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:

Brain, lifestyle and environmental pathways linking physical and mental health

Nature Mental Health volume 2, pages 1250–1261 (2024)Cite this article

Subjects

Abstract

Depression and anxiety are prevalent in people with a chronic physical illness. Increasing evidence suggests that co-occurring physical and mental illness is associated with shared biological pathways. However, little is known about the brain’s role in mediating links between physical and mental health. Here, using multimodal brain imaging and organ-specific physiological markers from the UK Biobank, we establish prospective associations between the baseline health of seven organs including cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic and metabolic systems, and mental health outcomes at 4–14 years’ follow-up, focusing on depression and anxiety. We reveal multiple pathways, mediated by the brain, through which poor organ health may lead to poor mental health. We identify lifestyle and environmental factors, including exercise, sedentary behavior, diet, sleep quality, smoking, alcohol intake, education and socioeconomic status that influence mental health through their selective impact on the physiology of specific organ systems and brain structure. Our work reveals the interplay between brain, body and lifestyle, and their collective influence on mental health. Pathways elucidated here may inform behavioral interventions to mitigate or prevent the synergistic co-occurrence of physical and mental disorders.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

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 study design.
Fig. 2: Brain GM volume mediates physical–mental health associations.
Fig. 3: Brain WM microstructure mediates physical–mental health associations.
Fig. 4: Mediating effects of GM and WM regions on physical–mental health associations.
Fig. 5: Physical and neurobiological pathways through which lifestyle factors influence depression severity.
Fig. 6: Physical and neurobiological pathways through which lifestyle factors influence neuroticism.

Similar content being viewed by others

Data availability

Data were obtained from the UK Biobank. Researchers can register to access all data used in this study via the UK Biobank Access Management System (https://bbams.ndph.ox.ac.uk/ams/).

Code availability

MATLAB code (R2022b, MathWorks) for computing organ health scores is available on GitHub (https://github.com/yetianmed/OrganHealthScore). SEM was performed using the lavaan package (v.0.6-16) in R. GAMLSS was performed using the gamlss package (v.5.4-3) in R. Connectome Workbench (v.2.0.0) was used to visualize brain images.

References

  1. Brandl, F. et al. Common and specific large-scale brain changes in major depressive disorder, anxiety disorders, and chronic pain: a transdiagnostic multimodal meta-analysis of structural and functional MRI studies. Neuropsychopharmacology 47, 1071–1080 (2022).

    PubMed  PubMed Central  Google Scholar 

  2. Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. Hettema, J. M. What is the genetic relationship between anxiety and depression? Am. J. Med. Genet. C Semin. Med. Genet. 148c, 140–146 (2008).

    PubMed  Google Scholar 

  4. Kessler, R. C. et al. Anxious and non-anxious major depressive disorder in the World Health Organization World Mental Health Surveys. Epidemiol. Psychiatr. Sci. 24, 210–226 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. Emsley, R. A. et al. Depressive and anxiety symptoms in patients with schizophrenia and schizophreniform disorder. J. Clin. Psychiatry 60, 747–751 (1999).

    PubMed  Google Scholar 

  6. Spoorthy, M. S. et al. Comorbidity of bipolar and anxiety disorders: an overview of trends in research. World J. Psychiatry 9, 7–29 (2019).

    PubMed  PubMed Central  Google Scholar 

  7. Schirmbeck, F. et al. Impact of comorbid affective disorders on longitudinal clinical outcomes in individuals at ultra-high risk for psychosis. Schizophr. Bull. 48, 100–110 (2021).

    PubMed Central  Google Scholar 

  8. Gold, S. M. et al. Comorbid depression in medical diseases. Nat. Rev. Dis. Primers 6, 69 (2020).

    PubMed  Google Scholar 

  9. Roy-Byrne, P. P. et al. Anxiety disorders and comorbid medical illness. Gen. Hosp. Psychiatry 30, 208–225 (2008).

    PubMed  Google Scholar 

  10. Moussavi, S. et al. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370, 851–858 (2007).

    PubMed  Google Scholar 

  11. Felez-Nobrega, M. et al. Multimorbidity, depression with anxiety symptoms, and decrements in health in 47 low- and middle-income countries. J. Affect. Disord. 317, 176–184 (2022).

