Skeletal Muscle Lipogenic Protein Expression Is Not Different between Lean and Obese Individuals: A Potential Factor in Ceramide Accumulation
Bibliographic record
Abstract
CONTEXT: Skeletal muscle lipid content is increased in obesity. Recent evidence suggests that fatty acid (FA) storage as triacylglycerol (TAG) represents a metabolically safe pool compared to the more bioactive diacylglycerol (DAG) and ceramide. OBJECTIVE/DESIGN: The purpose of this study was to compare the expression of lipogenic proteins and ceramide and DAG content in skeletal muscle of lean and obese humans. We hypothesized that lipogenic protein expression would be increased in obese to facilitate the storage of excess FA as TAG. PARTICIPANTS: Eighteen lean (BMI < or = 26 kg/m(2)) and 15 obese (BMI > 29 kg/m(2)) women participated in this study. RESULTS: There was no difference in the expression of any lipogenic (stearoyl-CoA desaturase-1, stearoyl retinol binding protein-1c, mitochondrial glycerol-3-phosphate acyltransferase, diacylglycerol acyltransferase-1) or sphingolipid proteins measured between lean and obese humans. Total ceramide was increased in muscle from obese humans (lean vs. obese, 529.4 +/- 54.8 vs. 672.4 +/- 57.4 nmol/g; P < 0.05), but there was no difference in total DAG content (lean vs. obese, 2244.1 +/- 278.2 vs. 1941.4 +/- 165.0 nmol/g). Content of protein phosphatase 2A, a ceramide target, was increased in muscle of obese humans (P < 0.05). CONCLUSIONS: We propose that in muscle of obese humans there is an insufficient lipogenic response to the lipid oversupply, allowing more FA to be stored as reactive lipid species, particularly ceramide, potentially contributing to subsequent metabolic complications.
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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 | 0.001 |
| 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".