Summary of Study ST002082

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, 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.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

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.

Study ID	ST002082
Study Title	Predicting dying: a study of the metabolic changes and the dying process in patients with lung cancer
Study Type	Observational study
Study Summary	Background: 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.
Institute	University of Liverpool Institute of Life Course & Medical Sciences
Last Name	Norman
First Name	Brendan
Address	William Henry Duncan Building, 6 West Derby Street, Liverpool, UK. L7 8TX
Email	bnorman@liverpool.ac.uk
Phone	(+44)151 794 9064
Submit Date	2022-01-24
Num Groups	6
Total Subjects	112
Num Males	67
Num Females	45
Raw Data Available	Yes
Raw Data File Type(s)	d
Analysis Type Detail	LC-MS
Release Date	2022-02-24
Release Version	1

Select appropriate tab below to view additional metadata details:

Project:

Project ID:	PR001322
Project DOI:	doi: 10.21228/M8TT44
Project Title:	Predicting dying: a study of the metabolic changes and the dying process in patients with lung cancer
Project Summary:	Background: 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.
Institute:	University of Liverpool Institute of Life Course Medical Sciences
Last Name:	Norman
First Name:	Brendan
Address:	William Henry Duncan Building, 6 West Derby Street, Liverpool, Merseyside, L7 8TX, United Kingdom
Email:	bnorman@liv.ac.uk
Phone:	(+44)151 794 9064
Funding Source:	This research received a Wellcome Trust Seed award for Science (202022/Z/16/Z) and a North West Cancer Research award (SI2018.11)
Contributors:	Séamus Coyle, Elinor Chapman, David Hughes, James Baker, Andrew S Davison, Brendan P Norman, Amara Callistus Nwosu, Mark Boyd, Catriona R Mayland, Stephen Mason, John Ellershaw, Chris Probert

Subject:

Subject ID:	SU002166
Subject Type:	Human
Subject Species:	Homo sapiens
Taxonomy ID:	9606
Age Or Age Range:	47-89 years
Gender:	Male and female
Human Inclusion Criteria:	Patients with lung cancer

