A single nucleotide polymorphism in CHAT influences response to acetylcholinesterase inhibitors in Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a devastating neurodegeneration with a characteristic deficit in cholinergic neurotransmission. Treatment with acetylcholinesterase (AChE) inhibitors aims to reverse this deficit and does ameliorate the decline in cognition in some AD patients, although response is variable. OBJECTIVE: To examine whether sequence variation in the gene encoding choline acetyltransferase (CHAT), which encodes the major catalytic enzyme of the cholinergic pathway, predicts response to AChE inhibitors. METHODS: Alzheimer's disease patients (121) were treated with cholinesterase inhibitors and the effect of treatment on cognition was measured using the Mini Mental State Examination (MMSE). Six polymorphisms in CHAT were analysed for association with change in MMSE score. RESULTS: After correction for multiple testing, we found one SNP, rs733722, in a promoter region of CHAT, is associated with response of AD patients to cholinesterase inhibitors (P = 0.03) and accounts for 6% of the variance in response to AChE inhibitors. CONCLUSION: Rs733722 represents a putative marker of response to AChE inhibitors in AD patients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".