Modelling the natural history of Huntingtons disease progression

Background: The lack of reliable biomarkers to track
disease progression is a major problem in clinical
research of chronic neurological disorders. Using
Huntington’s disease (HD) as an example, we describe a
novel approach to model HD and show that the
progression of a neurological disorder can be predicted
for individual patients.
Methods : Starting with an initial cohort of 343
patients with HD that we have followed since 1995, we
used data from 68 patients that satisfied our filtering
criteria to model disease progression, based on the
Unified Huntington’s Disease Rating Scale (UHDRS), a
measure that is routinely used in HD clinics worldwide.
Results : Our model was validated by: (A) extrapolating
our equation to model the age of disease onset, (B)
testing it on a second patient data set by loosening our
filtering criteria, (C) cross-validating with a repeated
random subsampling approach and (D) holdout
validating with the latest clinical assessment data from
the same cohort of patients. With UHDRS scores from
the past four clinical visits (over a minimum span of
2 years), our model predicts disease progression of
individual patients over the next 2 years with an
accuracy of 89–91%. We have also provided evidence
that patients with similar baseline clinical profiles can
exhibit very different trajectories of disease progression.
Conclusions : This new model therefore has important
implications for HD research, most obviously in the
development of potential disease-modifying therapies.
We believe that a similar approach can also be adapted
to model disease progression in other chronic
neurological disorders.