The Association between Green Tea Consumption and Cognitive Function: A Meta-Analysis of Current Evidence
Bibliographic record
Abstract
BACKGROUND: Tea is widely consumed around the world, with green tea showing potential protective effects against cognitive decline, as indicated by multiple studies. These effects are thought to stem from its polyphenols and neuroprotective properties. This study aimed to systematically review and meta-analyze recent observational research on the link between green tea consumption and the risk of cognitive impairment. METHODS: A systematic search was performed in PubMed, Embase, Web of Science, and the Cochrane Library for observational studies published between September 2004 and September 2024. The relationship between green tea consumption and cognitive impairment was summarized using odds ratios with 95% confidence intervals. Additionally, the study conducted subgroup analyses, assessed heterogeneity, evaluated publication bias, and performed sensitivity analyses. RESULTS: Eighteen studies were included, comprising a total of 58,929 participants. The quality of these studies was evaluated using the Newcastle-Ottawa Scale, and overall, the quality was found to be high. The random-effects meta-analysis indicated that green tea consumption was inversely associated with cognitive impairment OR 0.63 (95% CI: 0.54-0.73), with the greatest benefit observed in individuals aged 50-69 years. Subgroup analysis showed protective effects for dementia OR 0.74 (95% CI: 0.56-0.99) and mild cognitive impairment OR 0.64 (95% CI: 0.43-0.96). Additionally, a significant reduction in the risk of cognitive impairment was observed in Asian populations, whereas no such association was found in European populations. Both women OR 0.51 (95% CI: 0.28-0.95) and men OR 0.47 (95% CI: 0.28-0.80) showed significant associations. High consumption groups had reduced cognitive impairment risk OR 0.64 (95% CI: 0.50-0.82). CONCLUSION: Green tea consumption is associated with a reduced risk of cognitive impairment, suggesting potential cognitive benefits. However, large-scale longitudinal studies are needed to confirm dose-response relationships and long-term effects. Future studies should also investigate the long-term effects of green tea and its role in personalized nutrition based on genetic predispositions.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.054 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".