Predicting Stability of Mild Cognitive Impairment (MCI): Findings of a Community Based Sample

Background: Mild Cognitive Impairment (MCI) is a risk factor for Alzheimer’s disease (AD) and other forms of dementia. However, much heterogeneity concerning neuropsychological measures, prevalence and progression rates impedes distinct diagnosis and treatment implications. Objective: Aim of the present study was the identification of specific tests providing a high certainty for stable MCI and factors that precipitate instability of MCI in a community based sample examined at three measurement points. Method: 130 participants were tested annually with an extensive test battery including measures of memory, language, executive functions, intelligence and dementia screening tests. Exclusion criteria at baseline comprised, severe cognitive deficits (e.g. diagnosis of dementia, psychiatric or neurological disease). Possible predictors for stability or instability of MCI-diagnosis were analyzed using Regression and Receiver Operating Characteristic (ROC) curve analysis. Age, IQ and APOE status were tested for moderating effects on the interaction of test performances and group membership. Results: A high prevalence of MCI (49%) was observed at baseline with a reversion rate of 18% after two years. Stability of MCI was related to performances in four measures (VLMT: delayed recall, CERAD: recall drawings, CERAD: Boston Naming Test, Benton Visual Retention Test: number of mistakes). Conversion to MCI is associated with language functions. Reversion to ‘normal’ was primarily predicted by single domain impairment. There was no significant influence of demographic, medical or genetic variables. Conclusion: The results highlight the role of repeated measurements for a reliable identification of functional neuropsychological predictors and better diagnostic reliability. In cases of high uncertainty close monitoring over time is needed in order of estimating outcome.