Predicting accumulation and age at onset of amyloid-β from genetic risk and resilience for Alzheimer's disease
Bibliographic record
Abstract
Abstract 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 risk ) and resilience (PGS resilience ). 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 risk was associated with a greater risk of being an accumulator and a younger AAO-Aβ. When stratified by number of APOE ε4 alleles, PGS risk predicted Accumulator Status in APOE 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 resilience was not significantly associated with Accumulator Status, but higher PGS resilience 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.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".