Mapping Longitudinal Psychiatric Signatures in Huntington's Disease.

Area of Science:

• Neuroscience
• Psychiatry
• Genetics

Background:

• Huntington's disease (HD) is primarily known for motor symptoms, but psychiatric disturbances often precede them.
• Significant variability exists in the timing and presentation of psychiatric symptoms in HD.
• Identifying distinct psychiatric trajectories can improve prognostic accuracy and clinical management.

Purpose of the Study:

• To stratify longitudinal psychiatric signatures in individuals with Huntington's disease.
• To explore potential clinical applications for predicting disease prognosis based on psychiatric patterns.
• To investigate the early emergence of distinct psychiatric trajectories in premanifest HD.

Main Methods:

• Utilized the Disease Trajectories software, a machine-learning approach, for unsupervised clustering of psychiatric data.
• Analyzed data from 46 Huntington's disease gene carriers (premanifest and manifest) over up to six longitudinal visits.
• Assessed depression, irritability, apathy, and dysexecutive behaviors using the short-Problem Behavior Assessment.

Main Results:

• Identified two primary psychiatric trajectory clusters explaining 54% of the sample, encompassing both premanifest and manifest HD patients.
• Observed a dissociation in symptom development: one cluster showed increasing irritability without depression, while the other exhibited a rise-and-fall in depression without irritability.
• Both clusters demonstrated a significant longitudinal increase in apathy and dysexecutive behaviors.

Conclusions:

• Machine learning identified distinct psychiatric trajectories in HD, including a dissociation between depression and irritability, evident even in premanifest stages.
• These findings highlight individual differences in the longitudinal progression of psychiatric symptoms.
• The study supports patient stratification and precision medicine approaches for Huntington's disease based on psychiatric profiles.