A scalable model for Parkinson's disease genetic research in underrepresented populations: lessons from Kyrgyzstan
· 2026-09-09 · 原文
DOI：10.64898/2026.09.08.26362411v1?rss=1
Background Scalable models for Parkinson's disease (PD) genetics in underrepresented populations are needed, as most studies have focused on individuals of European-ancestry, limiting generalisability across ancestral and environmental contexts. Kyrgyzstan has been largely absent from global PD research. Using Kyrgyzstan as a case study, we aimed to develop and evaluate a scalable PD genetics model integrating research and local capacity building. Methods We implemented a stepwise capacity-building framework within a multi-site prospective PD genetics cohort that included participants with PD and controls, with longitudinal follow-up. The framework integrated community engagement, workforce development, specialist clinical services, data governance, clinical phenotyping, biobanking, and ge
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1. 人话版
Background Scalable models for Parkinson's disease (PD) genetics in underrepresented populations are needed, as most studies have focused on individuals of European-ancestry, limiting generalisability across ancestral and environmental contexts.
Kyrgyzstan has been largely absent from global PD research.
2. 领域脉络
Using Kyrgyzstan as a case study, we aimed to develop and evaluate a scalable PD genetics model integrating research and local capacity building.
3. 机制拆解
Methods We implemented a stepwise capacity-building framework within a multi-site prospective PD genetics cohort that included participants with PD and controls, with longitudinal follow-up.
The framework integrated community engagement, workforce development, specialist clinical services, data governance, clinical phenotyping, biobanking, and ge
4. 证据与数字
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5. 反例与边界
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6. 跨领域连接与意外收获
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8. 术语表
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