Potential Biomarkers for Depression Associated with Coronary Artery Disease: A Critical Review
Bibliographic record
Abstract
Depression, the most common mood disorder, is a leading contributor to the global burden of disease affecting more than 120 million individuals worldwide. Various pathophysiological processes underlie depression; this complexity renders it difficult to identify clinically useful diagnostic and prognostic markers, as well as treatment options. The current state of knowledge driving the management and treatment of depression remains incomplete, which underscores the need for further insight into pathways relevant to depression. Exploring co-morbid conditions, such as coronary artery disease, may be useful to further elucidate the etiopathology of depression. The present review therefore systematically identifies and critically evaluates relevant markers of depression as assessed in a high-risk population, namely patients with coronary artery disease. Biomarkers related to hypothalamicpituitary- adrenal axis dysregulation, inflammation, endothelial dysfunction, platelet activation and aggregation, serotonin activity, sympathetic nervous system activation, thyroid function, structural and morphological brain abnormalities, genetic variation, lipid metabolism, one-carbon metabolism, endocannabinoid signalling irregularities, and vitamin D deficiency are reviewed. Markers exhibiting the most consistent associations with depression include tumour necrosis factor-α, flow-mediated dilation, endothelin-1, endothelial progenitor cells, brain-derived neurotrophic factor, and docosahexaenoic acid. Further investigating the mechanisms underlying those markers and exploring novel pathways, such as oxidative stress, will extend the current state of knowledge and potentially lead to the identification of novel therapeutic targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 0.001 |
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".