HOMEPRODUCTSCOMPANYCONTACTFAQResearchDictionaryPharmaSign Up FREE or Login

Prediction of manifest Huntington's disease with clinical and imaging measures: a prospective observational study.

AbstractBACKGROUND:
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.
AuthorsJane S Paulsen, Jeffrey D Long, Christopher A Ross, Deborah L Harrington, Cheryl J Erwin, Janet K Williams, Holly James Westervelt, Hans J Johnson, Elizabeth H Aylward, Ying Zhang, H Jeremy Bockholt, Roger A Barker, PREDICT-HD Investigators and Coordinators of the Huntington Study Group
JournalThe Lancet. Neurology (Lancet Neurol) Vol. 13 Issue 12 Pg. 1193-201 (Dec 2014) ISSN: 1474-4465 [Electronic] England
PMID25453459 (Publication Type: Journal Article, Multicenter Study, Observational Study, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
CopyrightCopyright © 2014 Elsevier Ltd. All rights reserved.
Topics
  • Adult
  • Aged
  • Aged, 80 and over
  • Diagnostic Imaging (trends)
  • Female
  • Follow-Up Studies
  • Humans
  • Huntington Disease (diagnosis, epidemiology, genetics)
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Prospective Studies

Join CureHunter, for free Research Interface BASIC access!

Take advantage of free CureHunter research engine access to explore the best drug and treatment options for any disease. Find out why thousands of doctors, pharma researchers and patient activists around the world use CureHunter every day.
Realize the full power of the drug-disease research graph!


Choose Username:
Email:
Password:
Verify Password:
Enter Code Shown: