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Updated: Apr 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Association of dietary intake and lifestyle pattern with mild cognitive impairment in the elderly
1Rong Xiao, No.10 Xitoutiao, You An Men, Beijing 100069, PR China, xiaor22@ccmu.edu.cn, Tel: 86-010-83911512, Fax: 86-010-83911512.
Objectives:
Specific diets or lifestyles have an impact on cognitive function in previous studies. However, the association of the complex action of Chinese daily diets and lifestyle patterns with mild cognitive impairment (MCI) among elderly people had not been studied exactly. The aim of this study was to explore the association of dietary and lifestyle patterns with MCI among elderly people in Beijing.
Design:
Population-based and case-control design.
Setting:
The physical examination center in Xuanwu Hospital, Capital Medical University in Beijing.
Participants:
A total of 404 subjects, aged 60 years old or above, with or without MCI.
Measurements:
The Montreal cognitive assessment (MoCA) was used to screen the subjects with MCI. All subjects were required to complete a questionnaire which was comprised of their demographic information, health status, lifestyles, and food frequency questionnaire (FFQ). Binary multivariable logistic regression was used to evaluate the potential association between MCI and these factors.
Results:
With adjustment for some factors, higher daily intake of eggs (OR, 0.975, 95% CI, 0.959-0.992, P=.003) and marine products (OR, 0.96, 95% CI, 0.943-0.979, P=.000), longer time of watching TV (OR, 0.763, 95% CI, 0.628-0.928, P=.007), reading (OR, 0.540, 95% CI, 0.379-0.769, P=.001) and physical exercise (OR, 0.382, 95% CI, 0.185-0.789) had significantly decreased odds of suffering from MCI compared with the control group.
Conclusions:
Our findings suggested that daily higher intake of eggs and marine products, watching TV, reading and physical exercise were associated with preventing the development of MCI in this population-based samples.
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