Effects of L-carnitine supplementation on markers of exercise-induced muscle damage in healthy adults: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
L-carnitine supplementation is purported to attenuate exercise-induced muscle damage, however, individual studies revealed mixed results. Thus, we performed a systematic review and meta-analysis of randomized controlled trials to determine the pooled effects of L -carnitine supplementation on muscle soreness and indirect circulating biochemical markers of muscle damage (myoglobin, creatine kinase (CK), and lactate dehydrogenase (LDH)) in healthy adults. Searches were performed on PubMed, Scopus, and Web of Science for randomized controlled trials (RCTs) examining L -carnitine supplementation on muscle damage or related biomarkers. Meta-analyses were done to calculate the weighted mean difference (WMD) when sufficient data was available. A total of 14 RCTs, including 284 participants, were included. L -carnitine supplementation doses varied from 1 to 3 g/d, from a single dose administration to an 8-week chronic regimen. Pooled effects revealed that L -carnitine reduced muscle soreness at 24 h [WMD= -6.39, p = 0.001] and 48 h after exercise [WMD= -1.53, p = 0.03]. Myoglobin was reduced immediately [WMD= -11.55 ng/mL, p = 0.04] and 30–60 min after exercise [WMD= -41.09 ng/mL, p = 0.001], but not 24 h after exercise [WMD= -22.85 ng/mL, p = 0.07]. CK was reduced 2 h [WMD= -26.72 IU/L, p = 0.02] and 24 h after exercise [WMD= -48.72 IU/L, p = 0.006], with no significant changes immediately after exercise [WMD= -32.76 IU/L, p = 0.38]. A non-significant decrease in LDH [WMD= -25.66 IU/L, p = 0.14] was observed immediately after exercise. In conclusion, L -carnitine supplementation may reduce muscle soreness, myoglobin and CK post-exercise, in some instances. Further research is warranted to determine the effects of L -carnitine on exercise performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.058 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".