Autonomic reactivity to mental stress is associated with cardiovascular mortality
Bibliographic record
Abstract
Aims: The mechanisms linking acute psychological stress to cardiovascular disease (CVD) mortality are incompletely understood. We studied the relationship of electrocardiographic measures of autonomic dysfunction during acute mental stress provocation and CVD death. Methods and results: In a pooled cohort of 765 participants with stable CVD from two related studies, we collected Holter data during standardized laboratory-based mental stress testing with a speech task and followed them for events. We assessed autonomic function using low-frequency (LF) heart rate variability (HRV) in 5-min intervals before, during, and after stress induction, and specifically examined changes from rest to stress. We employed cause-specific survival models to examine its association with CVD and all-cause mortality, controlling for demographic and CVD risk factors. The mean (SD) age was 58 (10) years, 35% were women, and 44% self-identified as Black. After a median follow-up of 5.6 years, 37 (5%) died from CVD causes. A stress-induced LF HRV decrease (67% of sample), vs. increase, was associated with a hazard ratio (HR) of 3.48 (95% confidence interval-3.25, 3.73) for CVD mortality. Low rest LF HRV (bottom quartile) was also independently associated with CVD mortality, HR = 1.75 (1.58, 1.94), vs. normal rest LF HRV (upper three quartiles). The combination of stress-induced LF HRV decrease and low rest LF HRV was associated with HR = 5.73 (5.33, 6.15) vs. the normal stress/rest LF HRV reference. We found similar results with HF HRV. Conclusion: Stress-induced LF HRV decrease and low rest LF HRV are both independently and additively associated with a higher CVD mortality risk. Additional research is needed to assess whether targeting autonomic dysfunction may improve CVD outcomes.
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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 | 0.001 | 0.003 |
| 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".