Erythrocyte n-3 Fatty Acids and Metabolic Syndrome in Middle-Aged and Older Chinese
Bibliographic record
Abstract
CONTEXT: Few studies examined associations of circulating n-3 fatty acid levels with metabolic syndrome (MetS) among Chinese populations who have low consumption of these fatty acids and high risk of developing MetS. OBJECTIVE: The objective of the study was to determine associations between erythrocyte n-3 fatty acids and MetS as well as its components among middle-aged and older Chinese men and women. DESIGN AND PARTICIPANTS: Erythrocyte levels of docosahexaenoic acid (DHA), docosapentaenoic acid, eicosapentaenoic acid, and α-linolenic acid (ALA) were measured by gas chromatography among 2754 participants aged 50-70 yr living in Beijing and Shanghai. MetS was defined using the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans. RESULTS: After multivariable adjustment, higher levels of DHA, but neither eicosapentaenoic acid nor docosapentaenoic acid, were associated with lower odds of MetS as well as elevated blood pressure and triglycerides. Comparing extreme quartiles of DHA, odds ratios (95% confidence interval) were 0.75 (0.55, 1.01; P for trend = 0.04) for MetS; 0.70 (0.53, 0.92; P for trend = 0.01) for elevated blood pressure; and 0.64 (0.48, 0.87; P for trend = 0.005) for elevated triglycerides. In contrast, ALA concentrations were positively associated with MetS odds (odds ratio 4.06; 95% confidence interval 2.85, 5.80; P for trend <0.001). CONCLUSIONS: Higher concentrations of erythrocyte DHA were associated with lower odds of MetS, whereas higher concentrations of ALA were associated with increased odds among middle-aged and older Chinese. These findings warrant replication in other populations.
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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 | 0.001 | 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) | 0.001 | 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".