Lipid emulsion structure, digestion behavior, physiology, and health: a scoping review and future directions
Bibliographic record
Abstract
Research investigating the effects of the food matrix on health is needed to untangle many unresolved questions in nutritional science. Emulsion structure plays a fundamental role in this inquiry; however, the effects of oil-in-water emulsion structure on broad metabolic, physiological, and health-related outcomes have not been comprehensively reviewed. This systematic scoping review targets this gap and examines methodological considerations for the field of relating food structure and health. MEDLINE, Web of Science, and CAB Direct were searched from inception to December 2022, returning 3106 articles, 52 of which were eligible for inclusion. Many investigated emulsion lipid droplet size and/or gastric colloidal stability and their relation to postprandial weight-loss-related outcomes. The present review also identifies numerous novel relationships between emulsion structures and health-related outcomes. "Omics" endpoints present an exciting avenue for more comprehensive analysis in this area, yet interpretation remains difficult. Identifying valid surrogate biomarkers for long-term outcomes and disease risk will be a turning point for food structure research, leading to breakthroughs in the pace and utility of research that generates advancements in health. The review's findings and recommendations aim to support new hypotheses, future trial design, and evidence-based emulsion design for improved health and well-being.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".