Modulation of Strawberry/Cranberry Phenolic Compounds Glucuronidation by Co-Supplementation with Onion: Characterization of Phenolic Metabolites in Rat Plasma Using an Optimized μSPE–UHPLC-MS/MS Method
Bibliographic record
Abstract
Plant phenolic compounds are suggested to exert pharmacological activities in regards to obesity and type-2 diabetes, but their mode of action is poorly understood due to a lack of information about their bioavailability. This work aimed to study the bioavailability of GlucoPhenol phenolic compounds, a strawberry-cranberry extracts blend, by characterizing plasma phenolic profile in obese rats. A comparison was performed by co-supplementation with an onion extract. Using an optimized μSPE-UHPLC-MS/MS method, 21 phenolic metabolites were characterized, mostly conjugated metabolites and microbial degradation products of the native phenolic compounds. Their kinetic profiles revealed either an intestinal or hepatic formation. Among identified metabolites, isorhamnetin glucuronide sulfate was found in greater amount in plasma. Three glucuronidated conjugates of strawberry-cranberry phenolic compounds, p-hydroxybenzoic acid glucuronide, catechins glucuronide, and methyl catechins glucuronide were found in higher quantities when GlucoPhenol was ingested together with onion extract (+252%, +279%, and +118% respectively), suggesting a possible induction of glucuronidation processes by quercetin. This work allowed the characterization of actual phenolic metabolites generated in vivo following a phenolic intake, the analysis of their kinetics and suggested a possible synergistic activity of phenolic compounds for improving bioavailability.
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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) | 0.000 | 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 | 0.000 |
| 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".