Omega-3 Polyunsaturated Fatty Acids And Adipose Tissue Inflammation: Longitudinal Analysis in the PROMISE Cohort
Bibliographic record
Abstract
OBJECTIVES: Although preclinical studies have shown a beneficial impact of omega-3 (n-3) polyunsaturated fatty acids (PUFAs) on adipose tissue (AT) inflammation, the current literature from human studies is limited. Therefore, we aimed to evaluate the longitudinal associations of circulating levels of n-3 PUFAs with biomarkers of AT inflammation. METHODS: Longitudinal data from participants in the PROMISE cohort (n = 474) were used. AT inflammation was measured using circulating biomarkers at baseline and up to 2 follow-up visits. n-3 PUFAs were measured at baseline in 4 serum lipid fractions. Generalized estimating equations analyses evaluated longitudinal associations between n-3 PUFAs and AT inflammation, adjusting for covariates. RESULTS: Fully adjusted generalized estimating equation models indicated that higher baseline proportions of eicosapentaenoic acid, n-3 docosapentaenoic acid, and docosahexaenoic acid in total serum were significantly inversely associated with longitudinal change in soluble CD163 (all P < .05). A significant positive association of n-3 docosapentaenoic acid and docosahexaenoic acid with longitudinal change in adiponectin (P < .05) was also observed. Generally consistent associations were observed between n-3 PUFAs and soluble CD163 and adiponectin in the four lipid fractions. CONCLUSION: These findings will add to the limited evidence on the potential role n-3 PUFAs have in the prevention and management of AT inflammation in humans and may help inform future interventions targeting chronic inflammation at the level of AT.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".