Inter‐relationship Between the <i>In vivo</i> Metabolism of Apolipoprotein B <sub>100</sub> ‐Containing Lipoproteins and LDL Particle Size and LDL Particle Number
Bibliographic record
Abstract
Studies have shown that small dense LDL particles confer an increased risk of coronary heart disease (CHD) compared with large LDL. LDL particle number is also an important risk factor for CHD. The objective was to investigate the inter‐relationship between the in vivo kinetics of apolipoprotein (apo) B100‐containing lipoproteins and LDL particle size (LDLsi) and LDL particle number (LDL‐apoB). This analysis is based on data from 154 male and female subjects among whom in vivo lipoprotein kinetics were investigated using a bolus/infusion of D 3 ‐leucine. LDLsi was assessed by non‐denaturing polyacrylamide gradient gel electrophoresis. Participants' mean age (±SD) was 44.7±12.6 yrs. Mean body mass index (BMI) and triglyceride levels were 28.6±4.7 kg/m 2 and 1.97±1.4 mmol/l respectively. LDLsi correlated positively with plasma adiponectin levels (age and BMI‐adjusted Spearman r=0.41, P<0.001) and the fractional catabolic rate (FCR) of VLDL‐apoB (r=0.43, P<0.001) and negatively with plasma TG (r=‐0.42 P<0.001) and the pool size of VLDL‐apoB (r=‐0.38, P<0.001). Plasma LDL‐apoB levels correlated positively with the production rate of VLDL‐apoB (r=0.27, P=0.006) and negatively LDL‐apoB FCR (r=‐0.59, P<0.001). LDL‐apoB showed no correlation with plasma TG (r=0.07, P=0.47), LDLsi (r=0.18, P=0.08) and adiponectin (r=‐0.02, P=0.82). These data suggest that LDL size and LDL particle number are determined by distinct metabolic pathways. Funding provided by the Canadian Institutes of Health Research and the Chair of Nutrition, Université Laval
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".