Longitudinal Standards for Mid-life Cognitive Performance: Identifying Abnormal Within-Person Changes in the Wisconsin Registry for Alzheimer’s Prevention

Abstract

AbstractObjectives: A major challenge in cognitive aging is differentiating preclinical disease-related cognitive decline from changes associated with normal aging. Neuropsychological test authors typically publish single time-point norms, referred to here as unconditional reference values. However, detecting significant change requires longitudinal, or conditional reference values, created by modeling cognition as a function of prior performance. Our objectives were to create, depict, and examine preliminary validity of unconditional and conditional reference values for ages 40–75 years on neuropsychological tests. Method: We used quantile regression to create growth-curve–like models of performance on tests of memory and executive function using participants from the Wisconsin Registry for Alzheimer’s Prevention. Unconditional and conditional models accounted for age, sex, education, and verbal ability/literacy; conditional models also included past performance on and number of prior exposures to the test. Models were then used to estimate individuals’ unconditional and conditional percentile ranks for each test. We examined how low performance on each test (operationalized as <7th percentile) related to consensus-conference–determined cognitive statuses and subjective impairment. Results: Participants with low performance were more likely to receive an abnormal cognitive diagnosis at the current visit (but not later visits). Low performance was also linked to subjective and informant reports of worsening memory function. Conclusions: The percentile-based methods and single-test results described here show potential for detecting troublesome within-person cognitive change. Development of reference values for additional cognitive measures, investigation of alternative thresholds for abnormality (including multi-test criteria), and validation in samples with more clinical endpoints are needed. (JINS, 2019, 25, 1–14)