Assessment of the Longer Term Effects of a Dietary Portfolio of Cholesterol Lowering Foods in Hypercholesterolemia
Bibliographic record
Abstract
Objective To determine the effectiveness under real‐world conditions of dietary advice to consume a combination of cholesterol‐lowering foods (dietary portfolio)compared with published data from the same subjects eating the same diet under metabolic conditions or taking a statin. Methods For six months sixty‐six hyperlipidemic subjects were prescribed diets high in plant sterols (1.0 g/1000 kcal), soy protein (22.5 g/1000 kcal), viscous fibers (10 g/1000 kcal) and almonds (23 g/1000 kcal). Sixty‐one subjects completed the six‐month study. Four‐week data were also compared with published results on the same subjects (n=29) who had undergone separate one‐month metabolic trials of the diet and a statin. Results At 12 and 24 weeks, LDL‐cholesterol reductions appeared stable at 14.0±1.6% (P<0.001) and 13.1±1.6% (P<0.001), respectively. These reductions were significantly less than following the metabolic diet or a statin. Nevertheless, 32% of subjects on the ad libitum diet (n= 21/66) showed LDL‐cholesterol reductions ≥20% (mean 27.1±1.1%). The LDL‐C reductions in this group were not different from their respective metabolically controlled portfolio or statin treatments. A correlation was found between total compliance and LDL reduction (r=0.43, P<0.001). Only two subjects with less than 55% compliance (n=25) achieved ≥20% LDL‐cholesterol reduction at 24‐weeks. Conclusions Just over 30% of motivated subjects who ate the dietary portfolio of cholesterol‐lowering foods under real world conditions were able to lower LDL‐cholesterol ≥20%, which was not significantly different from their response to a first generation statin, taken under metabolically controlled conditions. Support: Almond Board of California
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".