Effect of Matcha green tea on cognitive functions and sleep quality in elderly adults with cognitive decline: a randomized controlled study over 12 months
Bibliographic record
Abstract
Abstract Background Nutrition is a pivotal factor in the prevention of dementia. Matcha green tea powder, which contains L‐theanine, caffeine, and epigallocatechin gallate, has the beneficial effects of each constituent on cognitive functions and mood. However, no long‐term clinical study has yet been performed to evaluate the effect of Matcha on psychological functions. Here, we performed a randomized, double‐blinded, placebo‐controlled, clinical study, conducted over 12 months, to investigate the effect of Matcha on cognitive functions and sleep quality. Furthermore, the relationship between blood and neuroimaging biomarkers and the effect of Matcha was investigated. Methods We recruited 939 community‐dwelling older adults aged 60–84 years and enrolled subjects with a diagnosis of subjective cognitive decline (SCD) and mild cognitive impairment (MCI). A total of 99 subjects (64 SCD, 35 MCI) were randomized, with 49 receiving Matcha (2 g/day, male 23, female 26) and 50 receiving placebo (male 20, female 30). The groups were adjusted for age, sex, and APOE4 genotype. Cognitive functions were assessed by MMSE, MoCA, ADAS‐cog, RBANS, and the CNS Vital Signs computerized neurocognitive battery. Sleep quality was measured by the Pittsburg Sleep Quality Index (PSQI). Plasma biomarkers and neuroimaging (Amyloid PET, MRI, SPECT, fNIRS) were also assessed. The change of outcome variables from the baseline to 12‐month was tested statistically using a mixed‐effects model. Results Compared to the placebo, the consumption of Matcha induced a significant improvement in social acuity assessed by perception of facial emotion (P = 0.034), while continuous performance showed a trend towards improvement. The PSQI differed by 0.86 between the groups, indicating an improvement in sleep quality in the Matcha group compared to the placebo group (P = 0.087). The MMSE score showed a slight increase in the Matcha group. Amyloid PET SUVR showed no change from baseline to 12‐month in either group, while the plasma Aβ42 was reduced in the Matcha group, suggesting increased clearance of peripheral Aβ42. Conclusion Facial emotion recognition is impaired in cognitive impairment. This long‐term intervention study suggests that Matcha consumption can improve emotion perception and attention, and sleep quality in elderly adults with cognitive decline.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".