title: Regression model for predicting dissociations of regional cerebral glucose metabolism in individuals at risk for Huntington's disease.
abstract: This article describes and partially validates a method for predicting whether an observed regional metabolic value is consistent with the observed value of another region. A regression equation was generated from a set of normal metabolic values, and then this equation was applied to patients with symptomatic Huntington's disease and patients at risk for this disorder. The results of the regression method were consistent with observations of the absolute rate for the normal subjects and Huntington's patients. For the at-risk patients, 6 of 18 were found to have reduced caudate metabolism relative to observed thalamic values. Since the initial scan, one of these identified at-risk individuals has developed symptomatic Huntington's disease. The method may be appropriate for other disorders where there are potential subgroups (e.g., schizophrenia) within a diagnostic category.
mesh_terms: D001921:Brain / Q000378:metabolism*; D005544:Forecasting; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D006816:Huntington Disease / Q000378:metabolism*; D008954:Models, Biological; D012044:Regression Analysis; D012306:Risk
chemical_list: D005947:Glucose