Obesity in familial hypercholesterolaemia: when precision medicine should meet precision population health
Bibliographic record
Abstract
Management of obesity in patients living with heterozygous familial hypercholesterolaemia (HeFH). (A) Current data indicate that overweight/obesity is associated with increased risk of premature coronary artery disease (CAD) in patients with HeFH. On that basis, a key remaining question is whether weight loss induced by a negative energy balance or by pharmacotherapy of obesity would lower CAD risk beyond pharmacologically reduced LDL particle concentration in these patients. (B) In the general population, cardiometabolic imaging studies have revealed that body mass index (BMI) is a black box that cannot properly assess individual variation in visceral adipose tissue accumulation and of fat deposition in normally lean tissues (e.g. heart, liver, and skeletal muscle), a phenomenon described as ectopic fat deposition. Waist circumference as a simple anthropometric index of abdominal adiposity has been shown to discriminate health risk at any BMI value. (B) Also highlights the fact that ‘lifestyle vital signs’ (cardiorespiratory fitness [CRF], physical activity [PA] and exercise, and overall diet quality) are also significant correlates of cardiovascular disease risk at any BMI value. Thus, whether going beyond BMI and weight-based strategies would be of value in the management of patients living with HeFH remains an important question to be addressed. Finally, as patients with HeFH are exposed to the same ‘chronic disease-promoting environment’ as the rest of the population, the illustration also emphasizes the need to target living/environmental/socioeconomic determinants of health.
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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 | 0.090 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.017 |
| 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".