Effects of Maca (Lepidium meyenii Walp.) on Physical Performance in Animals and Humans: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Walp. (LmW), known as maca, has been shown to increase physical performance. However, the effect size (ES) of LmW on the different manifestations of physical performance has not yet been described. OBJECTIVES: To examine and qualitatively describe the studies published up to 2024 that employed LmW supplementation to increase physical performance in animal and human experimentation. In addition, the ES associated with the different interventions was calculated. METHODS: guidelines for systematic reviews and meta-analyses, using Web of Science, Scopus, SPORTDiscus, PubMed, and MEDLINE databases until 2024. Randomized controlled studies with a pre- and post-test design, conducted both in vitro and in vivo in animals and humans, were included. Methodological quality assessment was performed using the CAMARADES tool for animal studies and the Newcastle Ottawa Scale for human studies. The main variables were the forced swimming test (FST), the rota-rod test (RRT), the grip strength test (GST), blood lactic acid (BLA), and lactic acid (LA). The analysis was conducted with a pooled standardized mean difference (SMD) through Hedges' g test (95% CI). RESULTS: Twenty-one studies were included in the systematic review and sixteen in the meta-analysis. They revealed a large effect for all outcomes (SMD: FST = 2.26, RRT = 6.26, GST = 5.23, LA = -1.01, and BLA = -1.70). CONCLUSIONS: The phytochemical compounds of LmW, mainly macamides, increase physical performance, showing a greater effect at higher doses (dose-response effect).
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 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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".