A tepary bean diet and exercise delays indices of type 2 diabetes in female fa/fa rats
Bibliographic record
Abstract
Legumes have been shown to protect against the development of type 2 diabetes (T2D). Previous work has focused on the impact of legumes on the glycemic index with little attention given to other physiological changes. Considerable evidence also demonstrates that exercise is beneficial for diabetic individuals. This study sought to determine the individual and synergistic effects of a legume diet and exercise on indices of T2D and tested the hypothesis that the synergy of these factors would protect against typical changes in glycemic hormones and lipids across the weight gain and insulin resistant stage of development in genetically obese rats. Fatty Zucker (fa/fa) rats, 6-7 wks of age were assigned to one of four treatment groups (n = 10/group); 1) tepary bean diet and exercise [TE], 2) tepary bean diet [T], 3) control diet and exercise [CE], 4) control diet [C]. A legume diet and exercise [TE] resulted in significantly less weight gain (126 g vs. 222 g in [C]) and lower body mass compared to animals in other treatment groups. The interaction of [TE] also resulted in significantly lower serum insulin compared to [C] animals across the study period. Diet [T] alone, significantly decreased serum triglycerides and cholesterol relative to [C] animals. Our results indicate that a tepary bean diet, with exercise, can decrease typical changes in weight gain, glycemia and lipid profile in fa/fa rats. The adoption of such a program in individuals showing signs of T2D would also likely serve to protect against these physiological changes. (Int J Diabetes Metab 15: 38-45, 2007)
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".