Polymorphisms in the MGLL gene are associated with plasma LDL‐C response to a marine n‐3 PUFA supplementation (1038.1)
Bibliographic record
Abstract
Omega‐3 (n‐3) polyunsaturated fatty acid (PUFA) supplementation from marine sources has an inconsistent effect on plasma low‐density lipoprotein cholesterol (LDL‐C) levels, as some individuals increase their levels while some others do not. Genetic factors may be involved in this inter‐individual variability observed in LDL‐C levels in response to an n‐3 PUFA supplementation. The objective of this study was to test whether SNPs within the MGLL gene, encoding the monoglyceride lipase, explain inter‐individual variability observed in plasma LDL‐C levels after an n‐3 PUFA supplementation. A total of 208 subjects consumed 5 g/d of a fish oil supplement, containing 1.9‐2.2g EPA and 1.1g DHA, for 6 weeks. Plasma lipids were measured before and after the supplementation. SNPs rs782440 and rs6776142 from MGLL were genotyped using the TaqMan technology (Life Technologies Inc. Burlington, ON, Canada). Results show that for rs782440 and rs6776142, there were differences in the mean variation in LDL‐C (delta LDL) between genotype groups (C/C, C/T and T/T) in an ANOVA adjusted for age, sex and BMI. Also, a chi‐square test showed differences in genotype frequencies for rs782440 (p<0.05) between responders and non‐responders defined on the basis of delta LDL, as well as a tendency for rs6776142 (p=0.099). In conclusion, two SNPs of the MGLL gene may influence the plasma LDL‐C variation in response to an n‐3 PUFA supplementation. Grant Funding Source : Supported by 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".