Effect of Gastric Bypass Surgery on the Absorption and Bioavailability of Metformin
Bibliographic record
Abstract
OBJECTIVE: Use of gastric bypass surgery is common and increasing. Over 40% of patients in diabetes remission after gastric bypass surgery may redevelop diabetes within 5 years. Metformin, the first-line drug for diabetes, has low bioavailability and slow, incomplete gastrointestinal absorption. We hypothesized that gastric bypass would further reduce the absorption and bioavailability of metformin. RESEARCH DESIGN AND METHODS: In a nonblinded, single-dose pharmacokinetic study, 16 nondiabetic post-gastric bypass patients and 16 sex- and BMI-matched control subjects (mean age 40 years and BMI 39.2 kg/m(2)) were administered two 500-mg metformin tablets. Plasma metformin levels were sampled at 0.5, 1, 1.5, 2, 3, 4, 6, 8, and 24 h. Metformin absorption, estimated by the area under the curve (AUC) of the plasma drug concentrations from time 0 to infinity (AUC(0-∞)), was the primary outcome, and metformin bioavailability, assessed by measuring 24-h urine metformin levels, was a secondary outcome. RESULTS: Compared with control subjects, metformin AUC(0-∞) was increased in gastric bypass subjects by 21% (13.7 vs. 11.4 μg/mL/h; mean difference 2.3 [95% CI -1.3 to 5.9]) and bioavailability was increased by 50% (41.8 vs. 27.8%; 14.0 [4.1-23.9]). Gastric bypass patients had significantly lower AUC glucose levels over 8 h compared with control subjects (35.8 vs. 41.7 μg/mL/h; 5.9 [3.1-8.8]), but this was likely a result of differences in baseline fasting glucose and not metformin absorption. CONCLUSIONS: Metformin absorption and bioavailability seem to be higher after gastric bypass, and this may have implications on dosing and toxicity risk. Studies are needed to confirm these findings and delineate potential mechanisms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".