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Multi-omic signatures and trajectories of cardiometabolic diseases and depression – Nature Mental Health

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Guangrui Yang
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Data availability

Access to individual-level UK Biobank data is available to bona fide researchers via application to the UK Biobank website at https://www.ukbiobank.ac.uk. Use of UK Biobank data was performed under application number 101169.

Code availability

The code used for all analyses in this study is publicly available via GitHub at https://github.com/Leonorya/Multimorbidity-trajectory.

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Acknowledgements

This study was supported by the AI4S Climber Initiative (awarded to V.W.Z.) and the Innovative Research Team of High-Level Local Universities in Shanghai (awarded to V.W.Z.).

Author information

Author notes

  1. These authors contributed equally: Guangrui Yang, Xuanwei Jiang.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Guangrui Yang, Xuanwei Jiang, Jingxuan Wang, Shuxiao Shi, Sujing Wang, Deshan Wu, Meng Chen, Yaqing Xu, Nannan Feng, Lan Xu, Xihao Du & Victor W. Zhong

Authors

  1. Guangrui Yang
  2. Xuanwei Jiang
  3. Jingxuan Wang
  4. Shuxiao Shi
  5. Sujing Wang
  6. Deshan Wu
  7. Meng Chen
  8. Yaqing Xu
  9. Nannan Feng
  10. Lan Xu
  11. Xihao Du
  12. Victor W. Zhong

Contributions

G.Y. and X.J. contributed equally to the paper as joint first authors. G.Y., X.J., X.D. and V.W.Z. designed the study. G.Y., X.J., J.W. and S.S. performed data analysis and figure generation. G.Y., X.J. and V.W.Z. drafted the manuscript. G.Y., X.J., J.W., S.S., S.W., D.W., M.C., Y.X., N.F., L.X., X.D. and V.W.Z. contributed to the interpretation of the results and critically reviewed and revised the manuscript. V.W.Z. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding authors

Correspondence to Xihao Du or Victor W. Zhong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

This research complies with all relevant ethical regulations. U.K.B. received approval from the National Information Governance Board for Health and Social Care and the National Health Service North West Centre for Research Ethics Committee. The HRS was approved by the Institutional Review Board at the University of Michigan and the National Institute on Aging.

Peer review

Peer review information

Nature Mental Health thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Flowchart of inclusions and exclusions.

A total of 502,536 individuals were enrolled in the UK Biobank. Participants were excluded if they had pre-existing cardiometabolic diseases (type 2 diabetes, coronary artery disease, stroke, or heart failure) or depression at baseline. A final analytical sample of 467,592 participants was included in the study.

Extended Data Fig. 2 Comparison of transition probabilities from healthy or disease states to cardiometabolic diseases and depression (n=467,592).

(A) Transition probabilities from healthy states to CMDs (Baseline-CMDs) compared to transition probabilities from depression to CMDs (DEP-CMDs) at 5, 10, and 15 years. (B) Transition probabilities from healthy states to depression (Baseline-DEP) compared to transition probabilities from CMDs to depression (CMDs-DEP) at 5, 10, and 15 years. (C)Transition probabilities from healthy states to CAD (Baseline-CAD) compared to transition probabilities from depression to CAD (DEP-CAD) at 5, 10, and 15 years. The transition probabilities were calculated using multistate models adjusted for age, sex, ethnicity, Townsend deprivation index, employment status, education level, smoking status, drinking status, physical activity, diet quality score, sleep duration, body mass index, and family history of type 2 diabetes, cardiovascular disease, and depression. Data are presented as estimated transition probabilities (center) with 95% confidence intervals (error bars). P values are two-sided and were calculated using t-tests. Abbreviations: CAD, coronary artery disease; CMDs, cardiometabolic diseases; DEP, depression.

Extended Data Fig. 3 Mean coefficients of multiomic predictors across nine transition prediction models.

Mean coefficients of polygenic risk scores (A), top 20 selected metabolites (B), top 20 selected proteins (C), and 45 multiomic predictors (D) across the nine transitions.

Extended Data Fig. 4 Associations and shared patterns of clinical makers.

(A) Volcano plots of markers with the nine transitions. The associations were estimated based on multistate models adjusted for age, sex, ethnicity, Townsend index, employment status, education level, smoking status, drinking status, physical activity, diet quality scores, sleep duration, body mass index, and family history of type 2 diabetes, cardiovascular disease, and depression. The top five most positive and top five most negative metabolites and proteins for nine transitions are labeled with the abbreviations, and their full names are provided in Supplementary Table S18. P values were two-sided and adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) correction, performed independently for each omics dataset. The dashed line indicates FDR-adjusted P = 0.05. (B) The number of shared and distinct markers for the nine transitions. (C) Frequent markers identified across nine outcomes.

Extended Data Fig. 5 Sensitivity analysis of transition patterns and probabilities.

(A) Complete-case analysis of transition probabilities in the UK Biobank at 5, 10, and 15 years (n=417,275). (B) Transition patterns in the HRS external validation cohort (n=8623). (C) Transition probabilities in the HRS cohort at 8 years. The transition probabilities were calculated using multistate models adjusted for age, sex, ethnicity, Townsend deprivation index, employment status, education level, smoking status, drinking status, physical activity, diet quality score, sleep duration, body mass index, and family history of type 2 diabetes, cardiovascular disease, and depression. Data are presented as estimated transition probabilities (center) with 95% confidence intervals (error bars). P values are two-sided and were calculated using t-tests. Abbreviations: CMDs, cardiometabolic diseases; DEP, depression.

Extended Data Fig. 6 Complete-case analysis of metabolomics and proteomics signatures in UK Biobank (n=417,275).

(A) Volcano plots of all metabolites with the nine transitions. (B) Volcano plots of all proteins with the nine transitions. The associations were estimated based on multistate models adjusted for age, sex, ethnicity, Townsend index, employment status, education level, smoking status, drinking status, physical activity, diet quality scores, sleep duration, body mass index, and family history of type 2 diabetes, cardiovascular disease, and depression. The top five most positive and top five most negative metabolites and proteins for nine transitions are labeled with the abbreviations, and their full names are provided in Supplementary Table S18. P values were two-sided and adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) correction, performed independently for each omics dataset. The dashed line indicates FDR-adjusted P = 0.05. Abbreviations: CMDs, cardiometabolic diseases; DEP, depression.

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Yang, G., Jiang, X., Wang, J. et al. Multi-omic signatures and trajectories of cardiometabolic diseases and depression. Nat. Mental Health (2026). https://doi.org/10.1038/s44220-026-00678-6

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