Do mice bred selectively for high locomotor activity have a greater reliance on lipids to power submaximal aerobic exercise?
Bibliographic record
Abstract
Patterns of fuel use during locomotion are determined by exercise intensity and duration, and are remarkably similar across many mammalian taxa. However, as lipids have a high yield of ATP per mole and are stored in large quantities, their use should be favored in endurance-adapted animals. To examine the capacity for alteration or differential regulation of fuel-use patterns, we studied two lines of mice that had been selectively bred for high voluntary wheel running (HR), including one characterized by small hindlimb muscles (HR(mini)) and one without this phenotype (HR(normal)), as well as a nonselected control line. We evaluated: 1) maximal aerobic capacity (Vo(2 max)); 2) whole body fuel use during exercise by indirect calorimetry; 3) cardiac properties; and 4) many factors involved in regulating lipid use. HR mice achieved an increased Vo(2 max) compared with control mice, potentially in part due to HR cardiac capacities for metabolic fuel oxidation and the larger relative heart size of HR(mini) mice. HR mice also exhibited enhanced whole body lipid oxidation rates at 66% Vo(2 max), but HR(mini), HR(normal), and control mice did not differ in the proportional mix of fuels sustaining exercise (% total Vo(2)). However, HR(mini) gastrocnemius muscle had elevated fatty acid translocase (FAT/CD36) sarcolemmal protein and cellular mRNA, fatty acid binding protein (H-FABP) cytosolic protein, peroxisome proliferator-activated receptor (PPAR) α mRNA, and mass-specific activities of citrate synthase, β-hydroxyacyl-CoA dehydrogenase, and hexokinase. Therefore, high-running mouse lines had whole body fuel oxidation rates commensurate with maximal aerobic capacity, despite notable differences in skeletal muscle metabolic phenotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".