Jiang Yukang, Zhao Bingxin, Wang Xiaopu, Tang Borui, Peng Huiyang, Luo Zidan, Shen Yue, Wang Zheng, Jiang Zhiwen, Wang Jie, Ye Jieping, Wang Xueqin, Zhu Hongtu
Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA.
Nat Commun. 2025 Apr 22;16(1):3767. doi: 10.1038/s41467-025-58724-3.
The rapid accumulation of biomedical cohort data presents opportunities to explore disease mechanisms, risk factors, and prognostic markers. However, current research often has a narrow focus, limiting the exploration of risk factors and inter-disease correlations. Additionally, fragmented processes and time constraints can hinder comprehensive analysis of the disease landscape. Our work addresses these challenges by integrating multimodal data from the UK Biobank, including basic, lifestyle, measurement, environment, genetic, and imaging data. We propose UKB-MDRMF, a comprehensive framework for predicting and assessing health risks across 1560 diseases. Unlike single disease models, UKB-MDRMF incorporates multimorbidity mechanisms, resulting in superior predictive accuracy, with all disease types showing improved performance in risk assessment. By jointly predicting and assessing multiple diseases, UKB-MDRMF uncovers shared and distinctive connections among risk factors and diseases, offering a broader perspective on health and multimorbidity mechanisms.
生物医学队列数据的快速积累为探索疾病机制、风险因素和预后标志物提供了机会。然而，当前的研究往往关注点较为狭窄，限制了对风险因素和疾病间相关性的探索。此外，流程碎片化和时间限制可能会阻碍对疾病全貌的全面分析。我们的工作通过整合来自英国生物银行的多模态数据来应对这些挑战，这些数据包括基础数据、生活方式数据、测量数据、环境数据、基因数据和影像数据。我们提出了UKB-MDRMF，这是一个用于预测和评估1560种疾病健康风险的综合框架。与单一疾病模型不同，UKB-MDRMF纳入了多病共患机制，从而具有更高的预测准确性，所有疾病类型在风险评估中的表现都有所改善。通过联合预测和评估多种疾病，UKB-MDRMF揭示了风险因素和疾病之间的共同及独特联系，为健康和多病共患机制提供了更广阔的视角。
