EP4500182A1 - Prognostic biomarkers - Google Patents

Prognostic biomarkers

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Publication number
EP4500182A1
EP4500182A1 EP23715223.6A EP23715223A EP4500182A1 EP 4500182 A1 EP4500182 A1 EP 4500182A1 EP 23715223 A EP23715223 A EP 23715223A EP 4500182 A1 EP4500182 A1 EP 4500182A1
Authority
EP
European Patent Office
Prior art keywords
biomarker associated
unknown
biomarker
acid
altered
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP23715223.6A
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German (de)
French (fr)
Inventor
Seamus COYLE
Chris Probert
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Clatterbridge Cancer Centre Nhs Foundation Trust
University of Liverpool
Original Assignee
Clatterbridge Cancer Centre Nhs Foundation Trust
University of Liverpool
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Clatterbridge Cancer Centre Nhs Foundation Trust, University of Liverpool filed Critical Clatterbridge Cancer Centre Nhs Foundation Trust
Publication of EP4500182A1 publication Critical patent/EP4500182A1/en
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/575—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/5752—Immunoassay; Biospecific binding assay; Materials therefor for cancer of the lungs
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/56—Staging of a disease; Further complications associated with the disease

Definitions

  • the present invention relates to prognostic biomarkers that can be used to help predict the how long an individual in the later stages of life might have before their death occurs.
  • the prognostic biomarkers are liquid phase biomarkers that are present in the urine or the blood.
  • the present invention provides a method of determining the probability that an individual will die within a specified period time, typically up to three months, by testing a blood or urine sample obtained from the individual concerned for the presence of certain prognostic biomarkers.
  • the present inventors hypothesised that a “dying process” would be associated with various detectable metabolic changes. To investigate this further, they investigated the changes in urinary biomarkers and their associated biochemical pathways in people with cancer in the last weeks and days of life. The urinary biomarkers identified were then developed into a prediction model for the last period (up to three months) of life.
  • the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
  • biomarker associated altered cellular energy metabolism a biomarker associated with disrupted mitochondrial fatty acid p-oxidation
  • a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis a biomarker associated with increased muscle loss, bone damage or bone resorption
  • a biomarker associated with increased mitochondrial dysfunction a biomarker associated with altered mitochondrial dysfunction
  • f) a biomarker associated with altered one carbon metabolism g) a biomarker associated with decreased RNA synthesis; h) a biomarker associated with decreased protein synthesis; i) a biomarker associated with oxidative stress; j) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) a biomarker associated with increased cell membrane breakdown; l
  • step (ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value; (iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
  • the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
  • biomarker associated altered cellular energy metabolism a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis, a biomarker associated with increased mitochondrial dysfunction, a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and b) Unknown metabolite 5:
  • step (ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
  • the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
  • biomarker associated altered cellular energy metabolism a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased mitochondrial dysfunction; d) a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and e) Unknown metabolite 5:
  • step (ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
  • the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
  • biomarker associated altered cellular energy metabolism a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased bone loss, bone damage or bone resorption; d) a biomarker associated with increased mitochondrial dysfunction; e) a biomarker associated with altered hormone production; f) a biomarker associated with decreased oral intake (food and drink); g) Unknown metabolites 5 and/or 7:
  • step (ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
  • the present invention further provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
  • step (ii) comparing the quantity of the one or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is: a) an increase in one or more of the following biomarkers relative to its reference value: i. a biomarker present in the liquid phase selected from:
  • a biomarker present in the liquid phase selected from: acetyl-lysine; adrenochrome; y-aminobutryic acid (GABA); 2- aminophenol; D-biotin; caffeic acid; caffeine; cortisol-21 -acetate; p- coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4- phenylenediamine; 1 ,2-dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L- hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3- methyladenine; 4-methylcatechol; methyl-p-D-Galactoside; 2- methylmaleate; 6-methylmercaptopurine; methyl-N-a-methylbuty
  • a volatile organic compound selected from: butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2- ylcyclohex-2-en-1-one); di-isobutyl cellosolve; 1 R,3S..1 ,3- dimethylcyclohexane; 1S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2,1-octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1.
  • the present invention further provides a method of analysing the variance data obtained by testing a urine or blood sample obtained from a patient for the presence and quantity of one or more of the biomarkers listed step (i) above; the method comprising the steps of:
  • step (ii) comparing the quantity of the one or more biomarkers listed step (i) above with a reference value for the biomarker concerned to determine whether there is: a. an increase in one or more of the biomarkers listed in step (ii) a) above relative to its reference value; and/or b. a decrease in one or more of the following biomarkers listed in step (ii) b) relative to its reference value;
  • step (i) of the method above by analysing for an increase or decrease in one or more of the prognostic biomarkers present in a urine or blood sample from an individual (as outlined in step (i) of the method above), compiling the variance data for each biomarker detected relative to its reference value (step (ii) of the method above) and analysing the variance data in a prediction model (step (iii) of the method above), enabled them to determine the likelihood of an individual dying within a period of time of up to three months.
  • the model can therefore provide an assessment of whether an individual is going to die within a time period of, for example, up to 3 months, up to 2 months, up to 1 month, up to 3 weeks, up to 2 weeks, up to 1 week, up to 6 days, up to 5 days, up to 4 days, up to 3 days, up to 48 hours, up to 12 hours.
  • This insight gives a clinician a much greater insight into when an individual patient is going to die and this enables appropriate communication with the individual concerned, their family members, and assists with the provision of the appropriate palliative care regime for the individual.
  • Figure 1 shows a Kaplan Meier survival curve using the 30 day Cox lasso logistic regression model. It shows 3 groupings; High, Medium and Low risk of dying.
  • Figure S1-A through to S1-CW show ANOVA graphs for stated metabolites.
  • Figure S2 shows AUC for the 30 day Cox lasso regression model (Model 1).
  • the black line shows the mean and dotted line the median.
  • the dark grey shows the confidence interval and the light grey shows the minimum and maximum values.
  • Figure S3 show calibration curves for the 30 day Cox lasso regression model (Model 1) at days 10, 20 and 30.
  • Figure S4 shows Kaplan Meier survival curve of the 30 day Cox lasso regression model using a penalty parameter that gave a minimum error within one standard deviation (Model 2). It shows 3 grouping; High, Medium and Low risk of dying.
  • Figure S5 shows AUC for the 30 day Cox lasso regression model using a penalty parameter that gave a minimum error within one standard deviation (Model 2).
  • the black line shows the mean and dotted line the median.
  • the dark grey shows the confidence interval and the light grey shows the minimum and maximum values.
  • Figure S6 shows calibration curves for the 30 day Cox lasso regression model using a penalty parameter that gave a minimum error within one standard deviation at days 10, 20 and 30 (Model 2).
  • the “reference value” is the mean amount of a biomarker present in urine or blood samples obtained from a cohort of reference individuals that are not within the final three months of their respective lives (i.e. they are greater than three months away from death).
  • the reference individuals could be normal healthy individuals or they may be patients suffering from a particular disease and/or condition (e.g. cancer). In the latter case where the reference individuals are patients suffering from a particular disease and/or condition (e.g. cancer), the reference individuals suitably have the same disease and/or condition as the individual providing a urine or blood sample for the prognostic method of the present invention.
  • Cox proportional hazards model with lasso penalty refers to the approach taken to derive a prediction model to analyse the variance data obtained for the selected urinary biomarkers in the example section set out herein. This model is described further in Wolfe RR, Regulation of skeletal muscle protein metabolism in catabolic states, Curr Opin Clin Nutr Metab Care. 2005;8(1):61-5 (reference (23) herein).
  • the Cox proportional hazards model with lasso penalty approach is similar to the standard Cox model but shrinks parameter estimates towards zero, reducing over-fitting due to the large number of potential metabolites to consider as possible predictors of death. The procedure followed involved the following steps:
  • Model calibration was assessed with each bootstrap sample by comparing the observed and expected survival probabilities, splitting the predicted risks into 3 groups (denoted low/medium/high survival).
  • the present invention relates to a method of determining the likelihood that an individual is going to die within a time period of up to 3 months.
  • step (i) of the methods defined herein the method comprises either:
  • a biomarker associated altered cellular energy metabolism a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis, and/or a biomarker associated with increased mitochondrial dysfunction, a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and b) Unknown metabolite 5:
  • Unknown metabolite 7 274.11 C analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased mitochondrial dysfunction; d) a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and e) Unknown metabolite 5:
  • Steps (ii) to (iv) of the methods of the invention involve:
  • step (ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
  • the method of the invention allows the approximate time to death for an individual/patient within the last three months of their life to be predicted. Importantly, the model allows for prediction of the approximate time to death within the last two weeks of life, which has hitherto been particularly hard to predict.
  • Step (i) of the method outlined above involves analysing a urine or blood sample obtained from the individual concerned.
  • the collected urine or blood sample can be analysed to identify the presence and quantity of the biomarkers listed above in step (i) of the process.
  • the analysis may be conducted by any suitable technique known in the art that is capable if identifying and quantifying the prognostic biomarkers listed in step (i) of the method defined herein.
  • the biomarkers are urinary biomarkers and sample tested is a sample of urine collected from the patient.
  • the biomarkers are blood biomarkers and sample tested is a sample of blood collected from the patient.
  • p-hydroxybutyrate is detected in the blood and all other biomarkers listed can be detected from a urine sample and/or a blood sample.
  • a biomarker present in the liquid phase of the urine or blood sample is analysed using a Liquid chromatography-mass spectrometry (LC-MS) technique.
  • LC-MS Liquid chromatography-mass spectrometry
  • Such techniques are well known in the art and include Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry (LC-QTOF-MS), which have been previously described [16],
  • biomarkers that have been identified are volatile organic compounds (i.e. any of those listed in step (i) b) of option E above) and these can also be analysed in the gaseous phase by, for example, gas chromatography mass spectrometry (GC-MS).
  • GC-MS gas chromatography mass spectrometry
  • the biomarker is not a protein or peptide biomarker.
  • the biomarker is a small molecule compound or metabolite that is capable of being excreted in the urine.
  • a total of 190 urinary biomarkers that were altered in the last stages of life have been identified.
  • the model could work with just one biomarker selected from the options listed in step (i).
  • the method involves detecting at least three, at least four, at least five, at least six or at least seven of the biomarkers identified herein.
  • the urine or blood sample is analysed to detect 1 to 190 of the biomarkers listed in step (i) of option E the method of present invention (e.g. by LC- QTOF-MS for those in step (i) a) of option E and GC-MS for those in step (i) b) of Option E).
  • the urine or blood sample is analysed to detect the presence and quantity of 1 to 50 of the biomarkers recited in step (i), option E above.
  • the urine or blood sample is analysed to detect the presence and quantity of 1 to 45, 1 to 40, 1 to 35, 1 to 30, 1 to 25, 1 to 20, 1 to 15, 1 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers recited in step (i), option E above.
  • the urine or blood sample is analysed to detect the presence and quantity of 2 to 45, 2 to 40, 2 to 35, 2 to 30, 2 to 25, 2 to 20, 2 to 15, 2 to 10, or 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers recited in step (i).
  • the urine or blood sample is analysed to detect the presence and quantity of 4 to 45, 4 to 40, 4 to 35, 4 to 30, 4 to 25, 4 to 20, 4 to 15, 4 to 10, or 4, 5, 6, 7, 8, 9 or 10 of the biomarkers recited in step (i).
  • the urine or blood sample is analysed to detect the presence and quantity of 6 to 45, 6 to 40, 6 to 35, 6 to 30, 6 to 25, 6 to 20, 6 to 15, 6 to 10, or 6, 7, 8, 9 or 10 of the biomarkers recited in step (i).
  • step (i) of the process are, in most cases, able to be correlated with altered behaviour and/or specific biological pathways that are altered in the final stages of life.
