Recent advances in dietary proteins and lipid metabolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is growing interest in defining the dietary approaches for the management of lipid disorders. This review focuses on dietary proteins. RECENT FINDINGS: Increasing protein intake was coupled with improved lipid profiles in humans and animals. However, most studies increased the protein content by reducing that of fat or carbohydrate or both, making unclear the role of protein alone. Mechanisms of action differ with the sources of proteins, because of amino acid composition and bioactive peptides encrypted in their sequences. Soy protein was investigated the most, and many studies show that its consumption reduces blood cholesterol. The role of other constituents including isoflavones is debated. Short-term studies are consistent in showing lipid-lowering properties of whey proteins, attributed to their relative high content in branched chain amino acids. A limited number of studies, the majority being on animals, have shown hypocholesterolemic activities of fish proteins. SUMMARY: Dietary proteins regulate lipid metabolism in a manner dependent on their quantity and composition. There is a general consensus that proteins slow lipid absorption and synthesis, and promote lipid excretion. The benefits of dietary proteins remain to be confirmed in individuals with lipid abnormalities, for formulation of optimal dietary alternatives for the management of lipid disturbances.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".