# Clinical Trial Readiness In X-Linked Dystonia Parkinsonism: Assessment of Sensor-Based And Blood Biomarkers for Early Detection and Monitoring Disease Progression

> **NIH NIH K23** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $197,640

## Abstract

PROJECT SUMMARY
Parkinsonism, the second most common movement disorder, affects over 900,000 Americans and its prevalence
is rising, while dystonia, the third most common, afflicts 250,000 Americans. X-linked dystonia parkinsonism
(XDP) is a rare neurogenetic movement disorder with a wide phenotypic spectrum ranging from a parkinsonism
indistinguishable from Parkinson's disease (PD), generalized dystonia, similar to DYT1 (a hereditary dystonia),
or combined dystonia and parkinsonism. Female XDP carriers are at risk for parkinsonism, given X-inactivation
and represents a possible genetic risk factor. As such, XDP serves as an excellent model of both dystonia and
parkinsonism, and insights can inform both phenotypes and mixed movement disorders, which are notoriously
challenging to assess. To provide adequate clinical trial endpoints, rater-independent, quantitative assessments
of motor function are urgently needed to identify early abnormalities which may predate overt clinical symptoms
and track disease progression, given the unreliability of rater-dependent clinical rating scales. This project will
provide Dr. Stephen with a skill set that will allow him to assess potential quantitative measures of disease
severity and progression in dystonia and parkinsonism using motion sensors and compare these measures with
proposed biochemical biomarkers, using machine learning to define optimal parameters. This project aims to
address three key knowledge gaps in dystonia and parkinsonism, using pure phenotypes (DYT1 and PD) and in
combination (XDP): Aim 1) to utilize technology-based evaluations as more sensitive and accurate measures of
dystonia in isolation (DYT1) vs. in combination with parkinsonism (XDP) compared to clinical scales; Aim 2) to
examine the accuracy of sensor-based monitoring of disease progression over 1 year in XDP, PD and DYT1
patients; and Aim 3) to analyze 2 proposed blood biomarkers (one specific to XDP, and neurofilament light chain,
a general marker of neurodegeneration) in XDP and combine these motor and blood markers, using machine
learning to predict disease phenotype/biotype and clinical course. This research complements the NINDS
objective of clinical trial readiness in rare neurological disorders, with a wider goal of better understanding these
common phenotypes in the context of a mixed movement disorder, using innovative technology and analysis
methods. The goal of this award is to prepare the candidate to become a fully independent investigator in the
quantitative assessment of dystonia, parkinsonism and other movement disorders, in the setting of expert
mentorship. The career development plan includes training goals: 1) learning transferrable motion analysis skills,
2) learning machine learning techniques to develop predictive models of motor behaviors and combining these
with blood biomarker data; and 3) an introduction to biomarker science and bioinformatics. Successful
completion of this project will put Dr. Stephen i...

## Key facts

- **NIH application ID:** 10475732
- **Project number:** 5K23NS118045-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Christopher D Stephen
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $197,640
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10475732

## Citation

> US National Institutes of Health, RePORTER application 10475732, Clinical Trial Readiness In X-Linked Dystonia Parkinsonism: Assessment of Sensor-Based And Blood Biomarkers for Early Detection and Monitoring Disease Progression (5K23NS118045-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10475732. Licensed CC0.

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