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The Effects of Consuming Smoothies Containing Faba Bean Ingredients on Post‐prandial Glycemia in Healthy Young Men

2017· article· en· W4389020042 on OpenAlexafffundabout
Rebecca C. Mollard, Hrvoje Fabek, G. Harvey Anderson, Camille Lagorse, Haizhou Wang, Peter J.H. Jones

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of ManitobaUniversity of TorontoCargill (Canada)
FundersSaskatchewan Pulse Growers
KeywordsMaltodextrinFood scienceMealGlycemicCrossover studyIngredientCalorieResistant starchStarchChemistryMedicineInsulinSpray dryingInternal medicine

Abstract

fetched live from OpenAlex

The food industry has shown interest in incorporating pulse ingredients into commercially available products so that consumers can more easily obtain the health benefits of pulses. Ready‐to‐drink beverages are a product type of interest; however the impact of incorporating pulse flours with different macronutrient content into beverages on glycemic response is unknown. Beverages often lead to high glycemic responses, but whether pulse flours can be used in beverage products to control glycemia requires investigation. The objective of this study was to test the effects of different faba bean (FB) flours in smoothies on the post‐prandial blood glucose before and after a later meal. In a repeated‐measures crossover trial, adult males (n=15) randomly consumed a 300 kcal smoothie containing: (1) 32 g corn maltodextrin (control), (2) 32 g whole FB flour (FB whole flour), (3) 33 g high starch FB flour (FB starch), (4) 32 g protein concentrate made from FB flour (FB protein concentrate), and (5) 32 g protein isolate made from FB flour (FB protein isolate). FB flours and maltodextrin contributed 40% of calories in smoothies. Blood glucose incremental area under the curve (iAUC) from 0–120 min (pre‐meal), 120–200 min (post‐meal) and 0–200 min (total) was calculated. For pre‐meal blood glucose, time (p<0.0001), treatment (p<0.0001) and time‐by‐treatment effects (p<0.0001) were observed, whereas there was only a time (p<0.0001) and time‐by‐treatment interaction (p=0.0003), but no treatment (p=0.08) effect on post‐meal blood glucose. At 15 min, blood glucose was lower (p<0.05) following FB protein concentrate smoothie compared to control. At 30 min, blood glucose was lower (p<0.05) after FB protein concentrate and FB protein isolate compared to FB whole flour, FB starch and control smoothies. The blood glucose response following FB whole flour smoothie was also lower (p<0.05) compared to control at 30 min. At 45 min, blood glucose was lower (p<0.05) after all FB smoothies compared to control and after FB protein isolate, and FB protein concentrate compared to FB starch smoothie. At 60 min, blood glucose was lower (p<0.05) after FB whole flour, FB protein concentrate, and FB protein isolate smoothies compared to control. At 90 min, blood glucose was lower (p<0.05) after all FB smoothies compared to control. At 170 min, blood glucose was lower (p<0.05) after FB protein isolate compared to FB starch smoothie. An effect of treatment on both pre‐meal (p<0.0001), post‐meal (p=0.007) and total (p=0.003) iAUC were observed. All FB flours led to lower (p<0.05) pre‐pizza blood glucose iAUC compared with control. However, post‐meal blood glucose iAUC was lower (p<0.05) following FB protein concentrate compared to FB starch smoothie. Total blood glucose iAUC was lower (p<0.05) following FB protein concentrate, and FB protein isolate smoothies compared to control. These data suggest that FB flours, particularly protein concentrate and isolate, can be used in smoothies designed for improved post‐prandial glycemic control. Support or Funding Information Saskatchewan Pulse Growers

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.277
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes3
Has abstractyes

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