Summary of Study ST002082

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001322. The data can be accessed directly via it's Project DOI: 10.21228/M8TT44 This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002082
Study TitlePredicting dying: a study of the metabolic changes and the dying process in patients with lung cancer
Study TypeObservational study
Study SummaryBackground: Accurately recognising that a person may be dying is central for improving their experience of care. Yet recognising dying is difficult and predicting dying frequently inaccurate. Methods: Serial urine samples from patients (n=112) with lung cancer were analysed using high resolution untargeted mass spectrometry. ANOVA and volcano plot analysis demonstrated metabolites that changed in the last weeks of life. Further analysis identified potential biological pathways affected. Cox lasso logistic regression was engaged to develop a multivariable model predicting the probability of survival within the last 30 days of life. Results: In total 124 metabolites changed. ANOVA analysis identified 93 metabolites and volcano plot analysis 85 metabolites. 53 metabolites changed using both approaches. Pathways altered in the last weeks included those associated with decreased oral intake, muscle loss, decreased RNA and protein synthesis, mitochondrial dysfunction, disrupted β-oxidation and one carbon metabolism. Epinephrine and cortisol increased in the last 2 weeks and week respectively. A model predicting time to death using 7 metabolites had excellent accuracy (AUC= 0.86 at day 30, 0.88 at day 20 and 0.85 at day 10) and enabled classification of patients at low, medium and high risk of dying on a Kaplan-Meier survival curve. Conclusions: Metabolomic analysis identified metabolites and their associated pathways that change in the last weeks and days of life in patients with lung cancer. Prognostic tests based on the metabolites identified have the potential to change clinical practice and improve the care of dying patients.
University of Liverpool Institute of Life Course & Medical Sciences
Last NameNorman
First NameBrendan
AddressWilliam Henry Duncan Building, 6 West Derby Street, Liverpool, UK. L7 8TX
Phone(+44)151 794 9064
Submit Date2022-01-24
Num Groups6
Total Subjects112
Num Males67
Num Females45
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2022-02-24
Release Version1
Brendan Norman Brendan Norman application/zip

Select appropriate tab below to view additional metadata details:

Combined analysis:

Analysis ID AN003396 AN003397 AN003398 AN003399
Analysis type MS MS MS MS
Chromatography type Reversed phase Reversed phase HILIC HILIC
Chromatography system Agilent 6550 Agilent 6550 Agilent 6550 Agilent 6550
Column Waters Atlantis dC18 (100 x 3mm,3um) Waters Atlantis dC18 (100 x 3mm,3um) Waters BEH Amide (150 x 3.0mm,1.7um) Waters BEH Amide (150 x 3.0mm,1.7um)
MS instrument type QTOF QTOF QTOF QTOF
MS instrument name Agilent 6550 QTOF Agilent 6550 QTOF Agilent 6550 QTOF Agilent 6550 QTOF
Units Values are raw peak area raw peak area raw peak area Values are raw peak area