DHA is more potent than EPA in attenuating cardiometabolic risk in men and women: a randomized double‐blind, placebo‐controlled crossover trial
Bibliographic record
Abstract
Background Most studies to date on the cardiometabolic effects of eicosapentaenoic (EPA) and docosahexaenoic (DHA) in humans have used a mixture of the two fatty acids in different forms and proportions. The objective of this study was to compare the individual effects of EPA vs. DHA supplementation (as re‐esterified triacylglycerol, 90% pure) on blood lipids and inflammatory markers in men and women at risk of cardiovascular diseases. Methods Using a double‐blind, placebo‐controlled crossover design, 48 men and 106 women with abdominal obesity were randomized to three treatment phases: 1‐EPA (3g/d), 2‐DHA (3g/d), 3‐corn oil (placebo, 3g/d). Treatments had durations of 10 weeks each and were separated by an 8‐week washout period. Blood lipids and inflammatory markers were measured before and after each treatment phase. Differences in study outcomes between treatments were assessed using the MIXED procedure for repeated measures with endpoint measures as primary outcomes. Potential confounders of the response to treatment, mainly obesity/body fat status, age, use of contraceptive agents (premenopausal women), menopausal status, treatment‐specific baseline values and sequence of treatments were considered. Results EPA vs. placebo decreased plasma TG concentrations (−11.1±2.4%, p<0.001). DHA vs. placebo decreased diastolic blood pressure (DBP, −2.2±0.9%, p<0.001), TG (−18.1±2.5%, p<0.001), cholesterol(C)/HDL‐C ratio (−2.5±1.4%, p=0.02), CRP (−8.4±5.0%, p=0.03), TNF‐α (−13.7±5.2%, p=0.02) and IL‐18 (−4.8±2.9%, p=0.01), and increased total C (+2.5±1.2%, p=0.001), HDL‐C (+6.1±1.2%, p<0.001) and LDL‐C (+5.0±1.9%, p<0.001). Changes in DBP (p=0.03), TG (p=0.02), total C (p<0.001), HDL‐C (p<0.001), LDL‐C (p=0.03), C/HDL‐C ratio (p=0.02), IL‐18 (p=0.007) and adiponectin (p<0.001) were greater with DHA than with EPA. Conclusion DHA is more effective than EPA in modulating blood lipids and inflammatory markers. Further studies are needed to determine the impact of a long term DHA supplementation on cardiovascular risk in men and women. Support or Funding Information This research was funded by the Instituts de recherche en santé du Canada (IRSC/CIHR). Janie Allaire is recipient of a scholarship from the Fonds de Recherche du Québec – Santé (FRQ‐S) for graduated students with a Professional Degree.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".