Chronic administration of ellagic acid improved the cognition in middle-aged overweight men
Bibliographic record
Abstract
This study aimed to investigate if ellagic acid has beneficial effects on cognitive deficits in middle-aged overweight individuals and to propose a possible mechanism. A total of 150 middle-aged male participants, including 76 normal-weight and 74 overweight men, aged between 45 to 55 years, were recruited for this study. Both normal-weight and overweight participants were administered either 50 mg ellagic acid or placebo cellulose daily for 12 weeks. Blood lipids, peripheral brain-derived neurotrophic factor (BDNF), and saliva cortisol were assessed on the last day of the procedure to investigate the effects induced by ellagic acid. The results revealed that ellagic acid treatment improved the levels of blood lipid metabolism with a 4.7% decline in total cholesterol, 7.3% decline in triglycerides, 26.5% increase in high-density lipoprotein, and 6.5% decline in low-density lipoprotein. Additionally, ellagic acid increased plasma BDNF by 21.2% in the overweight group and showed no effects on normal-weight participants. Moreover, the increased saliva cortisol level in overweight individuals was inhibited by 22.7% in a 12-week ellagic acid treatment. Also, compared with placebo, overweight individuals who consumed ellagic acid showed enhanced cognitive function as measured by the Wechsler Adult Intelligence Scale-Revised and the Montreal Cognitive Assessment. To the best of our knowledge, this is the first report showing that ellagic acid prevents cognitive deficits through normalization of lipid metabolism, increase in plasma BDNF level, and reduction of saliva cortisol concentration. These results indicate that ellagic acid has a potential to restore cognitive performance related to mild age-related declines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".