Gene‐diet interaction effects during a supplementation with n‐3 PUFA on both plasma and gene expression levels of inflammatory markers (1037.9)
Bibliographic record
Abstract
Objective: To test whether gene expression of inflammatory genes is altered following an n‐3 PUFA supplementation and to test for any possible gene‐diet interactions modulating plasma inflammatory biomarker levels. Methods: 210 subjects completed a 6‐wk n‐3 PUFA supplementation with 5g/d of fish oil. Gene expression of TNF and IL6 was assessed in peripheral blood mononuclear cells (PBMC) using the TaqMan technology adjusted for the endogenous control (GAPDH). Genotyping of 20 SNPs from the TNF‐LTA gene cluster, IL1β , IL6 and CRP genes was performed. Results: Gene expression of TNF and IL6 increased after the 6‐wk n‐3 PUFA supplementation as shown by 2 ‐ΔΔCt values >1 (mean±SD, 1.05±0.39 and 1.18±0.50, respectively). In a MIXED model for repeated measures adjusted for the effects of age, sex and BMI, gene by supplementation interactions effects were observed for rs1143627, rs16944, rs1800797, and rs2069840 on IL6 levels, for rs2229094 on TNF‐alpha levels and for rs1800629 on CRP levels (p<0.05 for all). Conclusions: An n‐3 PUFA supplementation could alter gene expression levels of TNF and IL6 in PBMC. Also, the large inter‐individual variability in plasma inflammatory markers following an n‐3 PUFA supplementation may be influenced by genetic variations in inflammatory genes. Grant Funding Source : CIHR
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".