Mechanisms of hyperglycemia‐ and hyperinsulinemia‐induced adiponectin resistance in cardiomyocytes
Bibliographic record
Abstract
Current literature has indicated that low adiponectin levels in obesity and type2 diabetes are a potential risk factor for heart failure, yet the mechanisms responsible for this remain unclear. Since alterations in myocardial substratae metabolism precede cardiac dysfuntion, in the present study we investigated the effect of globular (gAd) and full‐length multimeric adiponectin forms (fAd) on glucose and fatty acid uptake and metabolism. Differentiated H9c2 rat cardiomyocytes were pre‐treated with high glucose (HG: 25mM, 24h) or high insulin (HI: 100nM, 24h) followed by treatment with gAd (5μg/ml) or fAd (10μg/ml) for up to 2hr. Both gAd and fAd forms of adiponectin stimulated glucose and fatty acid uptake and oxidation, but there was no increase in glycogen synthesis and lactate production. The increase in glucose and fatty acid uptake was attenuated by HI and HG. We observed down regulation of adiponectin receptor1 (AdipoR1) and AdipoR2 mRNA expression by HI and HG in H9C2 cells. Recruitment of GFP‐APPL1 to plasma membrane was induced by adiponectin, although HI and HG did not alter APPL1 expression. The phosphorylation level of AMPK and ACC was stimulated by gAd and fAd, and this was significantly attenuated in HG and HI conditions. In summary, our results indicate that both hyperglycemia and hyperinsulinemia significantly diminish gAd and fAd functions on cardiomyocyte substrate metabolism, likely via decreasing receptor expression, ultimately leading to ‘adiponectin resistance’ in cardiomyocytes.
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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 | 0.001 |
| 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) | 0.001 | 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".