Acute Effects of Hemp Protein on Post‐prandial Glycemia and Insulin Responses in Adults
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».