Overview of food products and dietary constituents with antidiabetic properties and their putative mechanisms of action: A natural approach to complement pharmacotherapy in the management of diabetes
Bibliographic record
Abstract
Diabetes is one of the fastest growing chronic, noncommunicable diseases worldwide. Currently, 11 major classes of pharmacotherapy are available for the management of this metabolic disorder. However, the usage of these drugs is often associated with undesirable side effects, including weight gain and hypoglycemia. There is thus a need for new, safe and effective treatment strategies. Diet is known to play a major role in the prevention and management of diabetes. Numerous studies have reported the putative association of the consumption of specific food products, or their constituents, with the incidence of diabetes, and mounting evidence now suggests that some dietary factors can improve glycemic regulation. Foods and dietary constituents, similar to synthetic drugs, have been shown to modulate hormones, enzymes, and organ systems involved in carbohydrate metabolism. The present article reviews the major classes and modes of action of antidiabetic drugs, and examines the evidence on food products and dietary factors with antidiabetic properties as well as their plausible mechanisms of action. The findings suggest potential use of dietary constituents as a complementary approach to pharmacotherapy in the prevention and/or management of diabetes, but further research is necessary to identify the active components and evaluate their efficacy and safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".