Polymorphisms in FFAR4 (GPR120) Gene Modulate Insulin Levels and Sensitivity after Fish Oil Supplementation
Bibliographic record
Abstract
The objective was to test whether FFAR4 single nucleotide polymorphisms (SNPs) are associated with glycemic control-related traits in humans following fish oil supplementation. A total of 210 participants were given 3 g/day of omega-3 (n-3) fatty acids (FA) (1.9–2.2 g of eicosapentaenoic acid (EPA) and 1.1 g of docosahexaenoic acid (DHA)) during six weeks. Biochemical parameters were taken before and after the supplementation. Using the HapMap database and the tagger procedure in Haploview, 12 tagging SNPs in FFAR4 were selected and then genotyped using TaqMan technology. Transcript expression levels were measured for 30 participants in peripheral mononuclear blood cells. DNA methylation levels were measured for 35 participants in leukocytes. In silico analyses were also performed. Four gene–diet interactions on fasting insulin levels and homeostatic model assessment of insulin resistance (HOMA-IR) index values were found. rs17108973 explained a significant proportion of the variance of insulin levels (3.0%) and HOMA-IR (2.03%) index values. Splice site prediction was different depending on the allele for rs11187527. rs17108973 and rs17484310 had different affinity for transcription factors depending on the allele. n-3 FAs effectively improve insulin-related traits for major allele homozygotes of four FFAR4 SNPs as opposed to carriers of the minor alleles.
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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 | 0.001 |
| 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".