WAT apoC-I secretion: role in delayed chylomicron clearance in vivo and ex vivo in WAT in obese subjects
Bibliographic record
Abstract
Reduced white adipose tissue (WAT) LPL activity delays plasma clearance of TG-rich lipoproteins (TRLs). We reported the secretion of apoC-I, an LPL inhibitor, from WAT ex vivo in women. Therefore we hypothesized that WAT-secreted apoC-I associates with reduced WAT LPL activity and TRL clearance. WAT apoC-I secretion averaged 86.9 ± 31.4 pmol/g/4 h and 74.1 ± 36.6 pmol/g/4 h in 28 women and 11 men with BMI ≥27 kg/m(2), respectively, with no sex differences. Following the ingestion of a (13)C-triolein-labeled high-fat meal, subjects with high WAT apoC-I secretion (above median) had delayed postprandial plasma clearance of dietary TRLs, assessed from plasma (13)C-triolein-labeled TGs and apoB48. They also had reduced hydrolysis and storage of synthetic (3)H-triolein-labeled ((3)H)-TRLs in WAT ex vivo (i.e., in situ LPL activity). Adjusting for WAT in situ LPL activity eliminated group differences in chylomicron clearance; while adjusting for plasma apoC-I, (3)H-NEFA uptake by WAT, or body composition did not. apoC-I inhibited in situ LPL activity in adipocytes in both a concentration- and time-dependent manner. There was no change in postprandial WAT apoC-I secretion. WAT apoC-I secretion may inhibit WAT LPL activity and promote delayed chylomicron clearance in overweight and obese subjects. We propose that reducing WAT apoC-I secretion ameliorates postprandial TRL clearance in humans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".