Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Human Behaviour volume 8, pages 1784–1797 (2024)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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).

Similar content being viewed by others

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).

References

  1. Preventing Suicide: A Global Imperative (World Health Organization, 2014).

  2. Mars, B. et al. Predictors of future suicide attempt among adolescents with suicidal thoughts or non-suicidal self-harm: a population-based birth cohort study. Lancet Psychiatry 6, 327–337 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Janiri, D. et al. Risk and protective factors for childhood suicidality: a US population-based study. Lancet Psychiatry 7, 317–326 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Franklin, J. C. et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143, 187–232 (2017).

    Article  PubMed  Google Scholar 

  5. Turecki, G. et al. Suicide and suicide risk. Nat. Rev. Dis. Primers 5, 74 (2019).

    Article  PubMed  Google Scholar 

  6. Brezo, J. et al. Predicting suicide attempts in young adults with histories of childhood abuse. Br. J. Psychiatry 193, 134–139 (2008).

    Article  PubMed  Google Scholar 

  7. Millner, A. J., Robinaugh, D. J. & Nock, M. K. Advancing the understanding of suicide: the need for formal theory and rigorous descriptive research. Trends Cogn. Sci. 24, 704–716 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Joo, Y. Y. et al. Association of genome-wide polygenic scores for multiple psychiatric and common traits in preadolescent youths at risk of suicide. JAMA Netw. Open 5, e2148585–e2148585 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  9. DeVille, D. C. et al. Prevalence and family-related factors associated with suicidal ideation, suicide attempts, and self-injury in children aged 9 to 10 years. JAMA Netw. Open 3, e1920956–e1920956 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Strawbridge, R. J. et al. Identification of novel genome-wide associations for suicidality in UK Biobank, genetic correlation with psychiatric disorders and polygenic association with completed suicide. EBioMedicine 41, 517–525 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ioannidis, J. P. Neglecting major health problems and broadcasting minor, uncertain issues in lifestyle science. JAMA 322, 2069–2070 (2019).

    Article  PubMed  Google Scholar 

  12. Oquendo, M. A. et al. Toward a biosignature for suicide. Am. J. Psychiatry 171, 1259–1277 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mann, J. J. & Rizk, M. M. A brain-centric model of suicidal behavior. Am. J. Psychiatry 177, 902–916 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Turecki, G. & Brent, D. A. Suicide and suicidal behaviour. Lancet 387, 1227–1239 (2016).

    Article  PubMed  Google Scholar 

  15. Mullins, N. et al. GWAS of suicide attempt in psychiatric disorders and association with major depression polygenic risk scores. Am. J. Psychiatry 176, 651–660 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ruderfer, D. M. et al. Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide. Mol. Psychiatry 25, 2422–2430 (2020).

    Article  PubMed  Google Scholar 

  17. Goldman, D. Predicting suicide. Am. J. Psychiatry 177, 881–883 (2020).

    Article  PubMed  Google Scholar 

  18. Orri, M. et al. A genetically informed study on the association of cannabis, alcohol, and tobacco smoking with suicide attempt. Mol. Psychiatry 26, 5061–5070 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Richardson, T. G., Harrison, S., Hemani, G. & Davey Smith, G. An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. eLife 8, e43657 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Burgess, S. et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 30, 543–552 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lim, K. X. et al. Studying individual risk factors for self-harm in the UK Biobank: a polygenic scoring and Mendelian randomisation study. PLoS Med. 17, e1003137 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shen, X. et al. A phenome-wide association and Mendelian randomisation study of polygenic risk for depression in UK Biobank. Nat. Commun. 11, 2301 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen, S. D. et al. A phenome-wide association and mendelian randomization study for Alzheimer’s disease: a prospective cohort study of 502,493 participants from the UK biobank. Biol. Psychiatry 93, 790–801 (2023).

    Article  PubMed  Google Scholar 

  24. Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Hebbring, S. J. The challenges, advantages and future of phenome-wide association studies. Immunology 141, 157–165 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Denny, J. C., Bastarache, L. & Roden, D. M. Phenome-wide association studies as a tool to advance precision medicine. Annu. Rev. Genomics Hum. Genet. 17, 353–373 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Van Velzen, L. S. et al. Classification of suicidal thoughts and behaviour in children: results from penalised logistic regression analyses in the Adolescent Brain Cognitive Development study. Br. J. Psychiatry 220, 210–218 (2022).

    Article  PubMed  Google Scholar 

  28. Kim, D. J. et al. Examination of structural brain changes in recent suicidal behavior. Psychiatry Res. Neuroimaging 307, 111216 (2021).