    PubMed  Google Scholar 

  12. Agustini, B. et al. Patterns of association between depressive symptoms and chronic medical morbidities in older adults. J. Am. Geriatr. Soc. 68, 1834–1841 (2020).

    PubMed  PubMed Central  Google Scholar 

  13. Shinkov, A. et al. Increased prevalence of depression and anxiety among subjects with metabolic syndrome and known type 2 diabetes mellitus - a population-based study. Postgrad. Med. 130, 251–257 (2018).

    PubMed  Google Scholar 

  14. Ronaldson, A. et al. Associations between physical multimorbidity patterns and common mental health disorders in middle-aged adults: A prospective analysis using data from the UK Biobank. Lancet Reg. Health Eur. 8, 100149 (2021).

  15. Seo, J. et al. The relationship between multiple chronic diseases and depressive symptoms among middle-aged and elderly populations: results of a 2009 korean community health survey of 156,747 participants. BMC Public Health 17, 844 (2017).

    PubMed  PubMed Central  Google Scholar 

  16. Mulugeta, A. et al. Association between major depressive disorder and multiple disease outcomes: a phenome-wide Mendelian randomisation study in the UK Biobank. Mol. Psychiatry 25, 1469–1476 (2020).

    PubMed  Google Scholar 

  17. Purves, K. L. et al. A major role for common genetic variation in anxiety disorders. Mol. Psychiatry 25, 3292–3303 (2020).

    PubMed  Google Scholar 

  18. Milaneschi, Y. et al. Depression heterogeneity and its biological underpinnings: toward immunometabolic depression. Biol. Psychiatry 88, 369–380 (2020).

    PubMed  Google Scholar 

  19. Sheng, J. et al. The link between depression and chronic pain: neural mechanisms in the brain. Neural Plast. 2017, 9724371 (2017).

    PubMed  PubMed Central  Google Scholar 

  20. Berk, M. et al. So depression is an inflammatory disease, but where does the inflammation come from? BMC Med. 11, 200 (2013).

    PubMed  PubMed Central  Google Scholar 

  21. Zhao, Y. et al. The brain structure, immunometabolic and genetic mechanisms underlying the association between lifestyle and depression. Nat. Ment. Health 1, 736–750 (2023).

    Google Scholar 

  22. Heijmans, M. et al. The stress of being chronically ill: from disease-specific to task-specific aspects. J. Behav. Med. 27, 255–271 (2004).

    PubMed  Google Scholar 

  23. Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017).

    PubMed  Google Scholar 

  24. Harrewijn, A. et al. Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group. Transl. Psychiatry 11, 502 (2021).

    PubMed  PubMed Central  Google Scholar 

  25. Winter, N. R. et al. Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities. JAMA Psychiatry 79, 879–888 (2022).

    PubMed  PubMed Central  Google Scholar 

  26. van Velzen, L. S. et al. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Mol. Psychiatry 25, 1511–1525 (2020).

    PubMed  Google Scholar 

  27. Mrozek, S. et al. Crosstalk between brain, lung and heart in critical care. Anaesth. Crit. Care Pain Med. 39, 519–530 (2020).

    PubMed  Google Scholar 

  28. McCracken, C. et al. Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants. Nat. Commun. 13, 7839 (2022).

    PubMed  PubMed Central  Google Scholar 

  29. Delezie, J. et al. Endocrine crosstalk between skeletal muscle and the brain. Front. Neurol. 9, 698 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. Zhao, B. et al. Heart-brain connections: phenotypic and genetic insights from magnetic resonance images. Science 380, abn6598 (2023).

  31. Guo, B. et al. Causal associations of brain structure with bone mineral density: a large-scale genetic correlation study. Bone Res. 11, 37 (2023).

