Novel Phospholipid Signature of Depressive Symptoms in Patients With Coronary Artery Disease
Bibliographic record
Abstract
Background Depression in patients with coronary artery disease ( CAD ) is associated with increased cardiovascular morbidity. Given the proinflammatory actions of phospholipids, aberrant phospholipid metabolism may be an etiological mechanism linking CAD and depression. Our primary objective was to identify a phospholipid biomarker panel that characterizes CAD patients with significant depressive symptoms from those without. Methods and Results We performed a targeted lipidomic analysis on CAD patients with significant depressive symptoms (n=37, Center for Epidemiologic Studies Depression score ≥16) and those without (n=49). Phospholipid species were selected using partial least‐square discriminant analysis, and the ability of the resulting model to discriminate between groups was evaluated using receiver operator characteristic curves. Biosignature scores were calculated from this model, and analyses of covariance were performed to compare intergroup differences in biosignature scores, with adjustment for clinical differences between patients. Those with significant depressive symptoms had lower cardiopulmonary fitness, more prevalent history of depression, and a greater number of vascular risk factors. A model of 10 phospholipid species had an area under the curve value of 0.84 (95% confidence interval 0.72‐0.95), sensitivity of 0.73, and specificity of 0.71. This model passed permutation testing (n=1000, P <0.001). Biosignature scores were higher in those with significant depressive symptoms after adjustment for potential confounders (F[1.86]=14.39, P <0.0005). Conclusions The present findings support the role of proinflammatory phospholipid species in the presence of depression in CAD patients from the CAROTID trial (Coronary Artery Disease Randomized Omega‐3 Trial in Depression). Future investigations should aim to replicate findings in larger data sets and clarify possible pathophysiological mechanisms. Clinical Trial Registration URL : https://www.clinicaltrials.gov . Unique identifier: NCT 00981383.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".