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.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".