Dissecting clinical and biological heterogeneity in clinical states of bipolar disorder: a 10-year retrospective study from China

• Published in: Frontiers in psychiatry Vol. 14; p. 1128862
• Main Authors: Zhu, Ting, Kou, Ran, Hu, Yao, Yuan, Minlan, Yuan, Cui, Luo, Li, Zhang, Wei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 21.12.2023
• Subjects: biological heterogeneity; bipolar disorder; clinical states; electronic medical records (EMR); machine learning; Psychiatry; Shapley additive explanations (SHAP); bipolar disorder; clinical states; biological heterogeneity; electronic medical records (EMR); machine learning; Shapley additive explanations (SHAP)
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2023.1128862

To dissect clinical and biological heterogeneity in clinical states of bipolar disorder (BD), and investigate if neuropsychological symptomatology, comorbidity, vital signs, and blood laboratory indicators are predictors of distinct BD states. A retrospective BD cohort was established with data extracted from a Chinese hospital's electronic medical records (EMR) between 2009 and 2018. Subjects were inpatients with a main discharge diagnosis of BD and were assessed for clinical state at hospitalization. We categorized all subjects into manic state, depressive state, and mixed state. Four machine learning classifiers were utilized to classify the subjects. A Shapley additive explanations (SHAP) algorithm was applied to the classifiers to aid in quantifying and visualizing the contributions of each feature that drive patient-specific classifications. A sample of 3,085 records was included (38.54% as manic, 56.69% as depressive, and 4.77% as mixed state). Mixed state showed more severe suicidal ideation and psychomotor abnormalities, while depressive state showed more common anxiety, sleep, and somatic-related symptoms and more comorbid conditions. Higher levels of body temperature, pulse, and systolic and diastolic blood pressures were present during manic episodes. Xgboost achieved the best AUC of 88.54% in manic/depressive states classification; Logistic regression and Random forest achieved the best AUCs of 75.5 and 75% in manic/mixed states and depressive/mixed states classifications, respectively. Myocardial enzymes and the non-enzymatic antioxidant uric acid and bilirubin contributed significantly to distinguish BD clinical states. The observed novel biological associations with BD clinical states confirm that biological heterogeneity contributes to clinical heterogeneity of BD.