Increased Risk of Coronary Heart Disease in Patients with Anxiety Disorders: A Review of Underlying Biomarkers
Bibliographic record
Abstract
Anxiety is the most common mental health disorder in the United States, affecting nearly a third of the population.As for depression, it is associated with increased risk of incident coronary heart disease (CHD) and poor cardiac prognosis.The pathophysiological mechanism underlying this "deleterious" association is not well defined.While several hypotheses have been proposed, few seem proven in original studies.A narrative literature review was thus performed to identify all original studies that looked at any biomarkers that can be implicated in the relation between anxiety and CHD.Surprisingly, only four cohorts or observational studies on anxiety and CHD reported biophysiopathological variables.Of the overall populations studied, only 15% were women.In term of biomarkers, plasma lipid levels, C-reactive protein, cortisol, norepinephrine, body mass index, blood pressure and heart rate variability were mostly not significantly different between anxious patients and controls.Only two variables, myocardial perfusion and coronary artery calcium, were found different between the two groups.In summary, underlying biomarkers explaining the increased risk of CHD in anxious patients are still poorly understood.Although based on very limited data, myocardial perfusion and coronary artery calcium seem to be plausible biomarkers.Clearly, more studies are needed to better understand this problematic, especially in women.This step is essential so that personalized care for patients with both anxiety and CHD can be implemented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".