Elevated intramyocellular lipid concentration in obese subjects is not reduced after diet and exercise training
Bibliographic record
Abstract
To determine the effects of weight loss on intramyocellular energy substrates, vastus lateralis muscle biopsies were taken from six obese subjects (body mass index 34 +/- 5 kg/m(2)) before, after 15 wk of energy restriction (ER; -700 kcal/day), and after a further average 20.7 +/- 1.6 wk of endurance training plus low-fat diet (ET-LFD). Body weight fell from 100 +/- 6 to 89 +/- 6 kg during ER and to 84 +/- 4 kg after ET-LFD. Lipids and glycogen were histochemically measured in type I, IIA, and IIB fibers. Total muscle glycogen content (MGC; per 100 fibers) decreased after ER [from 72 +/- 13 to 55 +/- 8 arbitrary units (AU)]. A similar but not significant decrease was seen in total muscle lipid content (MLC; 14 +/- 5 to 9 +/- 1 AU). After ET-LFD, MGC returned to initial values (74 +/- 8 AU), and MLC approached near-initial values (12 +/- 3 AU). Individual fiber lipid concentration did not change throughout the protocol in all fiber types, whereas glycogen concentration increased after ET-LFD. The training effects of ET-LFD were measured as increasing activities of key mitochondrial enzymes. Although total muscle energy reserves can be reduced after weight loss, their concentration within individual myofibers remains elevated. Weight loss does not appear sufficient to correct the potential detrimental effects of high intracellular lipid concentrations.
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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 | 0.000 |
| 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".