Dose-related effects of red wine and alcohol on heart rate variability
Bibliographic record
Abstract
In healthy subjects a standard drink of either red wine (RW) or ethanol (EtOH) has no effect on muscle sympathetic nerve activity or on heart rate (HR), whereas two drinks increase both. Using time- and frequency-domain indexes of HR variability (HRV), we now tested in 12 subjects (24-47 yr, 6 men) the hypotheses that 1) this HR increase reflects concurrent dose-related augmented sympathetic HR modulation and 2) RW with high-polyphenol content differs from EtOH in its acute HRV effects. RW, EtOH, and water were provided on 3 days, 2 wk apart according to a randomized, single-blind design. Eight-minute segments were analyzed. One alcoholic drink increased blood concentrations to 36 + or - 2 mg/dl (mean + or - SE), and 2 drinks to 72 + or - 4 (RW) and 80 + or - 2 mg/dl (EtOH). RW quadrupled plasma resveratrol (P < 0.001). HR fell after both water drinks. When compared with respective baselines, one alcoholic drink had no effect on HR or HRV, whereas two glasses of both increased HR (RW, +5.4 + or - 1.2; and EtOH, +5.7 + or - 1.2 min(-1); P < 0.001), decreased total HRV by 28-33% (P < 0.05) and high-frequency spectral power by 32-42% (vagal HR modulation), and increased low-frequency power by 28-34% and the ratio of low frequency to high frequency by 98-119% (sympathetic HR modulation) (all, P < or = 0.01). In summary, when compared with water, one standard drink lowered time- and frequency-domain markers of vagal HR modulation. When compared with respective baselines, two alcoholic drinks increased HR by diminished vagal and augmented sympathetic HR modulation. Thus alcohol exerts dose-dependent HRV responses, with RW and EtOH having a similar effect.
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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 | 0.000 |
| 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".