  • the biomarker(s) detected in step (i) of the method may include a biomarker associated with one or more, two or more, three or more, four or more, five or more or six or more of the following: a) altered cellular energy metabolism b) disrupted mitochondrial fatty acid p-oxidation; c) increased muscle loss, muscle damage or rhabdomylosis; d) increased bone loss, bone damage or resorption; e) increased mitochondrial dysfunction; f) altered one carbon metabolism; g) decreased protein synthesis; h) decreased RNA synthesis; i) oxidative stress; j) altered nucleoside (purine, pyrimidine) metabolism; k) increased cell membrane breakdown; l) altered hormone production; m) altered amino acid metabolism; n) kynurenine pathway activation; and/or o) decreased oral intake (food and drink).
  • the biomarker(s) detected in step (i) of the method may include one or more, two or more, three or more, four or more, five or more or six or more biomarkers selected from one or more of the following options: a) a biomarker associated altered cellular energy metabolism selected from NAD or creatine; b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation selected from dicarboxylic acids (e.g.
  • the at least one biomarkers detected in step (i) of the method may include one or more biomarkers selected from one or more of the following: a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from creatine, sarcosine, carnitine, carnosine, 3- amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3- Phosphoethanolamine).
  • a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from creatine, sarcosine, carnitine, carnosine, 3- amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (
  • a biomarker associated with increased mitochondrial dysfunction selected from carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) a biomarker associated with altered hormone production selected from epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from UBDM1, UBDM2, UBDM3 and UBDM4.
  • a biomarker associated with decreased oral intake selected from caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • the method of the present invention also includes the possibility of detecting up to 15 compounds of unknown identity (although they are characterised by their molecular mass and retention times as shown below):
  • the present invention also includes the optional detection of one or more of these unknown compounds.
  • step (i) of the method involves detecting a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine).
  • a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidyl
  • a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4.
  • a biomarker associated with decreased oral intake selected from one or more caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • a biomarker associated with decreased oral intake selected from one or more caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with disrupted mitochondrial fatty acid p- oxidation; c) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) one biomarker associated with increased bone loss, bone damage or bone resorption; e) one biomarker associated with increased mitochondrial dysfunction; f) one biomarker associated with altered one carbon metabolism; g) one biomarker associated with decreased RNA synthesis; h) one biomarker associated with decreased protein synthesis; i) one biomarker associated with oxidative stress; j) one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism;
  • the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least the following biomarkers: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with altered hormone production; e) one biomarker associated with decreased oral intake (food and drink); f) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
  • biomarkers a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with altered hormone production; e) one biomarker associated with decreased oral intake (food and drink); f) one or more unknown metabolites selected from Unknown metabolites
  • the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with disrupted mitochondrial fatty acid p- oxidation; c) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) one biomarker associated with increased bone loss, bone damage or bone resorption; e) one biomarker associated with increased mitochondrial dysfunction; f) one biomarker associated with oxidative stress; g) one biomarker associated with altered hormone production; h) one biomarker associated with decreased oral intake (food and drink); i) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
  • the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with increased mitochondrial dysfunction; e) one biomarker associated with altered hormone production; f) one biomarker associated with decreased oral intake (food and drink); g) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
  • the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; b) one biomarker associated with increased bone loss, bone damage or bone resorption; c) one biomarker associated with altered hormone production; d) one biomarker associated with decreased oral intake (food and drink); e) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
  • step (i) the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of:
  • biomarker associated with disrupted mitochondrial fatty acid p-oxidation a biomarker associated with disrupted mitochondrial fatty acid p-oxidation
  • b a biomarker associated with increased bone loss, bone damage or bone resorption
  • c a biomarker associated with increased mitochondrial dysfunction
  • d a biomarker associated with altered one carbon metabolism
  • e a biomarker associated with decreased RNA synthesis
  • f a biomarker associated with decreased protein synthesis
  • g. a biomarker associated with oxidative stress h. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism
  • nucleoside purine, pyrimidine
  • a biomarker associated with increased cell membrane breakdown j. a biomarker associated with altered hormone production; k. a biomarker associated with altered amino acid metabolism; l. a biomarker associated with kynurenine pathway activation; m. a biomarker associated with decreased oral intake (food and drink); n. a biomarker associated with decreased DNA repair; o. one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
  • biomarker associated with disrupted mitochondrial fatty acid p-oxidation a biomarker associated with disrupted mitochondrial fatty acid p-oxidation
  • b a biomarker associated with increased bone loss, bone damage or bone resorption
  • c a biomarker associated with increased mitochondrial dysfunction
  • d a biomarker associated with altered one carbon metabolism
  • e a biomarker associated with oxidative stress
  • f a biomarker associated with increased cell membrane breakdown
  • g. a biomarker associated with altered hormone production h. a biomarker associated with altered amino acid metabolism
  • j a biomarker associated with decreased oral intake (food and drink);
  • k one or more of unknown metabolites selected from Unknown metabolites 1 to
  • biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with oxidative stress; f. a biomarker associated with altered hormone production; g. a biomarker associated with decreased oral intake (food and drink); h. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
  • biomarker associated with disrupted mitochondrial fatty acid p-oxidation a biomarker associated with disrupted mitochondrial fatty acid p-oxidation
  • b a biomarker associated with increased bone loss, bone damage or bone resorption
  • c a biomarker associated with altered one carbon metabolism
  • d a biomarker associated with decreased RNA synthesis
  • e a biomarker associated with decreased protein synthesis
  • f a biomarker associated with oxidative stress
  • g. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism h. a biomarker associated with increased cell membrane breakdown; i.
  • a biomarker associated with altered hormone production j. a biomarker associated with altered amino acid metabolism; k. a biomarker associated with kynurenine pathway activation; l. a biomarker associated with decreased oral intake (food and drink); m. a biomarker associated with decreased DNA repair; n. one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
  • biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with oxidative stress; e. a biomarker associated with altered hormone production; f. a biomarker associated with decreased oral intake (food and drink); g. one or more of unknown metabolites selected from Unknown metabolites 1 to
  • step (i) the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of:
  • a biomarker associated with oxidative stress j. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k. a biomarker associated with increased cell membrane breakdown; l. a biomarker associated with altered hormone production; m. a biomarker associated with altered amino acid metabolism; n. a biomarker associated with kynurenine pathway activation; o. a biomarker associated with decreased oral intake (food and drink); p. a biomarker associated with decreased DNA repair; q. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
  • biomarker associated altered cellular energy metabolism a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. one biomarker associated with increased bone loss, bone damage or bone resorption; e. one biomarker associated with increased mitochondrial dysfunction; f. one biomarker associated with altered one carbon metabolism; g. one biomarker associated with decreased RNA synthesis; h. one biomarker associated with decreased protein synthesis; i.
  • one biomarker associated with oxidative stress j. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k. one biomarker associated with increased cell membrane breakdown; l. one biomarker associated with altered hormone production; m. one biomarker associated with altered amino acid metabolism; n. one biomarker associated with kynurenine pathway activation; o. one biomarker associated with decreased oral intake (food and drink); p. one biomarker associated with decreased DNA repair; q. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
  • one biomarker associated with increased cell membrane breakdown j. one biomarker associated with altered hormone production; k. one biomarker associated with altered amino acid metabolism; l. one biomarker associated with kynurenine pathway activation; m. one biomarker associated with decreased oral intake (food and drink); n. one biomarker associated with decreased DNA repair; o. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
  • biomarker associated with disrupted mitochondrial fatty acid p-oxidation a biomarker associated with disrupted mitochondrial fatty acid p-oxidation
  • b one biomarker associated with increased bone loss, bone damage or bone resorption
  • c one biomarker associated with altered one carbon metabolism
  • d one biomarker associated with decreased RNA synthesis
  • e one biomarker associated with decreased protein synthesis
  • f one biomarker associated with oxidative stress
  • g. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism h. one biomarker associated with increased cell membrane breakdown; i.
  • one biomarker associated with altered hormone production j. one biomarker associated with altered amino acid metabolism; k. one biomarker associated with kynurenine pathway activation; l. one biomarker associated with decreased oral intake (food and drink); m. one biomarker associated with decreased DNA repair; n. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
  • (xiii) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
  • step (i) of the method involves detecting: a) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine); b) one biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) one biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol
  • step (i) of the method involves detecting a) creatine; b) carnitine; c) pyrocatechol; d) gluconic acid; e) UBRM 4; f) Unknown compound 5; and/or g) Unknown compound 7.
  • step (ii) of the process the quantity of the biomarker identified in step (i) of the method is correlated with a reference value in order to determine whether there is any variance (i.e. an increase or decrease) between the quality of the biomarker and the reference value.
  • the "reference value” is the mean amount of a biomarker present in urine or blood samples obtained from a cohort of reference individuals that are not within the final three months of their respective lives (i.e. they are greater than three months away from death).
  • Step (ii) of option E above the method comprises comparing the amount of the one or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is an increase for those markers listed in step (ii) a) of option E above or a decrease for those biomarkers listed in step (ii) b) of option E above.
  • the quantity of the biomarker present in the urine or blood sample is compared with the reference value for that biomarker and the data is analysed to see if there is an increase in the amount of any of the biomarkers recited in list a of step (ii) or a decrease in any of the biomarkers in list b of step (ii).
  • the inventors have found that the variance data for an increase in the level of one or more of the biomarkers in list a of step (ii), or a reduction in the level of any of the biomarkers listed in list b, can be used in the subsequent steps of the process to determine the likelihood that an individual in the final stages of life will die within a time period of up to three months.
  • Step (iii) of the method involves compiling the variance data obtained for the one or more biomarkers relative to their respective reference value(s) and step (iv) involves analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
  • the variance data is analysed using a prediction model which is a Cox proportional hazards model with lasso penalty as defined herein, and as described in the accompanying Example Section.
  • the method of the present invention may be applied to any individual or patient in the final stages of life.
  • the individual is cancer patient (e.g. a patient suffering with lung cancer or mesothelioma.
  • the methodology of the present invention may also further comprise a step of implement the appropriate palliative care for the individual based on the prediction from the method.
  • the present invention further provides a method of analysing the variance data obtained by testing a urine or blood sample obtained from a patient for the presence and quantity of one or more of the biomarkers listed step (i) above; the method comprising the steps of:
  • step (ii) comparing the quantity of the one or more biomarkers listed step (i) above with a reference value for the biomarker concerned to determine whether there is: a. an increase in one or more of the biomarkers listed in step (ii) a) above relative to its reference value; and/or b. a decrease in one or more of the following biomarkers listed in step (ii) b) relative to its reference value;
  • a method of determining the likelihood that an individual is going to die within a time period of up to 3 months comprising: (i) analysing a urine and/or blood sample obtained from the individual to detect the presence and quantity of one or more of the following: a) a biomarker present in the liquid phase selected from:
  • step (ii) comparing the quantity of the one or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is: a) an increase in one or more of the following biomarkers relative to its reference value: i.
  • a biomarker present in the liquid phase selected from: 4-acetamidobutanoate; N-acetylaspartate; N1-acetylspermine; adenine; adipic acid; allotetrahydrocortisol; 3-amino-4-hydroxybenzoic acid; 3- amino-isobutanoic acid; 3 carboxypropyl trimethylammonium; carnitine; carnosine; cortexolone; cortisol; creatine; cCMP; cGMP; cystathionine; deoxyadenosine; 5-deoxy-5-methylthio-adenosine; dihydrofolic acid; dihydrouracil; 2,4-dihydroxyacetophenone; dodecanoylcarnitine; epinephrine; ethylmalonic.acid; galactose-1 -phosphate; galactose-6- phosphate; glucosamine-6-phosphate; glucose- 1 -phosphat
  • a biomarker present in the liquid phase selected from: acetyl-lysine; adrenochrome; y-aminobutryic acid (GABA); 2- aminophenol; D-biotin; caffeic acid; caffeine; cortisol-21 -acetate; p- coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4- phenylenediamine; 1 ,2-dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L- hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3- methyladenine; 4-methylcatechol; methyl-p-D-Galactoside; 2- methylmaleate; 6-methylmercaptopurine; methyl-N-a-methylbuty
  • a volatile organic compound selected from: butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2- ylcyclohex-2-en-1-one); di-isobutyl cellosolve; 1 R,3S..1 ,3- dimethylcyclohexane; 1S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2,1-octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1.
