Caffeine Enhances Heart Rate Variability in Middle-Aged Healthy, But Not Heart Failure Subjects
Bibliographic record
Abstract
Background: In chronic heart failure (CHF) due to left ventricular dysfunction, diminished heart rate variability (HRV) is an independent predictor of poor prognosis. Caffeine has been shown to increase HRV in young healthy subjects. Such an increase may be of potential benefit to patients with CHF. Objective: We hypothesized that intravenous infusion of caffeine would increase HRV in CHF, and in age-matched healthy control subjects. Methods: On two separate days, 11 patients (1F) with CHF (age=51.3±4.6 years; left ventricular ejection fraction=18.6±2.7%; mean±standard error) and 10 healthy control subjects (age=48.0±4.0) according to a double-blind randomization design, received either saline or caffeine (4 mg/kg) infusion. We assessed HRV over 7 minutes of supine rest (fast Fourier Transform analysis) to determine total spectral power as well as its high-frequency (HF) (0.15–0.50 Hz) and low-frequency (LF) (0.05–0.15 Hz) components, and recorded muscle sympathetic nerve activity (MSNA) directly from the peroneal nerve (microneurography). Results: In healthy control subjects, compared with saline, caffeine reduced both heart rate and sympathetic nerve traffic (p≤0.003) and increased the ratio of HF/total power (p≤0.05). Baseline LF power and the ratio LF/HF were significantly lower in CHF compared with controls (p=0.02), but caffeine had no effect on any element of HRV. Conclusions: Caffeine increases cardiac vagal heart rate modulation and reduces MSNA in middle-aged healthy subjects, but not in those with CHF.
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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".