Cardiovascular disease prevention in heterozygous familial hypercholesterolemia: how important is a healthy diet in the era of long-lasting cholesterol-lowering drug therapies?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review aims to provide an in-depth perspective on the importance of diet for cardiovascular disease (CVD) prevention in heterozygous familial hypercholesterolemia (HeFH). RECENT FINDINGS: Even though data on diet and CVD prevention in HeFH are limited, the currently available evidence supports its cholesterol-lowering effect and its favorable association with CVD risk on the long-term. However, qualitative evidence from individuals with HeFH suggests that there is a common perception that diet is useless compared to medication, and this misconception serves as a barrier to healthy eating. On the other hand, evidence also suggests that individuals with HeFH are at higher risk of eating disorders compared with unaffected individuals. Family history of premature death and the chronic nature of the disease would be in cause. SUMMARY: Emphasizing a healthy diet needs to remain at the foundation of CVD prevention in HeFH. Evidence are limited but supportive of the cholesterol-lowering and cardioprotective potential effects of diet. Engaging in conversations about healthy dieting with individuals in HeFH is likely to help prevent misconceptions about diet. Additionally, it could help reduce the risk of eating disorders, which, altogether, is likely to improve overall CVD prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".