Abstract 127: Risk Factors for Cardiovascular Disease in Heterozygous Familial Hypercholesterolemia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objectives: To quantify the association between risk factors and cardiovascular disease (CVD) in heterozygous familial hypercholesterolemia (FH). Design: Systematic review, meta-analysis, and meta-regression. Data sources: MEDLINE, EMBASE, Global Health, the Cochrane Library, and PubMed for full-text articles published in English from 1990 - 2018. Methods: Studies reporting adjusted associations between: (1) traditional cardiovascular risk factors; (2) personal health practices; or (3) clinical characteristics and CVD with ≥ 100 participants (50 participants with and 50 without CVD) were included. A sole reviewer extracted data on study characteristics, risk factors, and outcomes of interest, with independent verification by a second reviewer. Odds ratios with 95% confidence intervals (CIs) were calculated for selected risk factors with random effects meta-analysis, from which values for population-attributable fraction and case impact number were derived. We also performed heterogeneity and publication bias investigations, five sensitivity analyses (by study size, design, adjustment, country and year of publication), and meta-regression. Results: We identified 25 studies representing 52,034 participants and 9,751 CVD events. Age [OR: 1.06; 95% CI: 1.05, 1.10], male sex [OR: 1.79; 95% CI: 1.47, 2.18], hypertension [OR: 1.76; 95% CI: 1.41, 2.21], diabetes [OR: 1.60; 95% CI: 1.18, 2.16], body mass index [OR: 1.04; 95% CI: 1.03, 1.05], smoking [OR: 1.69; 95% CI: 1.45, 1.98], lipoprotein a [OR: 1.78; 95% CI: 1.22, 2.60], high-density lipoprotein cholesterol [OR: 1.39; 95% CI: 1.20, 1.61], low-density lipoprotein (p = 0.045) and total cholesterol (p < 0.001) were associated with increased CVD. Smoking, hypertension, and diabetes were associated with 1 in 8, 1 in 10, and 1 in 38 cases of CVD, respectively. However, they accounted for a lower proportion of CVD risk in FH individuals than reported in the general population. Results were broadly consistent in sensitivity analyses. Conclusions: Several clinical risk factors are significantly and independently associated with CVD risk in patients with FH and should be targeted for risk factor modification. These data can also inform the selection of variables for prediction models to aid in risk stratifying 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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".