Effect of weight loss on lactate transporter expression in skeletal muscle of obese subjects
Bibliographic record
Abstract
The effects of weight loss on skeletal muscle lactate transporter [monocarboxylate transporter (MCT)] expression in obese subjects were investigated to better understand how lactate transporter metabolism is regulated in insulin-resistant states. Ten obese subjects underwent non-macronutrient-specific energy restriction for 15 wk. Anthropometric measurements and a needle biopsy of the vastus lateralis muscle before and after the weight loss program were performed. Enzymatic activity, fiber type distribution, and skeletal muscle MCT protein expression were measured. Muscle from nonobese control subjects was used for comparison of MCT levels. The program induced a weight loss of 9.2 +/- 1.6 kg. Compared with controls, muscle from obese subjects showed a strong tendency (P = 0.06) for elevated MCT4 expression (+69%) before the weight loss program. MCT4 expression decreased (-7%) following weight loss to reach levels that were not statistically different from control levels. There were no differences in MCT1 expression between controls and obese subjects before and after weight loss. A highly predictive regression model (R2 = 0.93), including waist circumference, citrate synthase activity, and percentage of type 1 fibers, was found to explain the highly variable MCT1 response to weight loss in the obese group. Therefore, in obesity, MCT1 expression appears linked both to changes in oxidative parameters and to changes in visceral adipose tissue content. The strong tendency for elevated expression of muscle MCT4 could reflect the need to release greater amounts of muscle lactate in the obese state, a situation that would be normalized with weight loss as indicated by decreased MCT4 levels.
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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".