Correlates of Coronary Artery Calcification Prevalence and Severity in Patients With Heterozygous Familial Hypercholesterolemia
Bibliographic record
Abstract
Background Determinants of coronary artery calcification (CAC) prevalence and severity in heterozygous familial hypercholesterolemia (HeFH) remain understudied. The objective of this cross-sectional study was to investigate correlates of CAC in patients with HeFH. Methods A CAC score was calculated by a noncontrast computed tomography scan in women (n = 68) and men (n = 78) with genetically defined HeFH. We classified CAC prevalence and severity using 3 categories: CAC score = 0 Agatston Unit (AU), CAC score = 1-100 AU, and CAC score > 100 AU. Information on potential correlates of CAC including familial and personal health history, cardiovascular risk factors, lipid-lowering medication, and lifestyle habits was collected. Results A total of 95 patients had prevalent CAC. Independent correlates of CAC prevalence and severity included age (odds ratio [OR] per 10 years: 5.06, 95% confidence interval [CI]: 3.19, 7.93, P < 0.0001), family history of premature cardiovascular disease (OR: 3.88, 95% CI: 1.71, 8.81, P = 0.001), male sex (OR: 3.40, 95% CI: 1.49, 7.78, P = 0.004), statin use (OR: 15.5, 95% CI: 1.89, 126, P = 0.01), diet quality assessed with the Alternative Healthy Eating Index score (OR per 1 standard deviation: 0.59, 95% CI: 0.39, 0.90, P = 0.01), ever smoking (OR: 3.06, 95% CI: 1.20, 7.81, P = 0.02), receptor-negative genotype (OR: 3.17, 95% CI: 1.16, 8.66, P = 0.02), lipoprotein(a) year-score (OR per 1 standard deviation of log-transformed year-score: 1.53, 95% CI: 0.99, 2.36, P = 0.05). Conclusions In individuals with HeFH, age, family history of premature cardiovascular disease, sex, statin use, diet quality, smoking status, the LDLR genotype, and lipoprotein(a) concentrations were independently associated with CAC prevalence and severity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".