Docosahexaenoic acid increases cellular adiponectin mRNA and secreted adiponectin protein, as well as PPARγ mRNA, in 3T3-L1 adipocytes
Bibliographic record
Abstract
Adiponectin, a protein secreted from adipose tissue, has been shown to have anti-diabetic and anti-inflammatory effects, but its regulation is not completely understood. Long-chain n-3 fatty acids eicosapentaenoic acid (20:5n-3; EPA) and docosahexaenoic acid (22:6n-3; DHA) may be involved in adiponectin regulation as they are potential ligands for peroxisome proliferator-activated receptor-γ (PPARγ), a key transcription factor for the adiponectin gene. To examine this, 3T3-L1 adipocytes were incubated with 125 µmol·L-1 EPA, DHA, palmitic, or oleic acids complexed to albumin, or with albumin alone (control) for 24 h. Adipocytes were also incubated for 24 h with EPA and DHA plus bisphenol-A-diglycidyl ether (BADGE), a PPARγ antagonist. Both EPA and DHA increased (p < 0.05) secreted adiponectin concentration compared with the control (44% and 102%, respectively), but did not affect cellular adiponectin protein content. Incubation with BADGE and DHA inhibited increases in secreted adiponectin protein, suggesting that DHA may act through a PPARγ-dependent mechanism. However, BADGE had no effect on EPA-induced increases in secreted adiponectin protein. Only DHA enhanced (p < 0.05) PPARγ and adiponectin mRNA expression compared wtih the control. Our results demonstrate that DHA increases cellular adiponectin mRNA and secreted adiponectin protein in 3T3-L1 adipocytes, possibly by a mechanism involving PPARγ. Moreover, DHA increased adiponectin concentration to a greater extent (40% more, p < 0.05) compared with EPA, emphasizing the need to consider the independent actions of EPA and DHA in adipocytes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".