Freezing of Gait in Parkinson’s Disease: Risk Factors, Their Interactions, and Associated Nonmotor Symptoms - David Gordon Lichter; Ralph Holmes Boring Benedict; Linda Ann Hershey (Parkinson's Disease)
Background. Freezing of gait (FOG) is a debilitating and incompletely understood symptom in Parkinson’s disease (PD). Objective. To determine the principal clinical factors predisposing to FOG in PD, their interactions, and associated nonmotor symptoms. Methods. 164 PD subjects were assessed in a cross-sectional retrospective study, using the MDS-UPDRS scale, MMSE, and Clinical Dementia Rating Scale. Clinical factors associated with FOG were determined using univariate analysis and nominal logistic regression. Receiver operating characteristic curves were computed, to obtain measures of sensitivity and specificity of predictors of FOG. Subgroups of patients with FOG were compared with those without FOG, based on defining aspects of their clinical phenotype. Results. Relative to non-FOG patients, those with FOG had a longer disease duration, higher PIGD and balance-gait score, higher LED, and more motor complications (p<0.0001) and were more likely to exhibit urinary dysfunction (p<0.0003), cognitive impairment, hallucinations, and psychosis (p=0.003). The balance-gait score and motor complications, at their optimum cutoff values, together predicted FOG with 86% accuracy. Interactions were noted between cognitive dysfunction and both the Bal-Gait score and motor complication status, cognitive impairment or dementia increasing the likelihood of FOG in subjects without motor complications (p=0.0009), but not in those with motor complications. Conclusions. Both disease and treatment-related factors, notably LED, influence the risk of FOG in PD, with a selective influence of cognitive dysfunction in patients with balance-gait disorder but not in those with motor fluctuations. These findings may help to inform clinical management and highlight distinct subgroups of patients with PD-FOG, which are likely to differ in their network pathophysiology.