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.

  • Review Article
  • Published:

Biological and functional multimorbidity—from mechanisms to management

Nature Medicine volume 29, pages 1649–1657 (2023)Cite this article

Subjects

Abstract

Globally, the number of people with multiple co-occurring diseases will increase substantially over the coming decades, with important consequences for patients, carers, healthcare systems and society. Addressing this challenge requires a shift in the prevailing clinical, educational and scientific thinking and organization—with a strong emphasis on the maintenance of generalist skills to balance the specialization trends of medical education and research. Multimorbidity is not a single entity but differs quantitively and qualitatively across life stages, ethnicities, sexes, socioeconomic groups and geographies. Data-driven science that quantifies the impact of disease co-occurrence—beyond the small number of currently well-studied long-term conditions (such as cardiometabolic diseases)—can help illuminate the pathological diversity of multimorbidity and identify common, mechanistically related, and prognostically relevant clusters. Broader access to data opportunities across modalities and disciplines will catalyze vertical and horizontal integration of multimorbidity research, to enable reconfiguring of medical services, clinical trials, guidelines and research in a way that accounts for the complexity of multimorbidity—and provides efficient, joined-up services for patients.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Mechanisms of multimorbidity.
Fig. 2: Clustering of diseases across life stages.
Fig. 3: Genomic regions linked to multiple diseases.

Similar content being viewed by others

References

  1. Kingston, A. et al. Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model. Age Ageing 47, 374–380 (2018).

    PubMed  PubMed Central  Google Scholar 

  2. Whitty, C. J. M. et al. Rising to the challenge of multimorbidity. BMJ 368, l6964 (2020).

    PubMed  PubMed Central  Google Scholar 

  3. Whitty, C. J. M. & Watt, F. M. Map clusters of diseases to tackle multimorbidity. Nature 579, 494–496 (2020).

    CAS  PubMed  Google Scholar 

  4. Kuan, V. et al. Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digit. Health 5, e16–e27 (2023).

    CAS  PubMed  Google Scholar 

  5. Kivimaki, M. et al. Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health 5, e140–e149 (2020).

    PubMed  Google Scholar 

  6. Barnett, K. et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 380, 37–43 (2012).

    PubMed  Google Scholar 

  7. Academy of Medical Sciences. Multiple Long-Term Conditions (Multimorbidity): a priority for global health research (2018). Available at: https://acmedsci.ac.uk/file-download/82222577

  8. van Leeuwen, F. E. & Ng, A. K. Long-term risk of second malignancy and cardiovascular disease after Hodgkin lymphoma treatment. Hematology Am. Soc. Hematol. Educ. Program 2016, 323–330 (2016).

    PubMed  PubMed Central  Google Scholar 

  9. Bjornevik, K. et al. Longitudinal analysis reveals high prevalence of Epstein–Barr virus associated with multiple sclerosis. Science 375, 296–301 (2022).

    CAS  PubMed  Google Scholar 

  10. Ho, I. S. et al. Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies. Lancet Public Health 6, e587–e597 (2021).

    PubMed  Google Scholar 

  11. Ho, I. S. S. et al. Measuring multimorbidity in research: Delphi consensus study. BMJ Med. 1, e000247 (2022).

    PubMed  PubMed Central  Google Scholar 

  12. Thygesen, J. H. et al. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. Lancet Digit. Health 4, e542–e557 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Raisi-Estabragh, Z. et al. Cardiovascular disease and mortality sequelae of COVID-19 in the UK Biobank. Heart 109, 119–126 (2022).

