Simone C Egli1, Daniela I Hirni1, Kirsten I Taylor2, Manfred Berres3, Axel Regeniter4, Achim Gass5, Andreas U Monsch1, Marc Sollberger6. 1. Memory Clinic, University Center for Medicine of Aging Basel, Felix Platter-Hospital, Basel, Switzerland. 2. Memory Clinic, University Center for Medicine of Aging Basel, Felix Platter-Hospital, Basel, Switzerland Centre for Speech, Language and the Brain, Department of Experimental Psychology, Cambridge, UK. 3. Department of Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany. 4. Department of Laboratory Medicine, University Hospital Basel, Basel, Switzerland. 5. Department of Neurology, Universitätsmedizin Mannheim, Mannheim, Germany. 6. Memory Clinic, University Center for Medicine of Aging Basel, Felix Platter-Hospital, Basel, Switzerland Department of Neurology, University Hospital Basel, Basel, Switzerland.
Abstract
BACKGROUND: Several cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. However, predictors might be more or less powerful depending on the characteristics of the MCI sample. OBJECTIVE: To investigate which cognitive markers and biomarkers predict conversion to AD dementia and course of cognitive functioning in a MCI sample with a high proportion of early-stage MCI patients. METHODS: Variables known to predict progression in MCI patients and hypothesized to predict progression in early-stage MCI patients were selected. Cognitive (long-delay free recall, regional primacy score), imaging (hippocampal and entorhinal cortex volumes, fornix fractional anisotropy), and CSF (Aβ1-42/t-tau, Aβ1-42) variables from 36 MCI patients were analyzed with Cox regression and mixed-effect models to determine their individual and combined abilities to predict time to conversion to AD dementia and course of global cognitive functioning, respectively. RESULTS: Those variables hypothesized to predict the course of early-stage MCI patients were most predictive for MCI progression. Specifically, regional primacy score (a measure of word-list position learning) most consistently predicted conversion to AD dementia and course of cognitive functioning. Both the prediction of conversion and course of cognitive functioning were maximized by including CSF Aβ1-42 and fornix integrity biomarkers, respectively, indicating the complementary information carried by cognitive variables and biomarkers. CONCLUSION: Predictors of MCI progression need to be interpreted in light of the characteristics of the respective MCI sample. Future studies should aim to compare predictive strengths of markers between early-stage and late-stage MCI patients.
BACKGROUND: Several cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. However, predictors might be more or less powerful depending on the characteristics of the MCI sample. OBJECTIVE: To investigate which cognitive markers and biomarkers predict conversion to AD dementia and course of cognitive functioning in a MCI sample with a high proportion of early-stage MCI patients. METHODS: Variables known to predict progression in MCI patients and hypothesized to predict progression in early-stage MCI patients were selected. Cognitive (long-delay free recall, regional primacy score), imaging (hippocampal and entorhinal cortex volumes, fornix fractional anisotropy), and CSF (Aβ1-42/t-tau, Aβ1-42) variables from 36 MCI patients were analyzed with Cox regression and mixed-effect models to determine their individual and combined abilities to predict time to conversion to AD dementia and course of global cognitive functioning, respectively. RESULTS: Those variables hypothesized to predict the course of early-stage MCI patients were most predictive for MCI progression. Specifically, regional primacy score (a measure of word-list position learning) most consistently predicted conversion to AD dementia and course of cognitive functioning. Both the prediction of conversion and course of cognitive functioning were maximized by including CSF Aβ1-42 and fornix integrity biomarkers, respectively, indicating the complementary information carried by cognitive variables and biomarkers. CONCLUSION: Predictors of MCI progression need to be interpreted in light of the characteristics of the respective MCI sample. Future studies should aim to compare predictive strengths of markers between early-stage and late-stage MCI patients.
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