The Green Tea Catechin, EGCg, Preserves Both Muscle and Bone in Aging Sarcopenic Rats
Bibliographic record
Abstract
Objectives Sarcopenia is the progressive decline in muscle mass, strength and function that is exacerbated with age. It is well known that there is an overlap between sarcopenia and osteopenia/osteoporosis in the aging population. Thus, addressing both muscle and bone loss during aging is important. Epigallocatechin gallate (EGCg) is the most abundant green tea catechin and has well established anti‐inflammatory and antioxidant benefits. Green tea consumption has also been associated with bone health. In this study we examined the impact of chronic EGCg treatment on muscle mass and bone structure in aging rats. Methods Aged male Sprague‐Dawley rats (21 months) were fed the following diets ad libitum: Control group (n=9) fed AIN93M diet, and a treatment group (n=10) fed AIN93M diet +EGCg (200 mg/kg) for 8 weeks. At the end of study, gastrocnemius muscles were weighed, and histological analysis was performed to measure muscle fiber cross sectional area (CSA) using hematoxylin & eosin staining. Bones were analyzed by MicroCT imaging (Burker) of the tibia at ~22 μm resolution and images were processed using Inveon Research Workplace software. All bone samples were analyzed for trabecular bone volume, trabecular number, trabecular spacing, trabecular thickness, trabecular pattern factor and bone density. Results Gastrocnemius muscle wet weights were significantly higher (p<0.05) and fiber CSA tended to be higher (p<0.06) in the EGCg group compared to controls. Tibia bone volume/total volume density was significantly greater (p=0.03) in animals treated with EGCg compared to control. Significant differences were found in trabecular microstructure, with a significant increase in trabecular number per given volume (p=0.01) and a corresponding significant decrease in trabecular spacing (p=0.02) in EGCg‐treated rats compared to control. No significant group differences were found in overall bone density or trabecular thickness. Differences in mean values between control and EGCg groups were determined by student’s t‐test. Conclusions These data present novel findings on the osteoprotective mechanism of EGCg on the aging bone through an increase in trabecular bone volume, trabecular number and trabecular spacing. Furthermore, the preservation of muscle mass and muscle fiber CSA suggests that the use of green tea catechin, EGCg, could be a potential nutritional therapy to prevent the progression of muscle and bone loss during sarcopenia. Support or Funding Information Abbott Nutrition
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".