    PubMed  Google Scholar 

  14. Wiersinga, W. J. et al. Melioidosis. Nat. Rev. Dis. Prim. 4, 17107 (2018).

    PubMed  Google Scholar 

  15. Shaw J, M. N. Opportunistic infections in HIV. Medicine 50, 294–297 (2022).

    Google Scholar 

  16. Ho, I. S. et al. Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies. BMJ Open 12, e057017 (2022).

    PubMed  PubMed Central  Google Scholar 

  17. Skou, S. T. et al. Multimorbidity. Nat. Rev. Dis. Prim. 8, 48 (2022).

    PubMed  Google Scholar 

  18. Jensen, A. B. et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5, 4022 (2014).

    CAS  PubMed  Google Scholar 

  19. Westergaard, D. et al. Uncovering the heritable components of multimorbidities and disease trajectories: a nationwide cohort study. Preprint at medRxiv https://doi.org/10.1101/2023.02.08.23285642 (2023).

  20. van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).

    PubMed  Google Scholar 

  21. Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41–58 (2019).

    CAS  PubMed  Google Scholar 

  22. Cowie, M. R. & Fisher, M. SGLT2 inhibitors: mechanisms of cardiovascular benefit beyond glycaemic control. Nat. Rev. Cardiol. 17, 761–772 (2020).

    CAS  PubMed  Google Scholar 

  23. Gram, H. The long and winding road in pharmaceutical development of canakinumab from rare genetic autoinflammatory syndromes to myocardial infarction and cancer. Pharmacol. Res. 154, 104139 (2020).

    CAS  PubMed  Google Scholar 

  24. Hingorani, A. D. et al. Improving the odds of drug development success through human genomics: modelling study. Sci. Rep. 9, 18911 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  PubMed  Google Scholar 

  26. Parisinos, C. A. et al. Variation in interleukin-6 receptor gene associates with risk of Crohn’s disease and ulcerative colitis. Gastroenterology 155, 303–306 (2018).

    CAS  PubMed  Google Scholar 

  27. Harrison, S. C. et al. Interleukin-6 receptor pathways in abdominal aortic aneurysm. Eur. Heart J. 34, 3707–3716 (2013).

    CAS  PubMed  Google Scholar 

  28. Interleukin-6 Receptor Mendelian Randomisation Analysis Consortium et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379, 1214–1224 (2012).

    Google Scholar 

  29. Ferreira, R. C. et al. Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases. PLoS Genet. 9, e1003444 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    CAS  PubMed  Google Scholar 

  31. King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. Cook, D. et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13, 419–431 (2014).

    CAS  PubMed  Google Scholar 

  33. Wheeler, J., McHale, M., Jackson, V. & Penny, M. Assessing theoretical risk and benefit suggested by genetic association studies of CCR5: experience in a drug development programme for maraviroc. Antivir. Ther. 12, 233–245 (2007).

    CAS  PubMed  Google Scholar 

  34. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr. & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

    CAS  PubMed  Google Scholar 

  35. Bovijn, J., Lindgren, C. M. & Holmes, M. V. Genetic variants mimicking therapeutic inhibition of IL-6 receptor signaling and risk of COVID-19. Lancet Rheumatol. 2, e658–e659 (2020).

    PubMed  PubMed Central  Google Scholar 

  36. Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).

    PubMed  PubMed Central  Google Scholar 

  38. Koprulu, M. et al. Proteogenomic links to human metabolic diseases. Nat. Metab. 5, 516–528 (2023).

    CAS  PubMed  Google Scholar 

  39. Barrio-Hernandez, I. et al. Network expansion of genetic associations defines a pleiotropy map of human cell biology. Nat. Genet. 55, 389–398 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Zuber, V. et al. Multi-response Mendelian randomization: identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes. Preprint at bioRxiv https://doi.org/10.1101/2023.02.01.526689 (2023).

  43. Pietzner, M. et al. Genetic architecture and shared mechanisms of common ‘neglected’ diseases. Preprint at MedRxiv https://doi.org/10.1101/2023.05.23.23290408 (2023).

  44. Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122–1131 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).

    PubMed  PubMed Central  Google Scholar 

  47. Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).

    CAS  PubMed  Google Scholar 

  48. Lotta, L. A. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat. Genet. 53, 54–64 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Pietzner, M. et al. ELF5 is a potential respiratory epithelial cell-specific risk gene for severe COVID-19. Nat. Commun. 13, 4484 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Surendran, P. et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat. Med. 28, 2321–2332 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Holmes, M. V. et al. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ 349, g4164 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. Biddinger, K. J. et al. Association of habitual alcohol intake with risk of cardiovascular disease. JAMA Netw. Open 5, e223849 (2022).

