Global Mapping of Flavonoids in Fruits and Vegetables: Targeting Insulin Receptors, <scp>GLP</scp> ‐ <scp>1R</scp> , and <scp>PPARs</scp> to Mitigate Diabetes
Bibliographic record
Abstract
Flavonoids, bioactive compounds abundant in fruits and vegetables, have great potential as natural therapeutic agents for managing type 2 diabetes (T2D) through the modulation of key molecular targets such as insulin receptors (IRs), GLP-1 receptors, and peroxisome proliferator-activated receptors. This paper aims to provide an in-depth review of the flavonoids found in various fruits and vegetables and the musculoskeletal mechanisms underlying their therapeutic potential in addressing diabetes through interactions with IRs, GLP-1/R, and PPARs. A systematic search was conducted across major databases, including PubMed, Scopus, Web of Science, Cochrane Library, and Embase. A combination of a comprehensive literature review approach and in silico analysis revealed that the compounds such as hesperidin, quercetin, luteolin and genistein showed significant affinity for these targets, even exceeding those of some conventional drugs such as metformin. These findings not only reinforce the scientific evidence on the benefits of flavonoid compounds in improving insulin sensitivity and regulating glucose and lipid metabolism, but also open up opportunities for the development of more sustainable and affordable nutrition-based therapies. It is important to acknowledge, however, that while promising, further high-quality studies, including large-scale clinical trials, are needed to firmly establish the clinical efficacy and optimal application of these flavonoid-based interventions in human populations. In addition, the study will also highlight the importance of an interdisciplinary approach between pharmacology, nutrition, and computational technology in facing evolving public health challenges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".