The acute effects of dietary pulses on postprandial glycemia in diabetes: a meta‐analysis (272.8)
Bibliographic record
Abstract
Background and Aims: Elevated postprandial glucose (PPG) levels have been associated with higher incidence of cardiovascular disease and all‐cause mortality. Dietary pulses including beans, lentils and chickpeas have resulted in low and medium PPG responses, however most studies have included few subjects and their overall PPG responses, when taken in equicarbohydrate amounts either alone or in mixed meals, have not been systematically summarized and quantitated. Objective: to synthesize the evidence of the effect of dietary pulses on PPG responses in individuals with diabetes. Methods: MEDLINE, EMBASE, CINAHL and Cochrane were searched through Oct 31, 2013 for all acute human trials in individuals with type 1 and type 2 diabetes reporting data for areas under the PPG curve or glycemic indices. Data were pooled by the generic inverse variance method using random effect models and expressed as ratio of means (RoM) with 95% confidence intervals (CI). Heterogeneity was assessed by Chi2 and quantified by I2. Results: Thirty‐six trials (n=259) from 12 published papers met the inclusion criteria. Pulses significantly reduced the relative PPG by 49% compared to an equicarbohydrate white bread control (RoM: 0.51, CI 0.45‐0.57). Heterogeneity was moderate and statistically significant (I2=30%; p<0.0001), however this effect was consistent across all types of pulses and in mixed meals. Canned pulses showed a weaker effect. Conclusion: Pooled analyses suggest that dietary pulses in diabetic patients result in 50% significantly lower PPG rise compared to an equicarbohydrate white bread control. Grant Funding Source : Pulse Canada and CIHR
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.051 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".