Association study in Alzheimer’s disease of single nucleotide polymorphisms implicated with coffee consumption
Bibliographic record
Abstract
Background There is evidence from animal and in vitro models of the protective effects of caffeine in Alzheimer’s disease. The suggested mechanisms through which caffeine may protect neurons against Alzheimer’s disease pathology include the facilitation of beta-amyloid clearance, upregulation of cholinergic transmission, and increased neuronal plasticity and survival. Epidemiological studies support that Alzheimer’s disease patients consume smaller amounts of coffee beverages throughout their lives as compared to age-matched cognitively healthy individuals. Objective The aim of the present study was to determine whether the negative association between Alzheimer’s disease and coffee consumption may be influenced by a common genetic predisposition, given the fact that the pattern of coffee consumption is determined by both environmental and genetic factors. Method We conducted an in silico search addressing the association between genetic polymorphisms related to coffee consumption and the diagnosis of Alzheimer’s disease. We further investigated the interactions between genes located in regions bearing these polymorphisms. Results Our analysis revealed no evidence for a genetic association (nor interaction between related proteins) involving coffee consumption and Alzheimer’s disease. Discussion The negative association between Alzheimer’s disease and coffee consumption suggested by epidemiological studies is most likely due to environmental factors that are not necessarily regulated by genetic background.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".