Genetic risk prediction of the plasma triglyceride response to independent supplementations with eicosapentaenoic and docosahexaenoic acids: the ComparED Study
Bibliographic record
Abstract
BACKGROUND: We previously built a genetic risk score (GRS) highly predictive of the plasma triglyceride (TG) response to an omega-3 fatty acid (n-3 FA) supplementation from marine sources. The objective of the present study was to test the potential of this GRS to predict the plasma TG responsiveness to supplementation with either eicosapentaenoic (EPA) or docosahexaenoic (DHA) acids in the Comparing EPA to DHA (ComparED) Study. METHODS: The ComparED Study is a double-blind, controlled, crossover trial, with participants randomized to three supplemented phases of 10 weeks each: (1) 2.7 g/day of DHA, (2) 2.7 g/day of EPA, and (3) 3 g/day of corn oil (control), separated by 9-week washouts. The 31 SNPs used to build the previous GRS were genotyped in 122 participants of the ComparED Study using TaqMan technology. The GRS for each participant was computed by summing the number of rare alleles. Ordinal and binary logistic models, adjusted for age, sex, and body mass index, were used to calculate the ability of the GRS to predict TG responsiveness. RESULTS: The GRS predicted TG responsiveness to EPA supplementation (p = 0.006), and a trend was observed for DHA supplementation (p = 0.08). The exclusion of participants with neutral TG responsiveness clarified the association patterns and the predictive capability of the GRS (EPA, p = 0.0003, DHA p = 0.01). CONCLUSION: Results of the present study suggest that the constructed GRS is a good predictor of the plasma TG response to supplementation with either DHA or EPA. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01810003. The study protocol was registered on March 4, 2013.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".