The effect of adding monounsaturated fat to a dietary portfolio of cholesterol‐lowering foods in hypercholesterolemia
Bibliographic record
Abstract
Background Diets high in monounsaturated fatty acids (MUFA) may raise HDL‐C without raising LDL‐C. We have therefore tested whether increasing the MUFA content of an effective LDL‐C lowering diet (dietary portfolio) increases HDL‐C and further reduces the total:HDL‐C ratio, two key risk factors for cardiovascular disease. Methods 24 hyperlipidemic subjects took a very low‐saturated‐fat therapeutic diet for one month and then were randomized to a low‐ or high‐MUFA dietary portfolio for a further month. Results Following the very low‐saturated‐fat therapeutic control diet, HDL‐C rose 13.1±3.6% (P=0.004) with the high‐MUFA dietary portfolio versus 2.7±3.6% (P=0.466) with the low‐MUFA dietary portfolio (treatment difference, P=.004). The respective figures for the total:HDL‐C ratio were 24.5±2.1% (P<0.001) and 17.6±3.0% (P<0.001); treatment difference (P=0.006). These treatment differences were associated with significantly higher ApoA1 concentrations on high‐MUFA dietary portfolio. CRP was also significantly reduced on the high‐MUFA dietary portfolio. Conclusions Monounsaturated fat increased the effectiveness of a cholesterol‐lowering dietary portfolio and may reduce cardiovascular risk through reducing CRP, raising HDL‐C and further lowering the ratio of total:HDL‐C. (Funded by CIHR; Canada Research Chair endowment; Loblaw Company)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".