Association of LDL-cholesterol subfractions with cardiovascular disorders: a systematic review
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disorders (CVDs) are the leading cause of death worldwide. This study aimed to evaluate the association between low-density lipoprotein (LDL) subfractions and cardiovascular disorders. METHODS: To ensure the rigor of the systematic review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. For this systematic review, a comprehensive search strategy was performed in important databases including PubMed, Scopus, Embase, International Statistical Institute (ISI) Web of Science, and google scholar from 2009 to February 2021. The following terms were used for systematic search: low-density lipoprotein, LDL, subfractions, subclasses, nuclear magnetic resonance, NMR, chromatography, high-pressure liquid, HPLC, cardiovascular disease, cerebrovascular, and peripheral vascular disease. Also, for evaluating the risk of bias, the Newcastle-Ottawa scale was employed. RESULTS: At the end of the search process, 33 articles were included in this study. The results of most of the evaluated studies revealed that a higher LDL particle number was consistently associated with increased risk for cardiovascular disease, independent of other lipid measurements. Also, small dense LDL was associated with an increased risk of CVDs. There was no association between LDL subfraction and CVDs in a small number of studies. CONCLUSIONS: Overall, it seems that the evaluation of LDL subclasses can be used as a very suitable biomarker for the assessment and diagnosis of cardiovascular diseases. However, further studies are required to identify the mechanisms involved.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".