Local and Systemic Effects of Bioactive Food Ingredients: Is There a Role for Functional Foods to Prime the Gut for Resilience?
Bibliographic record
Abstract
Scientific advancements in understanding the impact of bioactive components in foods on the gut microbiota and wider physiology create opportunities for designing targeted functional foods. The selection of bioactive ingredients with potential local or systemic effects holds promise for influencing overall well-being. An abundance of studies demonstrate that gut microbiota show compositional changes that correlate age and disease. However, navigating this field, especially for non-experts, remains challenging, given the abundance of bioactive ingredients with varying levels of scientific substantiation. This narrative review addresses the current knowledge on the potential impact of the gut microbiota on host health, emphasizing gut microbiota resilience. It explores evidence related to the extensive gut health benefits of popular dietary components and bioactive ingredients, such as phytochemicals, fermented greens, fibres, prebiotics, probiotics, and postbiotics. Importantly, this review distinguishes between the potential local and systemic effects of both popular and emerging ingredients. Additionally, it highlights how dietary hormesis promotes gut microbiota resilience, fostering better adaptation to stress-a hallmark of health. By integrating examples of bioactives, this review provides insights to guide the design of evidence-based functional foods aimed at priming the gut for resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".