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The Egyptian Dementia Network (EDN): A Multimodal Registry for Advancing Dementia Epidemiology and Precision Research in Egypt and Other LMICs
Dementia research has historically relied on evidence generated from Western populations, leaving low- and middle-income countries (LMICs), where dementia burden is rapidly increasing, substantially underrepresented. To address this gap, this thesis presents the conceptualization, operationalization...
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| Format: | Thesis |
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AUC Knowledge Fountain
2026
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| Summary: | Dementia research has historically relied on evidence generated from Western populations, leaving low- and middle-income countries (LMICs), where dementia burden is rapidly increasing, substantially underrepresented. To address this gap, this thesis presents the conceptualization, operationalization, and findings of the Egyptian Dementia Network (EDN), a culturally adapted, multicenter dementia registry in an African Arab African country. The EDN advances dementia research in Egypt from fragmented clinical observations to a standardized digital infrastructure that integrates epidemiological, clinical, cognitive, geospatial, and biomarker data.
Analysis of the EDN baseline cohort demonstrates that a complex interplay of social, environmental, and biological determinants shapes cognitive health in Egypt. Epidemiological findings identify lower educational attainment and social isolation as consistent risk factors associated with dementia status and cognitive performance. In addition, integrating geospatial metrics reveals important environmental inequities, showing that higher exposure to fine particulate matter (PM2.5) and lower neighborhood greenness, as measured by the Normalized Difference Vegetation Index (NDVI), are associated with increased dementia vulnerability among older adults.
This thesis also leverages the EDN’s linked biobank to advance precision dementia research in a historically underrepresented population. High-resolution proteomic profiling identified promising candidate blood-based biomarkers, including serpin G1, ApoA-II, and LBP, which may reflect biological pathways related to inflammation, lipid metabolism, and immune activation. To translate multimodal registry data into potential clinical utility, a localized machine learning model, the Dementia Riskometer, was developed. This predictive tool demonstrated improved performance compared with generic, externally derived diagnostic thresholds, underscoring the value of population-specific algorithms in dementia risk stratification.
Overall, the development and implementation of the EDN provide a scalable, resilient methodological framework for dementia surveillance and research in resource-limited settings. This thesis demonstrates that inclusive, context-specific research is not only an equity imperative but also a scientific prerequisite for external validity. The findings provide an evidence base to inform national dementia policy, guide environmentally conscious urban planning, and support the development of culturally sensitive and precision-oriented diagnostic strategies for dementia in Egypt and the broader Global South. |
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