Association between plasma omega-3 fatty acids and cardiovascular disease risk factors
Bibliographic record
Abstract
The consumption of omega-3 (n-3) fatty acids (FA), namely eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have been linked to reduced cardiovascular disease (CVD) risk. The objective of this study was to examine the relation between n-3 FA in plasma phospholipid (PL) levels and CVD risk factors. n-3 FA levels in plasma PL were determined using gas chromatography in 100 obese (body mass index (BMI), ≥30 kg·m(-2)) and 100 nonobese selected individuals from the Quebec City metropolitan area. The CVD risk factors analysed were BMI, blood pressure, plasma lipids levels, and fasting plasma glucose. Significantly higher levels of alpha-linolenic acid (ALA) and docosapentaenoic acid (DPA) were observed in obese subjects, whereas significantly higher levels of DHA were observed in nonobese subjects. For CVD risk factors, ALA levels were positively correlated with plasma triglyceride concentrations and negatively associated with diastolic blood pressure. None of the CVD risk factors studied was linked to EPA levels. In addition, DPA was negatively related to high-density lipoprotein cholesterol (HDL-C) and positively correlated with the total cholesterol/HDL-C ratio. DHA levels were negatively correlated with BMI, waist circumference, and plasma triglyceride levels, whereas a positive association was observed with HDL-C levels. Total n-3 FA percentages were negatively correlated with BMI. In conclusion, higher DHA percentages in plasma PL are associated with a more favourable CVD risk profile, whereas higher DPA percentages in plasma PL are associated with a more deteriorated CVD risk profile.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".