Mild Cold Stress at Ambient Temperature Elevates Muscle Calcium Cycling and Exercise Adaptations in Obese Female Mice
Bibliographic record
Abstract
CONTEXT: Housing temperature is a critical regulator of mouse metabolism and thermoneutral housing can improve model translation to humans. However, the impact of housing temperature on the ability of wheel running exercise training to rescue the detrimental effect of diet-induced obese mice is currently not fully understood. OBJECTIVE: To investigate how housing temperature affects muscle metabolism in obese mice with regard to calcium handling and exercise training (ET) adaptations in skeletal muscle, and benefits of ET on adiposity and glucometabolic parameters. METHODS: Lean or obese female mice were housed at standard ambient temperature (22 °C) or thermoneutrality (30 °C) with/without access to running wheels. The metabolic phenotype was investigated using glucose tolerance tests, indirect calorimetry, and body composition. Molecular muscle adaptations were measured using immunoblotting, qPCR, and spectrophotometric/fluorescent assays. RESULTS: Obese female mice housed at 22 °C showed lower adiposity, lower circulating insulin levels, improved glucose tolerance, and elevated basal metabolic rate compared to 30 °C housing. Mice exposed to voluntary wheel running exhibited a larger fat loss and higher metabolic rate at 22 °C housing compared to thermoneutrality. In obese female mice, glucose tolerance improved after ET independent of housing temperature. Independent of diet and training, 22 °C housing increased skeletal muscle sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) activity. Additionally, housing at 22 °C elevated the induction of training-responsive muscle proteins in obese mice. CONCLUSION: Our findings highlight that housing temperature significantly influences adiposity, insulin sensitivity, muscle physiology, and exercise adaptations in diet-induced obese female mice.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".