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Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank

Nature Human Behaviour (2024)Cite this article

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Abstract

Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case–control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.

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Fig. 1: Overview of the study.
Fig. 2: Significance plots for behaviour-related phenotypes associated with PRS for SA in PheWAS and associated with SA in case–control analyses.
Fig. 3: Maps of significant associations between PRS for SA and neuroimaging phenotypes.
Fig. 4: Significant associations between blood and metabolic biomarkers and proteins and PRS for SA.
Fig. 5: MR analysis between risk factors and SA.
Fig. 6: Predictor selection, SHAP visualization and performance of machine-learning classification model based on behaviour-related phenotypes.
Fig. 7: Mediation analysis of behaviour-related phenotypes (predictors) on SA (dependent variable) via brain structure and/or molecular biomarkers (mediators).

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Data availability

The data used in the present study are available from UK Biobank with restrictions applied. Data were used under licence and are thus not publicly available. Researchers can apply for access to the UK Biobank data via the Access Management System (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). Publicly available UK Biobank-based summary statistics for the GWAS of behavoural-related risk factors can be obtained from the MRC IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/). GWAS summary data for protein variables can be downloaded from the UK Biobank Pharma Proteomics Project (https://www.synapse.org/#!Synapse:syn51364943/). GWAS summary data for SA can be applied via the PGC SUI Data Access Portal (https://pgc.unc.edu/for-researchers/data-access-committee/data-access-portal/). European ancestry reference data from the 1000 Genomes Project can be found via https://github.com/getian107/PRScsx?tab=readme-ov-file.

Code availability

For the analyses conducted in R (version 4.2.3), the PHESANT package (v1.1) was used to perform PheWAS, TwoSampleMR (v0.5.6) to perform MR analysis, base ‘glm’ function to perform logistic regression analysis, and lavaan (v0.6-16) to perform mediation analysis. PLINK 2.0 were used to calculate PRS and perform GWAS analysis. PRS-CSx tool (v1.1.0) based on Python 3.9 was used to estimate PRS score using the PRS-CS method. LightGBM library (v3.3.2) based on Python 3.9 was used to develop the machine learning models. The primary code used in this study has been made publicly accessible through the GitHub repository (https://github.com/beimagic/Suicide_Risk_factors).

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Acknowledgements

This study used the UK Biobank Resource under application number 19542. We thank all participants and researchers from the UK Biobank. We are particularly grateful to N. Mullins, who helped conduct the new GWAS meta-analysis of SA. This work was partly supported by a grant from the National Key Research and Development Program of China (no. 2019YFA0709502 to J.F.), a grant from Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01 to J.F.), a grant from ZJ Lab, Shanghai Centre for Brain Science and Brain-Inspired Technology and a grant from the 111 Project (no. B18015 to J.F.). This work was partly supported by the Shanghai Rising-Star Program (no. 21QA1408700 to W.C.). This work was supported by a grant from Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project (no. 2022ZD0212800 to Y.J.). This work was partly supported by a grant from China Postdoctoral Science Foundation (no. 2022M710804 to B.Z.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China

    Bei Zhang, Jia You, Edmund T. Rolls, Jujiao Kang, Yuzhu Li, Wei Zhang, Shitong Xiang, Chun Shen, Yuchao Jiang, Chao Xie, Jintai Yu, Wei Cheng & Jianfeng Feng

  2. Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

    Bei Zhang, Jia You, Jujiao Kang, Yuzhu Li, Wei Zhang, Shitong Xiang, Chun Shen, Yuchao Jiang, Chao Xie, Wei Cheng & Jianfeng Feng

  3. Oxford Centre for Computational Neuroscience, Oxford, UK

    Edmund T. Rolls

  4. Department of Computer Science, University of Warwick, Coventry, UK

    Edmund T. Rolls, Ruohan Zhang & Jianfeng Feng

  5. Medical Psychological Centre, The Second Xiangya Hospital, Central South University, Changsha, China

    Xiang Wang

  6. Medical Psychological Institute, Central South University, Changsha, China

    Xiang Wang

  7. China National Clinical Research Centre on Mental Disorders (Xiangya), Changsha, China

    Xiang Wang

  8. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China

    Huifu Wang

  9. MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China

    Wei Cheng & Jianfeng Feng

  10. Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China

    Wei Cheng & Jianfeng Feng

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Contributions

B.Z., W.C. and J.F. conceived and designed the experiment. B.Z. did the analyses with support from J.Yo., J.K., Y.L., R.Z., W.Z., H.W. and C.S. B.Z. drafted the paper with contributions from W.C., E.T.R. and X.W. and comments from all other authors. Y.J., S.X. and C.X. contributed to the visualization of data. X.W., W.C., J.Yu. and J.F. contributed to the interpretation of results. B.Z., W.C. and J.F. had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final paper.

Corresponding authors

Correspondence to Wei Cheng or Jianfeng Feng.

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The authors declare no competing interests.

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Nature Human Behaviour thanks Sylvane Desrivières, Miguel Renteria and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary Figs. 1–3, Tables 1–3, results and methods.

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Zhang, B., You, J., Rolls, E.T. et al. Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01903-x

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