Effect of Legumes as Part of a Low Glycemic Index Diet on Glycemic Control and Cardiovascular Risk Factors in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Legumes, including beans, chickpeas, and lentils, are among the lowest glycemic index (GI) foods and have been recommended in national diabetes mellitus (DM) guidelines. Yet, to our knowledge, they have never been used specifically to lower the GI of the diet. We have therefore undertaken a study of low-GI foods in type 2 DM with a focus on legumes in the intervention. METHODS: A total of 121 participants with type 2 DM were randomized to either a low-GI legume diet that encouraged participants to increase legume intake by at least 1 cup per day, or to increase insoluble fiber by consumption of whole wheat products, for 3 months. The primary outcome was change in hemoglobin A1c (HbA1c) values with calculated coronary heart disease (CHD) risk score as a secondary outcome. RESULTS: The low-GI legume diet reduced HbA1c values by -0.5% (95% CI, -0.6% to -0.4%) and the high wheat fiber diet reduced HbA1c values by -0.3% (95% CI, -0.4% to -0.2%). The relative reduction in HbA1c values after the low-GI legume diet was greater than after the high wheat fiber diet by -0.2% (95% CI, -0.3% to -0.1%; P < .001). The respective CHD risk reduction on the low-GI legume diet was -0.8% (95% CI, -1.4% to -0.3%; P = .003), largely owing to a greater relative reduction in systolic blood pressure on the low-GI legume diet compared with the high wheat fiber diet (-4.5 mm Hg; 95% CI, -7.0 to -2.1 mm Hg; P < .001). CONCLUSION: Incorporation of legumes as part of a low-GI diet improved both glycemic control and reduced calculated CHD risk score in type 2 DM.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".