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Observational Study
. 2014 Dec;13(12):1193-201.
doi: 10.1016/S1474-4422(14)70238-8. Epub 2014 Nov 3.

Prediction of Manifest Huntington's Disease With Clinical and Imaging Measures: A Prospective Observational Study

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Free PMC article
Observational Study

Prediction of Manifest Huntington's Disease With Clinical and Imaging Measures: A Prospective Observational Study

Jane S Paulsen et al. Lancet Neurol. .
Free PMC article

Abstract

Background: Although the association between cytosine-adenine-guanine (CAG) repeat length and age at onset of Huntington's disease is well known, improved prediction of onset would be advantageous for clinical trial design and prognostic counselling. We compared various measures for tracking progression and predicting conversion to manifest Huntington's disease.

Methods: In this prospective observational study, we assessed the ability of 40 measures in five domains (motor, cognitive, psychiatric, functional, and imaging) to predict time to motor diagnosis of Huntington's disease, accounting for CAG repeat length, age, and the interaction of CAG repeat length and age. Eligible participants were individuals from the PREDICT-HD study (from 33 centres in six countries [USA, Canada, Germany, Australia, Spain, UK]) with the gene mutation for Huntington's disease but without a motor diagnosis (a rating below 4 on the diagnostic confidence level from the 15-item motor assessment of the Unified Huntington's Disease Rating Scale). Participants were followed up between September, 2002, and July, 2014. We used joint modelling of longitudinal and survival data to examine the extent to which baseline and change of measures analysed separately was predictive of CAG-adjusted age at motor diagnosis.

Findings: 1078 individuals with a CAG expansion were included in this analysis. Participants were followed up for a mean of 5·1 years (SD 3·3, range 0·0-12·0). 225 (21%) of these participants received a motor diagnosis of Huntington's disease during the study. 37 of 40 cross-sectional and longitudinal clinical and imaging measures were significant predictors of motor diagnosis beyond CAG repeat length and age. The strongest predictors were in the motor, imaging, and cognitive domains: an increase of one SD in total motor score (motor domain) increased the risk of a motor diagnosis by 3·07 times (95% CI 2·26-4·16), a reduction of one SD in putamen volume (imaging domain) increased risk by 3·32 times (2·37-4·65), and a reduction of one SD in Stroop word score (cognitive domain) increased risk by 2·32 times (1·88-2·87).

Interpretation: Prediction of diagnosis of Huntington's disease can be improved beyond that obtained by CAG repeat length and age alone. Such knowledge about potential predictors of manifest Huntington's disease should inform discussions about guidelines for diagnosis, prognosis, and counselling, and might be useful in guiding the selection of participants and outcome measures for clinical trials.

Funding: US National Institutes of Health, US National Institute of Neurological Disorders and Stroke, and CHDI Foundation.

Figures

Figure 1
Figure 1. Observed age of diagnosis by CAG expansion for N=225 prospective converters. Observations were slightly jittered horizontally to reveal overlapping cases
Figure 1 shows age of diagnosis as a function of CAG expansion for the N=225 people who obtained a motor diagnosis during the 12-year PREDICT-HD study (i.e., the converters). None of the individuals in Figure 1 were diagnosed at study entry and all have repeated measures, some with up to 12 years of observations (mean=5 years). The color change is at the median age for each CAG. As seen in the figure, the age at diagnosis can vary widely for individuals with the same CAG expansion. Considering CAG=40, the range is 31 years, and the difference between the first and third quartile is 15 years. The variability is indicative of the need to include variables in addition to CAG expansion with the goal of improving the accuracy of predicting the time of diagnosis. The main goal of this study is to improve predictive accuracy by identifying variables that are correlated with diagnosis, over and above CAG expansion. CAG=cytosine-adenine-guanine.
Figure 2
Figure 2. Cumulative hazard (accumulated risk rate) of motor diagnosis by CAG-Age Product (CAP) for various baseline predictor values
CAP is age corrected for CAG expansion and is the time metric of the horizontal axis. The solid black line denotes the cumulative hazard for a model with no predictor representing prediction based only on CAG and age (as summarized by CAP). The dash-dot lines are risk profiles for predictors representing no deterioration at baseline (e.g., Total Motor Score=0), whereas the colored solid lines are risk profiles for predictors representing advanced deterioration at baseline (e.g., Total Motor Score = 15). Putamen volume is ICV-corrected. As a reference, CAP=290 corresponds to age 31 and CAP=600 corresponds to age 64 for a person with CAG=43 (the sample mean). CAP=Age × (CAG − 33·66).CAG=cytosine-adenine-guanine.
Figure 3
Figure 3. Trajectories of motor, imaging, cognitive, functional, and psychiatric variables for N=225 converters
The top-two strongest predictors in each domain were selected (see Table 2).Dashed lines are individual empirical data, and solid lines are cubic spline curves (shading shows 95% confidence interval). ICV=Intracranial-corrected volumes. Smell ID=Smell Identification (University of Pennsylvania Smell Identification Test). FrSBe=Frontal Systems Behavior Scale. SCL90=Symptom Checklist 90. TFC=total functional capacity. FAS=functional activity scale.

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