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Related Experiment Video

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Constructing disease onset signatures using multi-dimensional network-structured biomarkers.

Xiang Li¹, Donglin Zeng², Karen Marder³

¹Statistics and Decision Sciences, Janssen Research & Development, LLC, Raritan, NJ, USA.

Biostatistics (Oxford, England)

|August 8, 2018

Summary

Developing new therapies for neurodegenerative diseases requires early diagnosis. This study introduces a novel biomarker model to identify pre-symptomatic individuals, improving clinical trial recruitment for conditions like Huntington

Keywords:

Clinical trial design Large-scale biomarkers Locally varying effect Network regularization Neurological disorders

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Area of Science:

Neuroscience
Biostatistics
Medical Imaging

Background:

Effective treatments for neurodegenerative disorders necessitate intervention before symptom onset.
Current diagnostic methods for neurological conditions primarily rely on clinical symptoms, identifying patients late in the disease course.
Early detection of pathological changes through biomarkers is crucial for timely diagnosis and recruiting pre-symptomatic individuals for prevention trials.

Purpose of the Study:

To develop and validate a novel statistical model for constructing biomarker signatures to predict time to disease onset in neurodegenerative disorders.
To integrate information on individual biomarker effects varying with baseline disease stage and network structures connecting biomarkers.
To enhance early diagnosis and facilitate the recruitment of pre-symptomatic subjects for clinical trials.

Main Methods:

Proposed a varying-coefficient hazards model to account for non-linear disease stage effects and network-based biomarker interactions.
Employed kernel smoothing of a regularized local partial likelihood for estimation and feature selection.
Developed an efficient algorithm for model fitting and analysis.

Main Results:

Numerical simulations demonstrated superior performance and computational efficiency compared to existing methods.
The model successfully identified structural network signatures associated with premanifest Huntington's disease (HD).
Analysis provided new insights into early pathological changes in HD.

Conclusions:

The proposed varying-coefficient hazards model effectively leverages biomarker network structures and baseline disease stage information for improved prediction of disease onset.
The method offers significant advancements in statistical modeling for neurodegenerative disease research.
Application to Huntington's disease highlights the potential for informing clinical trial design and identifying pre-symptomatic individuals.