Association Between Caffeine Intake and Alzheimer’s Disease Progression: A Systematic Review
Bibliographic record
Abstract
Alzheimer's disease (AD) is a growing global health challenge, prompting increased attention on modifiable lifestyle factors that might influence disease progression. Among these, caffeine consumption has emerged as a potential protective factor, though the evidence remains complex and incompletely understood. This study aims to systematically review and evaluate the available evidence regarding the association between caffeine intake and AD progression. A comprehensive literature search was conducted across major databases including PubMed/MEDLINE, Embase, Web of Science, and Cochrane Library, covering studies from database inception through October 2024. The review included studies examining the relationship between caffeine intake and AD progression in human subjects, with quality assessment performed using the Newcastle-Ottawa Scale for observational studies and appropriate tools for other study designs. Findings indicated that higher caffeine intake (>200 mg/day) was consistently associated with a reduced risk of cognitive decline and AD progression. Plasma caffeine levels exceeding 1200 ng/ml were notably linked to a reduced risk of conversion from mild cognitive impairment (MCI) to dementia. The Mendelian randomization study suggested a protective effect of genetically predicted higher plasma caffeine levels against AD, with an odds ratio of 0.87 (95% CI: 0.76-1.00), although this did not reach statistical significance. Overall, current evidence suggests a potentially protective role of moderate caffeine consumption against AD progression, particularly in individuals with MCI. The relationship appears dose-dependent and may be influenced by genetic factors and timing of exposure. Further research, particularly well-designed prospective studies and clinical trials, is needed to establish optimal dosing strategies and identify populations most likely to benefit.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".