Multi‐Target Modulation of Polyphenols in Diabetic Kidney Disease Therapeutics: A Comprehensive Review
Bibliographic record
Abstract
Diabetic kidney disease (DKD) is a severe complication of diabetes that presents as progressive kidney dysfunction and is the primary cause of end-stage kidney failure. Despite therapeutic advances, including the use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, sodium-glucose co-transporter-2 inhibitors, glucagon-like peptide-1 agonists, and non-steroidal mineralocorticoid receptor antagonists, managing DKD remains challenging. Current therapies mainly focus on glycemic control, hypertension management, and albumin reduction to mitigate kidney damage. Nevertheless, these approaches often fail to halt disease progression or restore renal function. There is therefore an urgent need for therapies with safer profiles that can be used singly to target the disease's underlying pathophysiology or integrated into traditional care. Polyphenols possess biological properties capable of addressing these unmet needs by targeting multiple underlying mechanisms involved in DKD pathogenesis. The literature search occurred between September 2024 and April 2025, with most articles sourced from the last 5 years. This review explores polyphenol classes that have demonstrated nephroprotective effects in vitro, in vivo, and in clinical trials. It also highlights the interconnected multi-pathways and molecular mediators potentially regulated by specific polyphenols for kidney function improvement. This multi-target therapeutic approach is especially beneficial for DKD, where several metabolic dysfunctions underlie its pathogenesis. Identifying polyphenols as a therapeutic option could lead to integrative patient care that embraces the strengths of conventional medicine and phytomedicine for better disease management and outcomes. There is therefore a need for more clinical trials to assess polyphenols' safety and efficacy in managing DKD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 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".