Plasma Levels of Acylation-Stimulating Protein Are Strongly Predicted by Waist/Hip Ratio and Correlate with Decreased LDL Size in Men
Bibliographic record
Abstract
The association of abdominal obesity with cardiovascular risk is often linked to altered secretion of adipose-derived factors and an abnormal lipid profile including formation of atherogenic small dense low density lipoprotein particles (sdLDL). Acylation-stimulating protein (ASP) is an adipose-derived hormone that exhibits potent lipogenic effects. Plasma ASP levels increase in obesity; however, the association of ASP levels with body fat distribution is not yet established, and no study to date has investigated the association of ASP with LDL size. In this study, we examined the association of ASP levels with abdominal obesity measures and the lipid profile including LDL size in 83 men with a wide range of abdominal girths. Regression analysis showed that waist/hip ratio was the main predictor of ASP levels ( β = 0.52, P < 0.0001), significantly followed by decreased LDL size. BMI and TG levels, although positively correlated with ASP levels, were excluded as significant predictors in regression analysis. No correlation was found with LDL-C or apoB levels. ASP levels were 62.5% higher in abdominally obese compared to nonobese men. Waist/hip ratio presenting as the main predictor of ASP levels, suggests increased ASP production by abdominal fat which, as proposed previously, may result from resistance to ASP function causing delayed TG clearance and subsequent formation of atherogenic sdLDL.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".