Dose-dependent decrease in mortality with no cognitive or muscle function improvements due to dietary EGCG supplementation in aged mice
Bibliographic record
Abstract
We have previously shown that a diet containing epigallocatechin gallate (EGCG) and beta-alanine is not effective in improving either cognitive or muscle function in aged (18 month) mice (Gibbons et al., Behav. Brain Res., 2014, 272:131-140; Pence et al., Appl. Physiol. Nutr. Metab., 2016, 41(2): 181-190). However, this diet reduced oxidative stress in the brain, and previous studies using longer term interventions and other doses have documented beneficial effects in cognitive and muscle function, especially with EGCG. Here we hypothesized that a different dose of EGCG or longer feeding period would be more efficacious in improving cognition. Aged (21-25 mo) Balb/cByJ male mice underwent 63 days of feeding with EGCG at 0, 0.091, or 3.67 mg/g AIN-93M diet and were then subjected to a battery of cognitive and muscle function tests. EGCG feeding at either of the 2 doses did not alter preference for novel versus familiar arm in the Y-maze test (p = 0.29) and did not affect learning in the active avoidance test (p = 0.76). Similarly, EGCG did not affect preference for novel versus familiar mice in a social discrimination test (p = 0.17). Likewise, there was no effect of EGCG on muscle function by grip strength (p = 0.16), rotarod (p = 0.18), or treadmill test to exhaustion (p = 0.25). EGCG reduced mortality in a dose-dependent fashion (p = 0.05, log-rank test for trend), with 91% of high EGCG, 72% of low EGCG, and 55% of control mice surviving to the end of the study. In conclusion, EGCG improves survival in aged mice but does not affect cognitive or muscle function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".