A new model for prediction of the age of onset and penetrance for Huntington's disease based on CAG length

Abstract

Huntington's disease (HD) is a neurodegenerative disorder caused by an unstable CAG repeat. For patients at risk, participating in predictive testing and learning of having CAG expansion, a major unanswered question shifts from "Will I get HD?" to "When will it manifest?" Using the largest cohort of HD patients analyzed to date (2913 individuals from 40 centers worldwide), we developed a parametric survival model based on CAG repeat length to predict the probability of neurological disease onset (based on motor neurological symptoms rather than psychiatric onset) at different ages for individual patients. We provide estimated probabilities of onset associated with CAG repeats between 36 and 56 for individuals of any age with narrow confidence intervals. For example, our model predicts a 91% chance that a 40-year-old individual with 42 repeats will have onset by the age of 65, with a 95% confidence interval from 90 to 93%. This model also defines the variability in HD onset that is not attributable to CAG length and provides information concerning CAG-related penetrance rates.

An image‐based model of brain volume biomarker changes in Huntington's disease

“…1C) for a visual representation). This is in agreement with recent work by27 who used change point linear regression based on a priori disease staging to estimate the initial time of atrophy of structural MRI in the PREDICT‐HD dataset, and observed a central‐to‐peripheral pattern of atrophy from the basal ganglia to deep white matter. The model we present here is drawn from a broader spectrum of stages in the patient cohort than in,27 facilitating a more complete picture of the whole disease time course, and avoids the confounds of a priori staging.…”

“…Their ordering, however, has not previously been observed: the EBM places the putamen strongly ahead of the caudate and pallidum, even under bootstrapping. Abnormalities in the insula white matter and nonventricular CSF are identified by the EBM as potential mid‐stage biomarkers, the former of which is also reported in the PREDICT‐HD dataset,27 and agrees with observations of white matter abnormality in premanifest and manifest subjects 31, 32, 33, 34…”

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

“…Ethical Approval was granted by the Local Research Ethics Committee's and informed consent was taken from participants. The preHD group was median‐split into preHD‐near and preHD‐far subgroups according to their estimated years to clinical diagnosis score calculated using the Langbehn model4 (median = 13.6 years). The cohort was then tracked for 5 years, during which time 8 preHD developed overt motor symptoms (HD‐converters; see Table 1 for demographics).…”

“…The prevalence of HD is just 12 per 100,0002 and with less than 1 in 5 “at‐risk” individuals undergoing predictive testing,3 the number of known premanifest HD gene carriers is small. Furthermore, HD is a slow progressing disease with a large variance in age of onset, especially for individuals with smaller CAG repeat lengths 4, 5. Currently, proximity to clinical diagnosis is estimated using statistical models based upon CAG repeat length and age 4, 6.…”

“…Furthermore, HD is a slow progressing disease with a large variance in age of onset, especially for individuals with smaller CAG repeat lengths 4, 5. Currently, proximity to clinical diagnosis is estimated using statistical models based upon CAG repeat length and age 4, 6. However, the CAG repeat length only accounts for between 50% and 69% of the variance observed in age at diagnosis 6, 7, 8.…”