N-3 Long-Chain Polyunsaturated Fatty Acids, Eicosapentaenoic and Docosahexaenoic Acid, and the Role of Supplementation during Cancer Treatment: A Scoping Review of Current Clinical Evidence
Bibliographic record
Abstract
This scoping review examines the evidence for n-3 long-chain polyunsaturated fatty acid [LCPUFA, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)] supplementation in clinical cancer therapy. A comprehensive literature search was performed to identify relevant clinical intervention studies conducted through August 2020. Fifty-seven unique cancer trials, assessing EPA and/or DHA supplementation pre- or post-treatment, concomitant with neoadjuvant chemotherapy, radiation or surgery, or in palliative therapy were included. Breast, head and neck, gastrointestinal, gastric, colorectal/rectal, esophageal, leukemia/lymphoma, lung, multiple myeloma and pancreatic cancers were investigated. Across the spectrum of cancers, the evidence suggests that supplementation increased or maintained body weight, increased progression-free and overall survival, improved overall quality of life, resulted in beneficial change in immune parameters and decreased serious adverse events. Taken together, the data support that EPA and/or DHA could be used to improve outcomes important to the patient and disease process. However, before incorporation into treatment can occur, there is a need for randomized clinical trials to determine the dose and type of n-3 LCPUFA intervention required, and expansion of outcomes assessed and improved reporting of outcomes.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".