Plant sterol whole body pool size in sitosterolemia is modulated by ezetimibe
Bibliographic record
Abstract
Sitosterolemia (STSL) is a sterol storage disorder characterized by very high plasma plant sterols levels. Ezetimibe (EZE), a sterol‐absorption inhibitor, has been shown to reduce plasma plant sterol levels in STSL. However, its effect on whole body sitosterol pool (WBSP) has not been well characterized. The study aimed to measure WBSP size in STSL, off and on EZE. STSL patients (pts, n=8) were taken off EZE for 14 wks. After 4 wks off EZE, they received an IV dose of D7‐sitosterol (1 mg/kg BW), and blood samples were serially collected over 10 wks. Afterward, pts resumed EZE therapy and the experiment repeated. D7‐sitosterol enrichment was measured by liquid chromatography‐tandem mass spectrometry and plasma sterols by gas chromatography. Data (mean±SEM ) were analyzed using paired t‐test. EZE reduced plasma sitosterol by 35% (5.1±0.5 vs 7.8±0.6 mg/dl; P<0.05), and WBSP by 53% compared with off treatment (106.1±15.5 vs 226.8±73.4 mg/kg BW; P=0.05) at 5 wks. Plasma cholesterol concentrations tended to decrease with EZE but not significantly (153.8±10.7 vs 178.3±13.9 mg/dl; P=0.09). Decline in WBSP was positively correlated with plasma sitosterol levels(r=0.58, p<0.05). These results indicated that plasma plant sterols, but not cholesterol levels were significantly reduced by EZE, suggesting plant sterol abnormality rather than cholesterol. Thus, EZE may work by reducing WBSP size in STSL pts. Funded by NIH and CIHR Grant Funding Source : 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) | 0.000 | 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 | 0.001 |
| 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".