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Record W4390200225 · doi:10.1002/alz.079764

Potential utility of using both <i>APOE</i>ε4 and Aβ positivity to enrich clinical trials of tau‐targeting therapies

2023· article· en· W4390200225 on OpenAlexaffabout
João Pedro Ferrari‐Souza, Pâmela C.L. Ferreira, Bruna Bellaver, Guilherme Povala, Firoza Z Lussier, Douglas Teixeira Leffa, Joseph Therriault, Cécile Tissot, Carolina Soares, Andréa Lessa Benedet, Yi‐Ting Wang, Mira Chamoun, Stijn Servaes, Arthur C. Macedo, Marie Vermeiren, Gleb Bezgin, Min Su Kang, Jenna Stevenson, Nesrine Rahmouni, Vanessa Pallen, Nina Margherita Poltronetti, Cristiano Schaffer Aguzzoli, Hussein Zalzale, Francieli Rohden, Annie Cohen, Oscar L. López, Dana Tudorascu, William E. Klunk, Victor L. Villemagne, Diogo O. Souza, Lucas Porcello Schilling, Thomas K. Karikari, Jean‐Paul Soucy, Serge Gauthier, Eduardo R. Zimmer, Pedro Rosa‐Neto, Tharick A. Pascoal

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsApolipoprotein EPositron emission tomographyDementiaInternal medicinePittsburgh compound BPopulationOncologyCohortPsychologyMedicineAlzheimer's Disease Neuroimaging InitiativeMagnetic resonance imagingClinical trialBiomarkerDiseaseNuclear medicineBiologyRadiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Background The use of enrichment strategies is crucial for selecting individuals with the highest probability of Alzheimer’s disease (AD)‐related progression in typical clinical trial time frames. Although both amyloid‐β (Aβ) pathology and the apolipoprotein E ε4 (APOEε4) genotype have been shown to accelerate tau accumulation, it is still not clear whether assessing both APOEε4 genotype and Aβ positivity is useful to enrich tau‐targeting trials using tau positron emission tomography (PET) as outcome. Here, we investigated the implications of considering APOEε4 carriership for population enrichment in trials testing drug effects on tau tangle deposition in cognitively impaired (CI) individuals across the AD continuum. Method We studied 29 Aβ positive CI individuals (16 with mild cognitive impairment [MCI] and 13 with AD dementia) from the McGill Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Study participants underwent clinical assessments, APOE genotyping, magnetic resonance imaging, PET for Aβ ([18F]AZD4694) and tau ([18F]MK6240) at baseline, as well as a follow‐up tau‐PET scan (mean follow‐up, 2.2 years). Aβ positivity was determined as global [18F]AZD4694 SUVR ≥ 1.55. Result No demographic differences were observed between APOEε4 carriers and noncarriers (Table 1). Regression analysis revealed that APOEε4 carriers had higher tau‐PET SUVR increase in temporal regions compared to APOEε4 noncarriers (Figure 1). The use of Aβ positivity alone for population enrichment of a clinical trial focusing on CI individuals would require a sample size of 436 individuals per study arm to test a 25% drug effect on tau‐PET accumulation (Figure 2). A similar clinical trial with a population enrichment strategy using Aβ positivity plus APOEε4 carriership would require a sample size of as few as 158 individuals per study arm (reduction of 64% in relation to using only Aβ positivity) to test the same drug effect (Figure 2). Conclusion Our results reveal that APOEε4 carriership is associated with increased tau tangle accumulation in CI individuals who are Aβ positive. Clinical trials testing drug effects on tangle deposition may benefit from assessing both APOEε4 carriership and Aβ positivity statuses as enrollment criteria to select individuals at higher risk of fast tau accumulation, resulting in a more cost‐effective trial.

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)0.060
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.147
GPT teacher head0.437
Teacher spread0.290 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2023
Admission routes2
Has abstractyes

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