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Updated: Dec 20, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Neuropsychiatric symptoms as prognostic makers for the elderly with mild cognitive impairment: a meta-analysis
Mingyue Hu1, Xinhui Shu2, Xinyin Wu3
1Department of Nursing, XiangYa School of Medicine, Central South University, Changsha, China.
Background:
Although several neuropsychiatric symptoms (NPSs) have been demonstrated to have value in the prediction of the progression of mild cognitive impairment (MCI) to dementia, these symptoms are less studied for the prediction of the transition from normal cognition (NC) to MCI.
Methods:
Prospective cohort studies were included if they reported on at least one NPS at baseline and had MCI as the outcome.
Results:
We obtained 13 cohort studies with a total population of 33,066. Depression was the most common neuropsychiatric symptom and could significantly predict transition to MCI (RR = 1.49, 95% CI: 1.13-1.86). However, depression was more capable of predicting amnestic MCI (RR=1.43, 95% CI: 1.04-1.83) than non-aMCI (RR= 0.96, 95% CI 95% CI: 0.60-1.33). Subgroup analysis suggested that the association between depression and MCI changed with depression severity, depression criteria, apolipoprotein-E-adjusted status, age, the percentage of females, and follow-up times, but some data were too sparse for a reliable estimate. Regarding other NPSs, there were insufficient data to assess their effect on the development of MCI. However, apathy, anxiety, sleep disturbances, irritability, and agitation might be risk factors for the prediction of NC-MCI transition with strong predictive value.
Conclusions:
Depression was associated with an approximately 1.5-fold sincreased risk of the progression to MCI in the population with normal cognition. Other NPSs with underlying predictive value deserve more attention.
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