Inhibition of Inflammatory Cytokine Expression Prevents High-Fat Diet-Induced Kidney Injury: Role of Lingonberry Supplementation
Bibliographic record
Abstract
L.) is rich in anthocyanins with demonstrated anti-inflammatory effect. In the current study, we investigated the potential renal protective effect of lingonberry and its anthocyanin (cyanidin-3-glucoside) in high-fat diet fed obese mice and in human proximal tubular cells. Prolonged consumption of high-fat diets is strongly associated with obesity, abnormal lipid and glucose metabolism. Mice (C57BL/6J) fed a high-fat diet (62% kcal fat) for 12 weeks developed renal injury as indicated by an elevation of blood urea nitrogen (BUN) level as well as an increase in renal kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL) and renin expression. Those mice displayed an activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and increased expression of inflammatory cytokines-monocyte chemoattractant-1 (MCP-1), tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6) in the kidneys. Mice fed a high-fat diet also had a significant elevation of inflammatory cytokine levels in the plasma. Dietary supplementation of lingonberry for 12 weeks not only attenuated high-fat diet-induced renal inflammatory response but also reduced kidney injury. Such a treatment improved plasma lipid and glucose profiles, reduced plasma inflammatory cytokine levels but did not affect body weight gain induced by high-fat diet feeding. Lingonberry extract or its active component cyanidin-3-glucoside effectively inhibited palmitic acid-induced NF-κB activation and inflammatory cytokine expression in proximal tubular cells. These results suggest that lingonberry supplementation can reduce inflammatory response and prevent chronic kidney injury. Such a renal protective effect by lingonberry and its active component may be mediated, in part, through NF-κB signaling pathway.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".