Expression and Sequence Variants of Inflammatory Genes; Effects on Plasma Inflammation Biomarkers Following a 6-Week Supplementation with Fish Oil
Bibliographic record
Abstract
(1) BACKGROUND: A growing body of literature suggest that polymorphisms (SNPs) from inflammation-related genes could possibly play a role in cytokine production and then interact with dietary n-3 fatty acids (FAs) to modulate inflammation. The aim of the present study was to test whether gene expression of selected inflammatory genes was altered following an n-3 PUFA supplementation and to test for gene-diet interactions modulating plasma inflammatory biomarker levels. (2) METHODS: 191 subjects completed a 6-week n-3 FA supplementation with 5 g/day of fish oil. Gene expression of TNF-α and IL6 was assessed in peripheral blood mononuclear cells (PBMCs) using the TaqMan technology. Genotyping of 20 SNPs from the TNF-LTA gene cluster, IL1β, IL6 and CRP genes was performed. (3) RESULTS: There was no significant reduction of plasma IL-6, TNF-α and C-reactive protein (CRP) levels after the 6-week fish oil supplementation. TNF-α and IL6 were slightly overexpressed in PBMCs after the supplementation (fold changes of 1.05 ± 0.38 and 1.18 ± 0.49, respectively (n = 191)), but relative quantification (RQ) within the -0.5 to 2.0 fold are considered as nonbiologically significant. In a MIXED model for repeated measures adjusted for the effects of age, sex and BMI, gene by supplementation interaction effects were observed for rs1143627, rs16944, rs1800797, and rs2069840 on IL6 levels, for rs2229094 on TNF-α levels and for rs1800629 on CRP levels (p < 0.05 for all). (4) CONCLUSIONS: This study shows that a 6-week n-3 FA supplementation with 5 g/day of fish oil did not alter gene expression levels of TNF-α and IL6 in PBMCs and did not have an impact on inflammatory biomarker levels. However, gene-diet interactions were observed between SNPs within inflammation-related genes modulating plasma inflammatory biomarker levels.
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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".