Comparing the serum TAG response to high-dose supplementation of either DHA or EPA among individuals with increased cardiovascular risk: the ComparED study
Bibliographic record
Abstract
Studies have shown that the reduction in serum TAG concentrations with long-chain n-3 fatty acid supplementation is highly variable among individuals. The objectives of the present study were to compare the proportions of individuals whose TAG concentrations lowered after high-dose DHA and EPA, and to identify the predictors of response to both modalities. In a double-blind, controlled, crossover study, 154 men and women were randomised to three supplemented phases of 10 weeks each: (1) 2·7 g/d of DHA, (2) 2·7 g/d of EPA and (3) 3 g/d of maize oil, separated by 9-week washouts. As secondary analyses, the mean intra-individual variation in TAG was calculated using the standard deviation from the mean of four off-treatment samples. The response remained within the intra-individual variation (±0·25 mmol/l) in 47 and 57 % of participants after DHA and EPA, respectively. Although there was a greater proportion of participants with a reduction >0·25 mmol/l after DHA than after EPA (45 υ. 32 %; P 0·25 mmol/l after both DHA and EPA had higher non-HDL-cholesterol, TAG and insulin concentrations compared with other responders at baseline (all P < 0·05). In conclusion, supplementation with 2·7 g/d DHA or EPA had no meaningful effect on TAG concentrations in a large proportion of individuals with normal mean TAG concentrations at baseline. Although DHA lowered TAG in a greater proportion of individuals compared with EPA, the magnitude of TAG lowering among them was similar.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".