Association between tea consumption and cognitive impairment in middle-aged and older adults
Bibliographic record
Abstract
BACKGROUND: Biologic studies have suggested that tea may have neuroprotective activity. However, tea's protective effect on cognitive function is controversial in human epidemiological studies, and no data, including the middle-aged, are available. The objective of this study was to investigate the association of habit, frequency, and types of tea consumption with incident cognitive impairment in middle-aged and older adults. METHODS: Data from the Asymptomatic Polyvascular Abnormalities in Community study were used (aged over 40y). We gathered information on tea consumption, including habit, frequency, and types, via a standardized questionnaire and assessed cognitive function by Mini-Mental State Examination (MMSE) and/or Montreal Cognitive Assessment (MoCA). Three thousand eight hundred sixty-eight and 806 participants were selected in MMSE and MoCA subgroups. Multivariate logistic regression models were utilized to examine associations between tea consumption and cognitive impairment in middle-aged and older participants. RESULTS: In MMSE analyses, after adjustment for potential confounding factors, habitual (odds ratio (OR) 0.47, [95% confidence interval (CI) 0.33-0.68], p < 0.001) and high frequency (p trend < 0.001) of tea intake were associated with a lower risk of cognitive impairment. The risk of cognitive impairment was lower in green tea consumption (OR 0.36, [95% CI 0.22-0.61], p < 0.001) than other types (OR 0.59, [95% CI 0.38-0.91], p = 0.017). In MoCA analyses, we got similar results. CONCLUSIONS: Habitual tea consumption, especially high-frequency and green tea consumption, was significantly associated with a lower prevalence of cognitive impairment in middle-aged and older individuals.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".