Analysis of Iron Dysregulation in High‐Fat and Iron‐Deficient Rat Models
Bibliographic record
Abstract
Iron (Fe) dysregulation has been identified as a contributor to insulin resistance (IR). Fe chelators and phlebotomy have been shown to improve insulin sensitivity. Due to the central role of the liver in Fe regulation, the objective of our study was to examine hepatic total Fe and critical Fe regulatory proteins in both high‐fat (HF) fed model of IR as well as a model of Fe deficiency (FeDEF). METHODS Rats in the HF group were fed a diet with 50% of calories from fat (lard and corn oil) or standard chow for a four‐week period. For the FeDEF model, rats were either fed an FeDEF diet (2–6 mg/kg Fe) or a control diet (50–58 mg/kg Fe). Western blots quantified levels for ferroportin (FPN), ferritin heavy chain (FHC), and ferritin light chain (FLC). Colorimetric analysis was used to quantify total hepatic Fe. RESULTS FPN and ferritin levels were reduced in the FeDEF group (FPN: −49%, p<0.01; ferritin: −44.7%, p < 0.01). While FHC did not show a significant difference in the HF group, FLC levels were elevated (42.1%, p<0.05) and FPN levels were reduced (−56.2%, p<0.05). Total hepatic Fe was found to be elevated in the HF group compared to control (64.1%, p < 0.05). CONCLUSIONS As expected, in the FeDEF group we observed reductions in both Fe storage proteins (ferritin) and the Fe export protein (FPN) compared to control. Interestingly, with the HF group we observed an increase in both Fe storage proteins and total hepatic Fe while the Fe exporting protein FPN was reduced. Our findings suggest the development of IR due to HF feeding is associated with the dysregulation of hepatic Fe. Support or Funding Information Funding was provided by Brigham Young University
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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 | 0.002 | 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) | 0.002 | 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".