The Addition of Faba Bean Ingredients to Crackers Reduces Acute Postprandial Glycemia in Healthy Young Men
Bibliographic record
Abstract
Background Consumers and therefore the food sector have a high interest in producing healthy commercially available snacks. One potential approach to meeting this demand is to utilize pulse ingredients to improve the nutritional quality of current commercial products. Many snack foods produce high blood glucose (BG) responses. However, there is limited information on the effects of addition of pulses and/or their components to snacks on post‐prandial glycemia (PPG). Additionally, BG levels are influenced by the properties of food ingredients and in particular starch microstructure, which remains to be studied in many pulse components such as faba beans (FB). Objective To test the effect of incorporating FB flour and FB flour fractions to wheat flour crackers on the acute, as well as the second‐meal effect, on PPG in healthy young men. Methods In a repeated‐measures, randomized crossover trial, adult males (n=15) consumed 225 kcal crackers made with: (1) 100% whole wheat flour (control), (2) 23.9g whole FB flour (FB flour) (3) 24.1g protein concentrate made from FB flour (FB protein concentrate) (4) 23.7g protein isolate made from FB flour (FB protein isolate), (5) 24.7g high starch FB flour (FB starch). All FB flours replaced 40% of calories of whole wheat flour in crackers. BG incremental area under the curve (iAUC) from 0–120 min (pre‐meal), 120–200 min (post‐meal) and 0–200 min (total) was calculated using PPG concentrations. FB flours were analyzed to ensure consistency for size using light scattering. Additionally, starch in FB flour and FB starch was analysed by differential scanning calorimetry and scanning electron microscopy to relate starch microstructure to PPG. Results All FB flours were finely ground with average sizes < 150 um. For pre‐meal BG (0–120 min), there was a time (p<0.0001), treatment (p<0.0001) and time‐by‐treatment effect (p<0.0001), whereas there was only a time (p<0.0001) but no time‐by‐treatment interaction or treatment effect (p=0.12) on post‐meal BG (120–200 min). At 30 and 45 min, BG was lower following FB protein concentrate and FB protein isolate crackers compared to wheat flour crackers with no effect of FB flour and FB starch crackers (p<0.05). At 60 min, BG was lower and similar after all FB crackers compared to wheat flour crackers (p<0.05). There was an effect of treatment pre‐meal (p<0.0001) but not post‐meal on BG iAUC. FB protein concentrate and FB protein isolate led to lower pre‐meal BG iAUC compared to wheat flour crackers and FB protein isolate compared to FB starch. Total BG iAUC (0–200 min) was lower following the FB protein isolate compared to wheat flour crackers (p<0.001). Starch gelatinization disrupts crystalline structure, which can increase PPG, and in FB starch and FB flour this occurred at 67°C, a temperature reached during baking. In addition, granules present in FB starch and FB flour showed evidence of surface erosion (exo‐ and endo‐corrosion), suggesting more efficient hydrolysis of starch and subsequent glucose release resulting in higher PPG. These results suggest that FB flours, particularly protein concentrate and isolate, are primary components of FB responsible for lowering PPG. Conclusion Addition of FB ingredients to high glycemic snacks such as crackers may aid in postprandial glucose control. Support or Funding Information This study was supported by the Saskatchewan Pulse Growers, Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".