Identifying Markers of Cardiovascular Event-Free Survival in Familial Hypercholesterolemia
Bibliographic record
Abstract
Familial hypercholesterolemia (FH) is an autosomal dominant trait characterized by elevated low-density lipoprotein-cholesterol (LDL-C) concentrations appearing at birth and is associated with increased risk of premature atherosclerotic cardiovascular disease (CVD). However, in some cases, FH subjects over 70 years of age have surprisingly never experienced any CVD symptoms throughout their entire lives. The objective of this study consists of identifying biological and environmental markers acting as cardioprotective factors and associated with unexpected survival in FH. Upon age and reported cardiovascular events (CVE) stratification, we identified a total of 458 French–Canadian FH subjects with premature reported CVE, and 1297 young adults as well as 24 elderly subjects (≥70 years) who have never reported CVE requiring hospitalization. Logistic regression models were used to depict cardioprotective markers among FH survivors (≥70 years). Regression analyses of the FH cohort showed that female sex (odds ratio (OR) = 12.92 (4.23–39.46); p < 0.0001), high levels of high-density lipoprotein (HDL)-C (OR = 6.76 (2.43–18.79); p = 0.0002) and elevated concentrations of adiponectin (OR = 71.40 (5.20–980.47); p = 0.001) were significant contributory factors in reducing FH-related CVD risk. Notably, female (OR = 11.45 (1.25–105.98); p = 0.031) and high HDL-C (OR = 9.78 (1.75–54.67); p = 0.009) were shown to be significant covariates associated with survival in FH. Non-smoking (OR = 11.73 (4.36–31.56); p < 0.0001) was also identified as an environmental factor associated with CVE-free survival. Based on this configured model of premature CVE occurrence, these results demonstrated that, beyond LDL-C levels, female sex, high HDL-C, elevated adiponectin and non-smoking are important markers that contribute to a reduced risk of CVD and CVE-free survival in FH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".