Vegan diet, processed foods, and type 1 diabetes: A secondary analysis of a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Plant-based diets lead to weight loss and improved insulin sensitivity. However, some plant foods are highly processed, raising the question as to their effect on body weight and insulin sensitivity. METHODS AND RESULTS: Fifty-eight adults with T1D were randomly assigned to an ad libitum low-fat vegan (n = 29) or a portion-controlled group (n = 29) for 12 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, Spearman correlations, and a linear regression model were used for statistical analysis. In the vegan group, the consumption of animal foods decreased in all categories, significantly so in categories 1, 2, and 4. Animal foods in category 1 decreased in the vegan group; effect size: -192 g/day (95 % CI -297 to -88); p < 0.001. Concomitantly, the intake of plant-based foods in category 1 increased in the vegan group; effect size: +334 g/day (95 % CI -24 to +693); p = 0.07. No significant changes were observed in plant-based foods in categories 2, 3, and 4 in either group. Changes in animal foods in category 1 were positively associated with changes in body weight (r = +0.52; p = 0.001) and negatively with changes in insulin sensitivity (r = -0.46; p = 0.005). A 140-g/day reduction in the consumption of animal foods in category 1 was associated with a 1-kg weight loss. CONCLUSIONS: These findings suggest that replacing animal products with plant-based foods may be an effective weight-loss strategy in people with type 1 diabetes, even when processed foods are included. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT04944316.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".