Analyses of serum and urinary metabolites in individuals with peripheral artery disease (PAD) consuming a bean-rich diet: relationships with drug metabolites
Bibliographic record
Abstract
Peripheral artery disease (PAD) has high morbidity and mortality rates. A metabolomics approach was employed to determine whether consumption of bean-rich diets for 8 weeks would impact the metabolomic profile of individuals with PAD. Serum and urine, collected from 54 participants with clinical PAD at baseline and after 8 weeks on 0.3 cups beans/day (n = 19), 0.6 cups beans/day (n = 20), or control (n = 23) diet, and the beans were extracted and analyzed using LC-QTOF-MS. As a result, PGE 2 p-acetamidophenyl ester, PGF 2α diethyl amide and 5-l-glutamyl-l-alanine were significantly changed in the serum or urine of bean groups compared with control. Significant changes (P < 0.05) in the profile and/or levels of 22 flavonoids present in bean extracts showed the potential importance of the mixture of beans used in this study. In a subset of participants taking metoprolol, after 8 weeks the bean-rich diets significantly elevated metoprolol in the serum while reducing it in urine compared with baseline. In addition, the diets significantly enhanced the urinary excretion of metformin. In conclusion, several biochemical pathways including prostaglandins and glutathione were affected by bean consumption. Significant changes in the metabolism of metoprolol and metformin with bean consumption suggested the presence of diet-drug interactions that may require adjustment of the prescribed dose. ClinicalTrials.gov Identifier: NCT01382056. Novelty: Bean consumption by people with PAD alters the levels of certain metabolites in serum and urine. Different bean types (black, red kidney, pinto, navy) have unique flavonoid profiles. Metabolomics revealed potential diet–drug interactions as serum and/or urinary levels of metoprolol and metformin are modified by bean consumption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".