Effects of vegan diets and lifestyle on adult body composition: a narrative review
Bibliographic record
Abstract
The health benefits of vegan diets are well documented, though achieving nutritional adequacy requires careful planning, as is the case with any well-designed diet. Vegan diets effectively address obesity, with emerging evidence suggesting that body composition analysis offers a more accurate assessment of body weight management than traditional body mass index (BMI) calculations. This narrative review evaluates the impact of vegan diets on adult body composition based on 16 human interventional studies (published between 01/2014-10/2024), sourced from the PubMed/Medline database across various countries, including the USA, Canada, Brazil, Chile, and several European countries. Findings indicate that vegan diets can lead to greater reductions in body weight and more favourable changes in body composition compared to control diets, including high carbohydrate lacto-ovo, traditional and vegan-type Mediterranean, animal-based ketogenic, portion-controlled, therapeutic omnivorous and Western-type diets. However, some studies report significant muscle mass loss. Strategies to mitigate this include regular physical activity, particularly resistance training, ensuring sufficient protein intake and applying modest energy restrictions without compromising nutrient adequacy. Individual factors such as baseline BMI and health status also influence outcomes. This review further addresses critical real-world questions and dilemmas to deepen understanding of the relationship between vegan diet, body composition, and overall health, thus contextualizing the theme. Future research should explore whether a well-designed vegan diet, combined with customized lifestyle interventions, can further improve muscle mass preservation and overall body composition outcomes compared to other dietary lifestyles.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".