High fat diet is protective against kidney injury in hypertensive-diabetic mice, but leads to liver injury
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a worldwide health burden with increases risk of end-stage renal function if left untreated. CKD induced in the context of metabolic syndrome (MS) increases risks of hypertension, hyperglycemia, excess body fat and dyslipidemia. To test if combining a high-fat diet (HFD) regimen onto the hypertensive/ diabetic phenotype would mimic features of MS induced-CKD in mice, hyperglycemia was induced in genetically hypertensive mice (Lin), followed by HFD regimen. For that, 8-week-old male were subjected to streptozotocin (STZ) intraperitoneal (i.p.) injections (50 mg/kg, 5 days consecutive). LinSTZ were fed a 60% kCal HFD for 8 weeks. Lin mice treated with STZ developed polydipsia, became hypertensive and hyperglycemic. HFD induced weight gain, protected against glomerular hypertrophy, scarring, and albuminuria at endpoint compared to regular diet fed LinSTZ. On the other hand, HFD induced steatosis, liver fibrosis, inflammation, and increase in AST/ALT ratio, characteristics of non-alcoholic liver disease. Taken together, our results show that LinSTZ mice fed a HFD did not lead to a more robust model of MS-induced CKD, protected against kidney injury, but inducing liver damage. More studies are necessary to understand the kidney protective mechanisms of HFD when superimposed with hypertension and type 1 diabetes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".