Lipoprotein Particle Profiles, Standard Lipids, and Peripheral Artery Disease Incidence
Bibliographic record
Abstract
Background: Despite strong and consistent prospective associations of elevated low-density lipoprotein (LDL) cholesterol concentration with incident coronary and cerebrovascular disease, data for incident peripheral artery disease (PAD) are less robust. Atherogenic dyslipidemia characterized by increased small LDL particle (LDL-P) concentration, rather than total LDL cholesterol content, along with elevated triglyceride-rich lipoproteins and low high-density lipoprotein (HDL) cholesterol (HDL-C), may be the primary lipid driver of PAD risk. Methods: The study population was a prospective cohort study of 27 888 women ≥45 years old free of cardiovascular disease at baseline and followed for a median of 15.1 years. We tested whether standard lipid concentrations, as well as nuclear magnetic resonance spectroscopy–derived lipoprotein measures, were associated with incident symptomatic PAD (n=110) defined as claudication and/or revascularization. Results: In age-adjusted analyses, while LDL cholesterol was not associated with incident PAD, we found significant associations for increased total and small LDL-P concentrations, triglycerides, and concentrations of very LDL (VLDL) particle (VLDL-P) subclasses, increased total cholesterol (TC):HDL-C, low HDL-C, and low HDL particle (HDL-P) concentration (all P for extreme tertile comparisons <0.05). Findings persisted in multivariable-adjusted models comparing extreme tertiles for elevated total LDL-P (adjusted hazard ratio [HR adj ] 2.03; 95% CI, 1.14–3.59), small LDL-P (HR adj 2.17; 95% CI, 1.10–4.27), very large VLDL-P (HR adj 1.68; 95% CI, 1.06–2.66), medium VLDL-P (HR adj 1.98; 95% CI, 1.15–3.41), and TC:HDL-C (HR adj , 3.11; 95% CI, 1.67–5.81). HDL was inversely associated with risk; HR adj for extreme tertiles of HDL-C and HDL-P concentration were 0.30 ( P trend < 0.0001) and 0.29 ( P trend < 0.0001), respectively. These components of atherogenic dyslipidemia, including small LDL-P, medium and very large VLDL-P, TC:HDL-C, HDL-C, and HDL-P, were more strongly associated with incident PAD than incident coronary and cerebrovascular disease. Finally, the addition of LDL-P and HDL-P concentration to TC:HDL-C measures identified women at heightened PAD risk. Conclusions: In this prospective study, nuclear magnetic resonance–derived measures of LDL-P, but not LDL cholesterol, were associated with incident PAD. Other features of atherogenic dyslipidemia, including elevations in TC:HDL-C, elevations in triglyceride-rich lipoproteins, and low standard and nuclear magnetic resonance–derived measures of HDL, were significant risk determinants. These data help clarify prior inconsistencies and may elucidate a unique lipoprotein signature for PAD compared to coronary and cerebrovascular disease. Clinical Trial Registration: URL: https://www.clinicaltrials.gov/ . Unique Identifier: NCT00000479.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".