Interaction between dietary protein and fat in triglyceride metabolism in the rat: Effects of soy protein and menhaden oil
Bibliographic record
Abstract
The objective of the present study was to determine the mechanisms by which dietary proteins interact with dietary lipids in the regulation of triglyceridemia in rats. Male Sprague-Dawley rats (n = 56) were subjected to 28-d experimental diets containing different combinations of proteins (20% w/w) and lipid sources (14% w/w): (i) casein-menhaden oil, (ii) casein-beef tallow, (iii) soy protein-menhaden oil, and (iv) soy protein-beef tallow. Significant protein-lipid interactions were observed on triglyceridemia and hepatic cholesterol in fasted rats. The combination of casein and beef tallow was associated with high plasma TG and hepatic cholesterol concentrations, which were reduced by substitution either of soy for casein or of menhaden oil for beef tallow. Therefore, triglyceridemia and liver cholesterol remained low with soy protein feeding, independently of the lipid source, as well as with menhaden oil feeding, regardless of the protein source. The menhaden oil diets reduced plasma cholesterol, hepatic TG, and TG secretion compared with beef tallow diets independently of the dietary protein source. Modifying the source of dietary proteins and lipids had no effect on post-heparin plasma lipoprotein lipase activity. These results demonstrate that soy protein can lower rat triglyceridemia relative to casein when associated with beef tallow consumption, whereas menhaden oil can attenuate hypertriglyceridemia when rats are fed casein. The data further suggest that part of the hypotriglyceridemic effect of soy protein in the rat may be mediated by reduced hepatic lipid synthesis, as is the case for menhaden oil.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".