  • Clause 2 A method of analysing the variance data obtained by testing a urine or blood sample obtained from a patient for the presence and quantity of one or more of the biomarkers listed step (i) of clause 1 ; the method comprising the steps of:
  • Clause 3 A method according to clause 1 or clause 2, wherein, in step (i) of the method, the urine or blood sample is analysed using Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry LC-QTOF-MS for biomarkers in step (i) a) of clause 1 or Gas Chromatography-Mass Spectrometry (GC-MS) for biomarkers in step (i) b) of clause
  • step (i) the method involves detecting 1 to 75, 1 to 50, 1 to 45, 1 to 40, 1 to 35, 1 to 30, 1 to 25, 1 to 20, 1 to 15, 1 to 10, 2 to 75, 2 to 50, 2 to 45, 2 to 40, 2 to 35, 2 to 30, 2 to 25, 2 to 20, 2 to 15, 2 to 10, 4 to 75, 4 to 50, 4 to 45, 4 to 40, 4 to 35, 4 to 30, 4 to 25, 4 to 20, 4 to 15, 4 to 10, 6 to 75, 6 to 50, 6 to 45, 6 to 40, 6 to 35, 6 to 30, 6 to 25, 6 to 20, 6 to 15, 6 to 10,1,
  • the biomarker(s) detected in step (i) of the method may include a biomarker associated with one or more, two or more, three or more, four or more, five or more or six or more of the following: a) altered cellular energy metabolism b) disrupted mitochondrial fatty acid p-oxidation; c) increased muscle loss, muscle damage or rhabdomylosis; d) increased bone loss, bone damage or resorption; e) increased mitochondrial dysfunction; f) altered one carbon metabolism; g) decreased protein synthesis; h) decreased RNA synthesis; i) oxidative stress; j) altered nucleoside (purine, pyrimidine) metabolism; k) increased cell membrane breakdown; l) altered hormone production; m) altered amino acid metabolism; n) kynurenine pathway activation; and/or o) decreased oral intake (food and drink).
  • biomarker(s) detected in step (i) of the method may include one or more two or more, three or more, four or more, five or more or six or more biomarkers selected from one or more of the following options: a) a biomarker associated altered cellular energy metabolism selected from one or more of NAD, creatine, creatine b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation selected from one or more of dicarboxylic acids (e.g.
  • a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, nitrotyrosine, creatinine.
  • a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1, UBDM2, UBDM3 and UBDM4.
  • a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl- glutaric.acid;
  • a biomarker associated with altered one carbon metabolism selected from one or more of dihydrofolate, sarcosine, and cystathionine and the lack of glycine or dTMP.
  • Altered one carbon metabolism has known consequences for purine biosynthesis including resultant decreased DNA synthesis and also the decreased production of sulfhydryl-containing reducing agents, impacting enzyme inhibition, enzyme reactivation or protection and labelling.
  • a biomarker associated with decreased RNA synthesis selected from one or more of uridine monophosphate, guanosine and increased purine degradation products xanthine and hypoxanthine; with known consequences on decreased protein synthesis.
  • a biomarker associated with decreased protein synthesis selected from one or more of essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine) and any biomarker associated with decreased RNA synthesis (see point (g)).
  • a biomarker associated with oxidative stress for example nitrotyrosine
  • a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism selected from one or more of adenine, guanosine, uridine monophosphate, galactose monophosphate, 5-deoxy, 5-methyl, thioadenosine, dihydrofolic acid, hypoxanthine, xanthine, 3-methyl deoxyadenosine, D-galactose-1-phosphate
  • k) a biomarker associated with increased cell membrane breakdown selected from one or more of various cholines (e.g.
  • l) a biomarker associated with altered hormone production selected from one or more of epinephrine (epinephrine, adrenochrome, vanillylmandelic.acid (VMA), pyrocatechol), GABA (GABA, acetamidobutanoate), cortisol (cortisol, allotetrahydrocortisol, cortexolone, cortisol-21 acetate), histamine (histidine), hydroxytryptophan, dopamine (dihydroxyphenylacetic acid, L- dopa) and serotonin; m) a biomarker associated with altered amino acid metabolism selected from one or more of alanine (eg dihydrouracil), cysteine (eg L- cystathionine) histidine, isoleucine, phenylalanine (eg phenylalanine (eg phenylalanine (eg phenylalanine) histidine, isoleucine
  • a biomarker associated with kynurenine pathway activation selected from one or more of 3-Hydroxyanthranilic acid or Kynurenic acid; r) a biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • a biomarker associated with decreased DNA repair eg Hypoxia Inducible Factor 1 (HIF1), phosphorylated and unphosphorylated ATR, phosphorylated and unphosphorylated FANCG.
  • biomarker(s) detected in step (i) of the method may include one or more biomarkers selected from one or more of the following: a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3- Phosphoethanolamine).
  • a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitroty
  • a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl- glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1, UBDM2, UBDM3 and UBDM4.
  • a biomarker associated with decreased oral intake selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • Clause 8 A method according to any one of clauses 5 to 7, wherein the method of the present invention also includes detecting the presence of one or more of the following compounds:
  • step (i) of the method involves detecting a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine).
  • a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidyl
  • a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4.
  • a biomarker associated with decreased oral intake selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • a biomarker associated with decreased oral intake selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
  • step (i) of the method involves detecting a) Creatine; b) carnitine; c) pyrocatechol; d) gluconic acid; e) UBRM 4; f) Unknown compound 5; and/or g) Unknown compound 7.
  • step (iv) the prediction model is a Cox proportional hazards model with Least Absolute Shrinkage and Selection Operator (LASSO) penalty.
  • LASSO Least Absolute Shrinkage and Selection Operator
  • Clause 13 A method according to any one of the preceding clauses, wherein the patient is a cancer patient.
  • Clause 14 A method according to clause 13, wherein the patient is a patient suffering from lung cancer.
  • Clause 15 A method according to any one of the preceding clauses, wherein the method determines the likelihood that a patient is going to die within a period of up to 2 months, up to 1 month, up to 3 weeks, up to 2 weeks, up to 1 week, up to 6 days, up to 5 days up to 4 days, up to 3 days, up to 48 hours, up to 24 hours.
  • Clause 16 A method according to any one of the preceding clauses, wherein if the method predicts that a patient is within the final stages of their life, the method further comprises a step of implementing appropriate palliative care.
  • Urine metabolomic studies were performed using Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry (LC-QTOF-MS) as previously described [16], In brief, analysis was performed on an Agilent 1290 Infinity LC coupled to an Agilent 6550 QTOF-MS equipped with a dual AJS electrospray ionization source (Agilent, UK), using two separate chromatographic methods.
  • LC-QTOF-MS Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry
  • LC Liquid chromatography
  • LC method 1 employed an Atlantis dC18 column (3x100 mm, 3 pm, 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.
  • LC method 2 used a BEH amide column (3x150 mm, 1.7 pm, 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 pL for both LC methods. The sampling needle was washed with a solution of water:methanol:isopropanol (45:45:10 v/v) between injections.
  • 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 previously [16],
  • 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 [16] (databases publicly available: https://doi.Org/10.6084/m9.figshare.c.4378235.v2).
  • MS/MS was also used in the confirmation of metabolite identity (i.e.
  • a penalty parameter (lambda) was imposed to determine the amount of smoothing chosen when 10-fold cross validation was performed. The value of lambda that gave minimum mean cross-validated error was used for both the prediction model and internal validation.
  • the model was internally validated using bootstrap resampling methods with 1000 bootstrap samples. The penalty parameter was fixed from the original cox lasso model fit to the whole dataset, and then for each bootstrap sample a Cox Lasso model was fit and time-dependent area under curve was calculated (24). Model calibration was assessed with each bootstrap sample by comparing the observed and expected survival probabilities, splitting the predicted risks into 3 groups (denoted low/medium/high survival). Calibration was performed at 10, 20 and 30 days.
  • the pathway analysis was undertaken using MetaboAnalyst 5.0 [22] and combines powerful pathway enrichment analysis with pathway topology analysis to identify the most relevant pathways involved with the conditions under study. Metabolites that showed a significant difference between groups were collated into relevant human KEGG physiological pathways to visualise which significant pathways were altered towards dying. Metabolites were matched using HMDB, PUBCHEM and KEGG databases.
  • Urine was prospectively collected from a total of 112 patients with mixed lung cancers from 6 institutions between June 2016 and August 2018. The demographic characteristics of those recruited are shown in (Table 1). Table 1 - Clinical Characteristics of the Patients number of samples collected
  • ANOVA analysis identified 76 metabolites that varied within the last weeks of life.
  • Volcano plot analysis identified 85 metabolites with a greater than 2-fold change for different time intervals; the last 4 weeks of life (0-4 weeks), last 2 weeks (0-2 weeks), last 5 days (0-5 days) and last 3 days (0-3 days) (see Tables S2 and S3 Supplementary Results). Twelve metabolites were associated with decreased oral intake, e.g. caffeine. Of the 73 metabolites, unrelated to oral intake, 41 were also identified by ANOVA.
  • ⁇ Disturbed represents a pathway where some metabolites increased and some metabolites decreased in abundance.
  • f UBRM 1 Unidentified Bone Derived Metabolite with molecular mass 1540.7544 and retention time 5.93.
  • t UBRM 2 Unidentified Bone Derived Metabolite with molecular mass 228.1124 and retention time 1.52.
  • nucleic acid metabolism Numerous changes suggest altered nucleic acid metabolism. Several critical building blocks accumulate, in particular UMP essential for RNA synthesis, as well as adenine and guanosine. In addition, purine degradation products xanthine, and hypoxanthine increase. Collectively these suggest depleting pools of substrates required for nucleic acid anabolism. Crucially this would suppress ribosomal biogenesis, impacting upon cellular capacity for protein synthesis, and importantly cellular stress monitoring and cell viability (23). In addition, ribosome synthesis makes high demands on cellular energy resources which appear to be low given the increase in NAD+.
  • nucleic acid metabolism alterations in one carbon metabolism, indicated by accumulating dihydrofolic acid, sarcosine, and cystathionine, suggest a reduced ability to generate nucleotides for DNA synthesis.
  • Raised inflammatory markers eg CRP are known to be associated with dying in cancer patients irrespective of malignancy type [13],
  • the Cox Lasso derived prediction model demonstrates it is possible to use urine metabolites to predict the dying process for each day within the last 30 days with good accuracy.
  • the high risk score predicted the majority of patients imminently dying.
  • the Low risk score identified those not in the last 2 weeks of life.
  • the model calibration remains stable at 20 and 10 days; it slightly over predicts dying in those with a higher probability and slightly under predicts those with a lower probability.
  • the low number of metabolites in the model suggests overfitting is limited.
  • Example 2 - GC analysis of volatile organic compounds emanating from urine samples
  • 1ml of urine was either freeze dried, or treated with acid or alkali.
  • a Perkin Elmer Clarus 500 GC-MS quadruple bench tcp system (Beaccnsfield, UK) was used in ccmbinaticn with a Ccmbi PAL autcsampler (CTC Analytics, Switzerland).
  • the GC cclumn used was a Zebrcn ZB-624 with inner diameter 0.25 mm, length 60 m, film thickness 1.4 pm (Phencmenex, Maccles field, UK).
