Low density lipoprotein delays clearance of triglyceride-rich lipoprotein by human subcutaneous adipose tissue
Bibliographic record
Abstract
Delayed clearance of triglyceride-rich lipoprotein (TRL) by white adipose tissue (WAT) promotes hypertriglyceridemia and elevated apoB-lipoproteins, which are primarily in the form of LDL. This study examines whether LDL promotes delayed clearance of TRL by WAT. Following the ingestion of a (13)C-triolein-labeled high-fat meal, obese women with high plasma apoB (> median 0.93 g/l, N = 11, > 98% as IDL/LDL) had delayed clearance of postprandial (13)C-triglyceride and (13)C-NEFA over 6 h compared with controls. AUC6 h of plasma (13)C-triglyceride and (13)C-NEFA correlated with plasma apoB but not with LDL diameter or adipocyte area. There was no group difference in (13)C-triolein oxidation rate, which suggests lower (13)C-NEFA storage in peripheral tissue in women with high apoB. Ex vivo/in vitro plasma apoB correlated negatively with WAT (3)H-lipid following a 4 h incubation of women's WAT with synthetic (3)H-triolein-TRL. LDL-differentiated 3T3-L1 adipocytes had lower (3)H-TRL hydrolysis and (3)H-NEFA storage. Treatment of women's WAT with their own LDL decreased (3)H-TRL hydrolysis and (3)H-NEFA uptake. Finally, LDL, although not an LPL substrate, reduced LPL-mediated (3)H-TRL hydrolysis as did VLDL and HDL. Exposure to LDL decreases TRL clearance by human WAT ex vivo. This may promote production of apoB-lipoproteins and hypertriglyceridemia through a positive-feedback mechanism in vivo.
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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) | 0.000 | 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".