Omega-3 Fatty Acid Prevents Heart Rate Variability Reductions Associated with Particulate Matter
Bibliographic record
Abstract
CONTEXT: Environmental exposure to particulate matter of 2.5 microm or less (PM2.5) has been associated with changes in heart rate variability (HRV). OBJECTIVE: To evaluate the effect of supplementation with omega-3 polyunsaturated fatty acids on the reduction of HRV associated with PM2.5 exposure. DESIGN: Randomized double-blind trial. SETTING: Mexico City, Mexico. PARTICIPANTS: 50 nursing home residents older than 60 yr. INTERVENTION: Randomization to either 2 g/d of fish oil versus 2 g/d of soy oil as the control, with 6 mo follow-up (1-mo presupplementation and 5-mo supplementation) or repeated HRV measurements. PM2.5 was monitored indoors and outdoors. MAIN OUTCOME MEASURE: The association between HRV and 1 SD change in PM2.5 (8 microg/m3). RESULTS: In the group receiving fish oil, the reduction in HRV-high-frequency log(10)-transformed associated with a 1-SD change in PM2.5 was -54% (95% confidence interval, -72, -24) in the presupplementation phase, and only -7% (95% confidence interval, -20,+7) in the supplementation phase (p < 0.01 for the effect of supplementation), with changes in other HRV parameters also being significantly less pronounced during supplementation. Small decreases in PM2.5-associated reductions in HRV parameters also occurred in the group receiving soy oil, but these were not significant. Fish oil supplementation was significantly better in preventing the reduction in percentage of successive normal RR intervals differing by more than 50 ms (p = 0.03) and the root square of the mean of the sum of the squares of differences between adjacent intervals (p = 0.05) than soy oil supplementation. INTERPRETATION: Supplementation with 2 g/d of fish oil prevented HRV decline related to PM2.5 exposure in the study population.
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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 | 0.001 | 0.001 |
| 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".