Objective Huntington's Disease (HD) is a degenerative disease with characteristic motor and behavioural symptoms, caused by an autosomal-dominant CAG repeat expansion mutation. Behavioural symptoms are important features of HD they contribute to functional decline and affect quality of life more than motor symptoms. The factors affecting the presence or absence of behavioural symptoms are complex and it is unclear to what extent behavioural symptoms are caused by underlying pathology. This study aimed to see which factors (CAG repeat length, gender, disease burden score (DBS), and age of onset) best predict the presence of 8 different neuropsychiatric symptoms.
Method Participants were taken from the European Huntington's Disease Network Registry study, which is an observational study of 1835 HD patients. 991 participants were given a clinical characteristics questionnaire, which recorded the presence and age of onset of motor symptoms and 8 common behavioural symptoms experienced by HD patients (depression, cognitive problems, irritability, apathy, obsession, aggression, psychosis and family history (FH) of psychosis). Statistics: A logistic regression model was used to analyse the predictive power of CAG length, gender, age of motor onset and DBS for the presence of each of these symptoms. Variables were chosen on the basis of preliminary comparisons of means and were entered into the model simultaneously.
Results Depression, cognitive problems, irritability, and apathy are all present in more than 50% of patients. The average onset of depression, irritability, and aggression is within 3 years of motor onset. Logistic regression analysis revealed that CAG length and age of disease onset significantly predict the presence of obsessive symptoms; these factors (along with DBS) also predict the presence of cognitive problems. Gender was a predictor of the presence of depression, while age of onset and gender predicted the presence of irritability and aggression.
Conclusion Psychiatric symptoms are highly prevalent in the HD population, and their presence can be predicted both by factors present in the general population (e.g. gender) and by disease specific factors (CAG length, age of disease onset). These findings could aid in the prognosis of HD and help anticipate symptoms in individual patients. Future work will involve distributing the clinical characteristics questionnaire to more participants in the EHDN database and further modelling of the time course and co-occurrence of these behavioural symptoms.