Tea and Blood–Brain Barrier Homeostasis: Potential Mechanisms and Improvement Strategies
Bibliographic record
Abstract
Abstract The blood–brain barrier (BBB), a selective interface regulating cerebral substance exchange, plays a crucial role in maintaining cognitive function and metabolic balance. While tea consumption has been traditionally associated with health benefits, its specific effects on BBB integrity warrant systematic investigation. This review demonstrates that tea bioactive compounds can cross the BBB through systemic absorption and metabolism, with their permeability determined by physicochemical properties, including molecular weight and lipophilicity. Notably, the tea bioactive compounds exhibit strong functional properties but low bioavailability. On one hand, tea can directly modulate the development of the BBB through vascular endothelial growth factor (VEGF), Wnt, and Notch1 signaling pathways, and delay BBB aging and dysfunction by alleviating CNS inflammation, oxidative stress, and p‐glycoprotein (P‐gp) activity. On the other hand, tea indirectly influences BBB homeostasis via gut microbiota‐mediated pathways, by regulating circadian rhythm disruptions, reducing psychosocial stress, and preventing metabolic syndrome. The review also discusses potential strategies to enhance tea's BBB‐protective effects, including optimization of tea leaf processing, beverage production, and nanoencapsulation of bioactive compounds. These findings provide valuable insights into the tea–BBB interaction and establish a theoretical framework for future research. This framework will support the development of dietary interventions for brain health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".