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Record W4415439474 · doi:10.1016/j.ebiom.2026.106329

Predicting accumulation and age at onset of amyloid-β from genetic risk and resilience for Alzheimer's disease

2025· preprint· en· W4415439474 on OpenAlexfundno aff
Eleanor K. O’Brien, Timothy Cox, Shane Fernandez, Pierrick Bourgeat, Tenielle Porter, Benjamin Goudey, James D. Doecke, Colin L. Masters, Jürgen Fripp, Kwangsik Nho, Victor L. Villemagne, Carlos Cruchaga, Christopher C. Rowe, Andrew J. Saykin, Vincent Doré, Simon M. Laws

Bibliographic record

VenueEBioMedicine · 2025
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilCanadian Institutes of Health ResearchAvid RadiopharmaceuticalsNational Institutes of HealthGenentechIXICOH. Lundbeck A/SNorthern California Institute for Research and EducationNovartis Pharmaceuticals CorporationServierBioClinicaUniversity of Southern CaliforniaBristol-Myers SquibbEli Lilly and CompanyBiogenEisaiMeso Scale DiagnosticsAlzheimer's Association
KeywordsDiseaseDementiaTraitPsychological resilienceAccumulator (cryptography)Association (psychology)PathologicalPolygenic risk score

Abstract

fetched live from OpenAlex

<title>Abstract</title> Accumulation of brain amyloid beta (Aβ) is a key pathological hallmark of Alzheimer’s disease (AD) and begins many years before cognitive symptoms. Being able to predict the risk of Aβ accumulation, or the age at which this accumulation exceeds a critical threshold, may enable early intervention and treatment to slow or prevent the onset of AD. We utilised published genome-wide association studies (GWAS) to develop polygenic scores (PGS) based on AD risk (PGS <sub>risk</sub> ) and resilience (PGS <sub>resilience</sub> ). We tested whether these could predict (i) whether an individual was an accumulator of Aβ (‘Accumulator Status’), and (ii) in accumulators, the age at which brain Aβ is estimated to exceed a threshold of 20 centiloids (CL)(‘Estimated Age at onset of Aβ’; AAO-Aβ) among 2175 participants (1158 with AAO Aβ) from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) study. Additionally, we conducted genome-wide association studies (GWAS) of these traits and developed phenotype-specific PGSs using cross-validation (CV). Higher PGS <sub>risk</sub> was associated with a greater risk of being an accumulator and a younger AAO-Aβ. When stratified by number of <italic>APOE</italic> ε4 alleles, PGS <sub>risk</sub> predicted Accumulator Status in <italic>APOE</italic> ε4 heterozygotes, and AAO-Aβ in ε4 non-carriers and heterozygotes, with the same directions of effect as were seen in the whole cohort. PGS <sub>resilience</sub> was not significantly associated with Accumulator Status, but higher PGS <sub>resilience</sub> was associated with later AAO-Aβ overall and in ε4 heterozygotes. Trait-specific PGSs, developed using CV, were not significantly associated with either trait overall and the direction of association varied across CV folds. Polygenic scores, alongside other risk factors, may be useful for identifying individuals at risk of accumulating Aβ, and predicting the age at which this exceeds a critical threshold. This could provide a window for administering disease-modifying treatment or lifestyle interventions to prevent or delay the onset of AD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.362
Teacher spread0.317 · 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 teacher head, not a consensus.

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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