Krill oil supplementation lowers serum triglycerides without increasing low-density lipoprotein cholesterol in adults with borderline high or high triglyceride levels
Bibliographic record
Abstract
The aim of the study was to explore the effects of 12 weeks daily krill oil supplementation on fasting serum triglyceride (TG) and lipoprotein particle levels in subjects whose habitual fish intake is low and who have borderline high or high fasting serum TG levels (150-499 mg/dL). We hypothesized that Krill oil lowers serum TG levels in subjects with borderline high or high fasting TG levels. To test our hypothesis 300 male and female subjects were included in a double-blind, randomized, multi-center, placebo-controlled study with five treatment groups: placebo (olive oil) or 0.5, 1, 2, or 4 g/day of krill oil. Serum lipids were measured after an overnight fast at baseline, 6 and 12 weeks. Due to a high intra-individual variability in TG levels, data from all subjects in the four krill oil groups were pooled to increase statistical power, and a general time- and dose-independent one-way analysis of variance was performed to assess efficacy. Relative to subjects in the placebo group, those administered krill oil had a statistically significant calculated reduction in serum TG levels of 10.2%. Moreover, LDL-C levels were not increased in the krill oil groups relative to the placebo group. The outcome of the pooled analysis suggests that krill oil is effective in reducing a cardiovascular risk factor. However, owing to the individual fluctuations of TG concentrations measured, a study with more individual measurements per treatment group is needed to increase the confidence of these findings.
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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) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".