Multi-organ network of cardiometabolic disease-depression multimorbidity revealed by phenotypic and genetic analyses of MR images

The development and progression of cardiometabolic diseases and depression multimorbidity involves pathophysiological processes across multiple organs. Using multi-organ imaging data from 31,246 UK Biobank participants, we investigate the multi-organ manifestations and their phenotypic connections and shared genetic architecture underlying the multimorbidity. Phenotypic analyses identify seven abdominal, 16 cardiac, and 107 brain traits forming 1418 abdomen-heart-brain cliques, with liver volume, myocardial wall thickness, and white matter hyperintensity volume as central nodes. Genetic analyses reveal 43 distinct genomic loci (21 novel) shared by these cliques, with the most widely shared loci mapped to genes NUDC, ARID1A, and CRHR1. The 224 protein-coding genes mapped by these loci are enriched in 39 biological processes related to cardiometabolic and neuropsychiatric functions, with 15 genes expressed across liver-heart-brain axis tissues. Combining biochemical and multi-organ imaging indicators significantly improves multimorbidity prediction. These findings uncover multi-organ network underlying physical-mental multimorbidity and highlight the necessity of holistic management.

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Publication Details

Journal
Nature Communications
Published
2026-01-07
DOI
https://doi.org/10.1038/s41467-025-68092-7
Citations
1
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
30.93
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article

Multi-organ network of cardiometabolic disease-depression multimorbidity revealed by phenotypic and genetic analyses of MR images

F. H. Liu, Hao Huang, M. L. Chen, Wei Fan et al.
1 citations
Nature Communications
Genetic Associations and Epidemiology
30.93
article

Multi-organ network of cardiometabolic disease-depression multimorbidity revealed by phenotypic and genetic analyses of MR images

F. H. Liu, Hao Huang, M. L. Chen, Wei Fan, Qiaoling Yan, Jian Wang, Zheng Zhang, Jun Lan, Xuanwei Jiang, Ye Wu, Shuxiao Shi, Lilong Wang, Victor W Zhong, Guangrui Yang, Zhongshang Yuan, Nannan Feng, Shuang Rong, Xiaoming Liu, Mianxin Liu
article en
1 citations

Abstract

The development and progression of cardiometabolic diseases and depression multimorbidity involves pathophysiological processes across multiple organs. Using multi-organ imaging data from 31,246 UK Biobank participants, we investigate the multi-organ manifestations and their phenotypic connections and shared genetic architecture underlying the multimorbidity. Phenotypic analyses identify seven abdominal, 16 cardiac, and 107 brain traits forming 1418 abdomen-heart-brain cliques, with liver volume, myocardial wall thickness, and white matter hyperintensity volume as central nodes. Genetic analyses reveal 43 distinct genomic loci (21 novel) shared by these cliques, with the most widely shared loci mapped to genes NUDC, ARID1A, and CRHR1. The 224 protein-coding genes mapped by these loci are enriched in 39 biological processes related to cardiometabolic and neuropsychiatric functions, with 15 genes expressed across liver-heart-brain axis tissues. Combining biochemical and multi-organ imaging indicators significantly improves multimorbidity prediction. These findings uncover multi-organ network underlying physical-mental multimorbidity and highlight the necessity of holistic management.

Nature CommunicationsVol. 17(1)
University of Science and Technology of China (CN), Union Hospital (HK), Shandong University (CN), Shanghai Jiao Tong University (CN), Tianjin Medical University General Hospital (CN), Lishui University (CN), Nanjing University of Science and Technology (CN), Beijing Academy of Artificial Intelligence (CN), Lishui Central Hospital (CN), Lishui City People's Hospital (CN), Wuhan Union Hospital (CN), Union Hospital (CN), Shanghai Artificial Intelligence Laboratory (CN), Huazhong University of Science and Technology (CN), Hubei University (CN)
Good health and well-being
Openalex Percentile: Top 2%
Genetic Associations and Epidemiology
30.93
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