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Record W7119625269 · doi:10.1002/alz70856_107470

<i>APOE</i> ε4 genotyping for populational enrichment of tau‐targeting clinical trials in cognitively impaired individuals

2025· article· en· W7119625269 on OpenAlexaff
Lucas Bastos Beltrami, João Pedro Ferrari‐Souza, Laura Rosso, Guilherme Povala, Douglas Teixeira Leffa, Firoza Z Lussier, Wagner S. Brum, Cristiano Schaffer Aguzzoli, Marco De Bastiani, Andrei Bieger, Giovanna Carello‐Collar, Wyllians Vendramini Borelli, Joseph Therriault, Arthur Macedo, Nesrine Rahmouni, Diogo O. Souza, Bruna Bellaver, Pamela C.L. Ferreira, Pedro Rosa‐Neto, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsClinical trialGenotypingPopulationGenotypeSample (material)

Abstract

fetched live from OpenAlex

Abstract Background The accumulation of tau tangle deposits is a potential target for clinical trials in Alzheimer's disease (AD). It is known that amyloid‐β (Aβ) pathology and the apolipoprotein E ε4 ( APOE ε4) allele accelerate tau pathology; yet, it is unclear whether assessing both variables could lead to more cost‐effective tau‐targeting trials using tau positron emission tomography (PET) as outcome. Here, we investigated the potential utility of considering APOE ε4 carriership for population enrichment in AD trials testing drug effects on tau tangle deposition in cognitively impaired (CI) individuals. Method Data was retrieved from the ADNI cohort. We selected CI participants with available clinical assessments, APOE genotyping, Aβ PET ([ 18 F]Florbetapir or [ 18 F]Florbetaben) and tau PET ([ 18 F]Flortaucipir) at baseline and a 2‐year follow‐up. Patients with global [ 18 F]Florbetapir SUVR >1.11 or [ 18 F]Florbetaben SUVR >1.08 were considered Aβ positive (Aβ+). We calculated required sample size and total costs for a hypothetical clinical trial testing a 25% drug effect on reducing tau PET accumulation in the medial temporal lobe (MTL) and neocortex (NEO) with 80% power at alpha level 0.05. Result We studied 78 CI individuals over a mean (SD) of 2.16 (0.31) years of follow up (Table 1). Figure 1 displays enrichment strategies based on the use of Aβ positivity alone or APOE ε4 carriership associated with Aβ positivity for the selection of patients for a hypothetical tau‐targeting trial in CI individuals. The addition of APOE ε4 carriership to Aβ positivity in the population enrichment strategy would notably reduce the required sample sizes (tau PET MTL = 53% and tau PET NEO = 41%), as well as trial costs (tau PET MTL = 55% and tau PET NEO = 44%), compared to using Aβ positivity alone (Figure 2). Conclusion Our results support that using APOE ε4 genotype together with Aβ positivity for population enrichment to select individuals at higher risk of fast tau accumulation could potentially reduce required sample sizes and costs for tau‐targeting trials focusing on CI individuals. Hence, this may be a cost‐effective strategy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.098
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.139
GPT teacher head0.447
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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