  • the carrier gas used was helium cf 99.996% purity (BOC, Sheffield, UK).
  • a Divinylbenzene/Carbcxen/Pclydimethylsilcxane (DVB/CAR/PDMS) SPME fibre was cbtained from Sigma-Aldrich, Dorset, UK and preconditioned before use.
  • Urine samples were placed in an incubation chamber at 60°C for 30 min, followed by the extraction of volatiles from the headspace of the vial and adsorption to the SPME fibre.
  • the fibre was then inserted into the GC component for desorption at 220°C for 5 mins.
  • the initial temperature of the GC oven was set at 40°C, held for 2 min before increasing to 220°C at a rate of 5 °C/min and then held for 4 min, with a total run time of 42 min.
  • a solvent delay was set for the first 4 min and the MS was operated in positive electron impact ionization EI+ mode, scanning from ion mass fragments 10-300 m/z, with an interscan delay of 0.1s and a resolution of 1000 at FWHM (Full Width at Half Maximum).
  • the helium gas flow rate was set at 1 ml/min. All samples were randomly injected. This was exactly the same as Aggio et al. 2016 [2],
  • the freeze-dried urine The freeze-dried urine:
  • the freeze-dried urine library was built by examining 10% of the ‘real’ samples.
  • the final library contained xxx unique VOCs (Table X).
  • a batch report was generated from AMDIS using our library with deconvolution settings as follows: component width of 10, adjacent peak subtraction of one, high resolution, high sensitivity, and low shape requirements.
  • An acid-alkali urine library was built by examining all the pooled QC samples run and 10% of the ‘real’ samples per treatment group.
  • the final library contained 173 unique VOCs (Table X).
  • a batch report was generated from AMDIS using our library with deconvolution settings as follows: component width of 10, adjacent peak subtraction of one, low resolution, low sensitivity, and high shape requirements.
  • R package Metab was used to generate a spreadsheet of VOCs per sample, using a half a minute time-window [4],
  • Surv10 compares samples from days 0-10 to days 11+.
  • Surv15 compares samples from days 0-15 to days 16+.
  • the model was tested for each of the last days of life (30days - last day) and shows an AUC of below 0.8 increasing in accuracy in the last 2 weeks to approximately 0.8. To get an idea of the prediction error, bootstrapping was used.
  • the model can place patients into low, medium and high risks groups and their survival predicted.
  • the Kaplan Meier curve shows patients in different risk groups have different survival risks.
  • the AUC increases from 0.8 in the last 3-4 weeks to >0.9 in the last days.
  • SurvIO compares samples from days 0-10 to days 11+.
  • Urine samples were collected 3 times a week from a patient and stored frozen.
  • the plot shows serial samples for the last 6 weeks of life from one patient.
  • the p-Actin and ATG lanes are controls to demonstrate equal loading of each well.
  • hypoxia Inducible Factor 1 HIF1
  • Phosphorylated ATR which is active in DNA repair becomes de-phosphorylated and therefore inactive in the last weeks.
  • Phosphorylated FANCG which is active in DNA repair becomes de-phosphorylated and therefore inactive in the last weeks.
  • Capillary blood was obtained using a lancet pen/device. Ketones were measured done with a glucometer (Freestyle Optium Neo glucometer, Abbott) using dedicated blood B- Ketone test strips (Abbott). An estimate of a patient’s food intake was recorded as being Normal, Reduced, Minimal or None. The participants Phase of Illness was also recorded (Masso M, Allingham SF, Banfield M, et al. Palliative Care Phase: inter-rater reliability and acceptability in a national study. Palliat Med. 2015;29(1):22-30. doi:10.1177/0269216314551814). This is a concept in palliative medicine consisting of five distinct phases (Stable, Unstable, Deteriorating, Dying and Bereavement). Sampling occurred up to 3 times a week depending on patient preference and researcher availability.
  • a boxplot shows an increase in ketone concentrations in the last 3 weeks. The increase is greatest in the last 3 days of life (see Figure 2). The data is not normally distributed, therefore a Kruskal-Wallis test was used. Post-hoc analysis, to see where the differences in the groups were, was performed using a Dunn test; P-values were adjusted with the Holm method.
  • a scatterplot of the ketone concentrations in the last month of life is shown in Figure 16.
  • a non-parametric regression model is plotted with the 95% confidence interval marked by the transparent gray-shaded area. Ketone concentrations gradually increase for the majority of participants in the last days of life. There is a cohort where the increase occurs in the last 3 weeks.
  • Example 5 Validation of the 7 biomarker panel identified in Table 3 of Example 1

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Abstract

The present invention relates to prognostic urinary or blood biomarkers that can be used to help predict the how long an individual in the later stages of life might have before their death occurs. Thus, the present invention therefore provides a method of determining the probability that an individual will die within a specified period time, typically up to three months, by testing a urine or blood sample obtained from the individual concerned for the presence of certain prognostic biomarkers and analysing the variance data with a predictive model.

Description

Prognostic Biomarkers
[0001] The present invention relates to prognostic biomarkers that can be used to help predict the how long an individual in the later stages of life might have before their death occurs. The prognostic biomarkers are liquid phase biomarkers that are present in the urine or the blood. Thus, the present invention provides a method of determining the probability that an individual will die within a specified period time, typically up to three months, by testing a blood or urine sample obtained from the individual concerned for the presence of certain prognostic biomarkers.
BACKGROUND
[0002] Recognising dying is incredibly difficult. The United Kingdom National Audit of Care at the End of Life (NACEL) of hospital deaths in 2019 found that 20% of people died within 8 hours of the recognition that death was imminent, the median was 36 hours and when dying was recognised, 50% of patients lacked the capacity to be directly involved in any decision-making [1], Systematic reviews show that physician’s predictions are frequently inaccurate and overoptimistic [2, 3], Some validated prognostic tools predict survival of patients with advanced cancer [4] including the Palliative Prognostic Index [5], Palliative Prognostic (PaP) Score [6] and Prognosis in Palliative Care Study (PiPS) [7] however they use subjective variables: e.g. symptoms, performance status and the physician’s prediction of survival. Although, the PiPs-B model for 14 and 56 days using clinical observations combined with blood results was as accurate as agreed multi-professional estimates of survival [8, 9], No existing model predicts death within the last 2 weeks of life.
[0003] We do not know how people die from cancer. Infection and pulmonary embolus are thought to be the major causes of death, based on post-mortem studies [10, 11], However, it is unusual for people with cancer to die suddenly as anticipated from a pulmonary embolus. About a third of patients with advanced cancer admitted to specialist palliative care units have a femoral deep vein thrombosis; thus venous thromboembolism is considered a manifestation of advanced disease rather than a cause of premature death [12], There is a difference between the physiological deterioration leading to death in the acutely unwell patient compared to people dying from cancer; there is no evidence of sepsis [13], This suggests that in those cancer patients that die with an infection or pulmonary embolus, they die with them, not necessarily from them.
[0004] In the last 2 weeks of life, there is evidence for deranged respiratory and renal function variables [13] however few patients have evidence of organ failure. A systematic review of biomarkers associated with dying identified common themes in cancer patients, irrespective of the type of malignancy; raised inflammatory markers (for example, C reactive protein), organ dysfunction (Kidney, Liver) and cachexia [14], Given the common features shared in patients dying from cancer, a "dying process" has been proposed [14, 15] but has not yet been demonstrated.
[0005] There is, therefore, a need for improved methods and tools for determining what stage an individual is at in the “dying process”. In particular, there is a need for methods and tools that can predict how long an individual might have before they die. The present invention was devised with the foregoing in mind.
BRIEF SUMMARY OF THE DISCLOSURE
[0006] The present inventors hypothesised that a “dying process” would be associated with various detectable metabolic changes. To investigate this further, they investigated the changes in urinary biomarkers and their associated biochemical pathways in people with cancer in the last weeks and days of life. The urinary biomarkers identified were then developed into a prediction model for the last period (up to three months) of life.
[0007] The present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
(i) analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) a biomarker associated with increased bone loss, bone damage or bone resorption; e) a biomarker associated with increased mitochondrial dysfunction; f) a biomarker associated with altered one carbon metabolism; g) a biomarker associated with decreased RNA synthesis; h) a biomarker associated with decreased protein synthesis; i) a biomarker associated with oxidative stress; j) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) a biomarker associated with increased cell membrane breakdown; l) a biomarker associated with altered hormone production; m) a biomarker associated with altered amino acid metabolism; n) a biomarker associated with kynurenine pathway activation; o) a biomarker associated with decreased oral intake (food and drink); p) a biomarker associated with decreased DNA repair; q) one or more unknown metabolites selected from Unknown metabolites 1 to 15:
Metabolite name Mass
Unknown metabolite 1 194.04
Unknown metabolite 2 188.08
Unknown metabolite 3 129.08
Unknown metabolite 4 295.14
Unknown metabolite 5 188.05
Unknown metabolite 6 304.08
Unknown metabolite 7 274.11
Unknown metabolite 8 225.06
Unknown metabolite 9 179.08
Unknown metabolite 10 194.04
Unknown metabolite 11 117.08
Unknown metabolite 12 212.07
Unknown metabolite 13 122.04
Unknown metabolite 14 205.07
Unknown metabolite 15 195.05
(ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value; (iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0008] In a further aspect, the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
(i) analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism, a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis, a biomarker associated with increased mitochondrial dysfunction, a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and b) Unknown metabolite 5:
Metabolite name Mass
Unknown metabolite 5 188.05
(ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0009] In a further aspect, the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
(i) analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased mitochondrial dysfunction; d) a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and e) Unknown metabolite 5:
Metabolite name Mass
Unknown metabolite 5 188.05
(ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0010] In a further aspect, the present invention provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
(i) analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased bone loss, bone damage or bone resorption; d) a biomarker associated with increased mitochondrial dysfunction; e) a biomarker associated with altered hormone production; f) a biomarker associated with decreased oral intake (food and drink); g) Unknown metabolites 5 and/or 7:
