Rapid development of systemic insulin resistance with overeating is not accompanied by robust changes in skeletal muscle glucose and lipid metabolism
Bibliographic record
Abstract
Prolonged overeating and the resultant weight gain are clearly linked with the development of insulin resistance and other cardiometabolic abnormalities, but adaptations that occur after relatively short periods of overeating are not completely understood. The purpose of this study was to characterize metabolic adaptations that may accompany the development of insulin resistance after 2 weeks of overeating. Healthy, nonobese subjects (n = 9) were admitted to the hospital for 2 weeks, during which time they ate ∼4000 kcals·day(-1) (70 kcal·kg(-1) fat free mass·day(-1)). Insulin sensitivity was estimated during a meal tolerance test, and a muscle biopsy was obtained to assess muscle lipid accumulation and protein markers associated with insulin resistance, inflammation, and the regulation of lipid metabolism. Whole-body insulin sensitivity declined markedly after 2 weeks of overeating (Matsuda composite index: 8.3 ± 1.3 vs. 4.6 ± 0.7, p < 0.05). However, muscle markers of insulin resistance and inflammation (i.e., phosphorylation of IRS-1-Ser(312), Akt-Ser(473), and c-Jun N-terminal kinase) were not altered by overeating. Intramyocellular lipids tended to increase after 2 weeks of overeating (triacylglyceride: 7.6 ± 1.6 vs. 10.0 ± 1.8 nmol·mg(-1) wet weight; diacylglyceride: 104 ± 10 vs. 142 ± 23 pmol·mg(-1) wet weight) but these changes did not reach statistical significance. Overeating induced a 2-fold increase in 24-h insulin response (area under the curve (AUC); p < 0.05), with a resultant ∼35% reduction in 24-h plasma fatty acid AUC (p < 0.05). This chronic reduction in circulating fatty acids may help explain the lack of a robust increase in muscle lipid accumulation. In summary, our findings suggest alterations in skeletal muscle metabolism may not contribute meaningfully to the marked whole-body insulin resistance observed after 2 weeks of overeating.
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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) | 0.001 | 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 | 0.001 |
| 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".