UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data

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.

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Publication Details

Journal
Nature Communications
Published
2025-04-22
DOI
https://doi.org/10.1038/s41467-025-58724-3
Citations
6
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
10.90

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article

UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data

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6 citations
Nature Communications
Genetic Associations and Epidemiology
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article

UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data

Jieping Ye, Borui Tang, Bingxin Zhao, Jie Wang, Zhenlin Luo, Hongtu Zhu, Xiaopu Wang, Xueqin Wang, Zheng Wang, Yukang Jiang, Yue Shen, Zhiwen Jiang, H. Peng
article en
6 citations

Abstract

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.

Nature CommunicationsVol. 16(1)
University of North Carolina at Chapel Hill (US), University of Science and Technology of China (CN), Alibaba Group (China) (CN), University of Pennsylvania (US)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 2%
Genetic Associations and Epidemiology
10.90
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