Similar body composition, muscle size, and strength adaptations to resistance training in lacto-ovo-vegetarians and non-vegetarians
Bibliographic record
Abstract
There is a popular belief that meat consumption is necessary to optimize adaptations to strength training (ST), but evidence to support this hypothesis is scarce. Therefore, this study aimed to compare ST adaptations in lacto-ovo-vegetarians (LOV) and non-vegetarians (NV) with adjusted protein intake per meal. Sixty-four LOV and NV performed 12 weeks of ST and were instructed to ingest at least 20 g of protein in each main meal during the experimental period. Quadriceps femoris muscle thickness (QFMT), knee extension one-repetition maximum (1RM), and isometric peak torque (PT), as well as participants’ body composition were assessed before and after the intervention. Dietary intake was assessed throughout the study. After 12 weeks, similar increases in QFMT (LOV: 9.2 ± 5.4; NV: 5.5 ± 8.1 mm), knee extension 1RM (LOV: 24.7 ± 11.1; NV: 21.6 ± 9.8 kg), and PT (LOV: 29.8 ± 33.4; NV: 17.5 ± 19.4 N m) and lean body mass (LOV: 1.3 ± 0.9; NV: 1.4 ± 1.4 kg), alongside a decrease in body fat mass (LOV: −0.5 ± 1.6; NV −0.8 ± 1.6 kg) were observed in both groups at the end of the training period ( p < 0.05). LOV had lower protein consumption than NV throughout the study ( p < 0.05), but participants reached intake of at least 1.2 g of protein/kg/day during the experimental period. In conclusion, LOV and NV displayed similar improvements in muscle mass, strength, and in body composition after 12 weeks of ST, suggesting that meat consumption and higher protein intake in NV did not bring about further benefits to early adaptations to ST. This study was registered in Clinical Trials (NCT03785002) on 24 December 2018.
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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 | 0.000 |
| 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".