TY - JOUR

T1 - Urinary metabolite model to predict the dying process in lung cancer patients

AU - Coyle, Séamus

AU - Chapman, Elinor

AU - Hughes, David M.

AU - Baker, James

AU - Slater, Rachael

AU - Davison, Andrew S.

AU - Norman, Brendan P.

AU - Roberts, Ivayla

AU - Nwosu, Amara C.

AU - Gallagher, James A.

AU - Ranganath, Lakshminarayan R.

AU - Boyd, Mark T.

AU - Mayland, Catriona R.

AU - Kell, Douglas B.

AU - Mason, Stephen

AU - Ellershaw, John

AU - Probert, Chris

PY - 2025/2/27

Y1 - 2025/2/27

N2 - 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.

AB - 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.

U2 - 10.1038/s43856-025-00764-3

DO - 10.1038/s43856-025-00764-3

M3 - Journal article

VL - 5

JO - communications medicine

JF - communications medicine

SN - 2730-664X

IS - 1

M1 - 49

ER -