Effects of maternal supplementation of guanidinoacetic acid on skeletal muscle growth and metabolism in beef offspring
Bibliographic record
Abstract
Guanidinoacetic acid (GAA) is a precursor of creatine and is an arginine-sparing compound that may improve energy metabolism and muscle growth. Its potential in beef cow–calf systems, however, is still poorly understood. This study evaluated the effects of supplementing pregnant cows with GAA during late gestation on muscle development and adipogenesis in beef calves. A total of 24 pregnant Brahman cows carrying male or female fetuses received either a control diet or a diet supplemented with 0.2% GAA from day 180 to day 270 of gestation. Cows were weighed at the beginning and at the end of the trial to assess body weight (BW), and daily feed intake was recorded. Blood was collected on day 227 of gestation for plasma amino acid profiling, and the carcass traits were assessed via ultrasound. At 45 days of age, muscle biopsies were collected for mRNA expression and protein abundance. All statistical analyses were performed in SAS Studio using a mixed model including the fixed effects of treatment and offspring sex. In cows, GAA supplementation did not affect the BW, average daily gain, or feed intake (p > 0.05), but increased the plasma arginine, citrulline, and ornithine levels (p ≤ 0.02) and the final ribeye area (p = 0.01). The calves from GAA-supplemented cows exhibited increased p-Akt/Akt (p = 0.03) and p-mTOR/mTOR (p < 0.01) ratios, with treatment × sex interactions (p = 0.02). The MYOD1 mRNA expression was upregulated (p = 0.01), whereas MYOG remained unchanged (p = 0.14). The PAX7 protein tended to be higher (p = 0.07) and PAX3 reduced (p = 0.01) in GAA calves. No differences were detected for the adipogenic markers. These findings suggest that maternal GAA supplementation can stimulate muscle development in beef calves without altering intramuscular adipogenesis, indicating a potential strategy to enhance muscle growth programming in cow–calf production systems.
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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) | 0.000 | 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 | 0.001 |
| 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".