Endothelial lipase is associated with inflammation in humans
Bibliographic record
Abstract
The aim of this study was to investigate the extent to which inflammation is linked with plasma endothelial lipase (EL) concentrations among healthy sedentary men. Plasma C-reactive protein (CRP) concentrations were measured with a highly sensitive commercial immunoassay, plasma interleukin-6 (IL-6) concentrations were measured using a commercial ELISA, and plasma secretory phospholipase A(2) type IIA (sPLA(2)-IIA) concentrations were measured using a commercial assay in a sample of 74 moderately obese men (mean body mass index, 29.8 +/- 5.2 kg/m(2)). Plasma EL concentrations were positively correlated with various indices of obesity, fasting plasma insulin, and plasma CRP, IL-6, and sPLA(2)-IIA concentrations. Multiple regression analyses revealed that plasma CRP concentrations explained 14.5% (P = 0.0008) of the variance in EL concentrations. When entered into the model, LPL activity accounted for 16.1% (P < 0.0001) and plasma CRP concentrations accounted for 20.9% (P < 0.0001) of the variance in EL concentrations. The combined impact of visceral adipose tissue (VAT) and of an inflammation score on EL concentrations was investigated. Among subjects with high or low VAT, those having a high inflammation score based on plasma CRP, IL-6, and sPLA(2)-IIA concentrations had increased plasma EL concentrations (P = 0.0005). In conclusion, our data reveal a strong association between proinflammatory cytokines and plasma EL concentrations among healthy people with low or high VAT levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".