Mapping the natural course of comorbid bipolar disorder via integration of multimodal data (MapCoBD): a study protocol

Bipolar disorder (BD) is a chronic, disabling psychiatric condition with a high global disease burden. Its course is marked by alternating episodes of mania and depression, with varying degrees of severity. While cyclicity is a defining feature, clinical patterns show significant inter-individual and intra-individual variability. Although naturalistic studies have described the course of BD, the impact of psychiatric comorbidities, particularly attention deficit hyperactivity disorder (ADHD) and borderline personality disorder (BPD), remains underexplored. MapCoBD is a 12-month, naturalistic observational study whose primary objective is to compare the longitudinal clinical course of BD among individuals with classic episodic BD, BD with comorbid ADHD, and BD with comorbid BPD. Secondary objectives are to characterize the clinical, neuropsychological, neuroimaging, and molecular features associated with these distinct clinical courses.

This project will recruit 150 BD individuals divided into three groups: (1) classic episodic BD (epiBD, n = 50), (2) BD with comorbid ADHD (BD-ADHD, n = 50), and (3) BD with comorbid BPD (BD-BPD, n = 50). Assessments will be conducted at baseline (T0), 6 months (T1), and 12 months (T2), including: mood symptom ratings, neuropsychological testing, fasting blood sampling (for serum, plasma, and microRNA), and 3T structural brain MRI scans. Daily mood ratings will also be collected prospectively throughout the study. The primary longitudinal clinical endpoint will be illness burden, defined as the proportion of evaluable follow-up days spent with moderate-to-severe psychopathological symptoms, based on prospective NIMH Life Chart Method assessments. Daily affective ratings will additionally be used to characterize mood instability. At each time point, peripheral biomarkers implicated in BD pathophysiology, including endocannabinoid (eCB) signaling, inflammatory cascade (IC), and epigenetic regulation (microRNA), will be quantified. Clinical, neuroimaging, and molecular data will also be integrated using supervised machine-learning approaches to explore the classification of clinical group status and the prediction of longitudinal mood instability and illness burden.

This study will determine whether ADHD and BPD comorbidity is associated with distinct longitudinal patterns of illness burden and mood instability in BD. The integration of clinical, neuroimaging, and molecular data may identify group-specific multimodal signatures and candidate predictors of clinical course, which will require independent validation before clinical application.