Skeletal muscle adaptation to fatty acid depends on coordinated actions of the PPARs and PGC1α: implications for metabolic disease
Bibliographic record
Abstract
Dyslipidemia and intramuscular accumulation of fatty acid metabolites are increasingly recognized as core features of obesity and type 2 diabetes. Emerging evidence suggests that normal physiological adaptations to a heavy lipid load depend on the coordinated actions of broad transcriptional regulators such as the peroxisome proliferator activated receptors (PPARs) and PPAR gamma coactivator 1 alpha (PGC1 alpha). The application of transcriptomics and targeted metabolic profiling tools based on mass spectrometry has led to our finding that lipid-induced insulin resistance is a condition in which upregulation of PPAR-targeted genes and high rates of beta-oxidation are not supported by a commensurate upregulation of tricarboxylic acid (TCA) cycle activity. In contrast, exercise training enhances mitochondrial performance, favoring tighter coupling between beta-oxidation and the TCA cycle, and concomitantly restores insulin sensitivity in animals fed a chronic high-fat diet. The exercise-activated transcriptional coactivator, PGC1 alpha, plays a key role in coordinating metabolic flux through these 2 intersecting metabolic pathways, and its suppression by overfeeding may contribute to diet-induced mitochondrial dysfunction. Our emerging model predicts that muscle insulin resistance arises from a mitochondrial disconnect between beta-oxidation and TCA cycle activity. Understanding of this "disconnect" and its molecular basis may lead to new therapeutic approaches to combatting metabolic disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".