Abstract
ObjectiveDetermining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.MethodsWe employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.ResultsThe model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.InterpretationWe used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.
Highlights
Huntington’s disease (HD) is a monogenic, autosomaldominant neurological disorder characterized by motor, cognitive, and behavioral symptoms that have a devastating effect on the life of the person affected.[1]
We highlight that these thresholds are not used by the event-based model (EBM); they are provided here just to illustrate the separation of the distributions
We have presented a uniquely fine-grained model of temporal progression of volume loss in premanifest and manifest HD that is robust under cross-validation
Summary
Huntington’s disease (HD) is a monogenic, autosomaldominant neurological disorder characterized by motor, cognitive, and behavioral symptoms that have a devastating effect on the life of the person affected.[1] Symptoms typically begin in early adult life and the disease is usually fatal, with a median survival rate of 18 years after motor onset.[2] Despite the disease being identifiable by a single genetic marker – an expanded cytosine-adenine-guanine (CAG) repeat in the huntingtin gene3 – an effective disease-modifying treatment has yet to be found This is complicated by the difficulty in assigning gene-positive subjects to suitable groups when conducting drug trials; within any group there may be a range of physiological and biophysical factors that cause a very different response to treatment. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association
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