Determination of exercise modality employing serum metabolomics profiling in type 2 diabetes: relation to clinical outcomes
Bibliographic record
Abstract
It is generally acknowledged that exercise training, both aerobic and resistance, is conducive to health and decreases chronic disease risk. However, little information exists on type of exercise that confers the greatest benefit. The purpose of this study is to use metabolomics strategy to i) interrogate the effects of exercise training modality on serum biochemistry and ii) compare changes in metabolomic profiles with clinical data (i.e. weight, BMI, HbA1c) to determine any correlation. Individuals with type 2 diabetes (n=40) were recruited as a part of the Diabetes Aerobic Resistance Exercise (DARE) study. After a 4‐week run‐in program, individuals were randomly assigned into one of four groups: control, aerobic, resistance or a combination of aerobic + resistance training for 22wk. Serum was collected at baseline and at 6mo. Serum samples were analyzed for metabolites using gas‐chromatography‐ mass spectrometry (GC‐MS) and Proton Nuclear Magnetic Resonance Spectroscopy (H1‐NMR). Changes in metabolism involved metabolites such as amino acids, organic acid, and carbohydrates. Aerobic exercise elevated the rate of tricarboxylic acid cycle and antioxidant activity, while resistance exercise lead to hypertrophy and urea markers. In summary, GC‐MS and NMR based metabolomics analysis proved to effectively reveal exercise modality and explain differential clinical outcomes between treatment groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".