Journal of the International Neuropsychological Society

Research Articles

Predicting cognitive decline and conversion to Alzheimer’s disease in older adults using the NAB List Learning test

BRANDON E. GAVETTa1a2a3 c1, AL OZONOFFa2a4, VLADA DOKTORa2, JOSEPH PALMISANOa2a5, ANIL K. NAIRa1a2, ROBERT C. GREENa1a2a6a7, ANGELA L. JEFFERSONa1a2a8a9 and ROBERT A. STERNa1a2a3

a1 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts

a2 Boston University Alzheimer’s Disease Center, Boston, Massachusetts

a3 Center for the Study of Traumatic Encephalopathy, Boston University, Boston, Massachusetts

a4 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts

a5 Data Coordinating Center, Boston University School of Public Health, Boston, Massachusetts

a6 Department of Medicine (Genetics), Boston University School of Medicine, Boston, Massachusetts

a7 Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts

a8 Department of Medicine (Geriatrics), Boston University School of Medicine, Boston, Massachusetts

a9 Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts

Abstract

To validate the Neuropsychological Assessment Battery (NAB) List Learning test as a predictor of future multi-domain cognitive decline and conversion to Alzheimer’s disease (AD), participants from a longitudinal research registry at a national AD Center were, at baseline, assigned to one of three groups (control, mild cognitive impairment [MCI], or AD), based solely on a diagnostic algorithm for the NAB List Learning test (Gavett et al., 2009), and followed for 1–3 years. Rate of change on common neuropsychological tests and time to convert to a consensus diagnosis of AD were evaluated to test the hypothesis that these outcomes would differ between groups (AD>MCI>control). Hypotheses were tested using linear regression models (n = 251) and Cox proportional hazards models (n = 265). The AD group declined significantly more rapidly than controls on Mini-Mental Status Examination (MMSE), animal fluency, and Digit Symbol; and more rapidly than the MCI group on MMSE and Hooper Visual Organization Test. The MCI group declined more rapidly than controls on animal fluency and CERAD Trial 3. The MCI and AD groups had significantly shorter time to conversion to a consensus diagnosis of AD than controls. The predictive validity of the NAB List Learning algorithm makes it a clinically useful tool for the assessment of older adults. (JINS, 2010, 16, 651–660.)

(Received October 13 2009)

(Reviewed March 19 2010)

(Accepted March 19 2010)

Correspondence:

c1 Correspondence and reprint requests to: Brandon E. Gavett, Instructor of Neurology, Boston University School of Medicine, 72 East Concord Street B-7800, Boston, MA 02118-2526. E-mail: begavett@bu.edu