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id	local_sample_id	Time before death (weeks)
SA198186	D0D1_WHI-011-106_2018-07-17	1
SA198187	D0D1_WHI-003_2018-04-16	1
SA198188	D0D1_WHI-102_2018-04-25	1
SA198189	D2D16_A-57_2017-08-22	1
SA198190	D2D16_MC-11_2018-07-01	1
SA198191	D0D1_WHI-001_2018-03-14	1
SA198192	D0D1_R-32_2018-07-13	1
SA198193	D0D1_C-04_2017-08-10	1
SA198194	D0D1_MC-05_2017-08-16	1
SA198195	D0D1_R-26_2018-04-20	1
SA198196	D0D1_R-29_2018-06-01	1
SA198197	D2D16_R-10_2016-09-15	1
SA198198	D2D16_WHI-015-107_2018-07-20	1
SA198199	G2_PS-R-09_01-11-2016	1
SA198200	G2_PS-R-21_15-11-2017	1
SA198201	G2_PS-WHI-027_04-09-2018	1
SA198202	G3_PS-WHI-101_11-04-2018	1
SA198203	G2_PS-MC-02_03-07-2017	1
SA198204	G1_PS-WHI-007_11-04-2018	1
SA198205	G1_PS-A-05_03-03-2017	1
SA198206	G1_PS-A-51_21-11-2017	1
SA198207	G1_PS-MC-06_14-08-2017	1
SA198208	G1_PS-R-02_11-10-2016	1
SA198209	D0D1_A-63_2018-03-28	1
SA198210	G1_PS-WHI-020_15-06-2018	1
SA198211	D0D1_A-09_2017-11-21	1
SA198212	A-20_2017-11-21	12+
SA198213	A-01_2017-02-09	12+
SA198214	LCC_PS-MC-09_19-10-2017	12+
SA198215	A-21_2018-01-24	12+
SA198216	LCC_PS-C-12_01-06-2017	12+
SA198217	A-24_2017-07-06	12+
SA198218	LCC_PS-MC-14_27-02-2018	12+
SA198219	A-40_2018-07-24	12+
SA198220	A-33_2017-12-12	12+
SA198221	A-15_2018-06-06	12+
SA198222	LCC_PS-C-01_09-03-2017	12+
SA198223	LCC_PS-A-13_06-07-2017	12+
SA198224	LCC_PS-A-08_26-07-2017	12+
SA198225	LCC_PS-A-07_02-03-2017	12+
SA198226	LCC_PS-A-06_20-07-2017	12+
SA198227	LCC_PS-A-19_06-07-2017	12+
SA198228	A-11_2018-05-17	12+
SA198229	LCC_PS-R-15_31-03-2017	12+
SA198230	LCC_PS-A-50_29-06-2017	12+
SA198231	LCC_PS-A-32_06-07-2017	12+
SA198232	A-12_2017-03-16	12+
SA198233	LCC_PS-R-22_08-12-2017	12+
SA198234	C-03_2017-03-20	12+
SA198235	C-02_2017-03-16	12+
SA198236	A-61_2018-03-15	12+
SA198237	WHI-016_2018-08-30	12+
SA198238	C-05_2017-04-20	12+
SA198239	C-06_2017-04-26	12+
SA198240	C-18_2017-06-21	12+
SA198241	C-13_2017-06-06	12+
SA198242	C-09_2017-05-10	12+
SA198243	R-30_2018-06-07	12+
SA198244	D17_A-65_2018-07-07	12+
SA198245	A-48_2017-06-29	12+
SA198246	A-47_2017-08-02	12+
SA198247	LCC_PS-WHI-002_14-03-2018	12+
SA198248	LCC_PS-R-24_11-01-2018	12+
SA198249	A-49_2017-06-28	12+
SA198250	LCC_PS-WHI-008_16-04-2018	12+
SA198251	MC-17_2018-09-12	12+
SA198252	MC-16_2018-09-12	12+
SA198253	MC-15_2018-09-19	12+
SA198254	Group 4_PS-R-18_20-06-2017	2
SA198255	R-13_2018-04-23	2
SA198256	G5_PS-R-01_15-06-2016	2
SA198257	G4_PS-WHI-012_01-05-2018	2
SA198258	D2D16_A-18_2017-04-04	2
SA198259	G3_PS-A-14_26-06-2017	2
SA198260	G3_PS-C-17_20-06-2017	2
SA198261	D2D16_WHI-026_2018-07-19	2
SA198262	D2D16_WHI-023_2018-08-29	2
SA198263	D2D16_A-56_2017-08-23	2
SA198264	G5_PS-MC-13_27-02-2018	2
SA198265	D2D16_WHI-014_2018-06-05	2
SA198266	G4_PS-A-62_15-02-2018	2
SA198267	G3_PS-R-12_02-03-2017	2
SA198268	G4_PS-R-17_14-06-2017	2
SA198269	G5_PS-MC-07_14-08-2017	2
SA198270	G4_PS-R-05_17-08-2016	2
SA198271	G4_PS-MC-10_06-03-2018	2
SA198272	D17_R-20_2017-11-22	3
SA198273	D17_A-64_2018-07-06	3
SA198274	R-11_2016-11-04	3
SA198275	D17_R-23_2017-12-13	3
SA198276	D17_WHI-004_2018-03-16	3
SA198277	MC-01_2017-07-05	3
SA198278	MC-08_2017-09-14	3
SA198279	D2D16_WHI-010_2018-04-23	3
SA198280	G5_PS-WHI-103_30-04-2018	3
SA198281	G5_PS-R-28_20-06-2018	3
SA198282	G6_PS-R-25_11-01-2018	3
SA198283	G5_PS-A-67_25-06-2018	3
SA198284	MC-04_2017-07-25	4
SA198285	R-31_2018-07-25	4+

Showing page 1 of 2 Results: 1 2 Next Showing results 1 to 100 of 112

Collection:

Collection ID:	CO002159
Collection Summary:	The study was conducted at six sites (hospitals and hospices) in the North West of England (UK) from June 2016 to September 2018. Patients with lung cancer were recruited prospectively and multiple urine samples were collected up to three times a week while an inpatient. Ethical approval was provided by North Wales (West) Research Ethics Committee (REC reference 15/WA/0464). Research team members collected 20 mL of urine from participants in a universal container. For those participants with a urinary catheter, the urine was collected using a needle and syringe from the catheter port. The samples were stored on site in a locked freezer at −20°C. An anonymised record of the medication administered was collected. Collection protocol described previously by Coyle et al. (BMJ Open. 2016; 6(11) e011763. doi: 10.1136/bmjopen-2016-011763).
Sample Type:	Urine
Volumeoramount Collected:	20 mL
Storage Conditions:	-20℃

Treatment:

Treatment ID:	TR002178
Treatment Summary:	Clinical observational study. Serial urine samples collected from patients with lung cancer in a palliative care setting at varying time points up until death; >12 weeks - 1 week before death (see 'study design' information). Although multiple samples were collected from patients, only the final sample was included in the analysis.

Sample Preparation:

Sampleprep ID:	SP002172
Sampleprep Summary:	Individual patient samples were thawed at room temperature, vortexed and separated into four replicate aliquots in individual 96-well plates (Waters, UK) which were stored at -80 °C until analysis by one of four different methods; two different chromatography conditions in negative and positive ionisation polarity. Pooled quality control samples were created following the protocol described by Norman et al. (Clin Chem. 2019;65(4):530-39. doi: 10.1373/clinchem.2018.295345). For each sample group (time before death), a separate representative pool was created by pooling an equal volume of each individual urine sample for quality control purposes. An overall pool was also created by pooling equal proportions of the above group pools. Analysis of individual and pooled samples was performed following dilution of 1:3 urine:deionised water (DIRECT-Q 3UV Millipore water purification system) as previously described by Norman et al. (2019).

