Urinary metabolite model to predict the dying process in lung cancer patients
Abstract Background Accurately recognizing that a person may be dying is central to improving their experience of care at the end-of-life. However, predicting dying is frequently inaccurate and often occurs only hours or a few days before death. Methods We performed urinary metabolomics analysis on...
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Nature Portfolio
2025-02-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00764-3 |
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| author | Séamus Coyle Elinor Chapman David M. Hughes James Baker Rachael Slater Andrew S. Davison Brendan P. Norman Ivayla Roberts Amara C. Nwosu James A. Gallagher Lakshminarayan R. Ranganath Mark T. Boyd Catriona R. Mayland Douglas B. Kell Stephen Mason John Ellershaw Chris Probert |
| author_facet | Séamus Coyle Elinor Chapman David M. Hughes James Baker Rachael Slater Andrew S. Davison Brendan P. Norman Ivayla Roberts Amara C. Nwosu James A. Gallagher Lakshminarayan R. Ranganath Mark T. Boyd Catriona R. Mayland Douglas B. Kell Stephen Mason John Ellershaw Chris Probert |
| author_sort | Séamus Coyle |
| collection | DOAJ |
| description | Abstract Background Accurately recognizing that a person may be dying is central to improving their experience of care at the end-of-life. However, predicting dying is frequently inaccurate and often occurs only hours or a few days before death. Methods We performed urinary metabolomics analysis on patients with lung cancer to create a metabolite model to predict dying over the last 30 days of life. Results Here we show a model, using only 7 metabolites, has excellent accuracy in the Training cohort n = 112 (AUC = 0·85, 0·85, 0·88 and 0·86 on days 5, 10, 20 and 30) and Validation cohort n = 49 (AUC = 0·86, 0·83, 0·90, 0·86 on days 5, 10, 20 and 30). These results are more accurate than existing validated prognostic tools, and uniquely give accurate predictions over a range of time points in the last 30 days of life. Additionally, we present changes in 125 metabolites during the final four weeks of life, with the majority exhibiting statistically significant changes within the last week before death. Conclusions These metabolites identified offer insights into previously undocumented pathways involved in or affected by the dying process. They not only imply cancer’s influence on the body but also illustrate the dying process. Given the similar dying trajectory observed in individuals with cancer, our findings likely apply to other cancer types. Prognostic tests, based on the metabolites we identified, could aid clinicians in the early recognition of people who may be dying and thereby influence clinical practice and improve the care of dying patients. |
| format | Article |
| id | doaj-art-73e14eea0b72408eae7d3bdd6b2880ef |
| institution | OA Journals |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-73e14eea0b72408eae7d3bdd6b2880ef2025-08-20T02:16:55ZengNature PortfolioCommunications Medicine2730-664X2025-02-015111110.1038/s43856-025-00764-3Urinary metabolite model to predict the dying process in lung cancer patientsSéamus Coyle0Elinor Chapman1David M. Hughes2James Baker3Rachael Slater4Andrew S. Davison5Brendan P. Norman6Ivayla Roberts7Amara C. Nwosu8James A. Gallagher9Lakshminarayan R. Ranganath10Mark T. Boyd11Catriona R. Mayland12Douglas B. Kell13Stephen Mason14John Ellershaw15Chris Probert16Liverpool Head and Neck Cancer Centre, University of LiverpoolInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolDepartment of Health Data Science, University of LiverpoolInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolDepartment of Clinical Biochemistry and Metabolic Medicine, Liverpool Clinical Laboratories, Liverpool University Hospitals Foundation TrustInstitute of Life Course & Medical Sciences, University of LiverpoolInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolLancaster Medical School, Lancaster UniversityInstitute of Life Course & Medical Sciences, University of LiverpoolDepartment of Clinical Biochemistry and Metabolic Medicine, Liverpool Clinical Laboratories, Liverpool University Hospitals Foundation TrustLiverpool Head and Neck Cancer Centre, University of LiverpoolPalliative Care Unit, Institute of Life Course & Medical Sciences, University of LiverpoolCentre for Metabolomics Research (CMR), Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of LiverpoolPalliative Care Unit, Institute of Life Course & Medical Sciences, University of LiverpoolPalliative Care Unit, Institute of Life Course & Medical Sciences, University of LiverpoolInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolAbstract Background Accurately recognizing that a person may be dying is central to improving their experience of care at the end-of-life. However, predicting dying is frequently inaccurate and often occurs only hours or a few days before death. Methods We performed urinary metabolomics analysis on patients with lung cancer to create a metabolite model to predict dying over the last 30 days of life. Results Here we show a model, using only 7 metabolites, has excellent accuracy in the Training cohort n = 112 (AUC = 0·85, 0·85, 0·88 and 0·86 on days 5, 10, 20 and 30) and Validation cohort n = 49 (AUC = 0·86, 0·83, 0·90, 0·86 on days 5, 10, 20 and 30). These results are more accurate than existing validated prognostic tools, and uniquely give accurate predictions over a range of time points in the last 30 days of life. Additionally, we present changes in 125 metabolites during the final four weeks of life, with the majority exhibiting statistically significant changes within the last week before death. Conclusions These metabolites identified offer insights into previously undocumented pathways involved in or affected by the dying process. They not only imply cancer’s influence on the body but also illustrate the dying process. Given the similar dying trajectory observed in individuals with cancer, our findings likely apply to other cancer types. Prognostic tests, based on the metabolites we identified, could aid clinicians in the early recognition of people who may be dying and thereby influence clinical practice and improve the care of dying patients.https://doi.org/10.1038/s43856-025-00764-3 |
| spellingShingle | Séamus Coyle Elinor Chapman David M. Hughes James Baker Rachael Slater Andrew S. Davison Brendan P. Norman Ivayla Roberts Amara C. Nwosu James A. Gallagher Lakshminarayan R. Ranganath Mark T. Boyd Catriona R. Mayland Douglas B. Kell Stephen Mason John Ellershaw Chris Probert Urinary metabolite model to predict the dying process in lung cancer patients Communications Medicine |
| title | Urinary metabolite model to predict the dying process in lung cancer patients |
| title_full | Urinary metabolite model to predict the dying process in lung cancer patients |
| title_fullStr | Urinary metabolite model to predict the dying process in lung cancer patients |
| title_full_unstemmed | Urinary metabolite model to predict the dying process in lung cancer patients |
| title_short | Urinary metabolite model to predict the dying process in lung cancer patients |
| title_sort | urinary metabolite model to predict the dying process in lung cancer patients |
| url | https://doi.org/10.1038/s43856-025-00764-3 |
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