Increased Soluble Fas Plasma Levels in Subjects at High Cardiovascular Risk
Bibliographic record
Abstract
OBJECTIVE: Increasing evidence indicates that the Fas/Fas ligand interaction is involved in atherogenesis. We sought to analyze soluble Fas (sFas) and soluble Fas ligand (sFasL) concentrations in subjects at high cardiovascular risk and their modulation by atorvastatin treatment. METHODS AND RESULTS: ACTFAST was a 12-week, prospective, multicenter, open-label trial which enrolled subjects (statin-free or statin-treated at baseline) with coronary heart disease (CHD), CHD-equivalent, or 10-year CHD risk > 20%. Subjects with LDL-C between 100 to 220 mg/dL (2.6 to 5.7 mmol/L) and triglycerides < or = 600 mg/dL (6.8 mmol/L) were assigned to a starting dose of atorvastatin (10 to 80 mg/d) based on LDL-C at screening. Of the 2117 subjects enrolled in ACTFAST, AIM sub-study included the 1078 statin-free patients. At study end, 85% of these subjects reached LDL-C target. Mean sFas levels were increased and sFasL were reduced in subjects at high cardiovascular risk compared with healthy subjects. Atorvastatin reduced sFas in the whole population as well as in patients with metabolic syndrome or diabetes. Minimal changes were observed in sFasL. CONCLUSIONS: sFas concentrations are increased and sFasL are decreased in subjects at high cardiovascular risk, suggesting that these proteins may be novel markers of vascular injury. Atorvastatin reduces sFas, indicating that short-term treatment with atorvastatin exhibits antiinflammatory effects in these subjects.
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 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) | 0.001 | 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 | 0.000 |
| 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".