Specific plasma oxylipins increase the odds of cardiovascular and cerebrovascular events in patients with peripheral artery disease
Bibliographic record
Abstract
Oxylipins and fatty acids may be novel therapeutic targets for cardiovascular disease. The objective was to determine if plasma oxylipins or fatty acids can influence the odds of cardiovascular/cerebrovascular events. In 98 patients (25 female, 73 male) with peripheral artery disease, the prevalence of transient ischemic attacks, cerebrovascular accidents, stable angina, and acute coronary syndrome was n = 16, 10, 16, and 24, respectively. Risk factors such as being male, diagnosed hypertension, diabetes mellitus, and hyperlipidemia were not associated with events. Plasma fatty acids and oxylipins were analyzed with gas chromatography and HPLC-MS/MS, respectively. None of 24 fatty acids quantified were associated with events. In contrast, 39 plasma oxylipins were quantified, and 8 were significantly associated with events. These 8 oxylipins are known regulators of vascular tone. For example, every 1 unit increase in Thromboxane B2/Prostaglandin F1α and every 1 nmol/L increase in plasma 16-hydroxyeicosatetraenoic acid, thromboxane B2, or 11,12-dihydroxyeicosatrienoic acid (DiHETrE) increased the odds of having had ≥2 events versus no event (p < 0.05). The greatest predictor was plasma 8,9-DiHETrE, which increased the odds of acute coronary syndrome by 92-fold. In conclusion, specific oxylipins were highly associated with clinical events and may represent specific biomarkers and (or) therapeutic targets of cardiovascular disease.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".