Plasma Levels of Eicosapentaenoic Acid Are Associated with Anti-TNF Responsiveness in Rheumatoid Arthritis and Inhibit the Etanercept-driven Rise in Th17 Cell Differentiation<i>in Vitro</i>
Bibliographic record
Abstract
Objective. To determine whether levels of plasma n-3 polyunsaturated fatty acids are associated with response to antitumor necrosis factor (anti-TNF) agents in rheumatoid arthritis (RA), and whether this putative effect may have its basis in altering anti-TNF–driven Th17 cell differentiation. Methods. Plasma was collected at baseline and after 3 months of anti-TNF treatment in 22 patients with established RA, and fatty acid composition of the phosphatidylcholine (PC) component was measured. CD4+CD25− T cells and monocytes were purified from the blood of healthy donors and cocultured in the presence of anti-CD3, with or without etanercept (ETN), eicosapentaenoic acid (EPA), or the control fatty acid, linoleic acid (LA). Expression of interleukin 17 and interferon-γ was measured by intracellular staining and flow cytometry. Results. Plasma PC EPA levels and the EPA/arachidonic acid ratio correlated inversely with change in the Disease Activity Score at 28 joints (DAS28) at 3 months (−0.51, p = 0.007 and −0.48, p = 0.01, respectively), indicating that higher plasma EPA was associated with a greater reduction in DAS28. Plasma PC EPA was positively associated with European League Against Rheumatism response (p = 0.02). An increase in Th17 cells post-therapy has been associated with nonresponse to anti-TNF. ETN increased Th17 frequenciesin vitro. Physiological concentrations of EPA, but not LA, prevented this. Conclusion. EPA status was associated with clinical improvements to anti-TNF therapyin vivoand prevented the effect of ETN on Th17 cellsin vitro. EPA supplementation might be a simple way to improve anti-TNF outcomes in patients with RA by suppressing Th17 frequencies.
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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".