Metabolite name Mass
Unknown metabolite 5 188.05
Unknown metabolite 7 274.11
(ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0011] The present invention further provides a method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
(i) analysing a urine and/or blood sample obtained from the individual to detect the presence and quantity of one or more of the following: a) a biomarker present in the liquid phase selected from:
4-acetamidobutanoate; N-acetylaspartate; N1-acetylspermine; adenine; adipic acid; allotetrahydrocortisol; 3-amino-4-hydroxybenzoic acid; 3-amino- isobutanoic acid; 3 carboxypropyl trimethylammonium; carnitine; carnosine; cortexolone; cortisol; creatine; cCMP; cGMP; cystathionine; deoxyadenosine;
5-deoxy-5-methylthio-adenosine; dihydrofolic acid; dihydrouracil; 2,4- dihydroxyacetophenone; dodecanoylcarnitine; epinephrine; ethylmalonic.acid; galactose-1-phosphate; galactose-6-phosphate; glucosamine-6-phosphate; glucose-1-phosphate; glucose-6-phosphate; glucuronic acid; guanosine; histidine; 3-hydroxybenzaldehyde; |3-hydroxybutyrate; 10-hydroxycapric acid; 12-hydroxydodecanoic acid; 3-hydroxy-3-methyl-glutaric.acid; 4- hydroxyphenyllactic acid (D-); 5-hydroxytryptophan; hypoxanthine; indolelactic acid; indole-3-methylacetate; isoleucine; kynurenic acid; malic acid; methioninesulfoximine; 3-methylglutaric acid; 3-methyloxindole (A-); nicotinamide adenine dinucleotide (NAD); nitrotyrosine; norleucine; oxoadipic acid; phenyl acetate; phenylalanine; phosphatidylcholine; pyrocatechol; resorcinol-monoacetate; sarcosine; suberic acid; sphingomyelin; tyrosine-methyl-ester-4-sulfate; UBDM 1 ; UBDM 2; UBDM 3; Unknown metabolite 1 ; Unknown metabolite 4; Unknown metabolite 5; Unknown metabolite 7; Unknown metabolite 10; Unknown metabolite 14; uridine monophosphate (UMP); vanillylmandelic acid; xanthine; xanthurenic acid; acetyl-lysine; adrenochrome; y-aminobutryic acid (GABA); 2-aminophenol; D-biotin; caffeic Acid; caffeine; cortisol-21 -acetate; p-coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4-phenylenediamine; 1,2- dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L-hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3-methyladenine; 4-methylcatechol; methyl-p- D-Galactoside; 2-methylmaleate; 6-methylmercaptopurine; methyl-N-a- methylbutyryl-glycine; 1-oleoyl-rac-glycerol; octanoic acid; paraxanthine; L- pipecolic acid; prolyl-threonine; quinic acid; rosmarinic acid; serotonin; tartaric acid; theobromine; theophylline; trigonelline; tryptamine; UBDM 4; Unknown metabolite 2; Unknown metabolite 3; Unknown metabolite 6; Unknown metabolite 8; Unknown metabolite 15; vanillactic acid; vanilloglycine; and/or b) a volatile organic compound selected from: acetic acid-1 R-2R or 2-methylcyclopentan-1-ol; acetoin; acetone; butan-2-one; 2-butoxyethoxy-ethanol; cyclohexanone; 2,6-dimethylaniline; 2,4- dimethylaniline; 1,2-dimethylcyclopentane; 3,4-dimethylhexan-2-one; 2,4- dimethyl-5H-1 ,3-oxazol-4-yl-methanol; 2,5-dimethyi-1 H-pyrrole; 2,6- dimethylpyrazine; 2-ethylhexane1-ol; 5-ethyl-5-methyloxolan-2-one; 3-ethyi-4- methylpyrrole-2, 5-dione; 5-ethyloxolan-2-one; 3-ethoxy-3-ethylpent-1-yne: heptan-3-one; heptan-4-one; E..hept-3-en-2-one or 3-Hepten-2-one; hexane- 3, 4-dione; E..hex-2-enyl.. acetate; 1-methoxypropan-2-ol; 4-methylpent-3-en-2- one; 4-methylpent-3-enoic-acid; noan-2-one or 5-methylhexan-2-one; E..non-3- en-2-one UP; Z..oct-2-enoic-acid; pentan-2-one; pent-3-en-2-one; phenol; propan-2-ol; propan-2-one; 1,2,4-triazole-3,4-diamine; 2,3,3- trimethylcydobutan-1-one; unknown (1S,5R..1,5-dimethyl-6,8-dioxabicyclo- 3,2,1 octane or E..hex-2-enyl.. acetate); butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2-ylcyclohex-2-en-1-one); di-isobutyl cellosolve;
1 R,3S..1 ,3-dimethylcyclohexane; 1 S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2, 1 - octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1 ,.2-furan-2-ylcyclopropyl ethenone; guanidine; heptan-2-one; 2-methoxy-2-methylpropane; 1- methoxypropan-2-one; 2..4-methylcyclohexa-2,4-dien-1-yl-propan-2-ol; Methyldisulfanylmethan; 3-methylheptan-2-one; 2-methylheptan-4-one; 5- methyl-2-propan-2-ylcyclohexan-1-ol-menthol; 5R..5-methyl-2-propan-2- ylidenecyclohexan-1-one (Pulegone); methylsulfonylmethane; oxime..methoxyphenyl; pentane-2, 3-dione; 2-prop-1-en-2-ylpyrazine; 1,2, 4, 5 tetrazine; 1 ,3,3- trimethyl-2-oxabicyclo-2,2,2-octane-eucalyptol;
(ii) comparing the quantity of the one or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is: a) an increase in one or more of the following biomarkers relative to its reference value: i. a biomarker present in the liquid phase selected from:
4-acetamidobutanoate; N-acetylaspartate; N1-acetylspermine; adenine; adipic acid; allotetrahydrocortisol; 3-amino-4-hydroxybenzoic acid; 3- amino-isobutanoic acid; 3 carboxypropyl trimethylammonium; carnitine; carnosine; cortexolone; cortisol; creatine; cCMP; cGMP; cystathionine; deoxyadenosine; 5-deoxy-5-methylthio-adenosine; dihydrofolic acid; dihydrouracil; 2,4-dihydroxyacetophenone; dodecanoylcarnitine; epinephrine; ethylmalonic.acid; galactose-1 -phosphate; galactose-6- phosphate; glucosamine-6-phosphate; glucose- 1 -phosphate; glucose-6- phosphate; glucuronic acid; guanosine; histidine; 3- hydroxybenzaldehyde; |3- hydroxy butyrate; 10-hydroxycapric acid; 12- hydroxydodecanoic acid; 3-hydroxy-3-methyl-glutaric.acid; 4- hydroxyphenyllactic acid (D-); 5-hydroxytryptophan; hypoxanthine; indolelactic acid; indole-3-methylacetate; isoleucine; kynurenic acid; malic acid; methionine-sulfoximine; 3-methylglutaric acid; 3- methyloxindole (A-); nicotinamide adenine dinucleotide (NAD); nitrotyrosine; norleucine; oxoadipic acid; phenyl acetate; phenylalanine; phosphatidylcholine; pyrocatechol; resorcinol-monoacetate; sarcosine; suberic acid; sphingomyelin; tyrosine-methyl-ester-4-sulfate; UBDM 1; UBDM 2; UBDM 3; Unknown metabolite 1 ; Unknown metabolite 4; Unknown metabolite 5; Unknown metabolite 7; Unknown metabolite 10; Unknown metabolite 14; uridine monophosphate (UMP); vanillylmandelic acid; xanthine; xanthurenic acid; and/or ii. a volatile organic compound selected from: acetic acid-1 R-2R or 2-methylcyclopentan-1-ol; acetoin; acetone; butan-
2-one; 2-butoxyethoxy-ethanol; cyclohexanone; 2,6-dimethylaniline; 2,4- dimethylaniline; 1 ,2-dimethylcyclopentane; 3,4-dimethylhexan-2- one; 2,4-dimethyl-5H-1 ,3-oxazol-4-yl-methanol; 2,5-dimethyl-1 H- pyrrole; 2,6-dimethylpyrazine; 2-ethylhexane1-ol; 5-ethyl-5- methyloxolan-2-one; 3-ethyl-4-methylpyrrole-2, 5-dione; 5-ethyloxolan-2- one; 3-ethoxy-3-ethylpent-1-yne; heptan-3-one; heptan-4-one; E..hept-
3-en-2-one or 3-Hepten-2-one; hexane-3, 4-dione; E..hex-2- eny I.. acetate; 1-methoxypropan-2-ol; 4-methylpent-3-en-2-one; 4- methylpent-3-enoic-acid; noan-2-one or 5-methylhexan-2-one; E..non-3- en-2-one UP; Z..oct-2-enoic-acid; pentan-2-one; pent-3-en-2-one; phenol; propan-2-ol; propan-2-one; 1 ,2,4-triazole-3,4-diamine; 2,3,3- trimethylcyclobutan-1-one; unknown (1S,5R..1,5-dimethyl-6,8- dioxabicyclo-3,2,1 octane or E..hex-2-enyl..acetate); and/or b) a decrease in one or more of the following biomarkers relative to its reference value: i. a biomarker present in the liquid phase selected from: acetyl-lysine; adrenochrome; y-aminobutryic acid (GABA); 2- aminophenol; D-biotin; caffeic acid; caffeine; cortisol-21 -acetate; p- coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4- phenylenediamine; 1 ,2-dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L- hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3- methyladenine; 4-methylcatechol; methyl-p-D-Galactoside; 2- methylmaleate; 6-methylmercaptopurine; methyl-N-a-methylbutyryl- glycine; 1-oleoyl-rac-glycerol; octanoic acid; paraxanthine; L-pipecolic acid; prolyl-threonine; quinic acid; rosmarinic acid; serotonin; tartaric acid; theobromine; theophylline; trigonelline; tryptamine; UBDM 4; Unknown metabolite 2; Unknown metabolite 3; Unknown metabolite 6; Unknown metabolite 8; Unknown metabolite 15; vanillactic acid; vanilloglycine; and/or ii. a volatile organic compound selected from: butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2- ylcyclohex-2-en-1-one); di-isobutyl cellosolve; 1 R,3S..1 ,3- dimethylcyclohexane; 1S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2,1-octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1. ,2-furan-2-ylcyclopropyl ethenone; guanidine; heptan-2-one; 2-methoxy-2-methylpropane; 1- methoxypropan-2-one; 2..4-methylcyclohexa-2,4-dien-1-yl-propan-2-ol; methyldisulfanylmethan; 3-methylheptan-2-one; 2-methylheptan-4-one; 5-methyl-2-propan-2-ylcyclohexan-1-ol-menthol; 5R..5-methyl-2-propan- 2-ylidenecyclohexan-1-one (Pulegone); methylsulfonylmethane; oxime.. methoxy-phenyl; pentane-2, 3-dione; 2-prop-1-en-2-ylpyrazine;
1 ,2,4,5 tetrazine; 1 ,3,3-trimethyl-2-oxabicyclo-2,2,2-octane-eucalyptol;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0012] The present invention further provides a method of analysing the variance data obtained by testing a urine or blood sample obtained from a patient for the presence and quantity of one or more of the biomarkers listed step (i) above; the method comprising the steps of:
(i) collecting the quantification data for the one or more biomarkers listed step (i) above;
(ii) comparing the quantity of the one or more biomarkers listed step (i) above with a reference value for the biomarker concerned to determine whether there is: a. an increase in one or more of the biomarkers listed in step (ii) a) above relative to its reference value; and/or b. a decrease in one or more of the following biomarkers listed in step (ii) b) relative to its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0013] The inventors surprisingly found that by analysing for an increase or decrease in one or more of the prognostic biomarkers present in a urine or blood sample from an individual (as outlined in step (i) of the method above), compiling the variance data for each biomarker detected relative to its reference value (step (ii) of the method above) and analysing the variance data in a prediction model (step (iii) of the method above), enabled them to determine the likelihood of an individual dying within a period of time of up to three months. The model can therefore provide an assessment of whether an individual is going to die within a time period of, for example, up to 3 months, up to 2 months, up to 1 month, up to 3 weeks, up to 2 weeks, up to 1 week, up to 6 days, up to 5 days, up to 4 days, up to 3 days, up to 48 hours, up to 12 hours. This insight gives a clinician a much greater insight into when an individual patient is going to die and this enables appropriate communication with the individual concerned, their family members, and assists with the provision of the appropriate palliative care regime for the individual.
[0014] This represents a major advance, especially in the ability to determine the likelihood that a patient will die within a period of two weeks or less.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Embodiments of the invention are further described hereinafter with reference to the accompanying drawings, in which:
Figure 1 shows a Kaplan Meier survival curve using the 30 day Cox lasso logistic regression model. It shows 3 groupings; High, Medium and Low risk of dying.
Figure S1-A through to S1-CW show ANOVA graphs for stated metabolites.
Figure S2 shows AUC for the 30 day Cox lasso regression model (Model 1). The black line shows the mean and dotted line the median. The dark grey shows the confidence interval and the light grey shows the minimum and maximum values.
Figure S3 show calibration curves for the 30 day Cox lasso regression model (Model 1) at days 10, 20 and 30.
Figure S4 shows Kaplan Meier survival curve of the 30 day Cox lasso regression model using a penalty parameter that gave a minimum error within one standard deviation (Model 2). It shows 3 grouping; High, Medium and Low risk of dying.
