Comparison between Gradient Gel Electrophoresis and Nuclear Magnetic Resonance Spectroscopy in Estimating Coronary Heart Disease Risk Associated with LDL and HDL Particle Size
Bibliographic record
Abstract
BACKGROUND: Gradient gel electrophoresis (GGE) and nuclear magnetic resonance (NMR) spectroscopy are both widely accepted methods for measuring LDL and HDL particle size. However, whether or not GGE- or NMR-measured LDL or HDL particle size predicts coronary heart disease (CHD) risk to a similar extent is currently unknown. METHODS: We used GGE and NMR to measure LDL and HDL particle size in a nested case-control study of 1025 incident cases of CHD and 1915 controls from the EPIC (European Prospective Investigation into Cancer and Nutrition)-Norfolk study. The study sample included apparently healthy men and women age 45-79 years followed for an average of 6 years. RESULTS: Pearson correlation coefficients showed that the overall agreement between NMR and GGE was better for the measurement of HDL size (r = 0.78) than for LDL size (r = 0.47). The odds ratio for future CHD among participants in the bottom tertile of LDL size (smallest LDL particles) was 1.35 (95% CI, 1.12-1.63) for GGE and 1.74 (1.41-2.15) for NMR. For HDL size, these respective odds ratios were 1.41 (1.16-1.72) and 1.85 (1.47-2.32). After adjustment for potential confounders, the relationship between small LDL or HDL particles and CHD was no longer significant, irrespective of the method. CONCLUSIONS: In this prospective population study, we found that the relationships between NMR-measured LDL and HDL sizes and CHD risk were slightly higher than those obtained with GGE.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".