Phenotype-genotype discrepancies in the prospective Huntington at-risk observational study

A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design

BACKGROUND

Applying machine-learning algorithms to large datasets such as those available in Huntington's disease offers the opportunity to discover hidden patterns, often not discernible to clinical observation.

OBJECTIVES

To develop and validate a model of Huntington's disease progression using probabilistic machine learning methods.

METHODS

Longitudinal data encompassing 2079 assessment measures from four observational studies (PREDICT-HD, REGISTRY, TRACK-HD, and Enroll-HD) were integrated and machine-learning methods (Bayesian latent-variable analysis and continuous-time hidden Markov models) were applied to develop a probabilistic model of disease progression. The model was validated using a separate Enroll-HD dataset and compared with existing clinical reference assessments (Unified Huntington's Disease Rating Scale [UHDRS] diagnostic confidence level, total functional capacity, and total motor scores) and CAG-age product.

RESULTS

Nine disease states were discovered based on 44 motor, cognitive, and functional measures, which correlated with reference assessments. The validation set included 3158 participants (mean age, 48.4 years) of whom 61.5% had manifest disease. Analysis of transition times showed that "early-disease" states 1 and 2, which occur before motor diagnosis, lasted ~16 years. Increasing numbers of participants had motor onset during "transition" states 3 to 5, which collectively lasted ~10 years, and the "late-disease" states 6 to 9 also lasted ~10 years. The annual probability of conversion from one of the nine identified disease states to the next ranged from 5% to 27%.

CONCLUSIONS

The natural history of Huntington's disease can be described by nine disease states of increasing severity. The ability to derive characteristics of disease states and probabilities for progression through these states will improve trial design and participant selection. © 2021 International Parkinson and Movement Disorder Society.

Do Changes in Synaptic Autophagy Underlie the Cognitive Impairments in Huntington's Disease?

Although Huntington's disease (HD) is classically considered from the perspective of the motor syndrome, the cognitive changes in HD are prominent and often an early manifestation of disease. As such, investigating the underlying pathophysiology of cognitive changes may give insight into important and early neurodegenerative events. In this review, we first discuss evidence from both HD patients and animal models that cognitive changes correlate with early pathological changes at the synapse, an observation that is similarly made in other neurodegenerative conditions that primarily affect cognition. We then describe how autophagy plays a critical role supporting synaptic maintenance in the healthy brain, and how autophagy dysfunction in HD may thereby lead to impaired synaptic maintenance and thus early manifestations of disease.