Adding MUFA to a dietary portfolio of cholesterol-lowering foods reduces apoAI fractional catabolic rate in subjects with dyslipidaemia
Bibliographic record
Abstract
The present randomised parallel study assessed the impact of adding MUFA to a dietary portfolio of cholesterol-lowering foods on the intravascular kinetics of apoAI- and apoB-containing lipoproteins in subjects with dyslipidaemia. A sample of sixteen men and postmenopausal women consumed a run-in stabilisation diet for 4 weeks. Subjects were then randomly assigned to an experimental dietary portfolio either high or low in MUFA for another 4 weeks. MUFA substituted 13·0% of total energy from carbohydrate (CHO) in the high-MUFA dietary portfolio. Lipoprotein kinetics were assessed after the run-in and portfolio diets using a primed, constant infusion of [2H3]leucine and multicompartmental modelling. The high-MUFA dietary portfolio resulted in higher apoAI pool size (PS) compared with the low-MUFA dietary portfolio (15·9% between-diet difference, P¼0·03). This difference appeared to be mainly attributable to a reduction in apoAI fractional catabolic rate (FCR) after the high-MUFA diet (25·6%, P¼0·02 v. pre-diet values), with no significant change in production rate. The high-MUFA dietary portfolio tended to reduce LDL apoB100 PS compared with the low-MUFA dietary portfolio (228·5% between-diet that adding MUFA to a dietary portfolio of cholesterol-lowering foods provides the added advantage of raising HDL primarily through a reduction in HDL clearance rate. Replacing CHO with MUFA in a dietary portfolio may also lead to reductions in LDL apoB100 concentrations primarily by increasing LDL clearance rate, thus potentiating further the well-known cholesterol-lowering effect of this diet.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".