Figure S5 shows AUC for the 30 day Cox lasso regression model using a penalty parameter that gave a minimum error within one standard deviation (Model 2). The black line shows the mean and dotted line the median. The dark grey shows the confidence interval and the light grey shows the minimum and maximum values.
Figure S6 shows calibration curves for the 30 day Cox lasso regression model using a penalty parameter that gave a minimum error within one standard deviation at days 10, 20 and 30 (Model 2).
Figures 2 to 13 are referred to in Examples 2 to 4 below.
Figure 14 is referred to in Example 5 below. DETAILED DESCRIPTION
[0016] In the methods of the present invention, the “reference value” is the mean amount of a biomarker present in urine or blood samples obtained from a cohort of reference individuals that are not within the final three months of their respective lives (i.e. they are greater than three months away from death). The reference individuals could be normal healthy individuals or they may be patients suffering from a particular disease and/or condition (e.g. cancer). In the latter case where the reference individuals are patients suffering from a particular disease and/or condition (e.g. cancer), the reference individuals suitably have the same disease and/or condition as the individual providing a urine or blood sample for the prognostic method of the present invention.
[0017] References to “Cox proportional hazards model with lasso penalty” herein refers to the approach taken to derive a prediction model to analyse the variance data obtained for the selected urinary biomarkers in the example section set out herein. This model is described further in Wolfe RR, Regulation of skeletal muscle protein metabolism in catabolic states, Curr Opin Clin Nutr Metab Care. 2005;8(1):61-5 (reference (23) herein). The Cox proportional hazards model with lasso penalty approach is similar to the standard Cox model but shrinks parameter estimates towards zero, reducing over-fitting due to the large number of potential metabolites to consider as possible predictors of death. The procedure followed involved the following steps:
• Only one sample per patient was included.
• Administrative censoring was applied if the individual was still alive 100 days after their sample was supplied.
• A penalty parameter was imposed to determine the amount of smoothing chosen when 10-fold cross validation was performed.
• The value of lambda that gave minimum mean cross-validated error was used. This results in a simpler model with fewer metabolites included and reduces the risk of over fitting.
• The ‘fitting’ method was used to perform the validation.
• Model calibration was assessed with each bootstrap sample by comparing the observed and expected survival probabilities, splitting the predicted risks into 3 groups (denoted low/medium/high survival).
• Calibration was performed at 10, 20 and 30 days.
• Kaplan-Meier curves were used to visually inspect the survival probabilities based on 30 day predicted risk. Log-rank tests were used to statistically compare the survival curves.
• Analysis was performed in R Studio Version 1.4.1717 and used the packages “glmnet”, “survival”, and “hdnom” (21).
• The model was internally validated using bootstrap resampling methods with 1000 bootstrap samples and a lambda that gives an error that is one standard error away from the minimum error.
• The tuning parameter was fixed from the original cox lasso model fit to the whole dataset, and then for each bootstrap sample a Cox Lasso model was fit and timedependent area under curve was calculated (24).
[0018] Throughout the description and claims of this specification, the words “comprise” and “contain” and variations of them mean “including but not limited to”, and they are not intended to (and do not) exclude other moieties, additives, components, integers or steps. Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.
[0019] Features, integers, characteristics, compounds, chemical moieties or groups described in conjunction with a particular aspect, embodiment or example of the invention are to be understood to be applicable to any other aspect, embodiment or example described herein unless incompatible therewith. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. The invention is not restricted to the details of any foregoing embodiments. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.
[0020] The reader's attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.
The prognostic method of the present invention [0021] As indicated above, in one aspect, the present invention relates to a method of determining the likelihood that an individual is going to die within a time period of up to 3 months.
[0022] In step (i) of the methods defined herein, the method comprises either:
A. analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) a biomarker associated with increased bone loss, bone damage or bone resorption; e) a biomarker associated with increased mitochondrial dysfunction; f) a biomarker associated with altered one carbon metabolism; g) a biomarker associated with decreased RNA synthesis; h) a biomarker associated with decreased protein synthesis; i) a biomarker associated with oxidative stress; j) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) a biomarker associated with increased cell membrane breakdown; l) a biomarker associated with altered hormone production; m) a biomarker associated with altered amino acid metabolism; n) a biomarker associated with kynurenine pathway activation; o) a biomarker associated with decreased oral intake (food and drink); p) a biomarker associated with decreased DNA repair; q) one or more unknown metabolites selected from Unknown metabolites 1 to 15:
Metabolite name Mass
Unknown metabolite 1 194.04 Unknown metabolite 2 188.08
Unknown metabolite 3 129.08
Unknown metabolite 4 295.14
Unknown metabolite 5 188.05
Unknown metabolite 6 304.08
Unknown metabolite 7 274.11
Unknown metabolite 8 225.06
Unknown metabolite 9 179.08
Unknown metabolite 10 194.04
Unknown metabolite 11 117.08
Unknown metabolite 12 212.07
Unknown metabolite 13 122.04
Unknown metabolite 14 205.07
Unknown metabolite 15 195.05
B. analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism, a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis, and/or a biomarker associated with increased mitochondrial dysfunction, a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and b) Unknown metabolite 5:
Metabolite name Mass
Unknown metabolite 5 188.05 c) Unknown metabolite 7:
Metabolite name Mass
Unknown metabolite 7 274.11 C. analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased mitochondrial dysfunction; d) a biomarker associated with increased bone loss, bone damage or bone resorption, a biomarker associated with altered hormone production, and/or a biomarker associated with decreased oral intake (food and drink); and e) Unknown metabolite 5:
Metabolite name Mass
Unknown metabolite 5 188.05 f) Unknown metabolite 7:
Metabolite name Mass
Unknown metabolite 7 274.11
D. analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least one biomarker selected from each of the following categories: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) a biomarker associated with increased bone loss, bone damage or bone resorption; d) a biomarker associated with increased mitochondrial dysfunction; e) a biomarker associated with altered hormone production; f) a biomarker associated with decreased oral intake (food and drink); g) Unknown metabolites 5 and/or 7: Metabolite name Mass
Unknown metabolite 5 188.05
Unknown metabolite 7 274.11
E. analysing a urine and/or blood sample obtained from the individual to detect the presence and quantity of one or more of the following: a) a biomarker present in the liquid phase selected from:
4-acetamidobutanoate; N-acetylaspartate; N1-acetylspermine; adenine; adipic acid; allotetrahydrocortisol; 3-amino-4-hydroxybenzoic acid; 3-amino- isobutanoic acid; 3 carboxypropyl trimethylammonium; carnitine; carnosine; cortexolone; cortisol; creatine; cCMP; cGMP; cystathionine; deoxyadenosine;
5-deoxy-5-methylthio-adenosine; dihydrofolic acid; dihydrouracil; 2,4- dihydroxyacetophenone; dodecanoylcarnitine; epinephrine; ethylmalonic.acid; galactose-1-phosphate; galactose-6-phosphate; glucosamine-6-phosphate; glucose-1-phosphate; glucose-6-phosphate; glucuronic acid; guanosine; histidine; 3-hydroxybenzaldehyde; |3-hydroxybutyrate; 10-hydroxycapric acid; 12-hydroxydodecanoic acid; 3-hydroxy-3-methyl-glutaric.acid; 4- hydroxyphenyllactic acid (D-); 5-hydroxytryptophan; hypoxanthine; indolelactic acid; indole-3-methylacetate; isoleucine; kynurenic acid; malic acid; methioninesulfoximine; 3-methylglutaric acid; 3-methyloxindole (A-); nicotinamide adenine dinucleotide (NAD); nitrotyrosine; norleucine; oxoadipic acid; phenyl acetate; phenylalanine; phosphatidylcholine; pyrocatechol; resorcinol-monoacetate; sarcosine; suberic acid; sphingomyelin; tyrosine-methyl-ester-4-sulfate; UBDM 1 ; UBDM 2; UBDM 3; Unknown metabolite 1 ; Unknown metabolite 4; Unknown metabolite 5; Unknown metabolite 7; Unknown metabolite 10; Unknown metabolite 14; uridine monophosphate (UMP); vanillylmandelic acid; xanthine; xanthurenic acid; acetyl-lysine; adrenochrome; y-arninobutryic acid (GABA); 2-aminophenol; D-biotin; caffeic Acid; caffeine; cortisol-21 -acetate; p-coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4-phenylenediamine; 1,2- dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L-hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3-methyladenine; 4-methylcatechol; methyl-p- D-Galactoside; 2-methylmaleate; 6-methylmercaptopurine; methyl-N-a- methylbutyryl-glycine; 1-oleoyl-rac-glycerol; octanoic acid; paraxanthine; L- pipecolic acid; prolyl-threonine; quinic acid; rosmarinic acid; serotonin; tartaric acid; theobromine; theophylline; trigonelline; tryptamine; UBDM 4; Unknown metabolite 2; Unknown metabolite 3; Unknown metabolite 6; Unknown metabolite 8; Unknown metabolite 15; vanillactic acid; vanilloglycine; and/or b) a volatile organic compound selected from: acetic acid-1 R-2R or 2-methylcyclopentan-1-ol; acetoin; acetone; butan-2-one; 2-butoxyethoxy-ethanol; cyclohexanone; 2,6-dimethylaniline; 2,4- dimethylaniline; 1,2-dimethylcyclopentane; 3,4-dimethylhexan-2-one; 2,4- dimethyl-5H-1 ,3-oxazol-4-yl-methanol; 2,5-dimethyl-1 H-pyrrole; 2,6- dimethylpyrazine; 2-ethylhexane1-ol; 5-ethyl-5-methyloxolan-2-one; 3-ethyl-4- methylpyrrole-2, 5-dione; 5-ethyloxolan-2-one; 3-ethoxy-3-ethylpent-1-yne; heptan-3-one; heptan-4-one; E..hept-3-en-2-one or 3-Hepten-2-one; hexane- 3, 4-dione; E..hex-2-enyl.. acetate; 1-methoxypropan-2-ol; 4-methylpent-3-en-2- one; 4-methylpent-3-enoic-acid; noan-2-one or 5-methylhexan-2-one; E..non-3- en-2-one UP; Z..oct-2-enoic-acid; pentan-2-one; pent-3-en-2-one; phenol; propan-2-ol; propan-2-one; 1,2,4-triazole-3,4-diamine; 2,3,3- trimethylcyclobutan-1-one; unknown (1S,5R.,1,5-dimethyl-6,8-dioxabicyclo- 3,2,1 octane or E..hex-2-enyL acetate); butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2-ylcyclohex-2-en-1-one); di-isobutyl cellosolve;
1 R,3S..1 ,3-dimethylcyclohexane; 1 S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2, 1 - octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1 ,.2-furan-2-ylcyclopropyl ethenone; guanidine; heptan-2-one; 2-methoxy-2-methylpropane; 1- methoxypropan-2-one; 2..4-methylcyclohexa-2,4-dien-1-yl-propan-2-ol;
Methyldisulfanylmethan; 3-methylheptan-2-one; 2-methylheptan-4-one; 5- methyl-2-propan-2-ylcyclohexan-1-ol-menthol; 5R..5-methyl-2-propan-2- ylidenecyclohexan-1-one (Pulegone); methylsulfonylmethane; oxime..methoxyphenyl; pentane-2, 3-dione; 2-prop-1-en-2-ylpyrazine; 1,2, 4, 5 tetrazine; 1 ,3,3- trimethyl-2-oxabicyclo-2,2,2-octane-eucalyptol.
[0023] Steps (ii) to (iv) of the methods of the invention involve:
(ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0024] The method of the invention allows the approximate time to death for an individual/patient within the last three months of their life to be predicted. Importantly, the model allows for prediction of the approximate time to death within the last two weeks of life, which has hitherto been particularly hard to predict.
