Effect of pulses as part of a low glycemic index diet compared to a high fiber diet on HbA1c and blood lipids in type 2 diabetes
Bibliographic record
Abstract
Objective Pulses including beans, lentils and chickpeas are low glycemic index (GI) foods however their effect on glycemic control and blood lipids diet has not been evaluated compared to a high fiber in type 2 diabetes. Methods 121 subjects with type 2 diabetes were randomized to one of two 12‐week treatments: 1. Dietary advice included pulses (1 cup cooked/day) and other low GI starches and fruit; 2. Dietary advice to consume a high fiber whole grain diet (40g/2000kcal) (control diet). The primary outcomes were changes in HbA1c and blood lipids. Results Treatment differences in pulse intakes and dietary GI were significantly different (+169g and −12.5%, respectively, on Pulse‐low GI diet, p<0.0001). The Pulse‐low GI diet reduced HbA1c by 0.5% units (p<0.001 vs. high fiber), total cholesterol by 0.22 mmol/L (p<0.001), LDL by 0.09 mmol/L (p=0.063) and triglycerides by 0.045 mmol/L (p=0.064). Pulse intake was inversely correlated to changes in HbA1c (r=−0.24, p=0.014), total cholesterol (r=−0.27, p=0.004) and triglycerides (r=−0.24, p=0.012). Conclusions Dietary advice to consume pulses within a low GI diet improved both glycemic control and the serum lipid profile in type 2 diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".