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Huntington Disease l: Introduction

Huntington Disease l: Introduction

Huntington disease or HD is a progressive, fatal neurodegenerative disorder inherited in an autosomal dominant pattern.PathophysiologyIt is caused by expansion of the CAG trinucleotide repeat in the HTT gene on chromosome 4 (4p16.3), producing an abnormal huntingtin protein with an expanded polyglutamine tract. This misfolded protein disrupts cellular function, leading to neuronal death. Normal alleles have ≤26 repeats, 27–35 are intermediate (risk of expansion), 36–39 show reduced penetrance,...

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Identifying time patterns in Huntington's disease trajectories using dynamic time warping-based clustering on

Alexia Giannoula¹, Audrey E De Paepe^2,3, Ferran Sanz²

¹Research Group on Integrative Biomedical Informatics (GRIB), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain. alexia.giannoula@upf.edu.

Scientific Reports

|January 24, 2025

Summary

This summary is machine-generated.

This study introduces a novel data-mining method using Dynamic Time Warping to cluster patient health data, revealing distinct temporal patterns for personalized medicine. It aids in stratifying patients with Huntington's disease by analyzing individual variability.

Keywords:

Longitudinal cohort analysis Multi-modal Real-World Data Patient stratification Precision medicine Time analysis Unsupervised clustering

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Area of Science:

Computational biology
Precision medicine
Data mining

Background:

Precision medicine aims to personalize patient care by stratifying individuals based on variability.
Extracting meaningful insights from real-world data (RWD), like electronic health records, faces methodological and computational challenges.
Understanding individual variability in disease progression is crucial for developing personalized strategies.

Purpose of the Study:

To present a Dynamic Time Warping-based unsupervised-clustering methodology for stratifying patient trajectories using multi-modal health data.
To incorporate time-varying clinical and imaging features into the clustering process.
To demonstrate the methodology's application in identifying temporal patterns in Huntington's disease (HD) patient data.

Main Methods:

Developed a Dynamic Time Warping-based unsupervised-clustering approach for multi-modal patient trajectories.
Incorporated time-varying clinical and imaging features using an adapted cost-minimization algorithm.
Applied the methodology to Huntington's disease data, analyzing feature weights and granularity thresholds.

Main Results:

Successfully clustered multi-modal HD patient trajectories based on shared temporal characteristics.
Identified distinct temporal patterns in disease progression influenced by feature weights and granularity.
Provided polynomial fits and averaged values for feature behavior within each identified cluster.

Conclusions:

The proposed data-mining methodology enables stratification of distinct temporal patterns in multi-modal health data for patients with the same diagnosis.
This approach allows for user-customized criteria beyond current clinical practices for analyzing individual variability.
Findings have implications for improved analysis of disease progression, paving the way for personalized preventative, diagnostic, and therapeutic strategies.