Metabolomic response to exercise training in lean and diet-induced obese mice
Bibliographic record
Abstract
Exercise training is a common therapeutic approach known to antagonize the metabolic consequences of obesity. The aims of the present study were to examine 1) whether short-term, moderate-intensity exercise training alters the basal metabolite profile and 2) if 10 days of mild exercise training can correct obesity-induced shifts in metabolic spectra. After being weaned, male C57BL/6J littermates were randomly divided into two diet groups: low fat (LF) or high fat (HF). After 12 wk of dietary manipulation, HF animals were obese and hyperglycemic compared with LF animals. Mice from each group were further divided into sedentary or exercise treatments. Exercise training consisted of wheel running exercise (2 h/day, 10 days, 5.64 m/min). After exercise training, animals were rested (36 h) and fasted (6 h) before serum collection. Samples were analyzed by high-resolution one-dimensional proton NMR. Fifty high- and medium-concentration metabolites were identified. Pattern recognition algorithms and multivariate modeling were used to identify and isolate significant metabolites changing in response to HF and exercise training. The results showed that while exercise can mitigate some of the abnormal patterns in metabolic spectra induced by HF diet feeding, they cannot negate it. In fact, when the effects of diet and exercise were compared, diet was a stronger predictor and had the larger influence on the metabolic profile. External validation of models showed that diet could be correctly classified with an accuracy of 89%, whereas exercise training could be classified 73% of the time. The results demonstrate metabolomics to effectively characterize obesity-induced perturbations in metabolism and support the concept that exercise is beneficial for this condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".