Genetic drivers of progression in Alzheimers disease are distinct from disease risk
Bibliographic record
Abstract
BACKGROUND: Recent trials in Alzheimer’s disease (AD) demonstrate encouraging outcomes. These trials target risk mechanisms identified through genetic analysis whilst directly aiming to reduce progression rates. Evidence from other neurodegenerative diseases suggests the genetics of progression is distinct from risk of disease. To expand these initial successes and improve clinical outcomes further we need to understand genetics of progression of disease. These can be deduced through rigorous analysis of meticulously phenotyped longitudinal cohorts. In this study we first looked at known genetic drivers of risk, namely polygenic risk scores for AD and APOE‑ε4, to assess their role in progression. This was then extended to a genome wide association analysis to identify the role of other genetic variants in progression of AD. METHODS: A total of 387 individuals with genetic data, amyloid positivity, and in active decline (ADNI (n = 222) and AIBL(n = 165)) were used to perform generalised mixed effects linear model genome wide association studies of longitudinal cognitive decline as measured by mini mental state examination (MMSE). The resulting summary statistics were subjected to functional annotation, and colocalisation analyses. RESULTS: Established AD risk factors, including APOE‑ε4 dosage and polygenic risk scores, were not associated with disease progression in amyloid positive individuals who are actively declining. A mixed effects GWAS meta-analysis revealed one genome-wide significant locus on chromosome 22 (rs78369883) and several nominally significant loci linked with AD progression. Functional annotation, finemapping, and colocalisation analyses implicated genes primarily involved in immune response, neurodegeneration (including tau pathology), brain resilience, and neurogenesis. These progression-related genes were significantly enriched in neuronal-interferon-microglial signalling pathways and normal homeostatic processes of neuronal networks, with specific enrichment in dopaminergic and inhibitory neuronal populations. CONCLUSION: These findings enhance our understanding of the biological underpinnings of AD progression, opening new avenues for therapeutic intervention.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".