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Enhancing global Alzheimer's disease drug outcomes by comprehensive African data integration

Domaine:

healthcare

Type de record:

paper
Créateur:
MicOriChiTem
Éditeur:
SAG
Hôte:
Alzheimer's disease (AD) remains the leading cause of dementia worldwide and continues to pose a substantial therapeutic challenge. Although recent advances in disease-modifying treatments targeting amyloid-β pathology have generated cautious optimism, their translational impact is limited by persistent gaps in population and geographic representation within clinical trials. African populations, the most genetically diverse worldwide, remain markedly underrepresented in AD genomic and therapeutic studies. This gap limits the identification of ancestry-specific genetic risk factors and differential treatment responses and may contribute to the high attrition rates observed across AD drug development pipelines. We examine how the limited inclusion of African cohorts restricts insights into AD pathobiology and reduces the external validity of emerging therapeutic strategies. We highlight opportunities arising from the systematic integration of African population and clinical data, which have the potential to reveal novel biological mechanisms and expand the global relevance of candidate interventions. Persistent barriers, including insufficient research infrastructure and frequent substitution of African American cohorts for indigenous African populations, continue to obscure population-specific variation and hinder the development of a representative evidence base. Advancing the field will require coordinated and context-appropriate recruitment strategies, predictive modeling approaches grounded in region-specific data, and long-term investment in research capacity across the continent. A globally representative scientific framework that captures the full spectrum of human genetic heterogeneity is essential for accelerating progress in AD drug development. Integrating African population and data into clinical research will strengthen scientific rigor, enhance generalizability, and facilitate the development of globally equitable therapeutic strategies.

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doi.org

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https://journals.sagepub.com/page/policies/text-and-data-mining-license

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