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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Factors predicting reversion from mild cognitive impairment to normal cognitive functioning: a population-based study
Perminder S Sachdev1, Darren M Lipnicki, John Crawford
1Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales Medicine, Sydney, New South Wales, Australia. p.sachdev@unsw.edu.au
Introduction:
Mild cognitive impairment (MCI) is associated with an increased risk of developing dementia. However, many individuals diagnosed with MCI are found to have reverted to normal cognition on follow-up. This study investigated factors predicting or associated with reversion from MCI to normal cognition.
Methods:
Our analyses considered 223 participants (48.9% male) aged 71-89 years, drawn from the prospective, population-based Sydney Memory and Ageing Study. All were diagnosed with MCI at baseline and subsequently classified with either normal cognition or repeat diagnosis of MCI after two years (a further 11 participants who progressed from MCI to dementia were excluded). Associations with reversion were investigated for (1) baseline factors that included diagnostic features, personality, neuroimaging, sociodemographics, lifestyle, and physical and mental health; (2) longitudinal change in potentially modifiable factors.
Results:
There were 66 reverters to normal cognition and 157 non-reverters (stable MCI). Regression analyses identified diagnostic features as most predictive of prognosis, with reversion less likely in participants with multiple-domain MCI (p = 0.011), a moderately or severely impaired cognitive domain (p = 0.002 and p = 0.006), or an informant-based memory complaint (p = 0.031). Reversion was also less likely for participants with arthritis (p = 0.037), but more likely for participants with higher complex mental activity (p = 0.003), greater openness to experience (p = 0.041), better vision (p = 0.014), better smelling ability (p = 0.040), or larger combined volume of the left hippocampus and left amygdala (p<0.040). Reversion was also associated with a larger drop in diastolic blood pressure between baseline and follow-up (p = 0.026).
Discussion:
Numerous factors are associated with reversion from MCI to normal cognition. Assessing these factors could facilitate more accurate prognosis of individuals with MCI. Participation in cognitively enriching activities and efforts to lower blood pressure might promote reversion.
Insights
Many individuals with mild cognitive impairment (MCI) revert to normal cognition. Factors like cognitive activity, sensory abilities, and brain structure predict reversion, suggesting lifestyle changes may aid recovery.
Area of Science:
- Neuroscience
- Gerontology
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) increases dementia risk.
- A significant portion of MCI cases revert to normal cognition.
- Predictors of MCI reversion are not fully understood.
Purpose of the Study:
- Investigate factors associated with reversion from MCI to normal cognition.
- Identify baseline characteristics and longitudinal changes influencing MCI prognosis.
- Inform more accurate prognostication for individuals with MCI.
Main Methods:
- Analysis of 223 participants (aged 71-89) from the Sydney Memory and Ageing Study.
- Baseline MCI diagnosis, with follow-up assessment after two years.
- Investigation of diagnostic features, sociodemographics, lifestyle, health, neuroimaging, and longitudinal changes.
Main Results:
- Reversion was less likely with multiple-domain MCI, severely impaired cognition, or memory complaints.
- Factors associated with higher reversion rates included complex mental activity, openness, better vision/smell, and larger hippocampal/amygdala volume.
- Reduced diastolic blood pressure over time also correlated with reversion.
Conclusions:
- Prognosis of MCI is influenced by a multitude of factors.
- Cognitively stimulating activities and blood pressure management may promote reversion.
- Assessing these factors can improve the prediction of MCI outcomes.
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