Potential mechanisms and consequences of cardiac triacylglycerol accumulation in insulin-resistant rats
Bibliographic record
Abstract
The accumulation of intracellular triacylglycerol (TG) is highly correlated with muscle insulin resistance. However, it is controversial whether the accumulation of TG is the result of increased fatty acid supply, decreased fatty acid oxidation, or both. Because abnormal fatty acid metabolism is a key contributor to the pathogenesis of diabetes-related cardiovascular dysfunction, we examined fatty acid and glucose metabolism in hearts of insulin-resistant JCR:LA-cp rats. Isolated working hearts from insulin-resistant rats had glycolytic rates that were reduced to 50% of lean control levels (P < 0.05). Cardiac TG content was increased by 50% (P < 0.05) in the insulin-resistant rats, but palmitate oxidation rates remained similar between the insulin-resistant and lean control rats. However, plasma fatty acids and TG levels, as well as cardiac fatty acid-binding protein (FABP) expression, were significantly increased in the insulin-resistant rats. AMP-activated protein kinase (AMPK) plays a major role in the regulation of cardiac fatty acid and glucose metabolism. When activated, AMPK increases fatty acid oxidation by inhibiting acetyl-CoA carboxylase (ACC) and reducing malonyl-CoA levels, and it decreases TG content by inhibiting glycerol-3-phosphate acyltransferase (GPAT), the rate-limiting step in TG synthesis. The activation of AMPK also stimulates cardiac glucose uptake and glycolysis. We thus investigated whether a decrease in AMPK activity was responsible for the reduced cardiac glycolysis and increased TG content in the insulin-resistant rats. However, we found no significant difference in AMPK activity. We also found no significant difference in various established downstream targets of AMPK: ACC activity, malonyl-CoA levels, carnitine palmitoyltransferase I activity, or GPAT activity. We conclude that hearts from insulin-resistant JCR:LA-cp rats accumulate substantial TG as a result of increased fatty acid supply rather than from reduced fatty acid oxidation. Furthermore, the accumulation of cardiac TG is associated with a reduction in insulin-stimulated glucose metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".