The mechanistic and ergogenic effects of phosphatidic acid in skeletal muscle
Bibliographic record
Abstract
Skeletal muscle mass plays a vital role in locomotion, whole-body metabolic health, and is a positive predictor of longevity. It is well established the mammalian target of rapamycin (mTOR) is a central regulator of skeletal muscle protein turnover. The pursuit to find novel nutrient compounds or functional food sources that possess the ability to activate mTOR and promote skeletal muscle protein accretion has been on going. Over the last decade, a key role has been proposed for the phospholipid phosphatidic acid (PA) in mTOR activation. Mechanical load-induced (i.e., resistance exercise) intramuscular PA can directly bind to and activate mTOR. In addition, PA provided exogenously in cell culture heightens mTOR activity, albeit indirectly. Thus, endogenously generated PA and exogenous provision of PA appear to act through distinct mechanisms that converge on mTOR and, potentially, may amplify muscle protein synthesis. In support of this notion, limited evidence from humans suggests that resistance exercise training combined with oral supplemental PA enhances strength gains and muscle hypertrophy. However, the precise mechanisms underpinning the augmented muscle remodelling response with supplemental PA remain elusive. In this review, we will critically examine available evidence from cell cultures and animal and human experimental models to provide an overview of the mechanisms through which endogenous and exogenous PA may act to promote muscle anabolism, and discuss the potential for PA as a therapeutic tool to maintain or restore skeletal muscle mass in the context of ageing and disease.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".