β-Cell Insulin Resistance Plays a Causal Role in Fat-Induced β-Cell Dysfunction In Vitro and In Vivo
Bibliographic record
Abstract
In the classical insulin target tissues of liver, muscle, and adipose tissue, chronically elevated levels of free fatty acids (FFA) impair insulin signaling. Insulin signaling molecules are also present in β-cells where they play a role in β-cell function. Therefore, inhibition of the insulin/insulin-like growth factor 1 pathway may be involved in fat-induced β-cell dysfunction. To address the role of β-cell insulin resistance in FFA-induced β-cell dysfunction we co-infused bisperoxovanadate (BPV) with oleate or olive oil for 48 hours in rats. BPV, a tyrosine phosphatase inhibitor, acts as an insulin mimetic and is devoid of any antioxidant effect that could prevent β-cell dysfunction, unlike most insulin sensitizers. Following fat infusion, rats either underwent hyperglycemic clamps for assessment of β-cell function in vivo or islets were isolated for ex vivo assessment of glucose-stimulated insulin secretion (GSIS). We also incubated islets with oleate or palmitate and BPV for in vitro assessment of GSIS and Akt (protein kinase B) phosphorylation. Next, mice with β-cell specific deletion of PTEN (phosphatase and tensin homolog; negative regulator of insulin signaling) and littermate controls were infused with oleate for 48 hours, followed by hyperglycemic clamps or ex vivo evaluation of GSIS. In rat experiments, BPV protected against fat-induced impairment of β-cell function in vivo, ex vivo, and in vitro. In mice, β-cell specific deletion of PTEN protected against oleate-induced β-cell dysfunction in vivo and ex vivo. These data support the hypothesis that β-cell insulin resistance plays a causal role in FFA-induced β-cell dysfunction.
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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) | 0.000 | 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 | 0.001 |
| 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".