Fanconi Anemia Links Reactive Oxygen Species to Insulin Resistance and Obesity
Bibliographic record
Abstract
AIMS: Insulin resistance is a hallmark of obesity and type 2 diabetes. Reactive oxygen species (ROS) have been proposed to play a causal role in insulin resistance. However, evidence linking ROS to insulin resistance in disease settings has been scant. Since both oxidative stress and diabetes have been observed in patients with the Fanconi anemia (FA), we sought to investigate the link between ROS and insulin resistance in this unique disease model. RESULTS: Mice deficient for the Fanconi anemia complementation group A (Fanca) or Fanconi anemia complementation group C (Fancc) gene seem to be diabetes-prone, as manifested by significant hyperglycemia and hyperinsulinemia, and rapid weight gain when fed with a high-fat diet. These phenotypic features of insulin resistance are characterized by two critical events in insulin signaling: a reduction in tyrosine phosphorylation of the insulin receptor (IR) and an increase in inhibitory serine phosphorylation of the IR substrate-1 in the liver, muscle, and fat tissues from the insulin-challenged FA mice. High levels of ROS, spontaneously accumulated or generated by tumor necrosis factor alpha in these insulin-sensitive tissues of FA mice, were shown to underlie the FA insulin resistance. Treatment of FA mice with the natural anti-oxidant Quercetin restores IR signaling and ameliorates the diabetes- and obesity-prone phenotypes. Finally, pairwise screen identifies protein-tyrosine phosphatase (PTP)-α and stress kinase double-stranded RNA-dependent protein kinase (PKR) that mediate the ROS effect on FA insulin resistance. INNOVATION: These findings establish a pathogenic and mechanistic link between ROS and insulin resistance in a unique human disease setting. CONCLUSION: ROS accumulation contributes to the insulin resistance in FA deficiency by targeting both PTP-α and PKR.
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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 | 0.001 | 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".