Angiotensin type 1 receptor antagonists induce human in-vitro adipogenesis through peroxisome proliferator-activated receptor-γ activation
Bibliographic record
Abstract
OBJECTIVE: In clonal animal cells, certain angiotensin receptor blockers (ARB) activate the peroxisome proliferator-activated receptor-gamma (PPARgamma). The aim of this work was to validate that observation in human cells and humans. METHODS: We investigated the induction of in-vitro adipogenesis and the activation of PPARgamma-target genes, adiponectin and lipoprotein lipase, by ARB in human preadipocytes. We also studied PPARgamma response-element-driven luciferase reporter gene activation in human adipocytes. Finally, we treated 14 obese men for 10 days with placebo crossed over with 150 mg/day irbesartan. Subcutaneous fat was analyzed for mRNA expression of adiponectin and lipoprotein lipase. RESULTS: Telmisartan and irbesartan, and to a lesser degree losartan, induced adipogenesis and activated PPARgamma-target genes. This stimulation of PPARgamma-target genes was prevented by the PPARgamma antagonist GW9662. Eprosartan had no effect. Paradoxically, all ARB activated the luciferase reporter gene. PPARgamma activity increased approximately two-fold with pioglitazone and 1.5-fold with the ARB in all assays. In the cross-over clinical study, irbesartan lowered blood pressure but had no effect on adiponectin or lipoprotein lipase mRNA expression. CONCLUSIONS: Our data are the first to show that ARB induce adipogenesis and PPARgamma-target gene expression in human adipocytes. Pharmacokinetic differences may contribute to the heterogeneous effects on metabolism and preadipocyte differentiation. In humans, larger doses of ARB, longer treatments, or both may be required to activate PPARgamma in adipose cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".