Coyle, S 
ORCID: 0000-0002-4761-9703, Chapman, E ORCID: 0000-0002-4398-1705, Hughes, DM ORCID: 0000-0002-1287-9994, Baker, J ORCID: 0000-0002-6562-1081, Slater, R ORCID: 0000-0001-9765-6978, Davison, AS ORCID: 0000-0001-5501-4475, Norman, BP ORCID: 0000-0001-9293-4852, Roberts, I ORCID: 0000-0003-3525-9787, Nwosu, AC ORCID: 0000-0003-0014-3741, Gallagher, JA ORCID: 0000-0002-0852-279X et al (show 7 more authors)
(2025)
Urinary metabolite model to predict the dying process in lung cancer patients
Communications Medicine, 5 (1).
49-.
ISSN 2730-664X, 2730-664X
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3205 Medical Biochemistry and Metabolomics, 4203 Health Services and Systems, 32 Biomedical and Clinical Sciences, 42 Health Sciences, Lung, Clinical Research, Women's Health, Lung Cancer, Health Disparities and Racial or Ethnic Minority Health Research, Health Disparities, Cancer, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Cancer |
| Divisions: | Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Life Courses & Medical Sciences Faculty of Health & Life Sciences > Inst. Life Courses & Medical Sciences > School of Medicine Faculty of Health & Life Sciences > Inst. Population Health Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Inst. Systems, Molec & Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 10 Apr 2025 08:13 |
| Last Modified: | 18 Mar 2026 05:38 |
| DOI: | 10.1038/s43856-025-00764-3 |
| Open Access URL: | https://doi.org/10.1038/s43856-025-00764-3 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3191327 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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