Fasting acylation-stimulating protein is predictive of postprandial triglyceride clearance
Bibliographic record
Abstract
Postprandial plasma triglyceride (ppTG) and NEFA clearance were stratified by plasma acylation-stimulating protein (ASP) and gender to determine the contribution of fasting ASP in a normal population (70 men; 71 women). In the highest ASP tertile only, ASP decreased over 8 h (90 +/- 9.7 nM to 70 +/- 5.9 nM, P<0.05 males; 61.9 +/- 4.0 nM to 45.6 +/- 6.2 nM, P<0.01 females). Fasting ASP correlated positively with ppTG response. ppTG (P<0.0001, 2-way ANOVA, both genders) and NEFA levels progressively increased from lowest to highest ASP tertile, with the greatest differences in males. By stepwise multiple regression, the best prediction of ppTG was: (fasting ASP + apolipoprotein B + insulin + TG; r=0.806) for men and (fasting ASP + total cholesterol; r=0.574) for women. Leptin, body mass index, and other fasting variables did not improve the prediction. Thus, in men and women, ASP significantly predicted ppTG and NEFA clearance and, based on lower ASP, women may be more ASP sensitive than men. Plasma ASP may be useful as a fasting variable that will provide additional information regarding ppTG and NEFA clearance.
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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 | 0.000 | 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) | 0.001 | 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".