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P177: Motoric cognitive risk syndrome is associated with MRI-derived brain age: the Arakawa Geriatric Cohort Study

Published online by Cambridge University Press:  02 February 2024

Shogyoku Bun
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
Daichi Sone
Affiliation:
Department of Psychiatry, Jikei University School of Medicine, Tokyo, Japan
Ryo Shikimoto
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
Hisashi Kida
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
Shinichiro Nakajima
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
Yoshihiro Noda
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
Hidehito Niimura
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
Masaru Mimura
Affiliation:
Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
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Abstract

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Objective:

Motoric cognitive risk (MCR) syndrome is characterized by slow gait speed and subjective cognitive decline, which could predict future dementia. Previous research reported the associations between MCR and gray matter volume reduction in total and specific cortical regions and increased white matter hyperintensities in the brain. However, knowledge is scarce on the relationship between MCR and neuroimaging-derived brain age. The present study explored the association between MCR and brain-predicted age differences.

Methods:

The present study is a cross-sectional study that involved 1,099 community- dwelling older adults in the Arakawa Ward, Tokyo, Japan, who were between 65 to 84 years old in 2016. In defining MCR, subjective cognitive decline was deemed positive for those who answered “Yes” to the question, “Do you feel that you have more problems with memory than most?” in the Geriatric Depression Scale. Slower gait speed was defined as walking slower than the -1 standard deviation of the age- and sex- stratified gait speed. Brain age was predicted on the 1,021 participants with brain magnetic resonance imaging without severe artifacts or lesions. We used the support vector regression algorithm using MATLAB's “fitrsvm” function, applying ten-fold cross-validation to the results of primary component analysis of the spatially normalized gray-matter images. We calculated the brain-predicted age difference (Brain-PAD) by subtracting the chronological age from the predicted brain age. After excluding the participants with dementia (N=23), the difference in the mean Brain-PAD between MCR+ and MCR- was compared with the Student's t-test. The association between MCR and Brain PAD was examined with multiple regression analyses, adjusting for clinical-demographical data.

Results:

The median ages were 72 for both MCR+ (N=96) and MCR- (N=902). The mean Brain-PAD was 3.29 for MCR+ and -0.19 for MCR- (p < .001, Hedges' g = -0.504). Multiple regression analysis showed a significant association between Brain- PAD and MCR (standardized β = 0.159, p < .001) after adjusting for covariates.

Conclusion:

The present findings suggest that MCR reflects accelerated brain aging, which may increase the risk of neurodegeneration. Future studies should examine the longitudinal trajectories of brain age and incident dementia in participants with MCR.

Type
Posters
Copyright
© International Psychogeriatric Association 2024