Acute Effects of Hemp Protein on Post‐prandial Glycemia and Insulin Responses in Adults
Bibliographic record
Abstract
The benefits of many protein sources on post‐prandial glycemic control have been extensively demonstrated; however, research on the effects of hemp protein is limited. Hemp is a good source of highly digestible protein, with the potential to impact post‐prandial glycemic control. The objective of this study was to examine the effects of hemp protein concentrate consumption on blood glucose and insulin responses before and after a fixed meal compared to soybean protein concentrate, and a carbohydrate control. In a repeated‐measures crossover design, adults (n=16) randomly consumed (1) 40g of hemp protein (hemp40), (2) 20g of hemp protein (hemp20), (3) 40 g of soybean protein (soy40), (4) 20g of soybean protein (soy20), and (5) carbohydrate control. Treatments were given in isocaloric, isovolumetric fruit shakes. A fixed calorie meal was provided at 60 min. Blood glucose and insulin incremental area under the curve (iAUC) was calculated pre‐meal (0–60 min) and post‐meal (60–200 min). Blood glucose response was affected by treatment (p=0.006), time (p<0.0001) and time‐by‐treatment (p<0.0001) from 0– 200 min. In a dose dependent manner, protein treatments led to lower pre‐meal blood glucose overall mean and iAUC. Specially, mean blood glucose response to both hemp40 and soy40 was lower (p<0.05) compared to hemp20, soy20, and control, whereas both hemp20 and soy20 resulted in lower (p<0.05) responses compared to control. In contrast, no difference in response to treatments was observed in post‐meal mean blood glucose levels, however, there was a time‐by‐treatment interaction (p<0.0001), explained by differences in the post‐meal blood glucose response to treatment overtime. Post‐meal, hemp40, soy40, soy20 and hemp20 led to higher (p<0.05) post‐meal blood glucose iAUC compared to control in a dose dependent manner. Insulin was affected by treatment (p=0.0001), time (p<0.0001) and time‐by‐treatment (p<0.0001) from 0–200 min. In the pre‐meal period, hemp40 and soy40 led to lower (p<0.05) overall mean insulin compared to hemp20, soy20, and control. In addition, hemp40 and soy40 led to lower (p<0.05) pre‐meal insulin iAUC compared to hemp20 and control, while soy40 also led to a lower (p<0.05) insulin iAUC compared to soy20. During the post‐meal period, although there were no differences between treatments in the overall mean insulin response, hemp40 led to higher (p<0.05) insulin iAUC compared to control. These data suggest that consumption of hemp protein, similar to soybean protein, dose dependently leads to lower post‐prandial blood glucose over 60 min compared to a carbohydrate control and that insulin follows a similar response pattern. Interestingly, a 40 g dose of hemp protein also leads to higher blood glucose and insulin responses following a meal consumed 60 min later. This increase in glucose and insulin following the fixed meal in response to the hemp40 treatment is novel and should be investigated, both over a longer period of time and in the absence of the fixed meal. These results support the use of hemp protein in fruit shakes to improve glycemic control. Support or Funding Information Governments of Manitoba and Canada through the Growing Forward 2, Growing Innovation – Agri‐Food Research and development Initiative. Matching funds were provided by Manitoba Harvest, Hempro Int. GmbH & Co. KG, and Hemp Oil 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".