Sampleprep ID:

SP002172

Sampleprep Summary:

Individual patient samples were thawed at room temperature, vortexed and separated into four replicate aliquots in individual 96-well plates (Waters, UK) which were stored at -80 °C until analysis by one of four different methods; two different chromatography conditions in negative and positive ionisation polarity. Pooled quality control samples were created following the protocol described by Norman et al. (Clin Chem. 2019;65(4):530-39. doi: 10.1373/clinchem.2018.295345). For each sample group (time before death), a separate representative pool was created by pooling an equal volume of each individual urine sample for quality control purposes. An overall pool was also created by pooling equal proportions of the above group pools. Analysis of individual and pooled samples was performed following dilution of 1:3 urine:deionised water (DIRECT-Q 3UV Millipore water purification system) as previously described by Norman et al. (2019).

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 Type	ESI	ESI	ESI	ESI
MS instrument type	QTOF	QTOF	QTOF	QTOF
MS instrument name	Agilent 6550 QTOF	Agilent 6550 QTOF	Agilent 6550 QTOF	Agilent 6550 QTOF
Ion Mode	NEGATIVE	POSITIVE	NEGATIVE	POSITIVE
Units	Values are raw peak area	raw peak area	raw peak area	Values are raw peak area

Chromatography:

Chromatography ID:	CH002511
Chromatography Summary:	LC method 1: employed an Atlantis dC18 column (3x100 mm, 3 µm, Waters, UK) maintained at 60 °C with flow rate at 0.4 mL/min. Mobile phases were (A) water and (B) methanol both containing 5 mmol/L ammonium formate and 0.1 % formic acid. The elution gradient started at 5 % B at 0 to 1 min increasing linearly to 100 % by 12 min, held at 100 % B until 14 min, returning to 95 % A for 5 min.
Instrument Name:	Agilent 6550
Column Name:	Waters Atlantis dC18 (100 x 3mm,3um)
Column Temperature:	60
Flow Gradient:	The elution gradient started at 5 % B at 0 to 1 min increasing linearly to 100 % by 12 min, held at 100 % B until 14 min, returning to 95 % A for 5 min
Flow Rate:	0.4 mL/min
Solvent A:	100% water; 0.1 % formic acid; 5 mM ammonium formate
Solvent B:	100% methanol; 0.1 % formic acid; 5 mM ammonium formate
Chromatography Type:	Reversed phase

Chromatography ID:	CH002512
Chromatography Summary:	LC method 2: used a BEH amide column (3x150 mm, 1.7 µm, Waters, UK) maintained at 40 °C with flow rate at 0.6 mL/min. Mobile phases were (A) water and (B) acetonitrile both containing 0.1 % formic acid. The elution gradient started at 99 % B, decreasing linearly to 30 % from 1 to 12 min, held at 30 % B until 12.6 min, returning to 99 % B for 3.4 min. Sample injection volume was 1 µL for both LC methods.
Instrument Name:	Agilent 6550
Column Name:	Waters BEH Amide (150 x 3.0mm,1.7um)
Column Temperature:	40
Flow Gradient:	The elution gradient started at 99% B, decreasing linearly to 30% from 1 to 12 min, held at 30% B until 12.6 min, returning to 99% B for 3.4 min.
Flow Rate:	0.6 mL/min
Solvent A:	100% water; 0.1% formic acid
Solvent B:	100% acetonitrile; 0.1% formic acid
Chromatography Type:	HILIC

MS:

MS ID:	MS003163
Analysis ID:	AN003396
Instrument Name:	Agilent 6550 QTOF
Instrument Type:	QTOF
MS Type:	ESI
MS Comments:	Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis. Unidentified compounds are 'known unknowns' of interest from previous experiments; detection by matched AMRT and named in the format 'RT_neutral-mass'. 'BDRM'; unidentified bone-derived metabolite.
Ion Mode:	NEGATIVE

MS ID:	MS003164
Analysis ID:	AN003397
Instrument Name:	Agilent 6550 QTOF
Instrument Type:	QTOF
MS Type:	ESI
MS Comments:	Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis. Unidentified compounds are 'known unknowns' of interest from previous experiments; detection by matched AMRT and named in the format 'RT_neutral-mass'. 'BDRM'; unidentified bone-derived metabolite.
Ion Mode:	POSITIVE

MS ID:	MS003165
Analysis ID:	AN003398
Instrument Name:	Agilent 6550 QTOF
Instrument Type:	QTOF
MS Type:	ESI
MS Comments:	Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis.
Ion Mode:	NEGATIVE

MS ID:	MS003166
Analysis ID:	AN003399
Instrument Name:	Agilent 6550 QTOF
Instrument Type:	QTOF
MS Type:	ESI
MS Comments:	Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis.
Ion Mode:	POSITIVE