Identifying Cardiovascular Disease Risk Endotypes of Adolescent Major Depressive Disorder Using Exploratory Unsupervised Machine Learning
Bibliographic record
Abstract
Objective: Adolescents with major depressive disorder (MDD) are at increased risk of premature atherosclerosis and cardiovascular disease (CVD). The ability to identify adolescents with MDD who are at increased CVD risk would facilitate personalized interventions and advance knowledge regarding the MDD-CVD association. This study aimed to identify adolescent MDD endotypes of increased CVD risk. Method: Youth with MDD (n = 189; 74% female; mean [SD] age 15.03 [1.85] years) were recruited through an outpatient psychiatry program in a large urban hospital. Individual and family (demographics, depression, anxiety symptoms, family conflict), physical examination (vital signs, body mass index), and laboratory (lipid profile, glucose, C-reactive protein) data were collected. Using demographic, clinical, and laboratory data, k-means clustering was performed; a subsequent model included only lipids. Continuous and categorical measures were compared between clusters. Results: The model containing all variables yielded 1 high and 1 low CVD risk cluster, which differed significantly in ethnicity, anthropometrics, laboratory data, and family conflict, but not in depression or anxiety severity. The lipid-only model yielded 2 high and 2 low CVD risk clusters that differed significantly in sex, ethnicity, body mass index, lipids, depression, and anxiety severity. Of the 2 CVD risk clusters, one was indicative of increased cardiometabolic risk, while the other comprised adolescents with MDD who had high low-density lipoprotein and no other cardiovascular risk factors. Conclusion: Endotypes of adolescent MDD associated with varying levels of CVD risk were identified. Results highlight the heterogeneity of adolescent MDD and the need for precision medicine approaches in management of MDD to improve both CVD and depression outcomes.
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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 | 0.001 |
| 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".