Jessica B Langbaum1, Suzanne B Hendrix2, Napatkamon Ayutyanont3, Kewei Chen4, Adam S Fleisher3, Raj C Shah5, Lisa L Barnes6, David A Bennett7, Pierre N Tariot8, Eric M Reiman9. 1. Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA. Electronic address: Jessica.Langbaum@bannerhealth.com. 2. Pentara Corporation, Salt Lake City, UT, USA. 3. Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA. 4. Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Department of Mathematics and Statistics, Arizona State University, Tempe, AZ, USA. 5. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Family Medicine, Rush University Medical Center, Chicago, IL, USA. 6. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA; Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA. 7. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA. 8. Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Department of Psychiatry, University of Arizona, Tucson, AZ, USA. 9. Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Department of Psychiatry, University of Arizona, Tucson, AZ, USA; Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA.
Abstract
BACKGROUND: There is growing interest in the evaluation of preclinical Alzheimer's disease (AD) treatments. As a result, there is a need to identify a cognitive composite that is sensitive to track preclinical AD decline to be used as a primary endpoint in treatment trials. METHODS: Longitudinal data from initially cognitively normal, 70- to 85-year-old participants in three cohort studies of aging and dementia from the Rush Alzheimer's Disease Center were examined to empirically define a composite cognitive endpoint that is sensitive to detect and track cognitive decline before the onset of cognitive impairment. The mean-to-standard deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of cognitive tests/subtests drawn from the neuropsychological battery in cognitively normal participants who subsequently progressed to clinical stages of AD during 2- and 5-year periods, using data from those who remained unimpaired during the same period to correct for aging and practice effects. Combinations that performed well were then evaluated for representation of relevant cognitive domains, robustness across individual years before diagnosis, and occurrence of selected items within top performing combinations. RESULTS: The optimal composite cognitive test score comprised seven cognitive tests/subtests with an MSDR = 0.964. By comparison, the most sensitive individual test score was Logical Memory Delayed Recall with an MSDR = 0.64. CONCLUSIONS: We have identified a composite cognitive test score representing multiple cognitive domains that has improved power compared with the most sensitive single test item to track preclinical AD decline and evaluate preclinical AD treatments. We are confirming the power of the composite in independent cohorts and with other analytical approaches, which may result in refinements, have designated it as the primary endpoint in the Alzheimer's Prevention Initiative's preclinical treatment trials for individuals at high imminent risk for developing symptoms due to late-onset AD.
BACKGROUND: There is growing interest in the evaluation of preclinical Alzheimer's disease (AD) treatments. As a result, there is a need to identify a cognitive composite that is sensitive to track preclinical AD decline to be used as a primary endpoint in treatment trials. METHODS: Longitudinal data from initially cognitively normal, 70- to 85-year-old participants in three cohort studies of aging and dementia from the Rush Alzheimer's Disease Center were examined to empirically define a composite cognitive endpoint that is sensitive to detect and track cognitive decline before the onset of cognitive impairment. The mean-to-standard deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of cognitive tests/subtests drawn from the neuropsychological battery in cognitively normal participants who subsequently progressed to clinical stages of AD during 2- and 5-year periods, using data from those who remained unimpaired during the same period to correct for aging and practice effects. Combinations that performed well were then evaluated for representation of relevant cognitive domains, robustness across individual years before diagnosis, and occurrence of selected items within top performing combinations. RESULTS: The optimal composite cognitive test score comprised seven cognitive tests/subtests with an MSDR = 0.964. By comparison, the most sensitive individual test score was Logical Memory Delayed Recall with an MSDR = 0.64. CONCLUSIONS: We have identified a composite cognitive test score representing multiple cognitive domains that has improved power compared with the most sensitive single test item to track preclinical AD decline and evaluate preclinical AD treatments. We are confirming the power of the composite in independent cohorts and with other analytical approaches, which may result in refinements, have designated it as the primary endpoint in the Alzheimer's Prevention Initiative's preclinical treatment trials for individuals at high imminent risk for developing symptoms due to late-onset AD.
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