    PubMed  PubMed Central  Google Scholar 

  32. Takeda, S. et al. Leptin regulates bone formation via the sympathetic nervous system. Cell 111, 305–317 (2002).

    PubMed  Google Scholar 

  33. Tian, Y. E. et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat. Med. 29, 1221–1231 (2023).

    PubMed  Google Scholar 

  34. Tian, Y. E. et al. Evaluation of brain-body health in individuals with common neuropsychiatric disorders. JAMA Psychiatry 80, 567–576 (2023).

    PubMed  PubMed Central  Google Scholar 

  35. Bosman, R. C. et al. Prevalence and course of subthreshold anxiety disorder in the general population: a three-year follow-up study. J. Affect. Disord. 247, 105–113 (2019).

    PubMed  Google Scholar 

  36. Rodríguez, M. R. et al. Definitions and factors associated with subthreshold depressive conditions: a systematic review. BMC Psychiatry 12, 181 (2012).

    PubMed  PubMed Central  Google Scholar 

  37. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat. Protoc. 17, 1711–1734 (2022).

    PubMed  PubMed Central  Google Scholar 

  40. Dutt, R. K. et al. Mental health in the UK Biobank: a roadmap to self-report measures and neuroimaging correlates. Hum. Brain Mapp. 43, 816–832 (2022).

    PubMed  Google Scholar 

  41. Davis, K. A. S. et al. Mental health in UK Biobank – development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open 6, e18 (2020).

    PubMed  PubMed Central  Google Scholar 

  42. Hettema, J. M. et al. A population-based twin study of the relationship between neuroticism and internalizing disorders. Am. J. Psychiatry 163, 857–864 (2006).

    PubMed  Google Scholar 

  43. Widiger, T. A. et al. Neuroticism is a fundamental domain of personality with enormous public health implications. World Psychiatry 16, 144–145 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. Ohrnberger, J. et al. The dynamics of physical and mental health in the older population. J. Econ. Ageing 9, 52–62 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. Momen, N. C. et al. Association between mental disorders and subsequent medical conditions. N. Engl. J. Med. 382, 1721–1731 (2020).

    PubMed  PubMed Central  Google Scholar 

  46. Murray, C. J. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).

    Google Scholar 

  47. Firth, J. et al. A meta-review of “lifestyle psychiatry”: the role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders. World Psychiatry 19, 360–380 (2020).

    PubMed  PubMed Central  Google Scholar 

  48. Penninx, B. W. et al. Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile. BMC Med. 11, 129 (2013).

    PubMed  PubMed Central  Google Scholar 

  49. Grieve, S. M. et al. Widespread reductions in gray matter volume in depression. Neuroimage Clin. 3, 332–339 (2013).

    PubMed  PubMed Central  Google Scholar 

  50. Gates, J. et al. Mental health starts with physical health: current status and future directions of non-pharmacological interventions to improve physical health in first-episode psychosis. Lancet Psychiatry 2, 726–742 (2015).

    PubMed  Google Scholar 

  51. Firth, J. et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry 6, 675–712 (2019).

    PubMed  Google Scholar 

  52. Rigby, R. A. et al. Generalized additive models for location, scale and shape. J. R. Stat. Soc. Ser. C Appl. Stat. 54, 507–554 (2005).

    Google Scholar 

  53. Marquand, A. F. et al. Conceptualizing mental disorders as deviations from normative functioning. Mol. Psychiatry 24, 1415–1424 (2019).

    PubMed  PubMed Central  Google Scholar 

  54. Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    PubMed  PubMed Central  Google Scholar 

  55. Borghi, E. et al. Construction of the World Health Organization child growth standards: selection of methods for attained growth curves. Stat. Med. 25, 247–265 (2006).

    PubMed  Google Scholar 

  56. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    PubMed  Google Scholar 

  57. Mori, S. et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40, 570–582 (2008).

    PubMed  Google Scholar 

  58. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166, 400–424 (2018).

    PubMed  Google Scholar 

  59. Brain Imaging Documentation (UK Biobank, 2024); https://biobank.ctsu.ox.ac.uk/showcase/showcase/docs/brain_mri.pdf

  60. Eysenck, H. J. et al. Manual of the Eysenck Personality Questionnaire: (EPQ-R Adult) (EdITS/Educational and Industrial Testing Service, 1994).

