Omega-3 Polyunsaturated Fatty Acids and Adipose Tissue Inflammation in Humans: A Scoping Review
Bibliographic record
Abstract
Adipose tissue (AT) inflammation is a topic of increasing interest given its role in initiating systemic subclinical inflammation. Evidence from preclinical studies suggests that n-3 polyunsaturated fatty acids (PUFAs) may ameliorate AT inflammation through various pathways. However, fewer data are available from humans, and existing studies are heterogeneous in design and findings. The objective of this scoping review was to identify, review, and map the current literature on the relationship between n-3 PUFAs and AT inflammation in healthy humans. MEDLINE, EMBASE, and Cochrane databases were searched from inception to August 4, 2022. Eligible studies included experimental trials and observational studies, enrolling nonpregnant adult study populations free of diagnosed chronic/infectious diseases. Screening and data extraction were performed on study characteristics. Overall, the 25 retained studies were heterogeneous in study design, intervention formulation/exposure assessment, comparator, study duration, and methods used to characterize AT inflammation. Most experimental trials used EPA+DHA [eicosapentaenoic acid (EPA, 20:5n-3) and docosapentaenoic acid (DPA, 22:5n-3)] supplementation and measured circulating adiponectin and leptin to characterize AT inflammation. A wide range of comparators were employed, including saturated/unsaturated oils, ketogenic diets, and n-6 PUFAs. All observational studies reported a significant association with ≥1 of their primary outcomes, while 15 of 20 experimental trials documented a significant effect of n-3 supplementation on ≥1 outcomes. Existing human literature on n-3 PUFAs and AT inflammation is inconclusive due to the limited number of studies available and their heterogeneous designs. Therefore, larger, longer-term longitudinal studies and experimental trials using AT biopsy measures or validated AT-specific biomarkers are needed. Registration: Open Science Framework (https://doi.org/10.17605/OSF.IO/29WGQ).
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".