Characterization of LDL Particle Size Among Carriers of a Defective or a Null Mutation in the Lipoprotein Lipase Gene
Bibliographic record
Abstract
OBJECTIVE: The objective of the present study was to compare the impact of the null P207L and defective D9N mutations in the LPL gene on LDL particle size among heterozygous carriers. METHODS AND RESULTS: LDL particle size was measured on whole plasma by 2% to 16% non-denaturing polyacrylamide gradient gel electrophoresis in a cohort of 206 heterozygous carriers of either the P207L or the D9N mutation. The P207L carriers (N=88) presented with a more atherogenic lipoprotein-lipid profile compared with the D9N carriers (N=118). Accordingly, LDL particle size was smaller in the P207L carriers than in the D9N subjects (248.8+/- 1.0 vs 254.5+/-1.0 A, P< 0.001), and the difference remained significant after adjustment for plasma triglyceride (TG) levels. The difference in LDL diameter between the P207L and the D9N carriers was 3-fold greater in individuals with plasma TG levels >3.5 mmol/L than in subjects with TG < or =3.5 mmol/L. The factors that statistically contributed to LDL particle size variation in multivariate analyses were plasma TG levels (11.6%) and age (6.4%) in subjects with TG levels < or =3.5 mmol/L and HDL cholesterol levels (15.5%) and the LPL gene mutation (null versus defective, 7.0%) in patients with TG levels >3.5 mmol/L. CONCLUSIONS: These results suggest that the null P207L mutation in the LPL gene has a greater impact on LDL particle size than the defective D9N mutation and that this mutation-specific effect is amplified at greater plasma TG concentrations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".