A comprehensive insight into the effect of glutamine supplementation on metabolic variables in diabetes mellitus: a systematic review
Bibliographic record
Abstract
Diabetes mellitus is one of the most important threats to human health in the twenty-first century. The use of complementary and alternative medicine to prevent, control, and reduce the complications of diabetes mellitus is increasing at present. Glutamine amino acid is known as a functional food. The purpose of this systematic review is to determine the potential role of glutamine supplementation on metabolic variables in diabetes mellitus. For this review, PubMed, SCOPUS, Embase, ProQuest, and Google Scholar databases were searched from inception through April 2020. All clinical trial and animal studies assessing the effects of glutamine on diabetes mellitus were eligible for inclusion. 19 studies of 1482 articles met the inclusion criteria. Of the 19 studies, nine studies reported a significant increase in serum GLP-1 levels. Also, eight studies showed reducing in serum levels of fasting blood sugar, four studies reducing in postprandial blood sugar, and triglyceride after glutamine supplementation. Although glutamine resulted in a significant increase in insulin production in seven studies, the findings on Hb-A1c levels were inconclusive. In addition to, despite of the results was promising for the effects of glutamine on weight changes, oxidative stress, and inflammation, more precise clinical trials are needed to obtain more accurate results. In conclusion, glutamine supplementation could improve glycemic control and levels of incretins (such as GLP-1 and GIP) in diabetes mellitus. However, more studies are needed for future studies.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".