Background
Niemann-Pick type C (NPC) is an autosomal recessive progressive neurodegenerative disorder caused by mutations in the NPC1 or NPC2 genes. Patients with this disorder have variable phenotypic presentations that often include neuropsychiatric manifestations, cognitive decline, and movement disorders. There is considerable interpatient variation in movement disorders, with limited quantitative measurements describing the movements observed. Objective measurements using wearable sensors provide clinically applicable monitoring of patients with Parkinson’s disease, and hence may be utilized in patients with NPC.
Objective
To explore the relationship between objective measurements of movement obtained via the use of the Personal KinetiGraph (PKG) with the clinical information obtained via questionnaires and clinical rating tools of patients with Niemann-Pick type C.
Methods
Twelve patients with Niemann-Pick type C were recruited who wore the PKG for 6 days during regular activities. A 6-day output was provided by the manufacturer, which provided bradykinesia (BK) and dyskinesia (DK) scores. BK and DK scores were further divided into their interquartile ranges. A fluctuation score (FDS), percentage time immobile (PTI), and percent time with tremors (PTT) were also provided. Clinical assessments included Abnormal Involuntary Movement Scale (AIMS), Epworth Sleepiness Score (ESS), Falls, Neuropsychiatric Unit Assessment Tool (NUCOG), Parkinson’s disease questionnaire (PDQ), and modified Unified Parkinson’s Disease Rating Scale (UPDRS) which were performed over telehealth within 2 weeks of PKG use. Pearson’s correlation analyses were utilized to explore the relationship between DK and BK quartiles and clinical measures.
Results
We found bradykinesia to be a feature among this cohort of patients, with a median BKS of 22.0 (7.4). Additionally, PTI scores were elevated at 4.9 (8.2) indicating elevated daytime sleepiness. Significant correlations were demonstrated between BK25 and Falls (r = − 0.74, 95% CI = [− 0.95, − 0.08]), BK50 and Falls (r = − 0.79, 95% CI = [− 0.96, − 0.19]), and BK75 and Falls (r = − 0.76, 95% CI = [− 0.95, − 0.11]). FDS correlated with PDQ (r = − 0.7, 95% CI = [− 0.92, − 0.18]), UPDRS IV (r = − 0.65, 95% CI = [− 0.90, − 0.09]), UPDRS (r = − 0.64, 95% CI = [− 0.9, − 0.06]), and AIMS (r = − 0.96, 95% CI = [− 0.99, − 0.49]). DK25 in comparison with NUCOG-A (r = 0.72, 95% CI = [0.17, 0.93]) and DK75 in comparison with NUCOG (r = 0.64, 95% CI = [0.02, 0.91]) and NUCOG-A (r = 0.63, 95% CI = [0.01, 0.90]) demonstrated significant correlations. Additionally, duration of illness in comparison with PTI (r = 0.72, 95% CI = [0.22, 0.92]) demonstrated significance.
Conclusions
Utilization of PKG measures demonstrated that bradykinesia is under recognized among NPC patients, and the bradykinetic patients were less likely to report concerns regarding falls. Additionally, the FDS rather than the DKS is sensitive to the abnormal involuntary movements of NPC—reflecting a differing neurobiology of this chorea compared to levodopa-induced dyskinesias. Furthermore, dyskinetic individuals performed better in cognitive assessments of attention which may indicate an earlier timepoint within disease progression.