    PubMed  PubMed Central  Google Scholar 

  53. Dale, C. E. et al. Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes and type 2 diabetes mellitus: a Mendelian randomization analysis. Circulation 135, 2373–2388 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. Venkatesh, S. S. et al. Obesity and risk of female reproductive conditions: a Mendelian randomisation study. PLoS Med. 19, e1003679 (2022).

    PubMed  PubMed Central  Google Scholar 

  55. Huang, J. et al. Genomics and phenomics of body mass index reveals a complex disease network. Nat. Commun. 13, 7973 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Pietzner, M. et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat. Med. 27, 471–479 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Proietti, M. et al. Association between clinical risk scores and mortality in atrial fibrillation: systematic review and network meta-regression of 669,000 patients. Eur. J. Prev. Cardiol. 27, 633–644 (2020).

    PubMed  Google Scholar 

  58. Tomic, D., Shaw, J. E. & Magliano, D. J. The burden and risks of emerging complications of diabetes mellitus. Nat. Rev. Endocrinol. 18, 525–539 (2022).

    PubMed  PubMed Central  Google Scholar 

  59. Farmer, C., Fenu, E., O’Flynn, N. & Guthrie, B. Clinical assessment and management of multimorbidity: summary of NICE guidance. BMJ 354, i4843 (2016).

    PubMed  Google Scholar 

  60. Aronson, J. K. Polypharmacy, appropriate and inappropriate. Br. J. Gen. Pract. 56, 484–485 (2006).

    PubMed  PubMed Central  Google Scholar 

  61. Aronson, J. K. In defence of polypharmacy. Br. J. Clin. Pharm. 57, 119–120 (2004).

    CAS  Google Scholar 

  62. Pirmohamed, M. et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 329, 15–19 (2004).

    PubMed  PubMed Central  Google Scholar 

  63. Dale, C. E. et al. The impact of the COVID-19 pandemic on cardiovascular disease prevention and management. Nat. Med. 29, 219–225 (2023).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank J. Carrasco-Zanini, M. Pietzner and H. Hemingway for assistance with references, figures and comments on an earlier draft of this Review. C.L. and A.D.H. are supported by the UK Research and Innovation (UKRI) Strategic Priority Fund Tackling multimorbidity at scale programme (grant number MR/V033867/1) delivered by the Medical Research Council and the National Institute for Health and Care Research (NIHR) in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. A.D.H. is funded by the NIHR University College London Hospitals NHS Trust Biomedical Research Centre.

Author information

Authors and Affiliations

  1. Precision Healthcare University Research Institute, Queen Mary University of London, London, UK

    Claudia Langenberg

  2. Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany

    Claudia Langenberg

  3. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK

    Claudia Langenberg

  4. UCL BHF Research Accelerator, University College London, London, UK

    Aroon D. Hingorani

  5. Institute of Cardiovascular Science, University College London, London, UK

    Aroon D. Hingorani

  6. University College London Hospitals NIHR Biomedical Research Centre, London, UK

    Aroon D. Hingorani

  7. Department of Health and Social Care, London, UK

    Christopher J. M. Whitty

  8. London School of Hygiene & Tropical Medicine, London, UK

    Christopher J. M. Whitty

Authors
  1. Claudia Langenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aroon D. Hingorani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher J. M. Whitty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudia Langenberg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Ioanna Tzoulaki, Heinz Freisling and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Karen O’Leary, in collaboration with the Nature Medicine team.

Additional information

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

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

Langenberg, C., Hingorani, A.D. & Whitty, C.J.M. Biological and functional multimorbidity—from mechanisms to management. Nat Med 29, 1649–1657 (2023). https://doi.org/10.1038/s41591-023-02420-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-023-02420-6

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