NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases

NDDRF 2.0:神经退行性疾病个性化预防风险因素知识库的更新和扩展

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Abstract

INTRODUCTION: Neurodegenerative diseases (NDDs) are chronic diseases caused by brain neuron degeneration, requiring systematic integration of risk factors to address their heterogeneity. Established in 2021, Knowledgebase of Risk Factors for Neurodegenerative Diseases (NDDRF) was the first knowledge base to consolidate NDD risk factors. NDDRF 2.0 expands focus to modifiable lifestyle-related factors, enhancing utility for NDD prevention. METHODS: Data from the past 4 years were comprehensively updated, while lifestyle factors were manually collected and filtered from 1975 to 2024. Each factor was embedded with International Classification of Diseases codes and clinical stage annotations, and then re-standardized, classified, and annotated in accordance with the Unified Medical Language System Semantic Network. RESULTS: NDDRF 2.0 encompasses 1971 risk factors classified under 151 subcategories across 20 NDDs, including 536 lifestyle-related factors covering six major categories and is freely accessible at http://sysbio.org.cn/NDDRF/. DISCUSSION: As the first lifestyle-specific and holistic knowledge base for NDDs, NDDRF 2.0 offers structured and deep phenotype information, enabling personalized prevention strategies and clinical decision support. HIGHLIGHTS: An enhanced lifestyle-specific and holistic knowledge base (Knowledgebase of Risk Factors for Neurodegenerative Diseases [NDDRF] 2.0) was built for neurodegenerative diseases (NDDs). NDDRF 2.0 provides detailed categorization and deep phenotypes to support targeted NDD prevention. NDDRF 2.0 provides a knowledge-driven resource that facilitates personalized risk assessment and proactive health management. NDDRF 2.0 provides clinicians, researchers, and at-risk populations with knowledge to develop and implement effective risk prevention strategies. NDDRF 2.0 can be used to build chatbots by enhancing large language models in the future.

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