Does lifestyle contribute to disease severity in patients with inherited lipid disorders?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with familial hypercholesterolemia, familial combined hyperlipidemia and hyperlipoprotein(a) are at high cardiovascular risk. Increasing evidence suggest that lifestyle-related risk factors such as physical inactivity, and poor diet quality could influence cardiovascular risk in these patients. Our objective is to review the evidence that supports the role of lifestyle-related factors in the prediction of cardiovascular risk in patients with inherited lipid disorders. RECENT FINDINGS: Recent studies have shown that smoking, a poor diet quality, physical inactivity, fitness levels, abdominal obesity, insulin resistance, and type 2 diabetes were associated with the presence of atherosclerosis and long-term cardiovascular outcomes in patients with familial hypercholesterolemia. Recent evidence also suggest that managing other cardiovascular risk factors such as cholesterol levels, obesity, glycemic control, blood pressure, smoking, physical inactivity, and diet quality could reduce long-term cardiovascular risk associated with hyperlipoprotein(a). Whether targeting these risk factors could ultimately decrease cardiovascular risk in these patients remains unknown. SUMMARY: Although reducing the number of atherogenic apolipoprotein-B containing particle with lipid-lowering therapy represents the cornerstone of treatment of patients with inherited lipid disorders, lifestyle-related risk factors such as physical inactivity and poor diet quality need to be targeted for the optimal management of these high-risk patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".