Swim Training Reduces Metformin Levels in Fructose-Induced Insulin Resistant Rats
Bibliographic record
Abstract
PURPOSE: Regular exercise training and metformin medication are widely considered to increase insulin sensitivity and protect against type 2 diabetes, however, evaluating the effect of exercise training on the disposition and pharmacokinetics of metformin is unclear. METHODS: We investigated the effect of a 4-wk swim training program (45 min/day, 5 days/wk) on the pharmacokinetics for the use of metformin in fructose-induced insulin resistant rats. Fructose-induced insulin resistant rats were assigned into two groups (n=6/group): swim training with metformin (SM), and non-swim training with metformin (CM). Blood samples were collected from 12 h-fasted rats at baseline and at 0.25, 0.5, 0.75, 1, 2, 3, 4, 6, 8, 10, and 12 h after an oral glucose tolerance test (OGTT) with administration of a single dose of metformin (450 mg/kg). RESULTS: Our study revealed that both glucose and insulin levels in the SM group were significantly lower than those in the CM group at 15 min following OGTT. The maximum concentration (C(max)) and area under the serum concentration-time curve (AUC) for the SM group were significantly lower than CM group. The apparent distribution volume (Vd) and the time-averaged total body clearance (CL) for the SM group were significantly higher than those for the CM group. There were no significant differences in the time to maximum concentration (T(max)) or the time to half-life concentration (t(1/2)) between the two groups. CONCLUSIONS: Our data demonstrate that swim training reduces metformin serum levels.
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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 | 0.000 | 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 | 0.001 |
| 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".