Dietary intake, body composition, and muscle function in resistance-untrained strict vegetarian and non-vegetarian women: an exploratory cross-sectional study
Bibliographic record
Abstract
Strict vegetarian and nonvegetarian diets may differ in quantity and quality of nutrient intake. This study aimed to compare dietary intake, lean mass, bone mineral content (BMC), bone mineral density (BMD), and muscle function between resistance-untrained strict vegetarians (SVs) and nonvegetarians (NVs) women. Seventy-one untrained women participated in this study, including 35 SV (28.2 ± 4.8 years) and 36 NV (29.6 ± 5.8 years). The SV group had adhered to their dietary pattern for 3.1 ± 2.1 years. Dietary intake was assessed using a 3-day food record, while total and regional lean mass, BMC, and BMD were measured by dual-energy X-ray absorptiometry. Muscle function was evaluated through knee extension peak torque (KEPT), knee flexion peak torque (KFPT) using an isokinetic dynamometer, and countermovement vertical jump (CMJ). No significant difference in total energy intake ( p = 0.546) was observed between groups. However, SV participants had a higher carbohydrate intake ( p = 0.001) and lower intakes of protein ( p < 0.001), fat ( p < 0.001), and calcium ( p = 0.049). Calcium intake was below the recommended level for both groups (SV: 345.2 mg, NV: 421.9 mg; p < 0.001). Additionally, no significant differences ( p > 0.05) were found between SV and NV in total or regional lean mass, BMC, BMD, KEPT, KFPT, and CMJ. Although SV consumed less protein, while still meeting minimum recommendations, total energy intake was similar between groups due to increased carbohydrate intake, supporting similar adaptations in lean mass, bone mineral content, bone mineral density, and muscle function. However, the observed calcium inadequacy highlights the need for nutritional counseling, particularly for strict vegetarians.
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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".