  61. Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Software https://doi.org/10.18637/jss.v048.i02 (2012).

    Article  Google Scholar 

  62. Kline, R. B. Principles and Practice of Structural Equation Modeling (Guilford Press, 2015).

  63. Byrne, B. M. Structural Equation Modeling with EQS and EQS/Windows (Sage Publications, 1994).

  64. Shan, Z. et al. Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care 38, 529–537 (2015).

    PubMed  Google Scholar 

  65. Smith, R. W. et al. Social isolation and risk of heart disease and stroke: analysis of two large UK prospective studies. Lancet Public Health 6, e232–e239 (2021).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was conducted using data from UK Biobank (https://www.ukbiobank.ac.uk/), a major biomedical database. We thank the UK Biobank for making the data available and all study participants for generously donating their time to make this resource possible. Y.E.T. was supported by a Mary Lugton Postdoctoral Fellowship and a National Health and Medical Research Council Investigator Grant (APP2026413). A.Z. was supported by a Senior Rebecca L. Cooper Fellowship. E.T.B. was supported by a senior investigator award from the National Institute of Health Research, UK, and the NIHR Cambridge Biomedical Research Centre.

Author information

Authors and Affiliations

  1. Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia

    Ye Ella Tian & Andrew Zalesky

  2. Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK

    James H. Cole

  3. Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK

    James H. Cole

  4. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Edward T. Bullmore

  5. Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK

    Edward T. Bullmore

  6. Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Australia

    Andrew Zalesky

Authors
  1. Ye Ella Tian
    View author publications

    Search author on:PubMed Google Scholar

  2. James H. Cole
    View author publications

    Search author on:PubMed Google Scholar

  3. Edward T. Bullmore
    View author publications

    Search author on:PubMed Google Scholar

  4. Andrew Zalesky
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.E.T. and A.Z. conceived the idea and designed the study. Y.E.T. compiled the data, performed the analyses, prepared the visualizations and drafted the paper. J.H.C. and E.T.B. provided critical conceptual input. All authors provided critical feedback and edited the final paper.

Corresponding author

Correspondence to Ye Ella Tian.

Ethics declarations

Competing interests

E.T.B. has consulted for GlaxoSmithKline, SR One, Sosei Heptares and Boehringer Ingelheim. The other authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Bruno Agustini, Lukas Roll and the other, anonymous, reviewers 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 Mental health assessment.

The severity of depressive (a), anxiety (b) and neuroticism (c) symptoms in healthy comparison (HC, n = 7,749) individuals and individuals who had a lifetime diagnosis of one of the following 4 mental disorders: schizophrenia (SCZ, n = 67), depression (DEP, n = 9,817), bipolar disorder (BD, n = 592) and generalized anxiety disorder (GAD, n = 2,041). Depressive symptom severity was represented by the total score of the Recent Depressive Symptom (RDS-4). The score was re-scaled so that a score of zero indicates no recent depressive symptoms. Anxiety symptom severity was represented by the total score of the Generalized Anxiety Disorder (GAD-7) scale. Neuroticism was assessed by the total score of the Eysenck Neuroticism (N-12) questionnaire. The bottom and top edges of the boxes indicate the 25th and 75th percentiles of the distribution, respectively. The central line indicates the median. The whiskers extend to the most extreme data points that are not considered outliers (1.5-times the interquartile range).

Extended Data Fig. 2 Associations between organ health and mental health.

Prospective associations between baseline organ health of 7 body systems and the severity of depression (a), anxiety (b) and neuroticism (c) symptoms at follow-up. Links are shown for significant associations between the health score of organ systems and mental health measures, adjusting for sex and age at baseline (p < 0.05, two-sided, t-test, false discovery rate (FDR) corrected across 7 organs). Edge thickness reflects standardized regression coefficients (\(\beta\)). Dep, depression; Anx, anxiety; N, neuroticism.

Extended Data Fig. 3 Associations between organ health and mental and brain health.

