Effects of the PPARγ agonist pioglitazone on lipoprotein metabolism in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Elevated plasma levels of VLDL triglycerides (TGs) are characteristic of patients with type 2 diabetes mellitus (T2DM) and are associated with increased production rates (PRs) of VLDL TGs and apoB. Lipoprotein lipase-mediated (LPL-mediated) lipolysis of VLDL TGs may also be reduced in T2DM if the level of LPL is decreased and/or the level of plasma apoC-III, an inhibitor of LPL-mediated lipolysis, is increased. We studied the effects of pioglitazone (Pio), a PPARgamma agonist that improves insulin sensitivity, on lipoprotein metabolism in patients with T2DM. Pio treatment reduced TG levels by increasing the fractional clearance rate (FCR) of VLDL TGs from the circulation, without changing direct removal of VLDL particles. This indicated increased lipolysis of VLDL TGs during Pio treatment, a mechanism supported by our finding of increased plasma LPL mass and decreased levels of plasma apoC-III. Lower apoC-III levels were due to reduced apoC-III PRs. We saw no effects of Pio on the PR of either VLDL TG or VLDL apoB. Thus, Pio, a PPARgamma agonist, reduced VLDL TG levels by increasing LPL mass and inhibiting apoC-III PR. These 2 changes were associated with an increased FCR of VLDL TGs, almost certainly due to increased LPL-mediated lipolysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".