Step (I) of the method of the invention
[0025] Step (i) of the method outlined above involves analysing a urine or blood sample obtained from the individual concerned. The collected urine or blood sample can be analysed to identify the presence and quantity of the biomarkers listed above in step (i) of the process. The analysis may be conducted by any suitable technique known in the art that is capable if identifying and quantifying the prognostic biomarkers listed in step (i) of the method defined herein.
[0026] In a particular method of the present invention, the biomarkers are urinary biomarkers and sample tested is a sample of urine collected from the patient.
[0027] In a further particular method of the present invention, the biomarkers are blood biomarkers and sample tested is a sample of blood collected from the patient.
[0028] Suitably, p-hydroxybutyrate is detected in the blood and all other biomarkers listed can be detected from a urine sample and/or a blood sample.
[0029] Suitably, a biomarker present in the liquid phase of the urine or blood sample (i.e. any of those listed in step (i) a) of option E above) is analysed using a Liquid chromatography-mass spectrometry (LC-MS) technique. Such techniques are well known in the art and include Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry (LC-QTOF-MS), which have been previously described [16],
[0030] Certain biomarkers that have been identified are volatile organic compounds (i.e. any of those listed in step (i) b) of option E above) and these can also be analysed in the gaseous phase by, for example, gas chromatography mass spectrometry (GC-MS). [0031] Suitably, the biomarker is not a protein or peptide biomarker. Typically, the biomarker is a small molecule compound or metabolite that is capable of being excreted in the urine.
[0032] A total of 190 urinary biomarkers that were altered in the last stages of life have been identified. The model could work with just one biomarker selected from the options listed in step (i). Suitably, the method involves detecting at least three, at least four, at least five, at least six or at least seven of the biomarkers identified herein. Suitably, in embodiments of the invention, the urine or blood sample is analysed to detect 1 to 190 of the biomarkers listed in step (i) of option E the method of present invention (e.g. by LC- QTOF-MS for those in step (i) a) of option E and GC-MS for those in step (i) b) of Option E).
[0033] In a particular method of the invention, the urine or blood sample is analysed to detect the presence and quantity of 1 to 50 of the biomarkers recited in step (i), option E above. In further methods of the invention, the urine or blood sample is analysed to detect the presence and quantity of 1 to 45, 1 to 40, 1 to 35, 1 to 30, 1 to 25, 1 to 20, 1 to 15, 1 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers recited in step (i), option E above.
[0034] In further methods of the invention, the urine or blood sample is analysed to detect the presence and quantity of 2 to 45, 2 to 40, 2 to 35, 2 to 30, 2 to 25, 2 to 20, 2 to 15, 2 to 10, or 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers recited in step (i).
[0035] In further methods of the invention, the urine or blood sample is analysed to detect the presence and quantity of 4 to 45, 4 to 40, 4 to 35, 4 to 30, 4 to 25, 4 to 20, 4 to 15, 4 to 10, or 4, 5, 6, 7, 8, 9 or 10 of the biomarkers recited in step (i).
[0036] In further methods of the invention, the urine or blood sample is analysed to detect the presence and quantity of 6 to 45, 6 to 40, 6 to 35, 6 to 30, 6 to 25, 6 to 20, 6 to 15, 6 to 10, or 6, 7, 8, 9 or 10 of the biomarkers recited in step (i).
[0037] The biomarkers listed in step (i) of the process are, in most cases, able to be correlated with altered behaviour and/or specific biological pathways that are altered in the final stages of life.
[0038] In some embodiments of the method of the present invention, the biomarker(s) detected in step (i) of the method may include a biomarker associated with one or more, two or more, three or more, four or more, five or more or six or more of the following: a) altered cellular energy metabolism b) disrupted mitochondrial fatty acid p-oxidation; c) increased muscle loss, muscle damage or rhabdomylosis; d) increased bone loss, bone damage or resorption; e) increased mitochondrial dysfunction; f) altered one carbon metabolism; g) decreased protein synthesis; h) decreased RNA synthesis; i) oxidative stress; j) altered nucleoside (purine, pyrimidine) metabolism; k) increased cell membrane breakdown; l) altered hormone production; m) altered amino acid metabolism; n) kynurenine pathway activation; and/or o) decreased oral intake (food and drink). p) altered DNA repair
[0039] In some embodiments of the method of the present invention, the biomarker(s) detected in step (i) of the method may include one or more, two or more, three or more, four or more, five or more or six or more biomarkers selected from one or more of the following options: a) a biomarker associated altered cellular energy metabolism selected from NAD or creatine; b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation selected from dicarboxylic acids (e.g. acetone, adipic acid, suberic acid, oxadipic acid, or ethylmalonic.acid), p-hydroxybutyrate, acetone, dodecanoylcarnitine or 12-hydroxy dodecanoic acid; c) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from creatine, sarcosine, carnitine, carnosine, 3- amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, nitrotyrosine, creatinine; d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from UBDM1, UBDM2, UBDM3 and UBDM4; e) a biomarker associated with increased mitochondrial dysfunction selected from carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; f) a biomarker associated with altered one carbon metabolism selected from dihydrofolate, sarcosine, and cystathionine and the lack of glycine or dTMP; [Altered one carbon metabolism has known consequences for purine biosynthesis including resultant decreased DNA synthesis and also the decreased production of sulfhydryl-containing reducing agents, impacting enzyme inhibition, enzyme reactivation or protection and labelling.] g) a biomarker associated with decreased RNA synthesis selected from uridine monophosphate, guanosine and increased purine degradation products xanthine and hypoxanthine; with known consequences on decreased protein synthesis; h) a biomarker associated with decreased protein synthesis selected from essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine) and any biomarker associated with decreased RNA synthesis (see point (g)); i) a biomarker associated with oxidative stress for example nitrotyrosine; j) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism selected from adenine, guanosine, uridine monophosphate, galactose monophosphate, 5-deoxy, 5-methyl, thioadenosine, dihydrofolic acid, hypoxanthine, xanthine, 3-methyl deoxyadenosine, D-galactose-1-phosphate; k) a biomarker associated with increased cell membrane breakdown selected from various cholines (e.g. 1,2-dipalmitoyl-rac-glycreo-3- phosphoethanolamine, L-phosphatidycholine); l) a biomarker associated with altered hormone production selected from epinephrine (epinephrine, adrenochrome, vanillylmandelic.acid (VMA), pyrocatechol), GABA (GABA, acetamidobutanoate), cortisol (cortisol, allotetrahydrocortisol, cortexolone, cortisol-21 acetate), histamine (histidine), hydroxytryptophan, dopamine (dihydroxyphenylacetic acid, L- dopa) and serotonin; m) a biomarker associated with altered amino acid metabolism selected from alanine (eg dihydrouracil), cysteine (eg L-cystathionine) histidine, isoleucine, phenylalanine (eg phenylalanine, phenyl acetate), tryptophan (eg hydroxytryptophan, , kynurenic acid, oxoadipic acid) or tyrosine (eg tyrosine, 4.Hydroxyphenyllactic.acid, tyrosine methylene ketone, L-tyrosine- methyl-ester-4-sulfate); n) a biomarker associated with kynurenine pathway activation selected from 3- Hydroxyanthranilic acid or Kynurenic acid; o) a biomarker associated with decreased oral intake (food and drink) selected from caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid; p) a biomarker associated with decreased DNA repair e.g. Hypoxia Inducible Factor 1 (HIF1), phosphorylated and unphosphorylated ATR, phosphorylated and unphosphorylated FANCG.
[0040] In a further embodiments of the method of the present invention, the at least one biomarkers detected in step (i) of the method may include one or more biomarkers selected from one or more of the following: a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from creatine, sarcosine, carnitine, carnosine, 3- amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3- Phosphoethanolamine). b) a biomarker associated with increased mitochondrial dysfunction selected from carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) a biomarker associated with altered hormone production selected from epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from UBDM1, UBDM2, UBDM3 and UBDM4. e) a biomarker associated with decreased oral intake (food and drink) selected from caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
[0041] The method of the present invention also includes the possibility of detecting up to 15 compounds of unknown identity (although they are characterised by their molecular mass and retention times as shown below):
Table of molecular mass and Retention Time (RT) for the unknown compounds.
Metabolite name Mass Retention
Time
Unknown metabolite 1 194.0426 1.3
Unknown metabolite 2 188.079 1.74 Unknown metabolite 3 129.0801 1.72
Unknown metabolite 4 295.142 4.31
Unknown metabolite 5 188.0472 6.77
Unknown metabolite 6 304.0794 4.28
Unknown metabolite 7 274.1148 3.48
Unknown metabolite 8 225.0638 4.77
Unknown metabolite 9 179.0787 1.22
Unknown metabolite 10 194.0425 1.29
Unknown metabolite 11 117.0793 1.64
Unknown metabolite 12 212.068 5.22
Unknown metabolite 13 122.0368 5.95
Unknown metabolite 14 205.0738 6.77
Unknown metabolite 15 195.0538 4.43
[0042] Thus, in addition, the present invention also includes the optional detection of one or more of these unknown compounds.
[0043] In an example of the method of the invention, step (i) of the method involves detecting a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine). b) a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4. e) a biomarker associated with decreased oral intake (food and drink) selected from one or more caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid. f) Unknown compound 5; and/or g) Unknown compound 7.
