Longitudinal expression changes are weak correlates of disease progression in Huntington's disease.
Christopher T MitchellIrina KrierJamshid ArjomandBeth BorowskySarah J TabriziBlair R Leavittnull nullRuth Luthi-CarterPublished in: Brain communications (2020)
Huntington's disease is a severe but slowly progressive hereditary illness for which only symptomatic treatments are presently available. Clinical measures of disease progression are somewhat subjective and may require years to detect significant change. There is a clear need to identify more sensitive, objective and consistent measures to detect disease progression in Huntington's disease clinical trials. Whereas Huntington's disease demonstrates a robust and consistent gene expression signature in the brain, previous studies of blood cell RNAs have lacked concordance with clinical disease stage. Here we utilized longitudinally collected samples from a well-characterized cohort of control, Huntington's disease-at-risk and Huntington's disease subjects to evaluate the possible correlation of gene expression and disease status within individuals. We interrogated these data in both cross-sectional and longitudinal analyses. A number of changes in gene expression showed consistency within this study and as compared to previous reports in the literature. The magnitude of the mean disease effect over 2 years' time was small, however, and did not track closely with motor symptom progression over the same time period. We therefore conclude that while blood-derived gene expression indicators can be of value in understanding Huntington's disease pathogenesis, they are insufficiently sensitive to be of use as state biomarkers.
Keyphrases
- gene expression
- clinical trial
- cross sectional
- dna methylation
- systematic review
- multiple sclerosis
- stem cells
- poor prognosis
- physical activity
- randomized controlled trial
- mesenchymal stem cells
- machine learning
- bone marrow
- depressive symptoms
- long non coding rna
- cell therapy
- blood brain barrier
- brain injury
- early onset
- electronic health record
- artificial intelligence
- big data