High-Dose DHA Has More Profound Effects on LDL-Related Features Than High-Dose EPA: The ComparED Study
Bibliographic record
Abstract
Context: Supplementation with high-dose docosahexaenoic acid (DHA) increases serum low-density lipoprotein (LDL) cholesterol (LDL-C) concentrations more than high-dose eicosapentaenoic acid (EPA). The mechanisms underlying this difference are unknown. Objective: To examine the phenotypic change in LDL and mechanisms responsible for the differential LDL-C response to EPA and DHA supplementation in men and women at risk of cardiovascular disease. Design, Setting, Participants, and Intervention: In a double-blind, controlled, crossover study, 48 men and 106 women with abdominal obesity and subclinical inflammation were randomized to a sequence of three treatment phases: phase 1, 2.7 g/d of EPA; phase 2, 2.7 g/d of DHA; and phase 3, 3 g/d of corn oil. All supplements were provided as three 1-g capsules for a total of 3 g/d. The 10-week treatment phases were separated by a 9-week washout period. Main Outcome Measure: In vivo kinetics of apolipoprotein (apo)B100-containing lipoproteins were assessed using primed-constant infusion of deuterated leucine at the end of each treatment in a subset of participants (n = 19). Results: Compared with EPA, DHA increased LDL-C concentrations (+3.3%; P = 0.038) and mean LDL particle size (+0.7 Å; P < 0.001) and reduced the proportion of small LDL (-3.2%; P < 0.01). Both EPA and DHA decreased proprotein convertase subtilisin/kexin type 9 concentrations similarly (-18.2% vs -25.0%; P < 0.0001 vs control). Compared with EPA, DHA supplementation increased both the LDL apoB100 fractional catabolic rate (+11.4%; P = 0.008) and the production rate (+9.4%; P = 0.03). Conclusions: The results of the present study have shown that supplementation with high-dose DHA increases LDL turnover and contributes to larger LDL particles compared with EPA.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".