The effects of docosahexaenoic acid (DHA) on plasma cytokines, oxylipins, and tumor-infiltrating lymphocytes from women with breast cancer undergoing neoadjuvant chemotherapy in the DHA-WIN trial
Bibliographic record
Abstract
Clinical trials on docosahexaenoic acid (DHA) supplementation and immune changes during breast cancer neoadjuvant chemotherapy (NAC) are limited. This study evaluated the impact of DHA supplementation during NAC on systemic and tumor immune modulation by assessing plasma inflammatory and cardiac damage markers, tumor-infiltrating lymphocyte (TIL) proportions, and n-6- and n-3-derived oxylipins produced in response to an ex vivo immune challenge. Venous blood was collected at baseline, 9, and 15 weeks during NAC from participants in the DHA for Women with Breast Cancer in the Neoadjuvant Setting (DHA-WIN) trial, which compared DHA-enriched algae (4.4g/day; n=23) with a placebo (n=26) over 18 weeks. Plasma markers were measured using electrochemiluminescence assays. CD4+ and CD8+ TILs were identified in tumor tissue by immunohistochemistry, and oxylipins were quantified in the supernatant of lipopolysaccharide-stimulated peripheral blood mononuclear cells via liquid chromatography-tandem mass spectrometry. DHA supplementation resulted in greater increases in the plasma cytokines IFN-γ and TNF-α compared to placebo (P-interaction < .05). In the DHA group, concentrations of these cytokines increased at 15 weeks compared to baseline (P < .05). No differences were found between groups for other immune markers or the proportion of TILs. Compared to the placebo, DHA led to an overall increase in total oxylipin concentrations (P < .05) and higher production of n-6 fatty acid-derived oxylipins, particularly prostanoids, and n-3 fatty acid-derived oxylipins, including 13-HDoHE. These results suggest that DHA may enhance immune responses by promoting an increase in oxylipin and cytokine concentrations, potentially benefiting patients during breast cancer NAC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".