Glucagon Like Peptide 1 and Evidence Around Diabetes Specific Nutrition
Bibliographic record
Abstract
There has been significant research and development in pharmaco-therapeutic molecules for management of type 2 diabetes mellitus (T2DM). Diabetes specific nutrition intervention & newer incretin-based therapies have gained a lot of attention. Since incretins play an essential role in augmenting the post-prandial release of insulin, it is important to understand the science behind incretins and modulation of same by diabetic-specific nutrition (DSN). The purpose of this article is to summarize the available science around glucagon like peptide 1 (GLP-1) and the known role of nutrition, particularly diabetes specific nutrition. Literature published in PubMed, Google scholar and Embase were studied up to the end of August 2018. The key words of GLP-1, T2DM and Nutrients were used in different combinations. It was found that macronutrient aspects of DSN like complex carbohydrate, soluble fibre, proteins and high monounsaturated fatty acids augment GLP-1 secretion from intestinal L-cells. This may be attributed to insulin-trophic effects of DSN as well as its effects in causing deceleration in gastric emptying and reducing food intake. Hence, it was concluded that augmenting GLP-1 secretion in response to the intake of certain nutrients helps in modulating insulin secretion, metabolic homeostasis as well as decelerating gastric emptying and reducing food intake. DSN increases endogenous GLP-1 secretion which in turn improves insulin secretion and sensitivity. Thus, integrating DSN in mainstay diabetes management plans may result in better glycaemic and metabolic controls, particularly when GLP-1 based therapies are concurrently in use. Key Messages: DSN containing complex carbohydrate, soluble fibre, proteins and high monounsaturated fatty acids (MUFAs) augments GLP-1 secretion which in turn improves insulin secretion and sensitivity.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".