Effect of Ezetimibe on Low‐and High‐Density Lipoprotein Subclasses in Sitosterolemia
Bibliographic record
Abstract
Sitosterolemia (STSL) is a rare sterol disease manifested by very high plasma phytosterols (PS) with normal to high total cholesterol (TC) levels and increased atherosclerosis risk. Low density lipoprotein (LDL), intermediate density lipoprotein (IDL) and very lowdensity lipoprotein (VLDL) cholesterols are proatherogenic while high density lipoprotein (HDL) cholesterol is antiatherogenic. Ezetimibe (EZ), a sterol absorption inhibitor, can reduce plasma PS and TC levels in STSL but its effect on lipoprotein subclasses has not been studied. We evaluated the effect of EZ on lipoprotein subclasses in STSL patients (pts, n=8) taken off EZ for 14 wks then placed on EZ (10 mg/d) for 14 wks. Serum total lipids and subfractions were measured enzymatically or with the Lipoprint system. Data (mean±SEM) were analyzed by paired t ‐test. EZ reduced serum TC (‐13±4%, p=0.02), VLDL (‐24±4%, p=0.002) and total LDL (‐17±6%, p=0.03) levels vs off EZ. Reduced LDL values were due to decreased IDLB and C (‐22±7 and ‐21±8%, p<0.05), not to large, buoyant LDL subclasses (LDL1: ‐8±8%, p=0.23 and LDL2: +35±55%, p=0.17). LDL size did not change with EZ (275±0 vs 274±1 Å, p=0.18). EZ increased HDL levels (26±8%, p=0.008) due to increased intermediate (34±14%, p=0.02) and large (33±16%, p=0.06) HDL subclasses. These data suggest EZ can favorably affect LDL and HDL subfractions distribution, thus providing potential clinical benefit in STSL beyond reducing TC and PS accrual. Funded by NIH and CIHR
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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 | 0.000 |
| 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) | 0.001 | 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".