Dietary phenolic compounds as promising therapeutic agents for diabetes and its complications: A comprehensive review
Bibliographic record
Abstract
In the middle of an ever-changing landscape of diabetes care, precision medicine, and lifestyle therapies are becoming increasingly important. Dietary polyphenols are like hidden allies found in our everyday meals. These biomolecules, found commonly in fruits, vegetables, and various plant-based sources, hold revolutionary potential within their molecular structure in the way we approach diabetes and its intimidating consequences. There are currently numerous types of diabetes medications, but they are not appropriate for all patients due to limitations in dosages, side effects, drug resistance, a lack of efficacy, and ethnicity. Currently, there has been increased interest in practicing herbal remedies to manage diabetes and its related complications. This article aims to summarize the potential of dietary polyphenols as a foundation in the treatment of diabetes and its associated consequences. We found that most polyphenols inhibit enzymes linked to diabetes. This review outlines the potential benefits of selected molecules, including kaempferol, catechins, rosmarinic acid, apigenin, chlorogenic acid, and caffeic acid, in managing diabetes mellitus as these compounds have exhibited promising results in in vitro, in vivo, in silico, and some preclinical trials study. This encompassing exploration reveals the multifaceted impact of polyphenols not only in mitigating diabetes but also in addressing associated conditions like inflammation, obesity, and even cancer. Their mechanisms involve antioxidant functions, immune modulation, and proinflammatory enzyme regulation. Furthermore, these molecules exhibit anti-tumor activities, influence cellular pathways, and activate AMPK pathways, offering a less toxic, cost-effective, and sustainable approach to addressing diabetes and its complications.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".