HDL-cholesterol concentration and its association with coronary artery calcification: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Coronary artery calcification (CAC) is a potential risk marker of coronary atherosclerosis that has high specificity and sensitivity. However, the association between high-density lipoprotein cholesterol (HDL-C) concentration and CAC incidence and progression is controversial. METHODS: PubMed, Embase, Web of Science, and Scopus were systematically searched to identify relevant observational studies up to March 2023 and assessed the methodological quality using Newcastle-Ottawa Scale (NOS) scale. Random-effects meta-analysis was used to estimate pooled odds ratios (OR) and 95% confidence interval considering heterogeneity across studies. RESULTS: Of the 2,411 records, 25 cross-sectional (n = 71,190) and 13 cohort (n = 25,442) studies were included in the systematic review. Ten cross-sectional and eight cohort studies were not eligible and were omitted from the meta-analysis. A total of 15 eligible cross-sectional studies (n = 33,913) were included in the meta-analysis and pooled results revealed no significant association between HDL-C and CAC > 0, CAC > 10, or CAC > 100 [pooled OR: 0.99 (0.97, 1.01)]. Meta-analysis of the 5 eligible prospective cohort studies (n = 10,721) revealed no significant protective effect of high HDL-C against CAC > 0 [pooled OR: 1.02 (0.93, 1.13)]. CONCLUSIONS: According to this analysis of observational studies, high HDL-C levels were not found to predict protection against CAC. These results suggest HDL quality rather than HDL quantity is important for certain aspects of atherogenesis and CAC. REGISTRATION NUMBER: CRD42021292077.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 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 teacher head, 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".