Dexamethasone decreases the autotaxin‐lysophosphatidate‐inflammatory axis in adipose tissue: implications for the metabolic syndrome and breast cancer
Bibliographic record
Abstract
Lysophosphatidate (LPA) signaling through 6 receptors is regulated by the balance of LPA production by autotaxin (ATX) vs. LPA degradation by lipid phosphate phosphatases (LPPs). LPA promotes an inflammatory cycle by increasing the synthesis of cyclooxygenase-2 and multiple inflammatory cytokines that stimulate further ATX production. We aimed to determine whether the anti-inflammatory glucocorticoid (GC) dexamethasone (Dex) functions partly by decreasing the ATX-LPA inflammatory cycle in adipose tissue, a major site of ATX secretion. Treatment of human adipose tissue with 10-1000 nM Dex decreased ATX secretion, increased LPP1 expression, and decreased mRNA expressions of IL-6, TNF-α, peroxisome proliferator-activated receptor (PPAR)-γ, and adiponectin. Cotreatment with rosiglitazone (an insulin sensitizer), insulin, or both abolished Dex-induced decreases in ATX and adiponectin secretion, but did not reverse Dex-induced decreases in secretions of 20 inflammatory cytokines and chemokines. Dex-treated mice exhibited lower ATX activity in plasma, brain, and adipose tissue; decreased mRNA levels for LPA and sphingosine 1-phosphate (S1P) receptors in brain; and decreased plasma concentrations of LPA and S1P. Our results establish a novel mechanism for the anti-inflammatory effects of Dex through decreased signaling by the ATX-LPA-inflammatory axis. The GC action in adipose tissue has implications for the pathogenesis of insulin resistance and obesity in metabolic syndrome and breast cancer treatment.-Meng, G., Tang, X., Yang, Z., Zhao, Y., Curtis, J. M., McMullen, T. P. W., Brindley, D. N. Dexamethasone decreases the autotaxin-lysophosphatidate-inflammatory axis in adipose tissue: implications for the metabolic syndrome and breast cancer.
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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) | 0.002 | 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".