Dietary fish oil reduces glomerular injury and elevated renal hydroxyeicosatetraenoic acid levels in the JCR:LA-<i>cp</i>rat, a model of the metabolic syndrome
Bibliographic record
Abstract
We have previously shown nutritional intervention with fish oil (n-3 PUFA) to reduce numerous complications associated with the metabolic syndrome (MetS) in the JCR:LA-corpulent (cp) rat. In the present study, we sought to explore the potential role of fish oil to prevent glomerulosclerosis in JCR:LA-cp rats via renal eicosanoid metabolism and lipidomic analysis. Male lean and MetS JCR:LA-cp rats were fed a lipid-balanced diet supplemented with fish oil (5 or 10 % of total fat). After 16 weeks of feeding, albuminuria was significantly reduced in MetS rats supplemented with 5 or 10 % fish oil ( - 53 and - 70 %, respectively, compared with the untreated MetS rats). The 5 % fish oil diet resulted in markedly lower glomerulosclerosis ( - 43 %) in MetS rats and to a lesser extent in those supplemented with 10 % fish oil. Interestingly, untreated MetS rats had higher levels of 11- and 12-hydroxyeicosatetraenoic acids (HETE) v. lean rats. Dietary fish oil reduced these levels, as well as other (5-, 9- and 15-) HETE. Whilst genotype did not alter prostanoid levels, fish oil reduced endogenous renal levels of 6-keto PGF1α (PGI2 metabolite), thromboxane B2 (TxB2), PGF2α and PGD2 by approximately 60 % in rats fed 10 % fish oil, and TxB2 ( - 50 %) and PGF2α ( - 41 %) in rats fed 5 % fish oil. In conclusion, dietary fish oil prevented glomerular damage in MetS rats and mitigated the elevation in renal HETE levels. These results suggest a potential role for dietary fish oil to improve dysfunctional renal eicosanoid metabolism associated with kidney damage during conditions of the 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".