SREBF1 gene variations modulate insulin sensitivity in response to a fish oil supplementation
Bibliographic record
Abstract
BACKGROUND: An important inter-individual variability in the response of insulin sensitivity following a fish oil supplementation has been observed. The objective was to examine the associations between single nucleotide polymorphisms (SNPs) within sterol regulatory element binding transcription factor 1 (SREBF1) gene and the response of insulin sensitivity to a fish oil supplementation. METHODS: Participants (n = 210) were recruited in the greater Quebec City area and followed a 6-week fish oil supplementation protocol (5 g/day: 1.9-2.2 g EPA; 1.1 g DHA). Insulin sensitivity was assessed by the quantitative insulin sensitivity check index (QUICKI). Three tag SNPs (tSNPs) within SREBF1 gene were genotyped according to TAQMAN methodology. RESULTS: Three tSNPs (rs12953299, rs4925118 and rs4925115) covered 100% of the known genetic variability within SREBF1 gene. None of the three tSNPs was associated with either baseline fasting insulin concentrations (rs12953299, rs4925118 and rs4925115) (p = 0.29, p = 0.20 and p = 0.70, respectively) or QUICKI (p = 0.20, p = 0.18 and p = 0.76, respectively). The three tSNPs (rs12953299, rs4925118 and rs4925115) were associated with differences in the response of plasma insulin levels (p = 0.01, p = 0.005 and p = 0.004, respectively) and rs12953299 as well as rs4925115 were associated with the insulin sensitivity response (p = 0.009 and p = 0.01, respectively) to the fish oil supplementation, independently of the effects of age, sex and BMI. CONCLUSIONS: The genetic variability within SREBF1 gene has an impact on the insulin sensitivity in response to a fish oil supplementation. TRIAL REGISTRATION: clinicaltrials.gov: NCT01343342.
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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) | 0.002 | 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".