Abstract 17559: Norepinephrine Levels are Associated With the Magnitude of Vasoconstriction During Mental Stress
Bibliographic record
Abstract
Background: Patients with CAD develop varying degrees of microvascular vasoconstriction in response to mental stress, leading to myocardial ischemia. Catecholamine increases during mental stress may explain this effect. We hypothesized, in a cohort of stable CAD patients, that increased epinephrine and norepinephrine levels from rest to peak mental stress predict peripheral microvascular vasoconstriction. Methods: We analyzed data from 204 patients with CAD and high quality vascular data. Subjects underwent a standardized mental stress test using a public speaking task. Peripheral arterial tonometry (PAT) was used to assess peripheral microvascular function during rest and mental stress. The PAT response was calculated as a ratio of the minimum pulse wave amplitude during the speaking task to the mean amplitude during rest. Plasma epinephrine and norepinephrine levels were obtained 15 minutes before and at peak mental stress. Linear regression modeling was used to adjust for potential confounders. Results: The mean (SD) age was 64 (8), 16% were women, and 28% were African American. Subjects in the quartile with the greatest vasoconstriction had the highest increase in norepinephrine levels (73.5 (31.4) ng/mL [mean (SEM]), while those in the quartile with the least vasoconstriction had decreased norepinephrine levels by 47.7 (32.5) ng/mL, p for trend<0.001 (Figure). There was no significant association between PAT and epinephrine. After multivariable adjustment for sociodemographics, CAD risk factors, medical history, medication use, major depression, and PTSD, the association between the PAT ratio and norepinephrine remained significant (p=0.003). Conclusion: Changes in circulating norepinephrine, but not epinephrine levels predict peripheral microvascular vasoconstriction during mental stress.
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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) | 0.005 | 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".