Effects of Pea Protein on Satiety, Postprandial Glucose Response and Appetite Hormones: A Literature Review
Bibliographic record
Abstract
Introduction: Type 2 Diabetes (T2D) is one of the leading causes of mortality with obesity being one of the greatest risk factors. Increased protein intake has been found to increase satiety, that could potentially aid in weight control. However, much of the research is elusive on the specifics of the effects of plant-based protein, specifically pea protein on satiety and responses linked to appetite. The purpose of this review was to investigate the effects of pea protein on satiety, postprandial glucose response and appetite. Methods: Studies of the existing literature were found, filtered, and analyzed from scientific databases Cochrane Library, PubMed, ScienceDirect, and Web of Science entering a combination of the keywords “pea protein”, “satiety”, and “postprandial response”. A total of 11 articles were analyzed to determine the relationship between pea protein consumption and postprandial response of satiety and appetite. Results: Pea protein consumption as a preload increased satiety and lowered food intake between 30 and 120 minutes after ingestion. Postprandial blood glucose was lowered and various appetite hormones increased at different time lapses. Discussion: Although the oral consumption of pea protein alone was seen to effectively induce satiety, other factors such as the addition of fibre, the method of administration, or rates of gastric emptying could significantly affect food intake. Conclusion: This literature review establishes a link between plant proteins and its benefits of feelings of satiety and appetite to promote incorporating more plant proteins in the diet. Future research should further investigate the link between postprandial responses and appetite hormones to identify benefits of pea protein for use in the food industry and increase public consumption of pea protein.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".