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Updated: Jun 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Outcome-specific risk factors challenge universal dementia prevention priorities: Findings from the Egyptian Dementia
Shimaa A Heikal1, Eman M Khedr2, Gharib Fawi3
1Institute of Global Health and Human Ecology, The American University in Cairo, Cairo, Egypt.
Background:
Dementia prevention targets modifiable factors from high-income cohorts, but most studies conflate dementia status and cognitive performance, distinct outcomes with potentially different outcomes. This distinction is critical to disentangle in resource-constrained low- and middle-income countries, where resource prioritization requires context-specific evidence.
Methods:
Cross-sectional analysis of 660 adults with dementia/mild cognitive impairment from the Egyptian Dementia Network registry (2022 to 2025). Parallel logistic/linear regression models identified factors associated with dementia status versus continuous Mini-Mental State Examination (MMSE) scores, comparing determinants across outcomes.
Results:
The mean age of participants was 68 years, 51% were male, and 81% had a primary education. Multivariable models showed only college education (odds ratio [OR] 0.30, 95% confidence interval [CI]: 0.10 to 0.86) and social inactivity (OR 3.00, 95% CI: 1.27 to 7.08) associated with dementia status. Eight factors (age, diabetes, hypertension, cholesterol, family history, apolipoprotein ε4, body mass index, and sex; all p < 0.05) strongly predicted MMSE but not diagnosis.
Discussion:
Dementia diagnosis and cognitive performance show mechanistically distinct cross-sectional associations. LMICs should prioritize education/social engagement for dementia prevention, framing vascular-metabolic management as cognitive maintenance.
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