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Disease clusters and their genetic determinants following a diagnosis of depression: analyses based on a novel three-dimensional disease network approach

Molecular Psychiatry (2025)Cite this article

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Abstract

Depression is strongly associated with a range of subsequent diseases. To elucidate key mechanistic pathways for targeted interventions, this study aimed to determine the main disease networks associated with depression as well as their underlying genetic determinants. We developed a novel three-dimensional network approach which refines disease association verification by incorporating regularized partial correlations, and facilitates robust identification and visualization of disease clusters (i.e., groups of depression-associated diseases with high within-group connectivity) through both non-temporal (illustrating by x-axis and y-axis) and temporal (by z-axis) dimensions. We applied this approach to a matched cohort of 54,284 middle aged patients diagnosed with depression and their 496,005 age- and sex-matched unexposed individuals from the Swedish national registers and validated our findings in a cohort from the UK Biobank. Additionally, we conducted genetic analyses, including polygenic risk score (PRS) and genome-wide association studies (GWAS), using genetic data from 10,754 depression patients in the UK Biobank. Our analysis of the Swedish cohort identified nine reliable disease clusters consisting of 85 component diseases associated with depression, of which six clusters with 30 diseases were successfully validated using the UK Biobank cohort. These were clusters characterized by central nervous system (CNS) diseases, respiratory system diseases, cardiovascular and metabolic diseases, gastrointestinal diseases, musculoskeletal diseases, and mental disorders. PRS analysis revealed a dose-response relationship between genetic liability to depression and the susceptibility for subsequent disease clusters, while GWAS identified eight genome-wide significant loci in four of the clusters. Overall, our novel three-dimensional disease network approach identified six robust disease clusters after depression across two large cohorts, each with shared and cluster-specific genetic underpinnings. These findings warrant further research on genetic-based risk prediction and the development of therapeutic interventions aimed at health improvement for patients with depression.

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Fig. 1
Fig. 2: PheWAS analysis results in the Swedish and UK Biobank cohorts and their comparison.
Fig. 3: Three-dimensional disease network and the reliable disease clusters following depression diagnosis.
Fig. 4: Genetic analyses results for the validated disease clusters following a depression diagnosis.

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Data availability

Researchers can request the original data for phenotypical analyses from the Swedish National Board of Health and Welfare and Statistics Sweden. Data from the UK Biobank (http://www.ukbiobank.ac.uk/) are available to all researchers upon making an application. GWAS summary data generated in the current study can be downloaded from Zenodo (https://zenodo.org/records/15380732).

Code availability

The Python code for conducting the 3D network analysis and visualizing the network is available on GitHub page (https://github.com/HZcohort/3D-Disease-Network).

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Acknowledgements

The authors thank the team members and colleagues involved in the West China Biomedical Big Data Center–UK Biobank project for their support. Part of this research was conducted using the UK Biobank research resource (application 54803). This work uses data provided by patients and collected by the NHS as part of their care and support. This research used data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation.

Funding

This work was supported by National Natural Science Foundation of China (grant 82404391 to Dr. Hou and 82471535 to Dr. Song); 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (grant ZYYC21005 to Dr. Song); the Sichuan Science and Technology Program (grant 2024NSFSC1568 to Dr. Hou); the University Cooperation Grant from NordForsk (PreciMent grant no. 164218 to Drs. Fang and Valdimarsdottir).

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Authors and Affiliations

  1. Mental Health Center and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China

    Can Hou

  2. Med-X Center for Informatics, Sichuan University, Chengdu, China

    Can Hou, Haowen Liu, Yu Zeng, Yike Gong, Huazhen Yang & Huan Song

  3. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China

    Haowen Liu, Yu Zeng, Yike Gong, Huazhen Yang & Huan Song

  4. Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden

    Weimin Ye

  5. Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden

    Fang Fang & Unnur A. Valdimarsdóttir

  6. Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland

    Unnur A. Valdimarsdóttir & Huan Song

  7. Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA

    Unnur A. Valdimarsdóttir

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Contributions

HS and UAV were responsible for the study’s concept and design. CH, HL, and YG did the method development. CH, YZ, and HY did the data collection and management. CH and YZ and did the data cleaning and analysis. CH, HL, YZ, WY, FF, UAV, and HS interpreted the data. CH, UAV, and HS drafted the manuscript. All the authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Huan Song.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The UK Biobank study has received full ethical approval from the NHS National Research Ethics Service (16/NW/0274), and all the participants provided written informed consent before data collection. The Swedish cohort study was approved by the Swedish Ethical Review Authority (Dnrs 2012/1814-31/4 and 2022-05745-02). The current study was approved by the biomedical research ethics committee of West China Hospital (2020.661). All methods were performed in accordance with the relevant guidelines and regulations.

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Hou, C., Liu, H., Zeng, Y. et al. Disease clusters and their genetic determinants following a diagnosis of depression: analyses based on a novel three-dimensional disease network approach. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03120-y

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