# Bipolar disorder integrative staging: incorporating biomarkers into progression across stages (BOARDING-PASS) – rationale and design

## TL;DR

This study aims to improve understanding of bipolar disorder progression by combining clinical, biological, and neuroimaging data with machine learning to create a more accurate staging model.

## Contribution

The study introduces an integrative, data-driven staging model for bipolar disorder using multimodal data and machine learning.

## Key findings

- The study will assess clinical stage progression in 97 participants over 18 months using a staging model.
- Epigenetic and inflammatory biomarkers will be analyzed alongside neuroimaging data to refine staging.
- A supervised machine learning algorithm will integrate multimodal data to develop a new staging model.

## Abstract

Bipolar disorder (BD) is a lifelong, recurrent condition with growing evidence supporting a neuroprogressive course, entailing the need to adopt staging models to guide stage-specific interventions. Although different approaches have been proposed, their application remains limited and largely based on clinical features. BOARDING-PASS is an Italian government-funded, multicenter, prospective, and observational study aimed at advancing current knowledge of BD progression through the integration of clinical, biological, neuroimaging data, alongside machine learning (ML) methodologies. The study enrolled 97 subjects (age 18–70 years), classified according to the Kupka & Hillegers’ staging model, and recruited from three secondary-level psychiatric services in Italy. The primary outcome is the longitudinal assessment of clinical stage progression over an 18-month period, with evaluations conducted at baseline (T0), T1 (6 months), T2 (12 months), and T3 (18 months after baseline). At each time point, clinical variables will be collected, as well as clinical stages assigned. Additionally, at T0, T2, and T3, peripheral blood and unstimulated saliva samples will be collected to assess epigenetic regulation of gene expression - including DNA methylation, histone modifications, and exosomal miRNAs - with a focus on key biomarkers such as C-reactive protein, proinflammatory cytokines, and BDNF, as well as microbial signatures of major oral bacterial phyla. Structural and resting-state functional MRI scans will also be acquired at the same time points: structural data will be used to compute the structural connectome based on gyrification-based covariance networks, while resting-state data will be used to assess functional connectome alterations via graph theory metrics. Finally, all multimodal data will be integrated within a supervised ML algorithm based on Support Vector Machine, with the goal of developing a refined, data-driven staging model for BD. BOARDING PASS project aligns with the growing need for a standardized, biologically informed staging framework that integrates clinical, inflammatory, epigenetic, and neuroimaging profiles to enhance prognostic accuracy and support tailored therapeutic interventions in BD.