Genome-wide association study of the plasma triglyceride response to an n-3 polyunsaturated fatty acid supplementation
Bibliographic record
Abstract
Studies have shown a large interindividual variability in plasma TG response to long-chain n-3 PUFA supplementation, which may likely be attributable to genetic variability within the populations studied. The objective is to compare the frequency of SNPs in a genome-wide association study between responders (reduction in plasma TG levels ≥0.01 mM) and nonresponders (increase in plasma TG of ≥0 mM) to supplementation. Genomic DNA from 141 subjects who completed a 2-week run-in period followed by 6-week supplementation with 5 g of fish oil daily (1.9-2.2 g EPA and 1.1 g DHA daily) were genotyped on Illumina HumanOmni-5-QuadBeadChip. Thirteen loci had frequency differences between responders and nonresponders (P < 1 × 10(-5)), including SNPs in or near IQCJ-SCHIP1, MYB, NELL1, NXPH1, PHF17, and SLIT2 genes. A genetic risk score (GRS) was constructed by summing the number of risk alleles. This GRS explained 21.53% of the variation in TG response to n-3 PUFA supplementation when adjusted for age, sex, and BMI (P = 0.0002). Using Fish Oil Intervention and Genotype as a replication cohort, the GRS was able to explain 2% of variation in TG response when adjusted. In conclusion, subjects who decrease their plasma TG levels following n-3 PUFA supplementation may have a different genetic profile than individuals who do not respond.
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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 | 0.002 |
| 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 | 0.000 |
| 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".