Iron overload reduces adiponectin receptor expression via a ROS/FOXO1‐dependent mechanism leading to adiponectin resistance in skeletal muscle cells
Bibliographic record
Abstract
Iron overload (IO) is a common yet underappreciated finding in metabolic syndrome (MetS) patients. With the prevalence of MetS continuing to rise, it is imperative to further elucidate cellular mechanisms leading to metabolic dysfunction. Adiponectin has many beneficial effects and is a therapeutic target for the treatment of MetS and cardiovascular diseases. IO positively correlates with reduced circulating adiponectin levels yet the impact of IO on adiponectin action is unknown. Here, we established a model of IO in L6 skeletal muscle cells and found that IO-induced adiponectin resistance. This was shown via reduced p38 mitogen-activated protein kinase phosphorylation in response to the small molecule adiponectin receptor (AdipoR) agonist, AdipoRon, in presence of IO. This correlated with reduced messenger RNA and protein levels of AdipoR1 and its facilitative signaling binding partner, APPL1. IO caused phosphorylation, nuclear extrusion, and thus inhibition of FOXO1, a known transcription factor regulating AdipoR1 expression. The antioxidant N-acetyl cystine attenuated the production of reactive oxygen species (ROS) by IO, and blunted its effect on FOXO1 phosphorylation and removal from the nucleus, as well as subsequent adiponectin resistance. In conclusion, our study identifies a ROS/FOXO1/AdipoR1 axis as a cause of skeletal muscle adiponectin resistance in response to IO. This new knowledge provides insight into a cellular mechanism with potential relevance to disease pathophysiology in MetS patients with IO.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".