%0 Journal Article %J J Alzheimers Dis %D 2020 %T Gait Disturbances are Associated with Increased Cognitive Impairment and Cerebrospinal Fluid Tau Levels in a Memory Clinic Cohort. %A Muurling, Marijn %A Rhodius-Meester, Hanneke F M %A Pärkkä, Juha %A van Gils, Mark %A Frederiksen, Kristian S %A Bruun, Marie %A Hasselbalch, Steen G %A Soininen, Hilkka %A Herukka, Sanna-Kaisa %A Hallikainen, Merja %A Teunissen, Charlotte E %A Visser, Pieter Jelle %A Scheltens, Philip %A van der Flier, Wiesje M %A Mattila, Jussi %A Lötjönen, Jyrki %A de Boer, Casper %X

BACKGROUND: Gait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer's disease and other dementias. It is not clear, however, which gait features are most informative and how these measures relate to Alzheimer's disease pathology.

OBJECTIVE: In this study, we tested if calculated features of gait 1) differ between cognitively normal subjects (CN), mild cognitive impairment (MCI) patients, and dementia patients, 2) are correlated with cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease, and 3) predict cognitive decline.

METHODS: Gait was measured using tri-axial accelerometers attached to the fifth lumbar vertebra (L5) in 58 CN, 58 MCI, and 26 dementia participants, while performing a walk and dual task. Ten gait features were calculated from the vertical L5 accelerations, following principal component analysis clustered in four domains, namely pace, rhythm, time variability, and length variability. Cognitive decline over time was measured using MMSE, and CSF biomarkers were available in a sub-group.

RESULTS: Linear mixed models showed that dementia patients had lower pace scores than MCI patients and CN subjects (p < 0.05). In addition, we found associations between the rhythm domain and CSF-tau, especially in the dual task. Gait was not associated with CSF Aβ42 levels and cognitive decline over time as measured with the MMSE.

CONCLUSION: These findings suggest that gait-particularly measures related to pace and rhythm-are altered in dementia and have a direct link with measures of neurodegeneration.

%B J Alzheimers Dis %V 76 %P 1061-1070 %8 2020 Aug 04 %G eng %N 3 %R 10.3233/JAD-200225 %0 Journal Article %J J Alzheimers Dis %D 2020 %T Metabolic Profiles Help Discriminate Mild Cognitive Impairment from Dementia Stage in Alzheimer's Disease. %A Jääskeläinen, Olli %A Hall, Anette %A Tiainen, Mika %A van Gils, Mark %A Lötjönen, Jyrki %A Kangas, Antti J %A Helisalmi, Seppo %A Pikkarainen, Maria %A Hallikainen, Merja %A Koivisto, Anne %A Hartikainen, Päivi %A Hiltunen, Mikko %A Ala-Korpela, Mika %A Soininen, Pasi %A Soininen, Hilkka %A Herukka, Sanna-Kaisa %X

Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.

%B J Alzheimers Dis %V 74 %P 277-286 %8 2020 Mar 10 %G eng %N 1 %R 10.3233/JAD-191226