Susceptibility to Fatty Acid-Induced β-Cell Dysfunction Is Enhanced in Prediabetic Diabetes-Prone BioBreeding Rats: A Potential Link Between β-Cell Lipotoxicity and Islet Inflammation
Bibliographic record
Abstract
β-Cell lipotoxicity is thought to play an important role in the development of type 2 diabetes. However, no study has examined its role in type 1 diabetes, which could be clinically relevant for slow-onset type 1 diabetes. Reports of enhanced cytokine toxicity in fat-laden islets are consistent with the hypothesis that lipid and cytokine toxicity may be synergistic. Thus, β-cell lipotoxicity could be enhanced in models of autoimmune diabetes. To determine this, we examined the effects of prolonged free fatty acids elevation on β-cell secretory function in the prediabetic diabetes-prone BioBreeding (dp-BB) rat, its diabetes-resistant BioBreeding (dr-BB) control, and normal Wistar-Furth (WF) rats. Rats received a 48-h iv infusion of saline or Intralipid plus heparin (IH) (to elevate free fatty acid levels ~2-fold) followed by hyperglycemic clamp or islet secretion studies ex vivo. IH significantly decreased β-cell function, assessed both by the disposition index (insulin secretion corrected for IH-induced insulin resistance) and in isolated islets, in dp-BB, but not in dr-BB or WF, rats, and the effect of IH was inhibited by the antioxidant N-acetylcysteine. Furthermore, IH significantly increased islet cytokine mRNA and plasma cytokine levels (monocyte chemoattractant protein-1 and IL-10) in dp-BB, but not in dr-BB or WF, rats. All dp-BB rats had mononuclear infiltration of islets, which was absent in dr-BB and WF rats. In conclusion, the presence of insulitis was permissive for IH-induced β-cell dysfunction in the BB rat, which suggests a link between β-cell lipotoxicity and islet inflammation.
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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 | 0.001 | 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".