Affiliations: [a] VTT Technical Research Centre of Finland, Tampere, Finland | [b] Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
Correspondence:
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Correspondence to: Hilkka Runtti, VTT Technical Research Centre of Finland, P.O. Box 1300, FIN-33101 Tampere, Finland. Tel.: +358 40 152 6627; Fax: +358 20 722 3499; E-mail: [email protected].
Abstract: Several neuropsychological tests and biomarkers of Alzheimer's disease (AD) have been validated and their evolution over time has been explored. In this study, multiple heterogeneous predictors of AD were combined using a supervised learning method called Disease State Index (DSI). The behavior of DSI values over time was examined to study disease progression quantitatively in a mild cognitive impairment (MCI) cohort. The DSI method was applied to longitudinal data from 140 MCI cases that progressed to AD and 149 MCI cases that did not progress to AD during the follow-up. The data included neuropsychological tests, brain volumes from magnetic resonance imaging, cerebrospinal fluid samples, and apolipoprotein E from the Alzheimer's Disease Neuroimaging Initiative database. Linear regression of the longitudinal DSI values (including the DSI value at the point of MCI to AD conversion) was performed for each subject having at least three DSI values available (147 non-converters, 126 converters). Converters had five times higher slopes and almost three times higher intercepts than non-converters. Two subgroups were found in the group of non-converters: one group with stable DSI values over time and another group with clearly increasing DSI values suggesting possible progression to AD in the future. The regression parameters differentiated between the converters and the non-converters with classification accuracy of 76.9% for the slopes and 74.6% for the intercepts. In conclusion, this study demonstrated that quantifying longitudinal patient data using the DSI method provides valid information for follow-up of disease progression and support for decision making.
Keywords: Alzheimer's disease, biomarkers, data mining, decision support techniques, early diagnosis, mild cognitive impairment
