Vegan diet, processed foods, and body weight: a secondary analysis of a randomized clinical trial
Bibliographic record
Abstract
Low-fat plant-based diets cause weight loss in clinical trials. However, many foods are highly processed, raising the question as to their effect on body weight. This secondary analysis assessed the associations between changes in processed food intake and weight loss in 244 overweight adults randomly assigned to a vegan (n = 122) or control group (n = 122) for 16 weeks. Three-day dietary records were analyzed using the NOVA system, which categorizes foods from 1 to 4, based on degree of processing. A repeated measure ANOVA, Pearson correlations, and a multivariate regression model were used for statistical analysis. The consumption of animal foods in categories 1-4 decreased in the vegan group, compared with the control group. Body weight decreased in the vegan group (treatment effect - 5.9 kg [95% CI -6.7 to -5.0]; Gxt, p < 0.001). Changes in consumption of animal foods in categories 1-4 were positively associated with changes in body weight: r = + 0.34; p < 0.001 for category 1; r = + 0.18; p = 0.008 for category 2; r = + 0.17; p = 0.01 for category 3; and r = + 0.22; p = 0.001 for category 4. In no NOVA category was the consumption of plant-based processed foods positively and significantly associated with weight gain. The top three independent predictors of weight loss were reduced intakes of processed, unprocessed or minimally processed, and ultra-processed animal foods. These findings suggest that replacing animal products with plant-based foods may be an effective weight-loss strategy, even when processed plant-based foods are included.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".