Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse...

Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lithium response is critical for personalization strategies in real-world settings.

In this cross-sectional study, 179 individuals with BD were assessed using a broad range of standardized clinical instruments and the Brief TEMPS-M to evaluate affective temperaments. Lithium response was retrospectively classified via the Alda Scale. Multivariate linear regressions and three supervised machine learning classifiers (random forest, elastic net, and XGBoost) were applied to identify predictors of treatment response. Class imbalance was addressed by down-sampling during training.

Responders (12.3 % of participants) were more likely to be employed, diagnosed with BD-II, had fewer lifetime depressive and hypomanic episodes, fewer suicide attempts, and significantly lower anxious temperament scores. In multivariable models, lifetime psychotic and mixed features, as well as higher counts of hypomanic and manic episodes, were independently associated with poorer lithium response. Machine learning models confirmed the importance of these variables, possibly with remarkably high predictive validity (random forest AUC = 1.00; elastic net and XGBoost F1 = 0.86). Hyperthymic temperament was positively associated with lithium response, whereas cyclothymic and depressive traits were associated with poorer lithium response.

The present exploratory study should be intended as a primer for larger samples and more balanced replication studies employing reliable machine learning techniques, allowing for firm conclusions about suggestive clinical predictors of lithium response in BD, and their complex interaction.