Unraveling the Inhibition of Intestinal Glucose Transport by Dietary Phenolics: A Review
Bibliographic record
Abstract
BACKGROUND: Glucose transport across the intestinal brush border membrane plays a key role in metabolic regulation. Depending on the luminal glucose concentration, glucose is mainly transported by the sodium- dependent glucose transporter (SGLT1) and the facilitated-transporter glucose transporter (GLUT2). SGLT1 is apical membrane-constitutive and it is active at a low luminal glucose concentration, while at concentrations higher than 50 mM, glucose is mainly transported by GLUT2 (recruited from the basolateral membrane). Dietary phenolic compounds can modulate glucose homeostasis by decreasing the postprandial glucose response through the inhibition of SGLT1 and GLUT2. METHODS: Phenolic inhibition of intestinal glucose transport has been examined using brush border membrane vesicles from rats, pigs or rabbits, Xenopus oocytes and more recently Caco-2 cells, which are the most promising for harmonizing in vitro experiments. RESULTS: Phenolic concentrations above 100 µM has been proved to successfully inhibit the glucose transport. Generally, the aglycones quercetin, myricetin, fisetin or apigenin have been reported to strongly inhibit GLUT2, while quercetin-3-O-glycoside has been demonstrated to be more effective in SGLT1. Additionally, epigallocatechin as well as epicatechin and epigallocatechin gallates were observed to be inhibited on both SGLT1 and GLUT2. CONCLUSION: Although, valuable information regarding the phenolic glucose transport inhibition is known, however, there are some disagreements about which flavonoid glycosides and aglycones exert significant inhibition, and also the inhibition of phenolic acids remains unclear. This review aims to collect, compare and discuss the available information and controversies about the phenolic inhibition of glucose transporters. A detailed discussion on the physicochemical mechanisms involved in phenolics-glucose transporters interactions is also included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".