Identifying Cardiovascular Disease Risk Endotypes of Adolescent Major Depressive Disorder Using Exploratory Unsupervised Machine Learning.

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.