohort that underwent clinical assessments, APOE genotyping, PET for Aβ ([ 18 F]Florbetapir or [ 18 F]Florbetaben) and tau ([ 18 F]Flortaucipir) at baseline, along with a 2‐year follow‐up. Aβ positivity was determined as global [ 18 F]Florbetapir SUVR >1.11 or [ 18 F]Florbetaben SUVR >1.08. We calculated the sample size required for a hypothetical clinical trial testing a 25% drug effect, with 80% power at alpha level 0.05, to reduce tau‐PET accumulation in the medial temporal lobe (MTL) and neocortex (NEO), along with the total trial costs. Result Table 1 reports the demographic information of the study population. Figure 1 shows enrichment strategies for the selection of participants in a clinical trial aiming at tau PET reduction in CU individuals. In comparison to using only Aβ positivity, the use of APOE ε4 genotyping together with Aβ positivity for population enrichment would reduce the sample size and total costs, respectively, by 28% and 34% in trials targeting tau PET MTL , and by 24% and 36%, respectively, in trials targeting tau PET NEO (Figure 2). Conclusion Our findings suggest that combining APOE ε4 status with Aβ positivity may be a cost‐effective strategy for enriching participant selection in AD tau‐targeting trials focusing on asymptomatic individuals, reducing required sample sizes and trial costs.
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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 | 0.054 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".