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Lipoprotein Particle Profiles, Standard Lipids, and Peripheral Artery Disease Incidence

2018· article· en· W2901244157 on OpenAlexaff
Aaron W. Aday, Patrick R. Lawler, Nancy R. Cook, Paul M. Ridker, Samia Mora, Aruna D. Pradhan

Bibliographic record

VenueCirculation · 2018
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsUniversity Health Network
FundersNational Cancer InstituteNational Heart, Lung, and Blood Institute
KeywordsMedicineInternal medicineVery low-density lipoproteinLipoprotein particleLipoproteinCholesterolHazard ratioCardiologyPopulationProspective cohort studyDyslipidemiaTriglycerideEndocrinologyApolipoprotein BCoronary artery diseaseDiseaseConfidence interval

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.261
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations142
Published2018
Admission routes1
Has abstractyes

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