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Updated: Dec 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Ethnic Differences Between Hispanics and Non-Hispanic Whites in Neuropsychiatric Symptoms Predict Conversion to Mild
Bhaskar Thakur1, Luis Alvarado2, Christopher Dodoo2
1Division of Biostatistics and Epidemiology, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, 158161Texas Tech University Health Sciences Center El Paso, TX, USA.
Abstract:
The aim of the study is to ascertain the neuropsychiatric symptoms (NPS) subtypes significantly influencing progression to mild cognitive impairment (MCI) by ethnicity. In this retrospective cohort study, we included 386 cognitively normal individuals participating in the longitudinal Texas Alzheimer's Research and Care Consortium between February 2007 and August 2014. The primary outcome was time to incident MCI. Data driven NPS subtypes at baseline were identified and the effects of these subtypes on the outcome were obtained for Hispanic and non-Hispanic ethnic cohorts and summarized with a hazard ratio (HR). Three NPS subtypes were identified and internally validated: psychomotor apathy factor (including agitation, irritability, apathy), affective mood factor (including depression, anxiety), and physical behavior factor (including nighttime behavior, eating/appetite disturbances). In adjusted analysis, a psychomotor apathy score of NPS was the best predictor for MCI (HR = 2.19, p = 0.037) among non-Hispanics whereas physical behavior score was the most predictive of MCI (HR = 2.55, p = 0.029) among Hispanics. A high score of affective mood factor also tended to increase the risk of MCI (HR = 2.09, p = 0.06) in Hispanics. Progression from normal cognition to MCI was differentially predicted by NPS subtypes in Hispanics and non-Hispanic whites. These data may inform the allocation of efforts for monitoring individuals at-risk of MCI.
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