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J01 Improving Prediction Of Huntington Disease Onset With Clinical And Imaging Measures: A 10-year Preopective Study Of Converters
  1. J Paulsen1,2,3,
  2. JD Long1,4,
  3. C Ross5,
  4. D Harrington6,7,
  5. C Erwin8,
  6. J Williams9,
  7. H Westervelt10,11,
  8. H Johnson1,12,
  9. E Aylward13,
  10. Y Zhang14,
  11. J Bockholt15,
  12. R Barker16
  13. PREDICT-HD Investigators, and Coordinators of the Huntington Study Group
  1. 1Department of Psychiatry, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
  2. 2Department of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
  3. 3Department of Psychology,The University of Iowa, Iowa City, IA, USA
  4. 4Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, IA, USA
  5. 5Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, Johns Hopkins University, Baltimore, MD, USA
  6. 6Department of Radiology, University of California, San Diego, School of Medicine, San Diego, CA, USA
  7. 7Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
  8. 8Director, Center of Excellence for Ethics, Humanities and Spirituality, Texas Tech University Health Science Center School of Medicine, Lubbock, TX, USA
  9. 9College of Nursing, The University of Iowa, Iowa City, IA, USA
  10. 10Department of Psychiatry and Human Behavior, Division of Biology and Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA
  11. 11Department of Psychiatry, Rhode Island Hospital, Providence, RI, USA
  12. 12Departments of Electrical and Computer Engineering and Biomedical Engineering, College of Engineering, The University of Iowa, Iowa City, IA, USA
  13. 13Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA, USA
  14. 14Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
  15. 15Advanced Biomedical Informatics Group, LLC, Iowa City, IA, USA
  16. 16Department of Clinical Neurosciences, John Van Geest Centre for Brain Repair, University of Cambridge, Cambridge, UK

Abstract

Background Although correlation between CAG repeat length and age of HD onset is well known, improved prediction of onset would be advantageous for clinical trial sample enrichment and genetic counselling.

Aims To examine the predictive utility of genetic, demographic, motor, cognitive, psychiatric, functional and imaging measures for predicting conversion to manifest HD.

Methods Over 1000 research participants with the gene mutation for HD from 33 worldwide sites were followed for up to 10 years (average=6; range 1–10). A subset of 211 participants prospectively converted to manifest HD according to standard motor criteria.

Results The association between CAG repeats and onset age was replicated using a prospectively diagnosed cohort. Further, cross-sectional and longitudinal clinical and imaging measures were highly predictive of motor diagnosis beyond CAG repeat length and age. For example, a one standard deviation (SD) difference in total motor score increased the likelihood of motor diagnosis by 3X6 times, one SD loss in putamen volume by 2X9 times and one SD cognitive decline 2X2 times.

Conclusions Prediction of HD onset can considerably improve above and beyond that obtained by CAG repeat length and age alone. Many of the measures shown are inexpensive and efficient and could readily be integrated into clinical care at bedside, the clinic, or in the field. Refined predictors of onset can provide evidence for revised diagnostic guidelines and may be utilised for prognostic counselling. Prediction models can be used for enrichment of clinical trial samples and predictors can more precisely determine covariates to reduce costs of clinical trials.

Funding National Institutes for Health, National Institute of Neurological Disorders and Stroke 040068, and CHDI Foundation, Inc.

KeyWords
  • Prediction
  • diagnosis
  • onset
  • HD
  • premanifest

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