Dietary phenolic compounds as promising therapeutic agents for diabetes and its complications: A comprehensive review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it