Prospective associations between baseline organ health of 7 body systems and mental health outcome (a) as well as brain health (b) at follow-up. Links are shown for significant associations between the health score of organ systems and mental/brain health measures, adjusting for sex, age at baseline and individual variation in the time interval between baseline and follow-up assessments (p < 0.05, two-sided, t-test, false discovery rate (FDR) corrected across 7 organs). Edge thickness reflects standardized regression coefficients (\(\beta\)). Dep, depression; Anx, anxiety; N, neuroticism. GMV, total gray matter volume; FA, fractional anisotropy.

Extended Data Fig. 4 Associations between organ health and mental health in patients and healthy individuals.

Prospective associations between baseline organ health of 7 body systems and the severity of depression, anxiety and neuroticism symptoms at follow-up in patients (a) and healthy individuals (HC, b) separately. Links are shown for significant associations between the health score of organ systems and mental health measures, adjusting for sex and age at baseline (p < 0.05, two-sided, t-test, false discovery rate (FDR) corrected across 7 organs). Edge thickness reflects standardized regression coefficients (\(\beta\)). Dep, depression; Anx, anxiety; N, neuroticism.

Extended Data Fig. 5 Mediating effects of gray and white matter on physical-mental health associations.

Pathways linking organ health, brain gray matter volume (a)/ fractional anisotropy (FA) of white matter (b), and mental health. A structural equation model (SEM) linking the health of each organ system to a summary measure of mental health (MH) across depression, anxiety and neuroticism. The summary measure was indicated by the first principal component of the three measures. Links are shown for significant paths linking organ health (exogenous) and mental health (outcome) via brain gray matter volume (a) or white matter tracts (b, mediator) inferred from SEM (p < 0.05, two-sided, false discovery rate corrected for 7 organs). Node size of organs is modulated by the direct effect from organs to mental health outcome. Node size of the brain is modulated by its mediating effect and ranked in decreasing order. Edge thickness reflects regression coefficients estimated for edges comprising the SEM. Non-significant mediating effect of the brain is indicated by a dashed square. Pulmon., pulmonary; Muscle., musculoskeletal; Cardiovas., cardiovascular; Dep, depression; Anx, anxiety; N, neuroticism; n.s., non-significant.

Extended Data Fig. 6 Mediating effects of gray and white matter regions on physical-mental health associations.

A structural equation model (SEM) was fitted for each gray matter region, white matter tract and for each organ system. Significant mediating effects (z-statistic) of brain regions (p < 0.05, two-sided, FDR-corrected for 33 gray and 27 white matter regions) were averaged across 7 organ systems to provide a consensus mediating effect map for overall mental health. The overall mental health (MH) score was indicated by the first principal component across three measures, that is, depression, anxiety and neuroticism. a, The average z-statistic for cortical gray matter volume (Desikan-Killianny atlas) are rendered on cortical surface for visualization. Word clouds show top‐ranked cortical and subcortical regions. The font size is scaled according to the absolute value of the average z-statistic. b, Similarly, the average z-statistic for regional fractional anisotropy (JHU ICBM-DTI-81 atlas) are rendered in standard Montreal Neurological Institute (MNI)-152 anatomical space. Word clouds show top‐ranked white matter tracts. The font size is scaled according to the absolute value of the average z-statistic.

Extended Data Fig. 7 Associations between lifestyle factors and mental health.

Bar plots show the prospective associations between baseline exposure to lifestyle factors and the severity of depression (a), anxiety (b) and neuroticism (c) symptoms at follow-up. Colored bars indicate lifestyle factors with significant associations, adjusting for sex and age at baseline (p < 0.05, two-sided, FDR corrected across 14 lifestyle factors). Lifestyle factors are ordered from top to bottom according to decreasing regression coefficient.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9.

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

Tian, Y.E., Cole, J.H., Bullmore, E.T. et al. Brain, lifestyle and environmental pathways linking physical and mental health. Nat. Mental Health 2, 1250–1261 (2024). https://doi.org/10.1038/s44220-024-00303-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44220-024-00303-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research