    Article  PubMed  Google Scholar 

  29. Auerbach, R. P., Chase, H. W. & Brent, D. A. The elusive phenotype of preadolescent suicidal thoughts and behaviors: can neuroimaging deliver on its promise? Am. J. Psychiatry 178, 285–287 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Beautrais, A. L. et al. Prevalence and comorbidity of mental disorders in persons making serious suicide attempts: a case-control study. Am. J. Psychiatry 153, 1009–1014 (1996).

    Article  CAS  PubMed  Google Scholar 

  31. Melhem, N. M. et al. Severity and variability of depression symptoms predicting suicide attempt in high-risk individuals. JAMA Psychiatry 76, 603–613 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Brezo, J., Paris, J. & Turecki, G. Personality traits as correlates of suicidal ideation, suicide attempts, and suicide completions: a systematic review. Acta Psychiatr. Scand. 113, 180–206 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Shneidman, E. S. Suicide as Psychache: A Clinical Approach to Self-Destructive Behavior (Jason Aronson, 1993).

  34. Vidal-Ribas, P. et al. Multimodal neuroimaging of suicidal thoughts and behaviors in a US population-based sample of school-age children. Am. J. Psychiatry 178, 321–332 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  35. van Velzen, L. S. et al. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium. Mol. Psychiatry 27, 4550–4560 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Schmaal, L. et al. Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies. Mol. Psychiatry 25, 408–427 (2020).

    Article  PubMed  Google Scholar 

  37. Soloff, P. H. et al. Structural brain abnormalities and suicidal behavior in borderline personality disorder. J. Psychiatr. Res. 46, 516–525 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Giakoumatos, C. I. et al. Are structural brain abnormalities associated with suicidal behavior in patients with psychotic disorders? J. Psychiatr. Res. 47, 1389–1395 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hwang, J.-P. et al. Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry. J. Geriatr. Psychiatry Neurol. 23, 171–184 (2010).

    Article  PubMed  Google Scholar 

  40. Johnston, J. A. et al. Multimodal neuroimaging of frontolimbic structure and function associated with suicide attempts in adolescents and young adults with bipolar disorder. Am. J. Psychiatry 174, 667–675 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Jollant, F. et al. Decreased activation of lateral orbitofrontal cortex during risky choices under uncertainty is associated with disadvantageous decision-making and suicidal behavior. NeuroImage 51, 1275–1281 (2010).

    Article  PubMed  Google Scholar 

  42. Jollant, F. et al. Orbitofrontal cortex response to angry faces in men with histories of suicide attempts. Am. J. Psychiatry 165, 740–748 (2008).

    Article  PubMed  Google Scholar 

  43. Sudol, K. & Mann, J. J. Biomarkers of suicide attempt behavior: towards a biological model of risk. Curr. Psychiatry Rep. 19, 1–13 (2017).

    Article  Google Scholar 

  44. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Docherty, A. R. et al. GWAS meta-analysis of suicide attempt: identification of 12 genome-wide significant loci and implication of genetic risks for specific health factors. Am. J. Psychiatry 180, 723–738 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ge, T., Chen, C. Y., Ni, Y., Feng, Y. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  49. Rolls, E. T., Joliot, M. & Tzourio-Mazoyer, N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage 122, 1–5 (2015).

    Article  PubMed  Google Scholar 

  50. Sun, B. B. et al. Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants. Preprint at bioRxiv https://doi.org/10.1101/2022.06.17.496443 (2022).

  51. Sun, B. B. et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 622, 329–338 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhang, Y. et al. Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank. Nat. Hum. Behav. 7, 1185–1195 (2023).

    Article  PubMed  Google Scholar 

  53. Choi, K. W. et al. An exposure-wide and Mendelian randomization approach to identifying modifiable factors for the prevention of depression. Am. J. Psychiatry 177, 944–954 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Nassan, M. et al. Genetic evidence for a potential causal relationship between insomnia symptoms and suicidal behavior: a Mendelian randomization study. Neuropsychopharmacology 47, 1672–1679 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Ke, G. et al. Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inform. Process. Syst. 30, 3149–3157 (2017).

    Google Scholar 

  58. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inform. Process. Syst. 30, 4768–4777 (2017).

    Google Scholar 

Download references

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.

Author information

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

Authors
  1. Bei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia You
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edmund T. Rolls
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jujiao Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuzhu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ruohan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Huifu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shitong Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Chun Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yuchao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Chao Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jintai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Wei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jianfeng Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

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

Supplementary information

Supplementary Information

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

Reporting Summary

Peer Review File

Supplementary Data

Supplementary data: summary of key findings.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 8, 1784–1797 (2024). https://doi.org/10.1038/s41562-024-01903-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-024-01903-x

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing