The Role of Mood Disorders in Exercise-Induced Cardiovascular Reactivity
Bibliographic record
Abstract
Objective: Increased cardiovascular (CV) reactivity has been associated with worse CV prognosis. Though mood disorders (MDs) have been associated with increased CV reactivity during behavioral stressors, the extent to which MDs and their interaction with coronary heart disease (CHD) influences exercise-induced CV reactivity has not been evaluated. Methods: Five hundred twenty-six patients underwent nuclear exercise stress testing. Cardiovascular parameters were assessed at rest, every 2 minutes during exercise, and at peak exercise. MDs were measured using a structured psychiatric interview, the Primary Care Evaluation of Mental Disorders, and CHD was defined as having a history of myocardial infarction, revascularization, heart failure, and/or cerebrovascular event. Results: CHD patients exhibited lower peak exercise heart rate (F = 9.40, p = .002) compared with patients without CHD. Submaximal data showed that patients with CHD had a slower rate of increase of heart rate (F = 4.29, p = .04) and diastolic blood pressure (F = 3.27, p = .04). There was an interaction of CHD and MDs, indicating that in patients with CHD, the rate of submaximal increase in systolic blood pressure (F = 3.08, p = .047) and rate-pressure product (F = 5.13, p = .006) was greater in patients with a MD compared with those without a MD. These differences were not observed in patients with no CHD. No other main or interaction effects of MDs and CHD were observed. Conclusion: Though MDs alone do not seem to be associated with higher levels of stress CV reactivity, their combination with CHD leads to increased submaximal exercise-induced CV reactivity. Prospective studies are needed to explore the causal relationship between these variables. CV = cardiovascular; HR = heart rate; CHD = coronary heart disease; SBP = systolic blood pressure; MDs = mood disorders; DBP = diastolic blood pressure; RPP = rate-pressure product; BP = blood pressure; SPECT = single photon emission computed tomography; SES = socioeconomic status; BRS = baroreceptor sensitivity; BMI = body mass index; MI = myocardial infarction; MOSMI = Mechanisms and Longitudinal Outcomes of Silent Myocardial Ischemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".