Daily Intake of Grape Powder Prevents the Progression of Kidney Disease in Obese Type 2 Diabetic ZSF1 Rats
Bibliographic record
Abstract
Individuals living with metabolic syndrome (MetS) such as diabetes and obesity are at high risk for developing chronic kidney disease (CKD). This study investigated the beneficial effect of whole grape powder (WGP) diet on MetS-associated CKD. Obese diabetic ZSF1 rats, a kidney disease model with MetS, were fed WGP (5%, w/w) diet for six months. Kidney disease was determined using blood and urine chemical analyses, and histology. When compared to Vehicle controls, WGP intake did not change the rat bodyweight, but lowered their kidney, liver and spleen weight, which were in parallel with the lower serum glucose and the higher albumin or albumin/globin ratio. More importantly, WGP intake improved the renal function as urination and proteinuria decreased, or it prevented kidney tissue damage in these diabetic rats. The renal protection of WGP diet was associated with up-regulation of antioxidants (Dhcr24, Gstk1, Prdx2, Sod2, Gpx1 and Gpx4) and downregulation of Txnip (for ROS production) in the kidneys. Furthermore, addition of grape extract reduced H2O2-induced cell death of cultured podocytes. In conclusion, daily intake of WGP reduces the progression of kidney disease in obese diabetic rats, suggesting a protective function of antioxidant-rich grape diet against CKD in the setting of MetS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".