Development and Validation of a Multivariate Diagnostic Model for Major Depressive Disorder With Comorbid Insomnia Based on Lymphocyte Subsets and Resting-State Functional MRI.

Objective: This study aimed to investigate the relationship between alterations in lymphocyte subsets and resting-state functional magnetic resonance imaging (rs-fMRI) patterns in patients with comorbid major depressive disorder (MDD) and insomnia disorder (ID). Methods: A total of 114 patients with MDD, 108 with ID, 126 with comorbid MDD and ID, and 168 healthy controls (HCs) were recruited, all experiencing their first episode. Emotional and sleep quality were assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17), self-rating depression scale (SDS), Hamilton Anxiety Scale, self-rating anxiety scale (SAS), Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI). rs-fMRI data and lymphocyte subsets were analyzed. Multivariate prediction models were constructed using correlation analysis, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, and logistic regression. Model performance was evaluated with calibration curves and receiver operating characteristic (ROC) analysis. Results: No significant differences were observed in age ( Conclusions: The identified risk predictors might facilitate individualized clinical decision-making for iMDD patients. While the multivariable prediction model demonstrated strong internal diagnostic accuracy, further external validation using independent cohorts is needed to confirm its generalizability.