title: Informativeness of Early Huntington Disease Signs about Gene Status.
abstract: BACKGROUND
  The cohort-level risk of Huntington disease (HD) is related to the age and symptom level of the cohort, but this relationship has not been made precise.
  
  
  OBJECTIVE
  To predict the evolving likelihood of carrying the Huntington disease (HD) gene for at-risk adults using age and sign level.
  
  
  METHODS
  Using data from adults with early signs and symptoms of HD linked to information on genetic status, we use Bayes' theorem to calculate the probability that an undiagnosed individual of a certain age and sign level has an expanded CAG repeat.
  
  
  RESULTS
  Both age and sign levels have substantial influence on the likelihood of HD onset, and the probability of eventual diagnosis changes as those at risk age and exhibit (or fail to exhibit) symptoms. For example, our data suggest that in a cohort of individuals age 26 with a Unified Huntington's Disease Rating Scale (UHDRS) motor score of 7-10 70% of them will carry the HD mutation. For individuals age 56, the same motor score suggests only a 40% chance of carrying the mutation. Early motor signs of HD, overall and the chorea subscore, were highly predictive of disease onset at any age. However, body mass index (BMI) and cognitive performance scores were not as highly predictive.
  
  
  CONCLUSIONS
  These results suggest that if researchers or clinicians are looking for early clues of HD, it may be more foretelling to look at motor rather than cognitive signs. Application of similar approaches could be used with other adult-onset genetic conditions.
authors.affiliation: Department of Economics, Brown University, Providence, RI, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Oster
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
authors.forename: Shirley W
authors.initials: SW
authors.lastname: Eberly
authors.affiliation: Department of Neurology and CHET, University of Rochester Medical Center, Rochester, NY, USA.
authors.forename: E Ray
authors.initials: ER
authors.lastname: Dorsey
authors.affiliation: Department of Neurology and CHET, University of Rochester Medical Center, Rochester, NY, USA.
authors.forename: Elise
authors.initials: E
authors.lastname: Kayson-Rubin
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
authors.forename: David
authors.initials: D
authors.lastname: Oakes
authors.affiliation: Department of Neurology, Georgetown University, Washington, DC, USA.
authors.forename: Ira
authors.initials: I
authors.lastname: Shoulson
mesh_terms: D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001499:Bayes Theorem; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D006816:Huntington Disease / Q000175:diagnosis* / Q000235:genetics*; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D012720:Severity of Illness Index
keywords: Huntington disease (HD); bayesian; diagnosis; early symptoms; predictive