Plasma Metabolite Profiles in Response to Chronic Exercise
Bibliographic record
Abstract
PURPOSE: High-throughput profiling of metabolic status (metabolomics) allows for the assessment of small-molecule metabolites that may participate in exercise-induced biochemical pathways and corresponding cardiometabolic risk modification. We sought to describe the changes in a diverse set of plasma metabolite profiles in patients undergoing chronic exercise training and assess the relationship between metabolites and cardiometabolic response to exercise. METHODS: A secondary analysis was performed in 216 middle-age abdominally obese men and women (mean ± SD, 52.4 ± 8.0 yr) randomized into one of four groups varying in exercise amount and intensity for 6-month duration: high amount high intensity, high amount low intensity, low amount low intensity, and control. One hundred forty-seven metabolites were profiled by liquid chromatography-tandem mass spectrometry. RESULTS: No significant differences in metabolite changes between specific exercise groups were observed; therefore, subsequent analyses were collapsed across exercise groups. There were no significant differences in metabolite changes between the exercise and control groups after 24 wk at a Bonferroni-adjusted statistical significance (P < 3.0 × 10). Seven metabolites changed in the exercise group compared with the control group at P < 0.05. Changes in several metabolites from distinct metabolic pathways were associated with change in cardiometabolic risk traits, and three baseline metabolite levels predicted changes in cardiometabolic risk traits. CONCLUSIONS: Metabolomic profiling revealed no significant plasma metabolite changes between exercise and control after 24 wk at Bonferroni significance. However, we identified circulating biomarkers that were predictive or reflective of improvements in cardiometabolic traits in the exercise group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".