Does Encapsulation Improve the Bioavailability of Polyphenols in Humans? A Concise Review Based on In Vivo Human Studies
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Polyphenols offer an array of health benefits that can contribute to well-being. Nevertheless, their bioactivity can be compromised due to their low bioavailability. Encapsulation has been explored as a strategy to enhance the stability and bioavailability of polyphenols. During encapsulation, polyphenols are protected from degradation by a wall material that acts as a protective coating. This coating shields the polyphenols from the harsh physiological conditions of digestion, ensuring their delivery to the intestine. However, the majority of evidence, particularly regarding bioavailability after digestion, is derived from in vitro studies. While these studies provide valuable preliminary insights, they cannot definitively confirm the effects in vivo due to their inability to accurately replicate physiological conditions and the complex gut microbial ecosystem. Consequently, this review seeks to evaluate the current evidence from in vivo human studies to elucidate the efficacy of encapsulation in improving polyphenols' bioavailability. RESULTS AND CONCLUSIONS: Current clinical evidence on the impact of encapsulation on polyphenol bioavailability is primarily focused on polyphenols derived from grape pomace, cocoa, and bilberries, as well as individual polyphenols such as fisetin, hesperidin, and curcumin. Encapsulation has been an effective technique in improving the bioavailability of individual polyphenols like hesperidin, fisetin, and curcumin. However, this approach has not yielded consistent results when applied to groups of polyphenols, such as bilberry anthocyanins or cocoa phenolic acids. Encapsulation by micellization has shown promising results in improving the bioavailability of curcumin in a nutraceutical context. Further studies are needed to explore the bioavailability of encapsulated polyphenols, especially in the functional food context.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".