[0044] In a particular embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with disrupted mitochondrial fatty acid p- oxidation; c) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) one biomarker associated with increased bone loss, bone damage or bone resorption; e) one biomarker associated with increased mitochondrial dysfunction; f) one biomarker associated with altered one carbon metabolism; g) one biomarker associated with decreased RNA synthesis; h) one biomarker associated with decreased protein synthesis; i) one biomarker associated with oxidative stress; j) one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) one biomarker associated with increased cell membrane breakdown; l) one biomarker associated with altered hormone production; m) one biomarker associated with altered amino acid metabolism; n) one biomarker associated with kynurenine pathway activation; o) one biomarker associated with decreased oral intake (food and drink); p) one biomarker associated with decreased DNA repair; q) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
[0045] In a further embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least the following biomarkers: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with altered hormone production; e) one biomarker associated with decreased oral intake (food and drink); f) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
[0046] In a further embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with disrupted mitochondrial fatty acid p- oxidation; c) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) one biomarker associated with increased bone loss, bone damage or bone resorption; e) one biomarker associated with increased mitochondrial dysfunction; f) one biomarker associated with oxidative stress; g) one biomarker associated with altered hormone production; h) one biomarker associated with decreased oral intake (food and drink); i) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
[0047] In a further embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with increased mitochondrial dysfunction; e) one biomarker associated with altered hormone production; f) one biomarker associated with decreased oral intake (food and drink); g) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
[0048] In a further embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; b) one biomarker associated with increased bone loss, bone damage or bone resorption; c) one biomarker associated with altered hormone production; d) one biomarker associated with decreased oral intake (food and drink); e) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
[0049] In a further embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of:
(i) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with decreased RNA synthesis; f. a biomarker associated with decreased protein synthesis; g. a biomarker associated with oxidative stress; h. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; i. a biomarker associated with increased cell membrane breakdown; j. a biomarker associated with altered hormone production; k. a biomarker associated with altered amino acid metabolism; l. a biomarker associated with kynurenine pathway activation; m. a biomarker associated with decreased oral intake (food and drink); n. a biomarker associated with decreased DNA repair; o. one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
(ii) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with oxidative stress; f. a biomarker associated with increased cell membrane breakdown; g. a biomarker associated with altered hormone production; h. a biomarker associated with altered amino acid metabolism; i. a biomarker associated with kynurenine pathway activation; j. a biomarker associated with decreased oral intake (food and drink); k. one or more of unknown metabolites selected from Unknown metabolites 1 to
15 disclosed herein; or
(iii) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with oxidative stress; f. a biomarker associated with altered hormone production; g. a biomarker associated with decreased oral intake (food and drink); h. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
(iv) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with increased bone loss, bone damage or bone resorption; b. a biomarker associated with altered hormone production; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with decreased oral intake (food and drink); e. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
(v) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with decreased RNA synthesis; e. a biomarker associated with decreased protein synthesis; f. a biomarker associated with oxidative stress; g. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; h. a biomarker associated with increased cell membrane breakdown; i. a biomarker associated with altered hormone production; j. a biomarker associated with altered amino acid metabolism; k. a biomarker associated with kynurenine pathway activation; l. a biomarker associated with decreased oral intake (food and drink); m. a biomarker associated with decreased DNA repair; n. one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein; or
(vi) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with oxidative stress; e. a biomarker associated with increased cell membrane breakdown; f. a biomarker associated with altered hormone production; g. a biomarker associated with altered amino acid metabolism; h. a biomarker associated with kynurenine pathway activation; i. a biomarker associated with decreased oral intake (food and drink); j. one or more of unknown metabolites selected from Unknown metabolites 1 to
15 disclosed herein; or
(vii) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with oxidative stress; e. a biomarker associated with altered hormone production; f. a biomarker associated with decreased oral intake (food and drink); g. one or more of unknown metabolites selected from Unknown metabolites 1 to
15 disclosed herein; or (viii) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with increased bone loss, bone damage or bone resorption; b. a biomarker associated with altered hormone production; c. a biomarker associated with decreased oral intake (food and drink); d. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
[0050] In a further embodiment of the invention, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of:
(i) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated altered cellular energy metabolism; b. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. a biomarker associated with increased bone loss, bone damage or bone resorption; e. a biomarker associated with increased mitochondrial dysfunction; f. a biomarker associated with altered one carbon metabolism; g. a biomarker associated with decreased RNA synthesis; h. a biomarker associated with decreased protein synthesis; i. a biomarker associated with oxidative stress; j. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k. a biomarker associated with increased cell membrane breakdown; l. a biomarker associated with altered hormone production; m. a biomarker associated with altered amino acid metabolism; n. a biomarker associated with kynurenine pathway activation; o. a biomarker associated with decreased oral intake (food and drink); p. a biomarker associated with decreased DNA repair; q. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(ii) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated altered cellular energy metabolism; b. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. one biomarker associated with increased bone loss, bone damage or bone resorption; e. one biomarker associated with increased mitochondrial dysfunction; f. one biomarker associated with altered one carbon metabolism; g. one biomarker associated with decreased RNA synthesis; h. one biomarker associated with decreased protein synthesis; i. one biomarker associated with oxidative stress; j. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k. one biomarker associated with increased cell membrane breakdown; l. one biomarker associated with altered hormone production; m. one biomarker associated with altered amino acid metabolism; n. one biomarker associated with kynurenine pathway activation; o. one biomarker associated with decreased oral intake (food and drink); p. one biomarker associated with decreased DNA repair; q. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(iii) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated altered cellular energy metabolism; b. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. one biomarker associated with increased bone loss, bone damage or bone resorption; e. one biomarker associated with increased mitochondrial dysfunction; f. one biomarker associated with altered hormone production; g. one biomarker associated with decreased oral intake (food and drink); h. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(iv) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated altered cellular energy metabolism; b. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c. one biomarker associated with increased bone loss, bone damage or bone resorption; d. one biomarker associated with increased mitochondrial dysfunction; e. one biomarker associated with altered hormone production; f. one biomarker associated with decreased oral intake (food and drink); g. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(v) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(vi) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered one carbon metabolism; e. one biomarker associated with decreased RNA synthesis; f. one biomarker associated with decreased protein synthesis; g. one biomarker associated with oxidative stress; h. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; i. one biomarker associated with increased cell membrane breakdown; j. one biomarker associated with altered hormone production; k. one biomarker associated with altered amino acid metabolism; l. one biomarker associated with kynurenine pathway activation; m. one biomarker associated with decreased oral intake (food and drink); n. one biomarker associated with decreased DNA repair; o. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(vii) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered one carbon metabolism; e. one biomarker associated with oxidative stress; f. one biomarker associated with increased cell membrane breakdown; g. one biomarker associated with altered hormone production; h. one biomarker associated with decreased oral intake (food and drink); i. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(viii) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered one carbon metabolism; e. one biomarker associated with altered hormone production; f. one biomarker associated with decreased oral intake (food and drink); g. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(ix) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(x) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with increased bone loss, bone damage or bone resorption; b. one biomarker associated with altered hormone production; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with decreased oral intake (food and drink); e. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(xi) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with decreased RNA synthesis; e. one biomarker associated with decreased protein synthesis; f. one biomarker associated with oxidative stress; g. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; h. one biomarker associated with increased cell membrane breakdown; i. one biomarker associated with altered hormone production; j. one biomarker associated with altered amino acid metabolism; k. one biomarker associated with kynurenine pathway activation; l. one biomarker associated with decreased oral intake (food and drink); m. one biomarker associated with decreased DNA repair; n. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(xii) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with oxidative stress; e. one biomarker associated with increased cell membrane breakdown; f. one biomarker associated with altered hormone production; g. one biomarker associated with decreased oral intake (food and drink); h. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(xiii) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(xiv) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered hormone production; d. one biomarker associated with decreased oral intake (food and drink); e. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; or
(xv) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with increased bone loss, bone damage or bone resorption; b. one biomarker associated with altered hormone production; c. one biomarker associated with decreased oral intake (food and drink); d. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein.
[0051] In a further embodiment of the invention, step (i) of the method involves detecting: a) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine); b) one biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) one biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol; d) one biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4; e) one biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid; f) Unknown compound 5; and/or g) Unknown compound 7.
[0052] In a particular example of the method of the invention, step (i) of the method involves detecting a) creatine; b) carnitine; c) pyrocatechol; d) gluconic acid; e) UBRM 4; f) Unknown compound 5; and/or g) Unknown compound 7.
[0053] In step (ii) of the process, the quantity of the biomarker identified in step (i) of the method is correlated with a reference value in order to determine whether there is any variance (i.e. an increase or decrease) between the quality of the biomarker and the reference value. As noted above, the "reference value” is the mean amount of a biomarker present in urine or blood samples obtained from a cohort of reference individuals that are not within the final three months of their respective lives (i.e. they are greater than three months away from death).
[0054] Step (ii) of option E above, the method comprises comparing the amount of the one or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is an increase for those markers listed in step (ii) a) of option E above or a decrease for those biomarkers listed in step (ii) b) of option E above. For each biomarker that is detected in step (i), the quantity of the biomarker present in the urine or blood sample is compared with the reference value for that biomarker and the data is analysed to see if there is an increase in the amount of any of the biomarkers recited in list a of step (ii) or a decrease in any of the biomarkers in list b of step (ii). The inventors have found that the variance data for an increase in the level of one or more of the biomarkers in list a of step (ii), or a reduction in the level of any of the biomarkers listed in list b, can be used in the subsequent steps of the process to determine the likelihood that an individual in the final stages of life will die within a time period of up to three months.
Steps (Hi) and (iv) of the method of the invention
[0055] Step (iii) of the method involves compiling the variance data obtained for the one or more biomarkers relative to their respective reference value(s) and step (iv) involves analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0056] Suitably, the variance data is analysed using a prediction model which is a Cox proportional hazards model with lasso penalty as defined herein, and as described in the accompanying Example Section.
[0057] After analysing the data in the prediction model, it is possible to predict the time frame within which the individual is likely to die, e.g, greater than three months, up to 3 months, up to 2 months, up to 1 month, up to 3 weeks, up to 2 weeks, up to 1 week, up to 6 days, up to 5 days, up to 4 days, up to 3 days, up to 48 hours, up to 12 hours, or 1 to 3 days, 3 to 7 days, 7 to 14 days, 14 to 28 days or over 28 days. [0058] Based on the data presented in the accompany example section, the method of the present invention may be applied to any individual or patient in the final stages of life. In a particular embodiment of the invention, the individual is cancer patient (e.g. a patient suffering with lung cancer or mesothelioma.
[0059] The methodology of the present invention may also further comprise a step of implement the appropriate palliative care for the individual based on the prediction from the method.
The method of analysing the variance data
[0060] The present invention further provides a method of analysing the variance data obtained by testing a urine or blood sample obtained from a patient for the presence and quantity of one or more of the biomarkers listed step (i) above; the method comprising the steps of:
(i) collecting the quantification data for the one or more biomarkers listed step (i) above;
(ii) comparing the quantity of the one or more biomarkers listed step (i) above with a reference value for the biomarker concerned to determine whether there is: a. an increase in one or more of the biomarkers listed in step (ii) a) above relative to its reference value; and/or b. a decrease in one or more of the following biomarkers listed in step (ii) b) relative to its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
[0061] The details provided above for steps (i) to (iv) apply equally to this aspect of the invention.
Numbered Clauses
Clause 1. A method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising: (i) analysing a urine and/or blood sample obtained from the individual to detect the presence and quantity of one or more of the following: a) a biomarker present in the liquid phase selected from:
4-acetamidobutanoate; N-acetylaspartate; N1-acetylspermine; adenine; adipic acid; allotetrahydrocortisol; 3-amino-4-hydroxybenzoic acid; 3-amino- isobutanoic acid; 3 carboxypropyl trimethylammonium; carnitine; carnosine; cortexolone; cortisol; creatine; cCMP; cGMP; cystathionine; deoxyadenosine;
5-deoxy-5-methylthio-adenosine; dihydrofolic acid; dihydrouracil; 2,4- dihydroxyacetophenone; dodecanoylcarnitine; epinephrine; ethylmalonic.acid; galactose-1-phosphate; galactose-6-phosphate; glucosamine-6-phosphate; glucose-1-phosphate; glucose-6-phosphate; glucuronic acid; guanosine; histidine; 3-hydroxybenzaldehyde; |3-hydroxybutyrate; 10-hydroxycapric acid; 12-hydroxydodecanoic acid; 3-hydroxy-3-methyl-glutaric.acid; 4- hydroxyphenyllactic acid (D-); 5-hydroxytryptophan; hypoxanthine; indolelactic acid; indole-3-methylacetate; isoleucine; kynurenic acid; malic acid; methioninesulfoximine; 3-methylglutaric acid; 3-methyloxindole (A-); nicotinamide adenine dinucleotide (NAD); nitrotyrosine; norleucine; oxoadipic acid; phenyl acetate; phenylalanine; phosphatidylcholine; pyrocatechol; resorcinol-monoacetate; sarcosine; suberic acid; sphingomyelin; tyrosine-methyl-ester-4-sulfate; UBDM 1 ; UBDM 2; UBDM 3; Unknown metabolite 1; Unknown metabolite 4; Unknown metabolite 5; Unknown metabolite 7; Unknown metabolite 10; Unknown metabolite 14; uridine monophosphate (UMP); vanillylmandelic acid; xanthine; xanthurenic acid; acetyl-lysine; adrenochrome; y-arninobutryic acid (GABA); 2-aminophenol; D-biotin; caffeic Acid; caffeine; cortisol-21 -acetate; p-coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4-phenylenediamine; 1,2- dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L-hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3-methyladenine; 4-methylcatechol; methyl-p- D-Galactoside; 2-methylmaleate; 6-methylmercaptopurine; methyl-N-a- methylbutyryl-glycine; 1-oleoyl-rac-glycerol; octanoic acid; paraxanthine; L- pipecolic acid; prolyl-threonine; quinic acid; rosmarinic acid; serotonin; tartaric acid; theobromine; theophylline; trigonelline; tryptamine; UBDM 4; Unknown metabolite 2; Unknown metabolite 3; Unknown metabolite 6; Unknown metabolite 8; Unknown metabolite 15; vanillactic acid; vanilloglycine; and/or b) a volatile organic compound selected from: acetic acid-1 R-2R or 2-methylcyclopentan-1-ol; acetoin; acetone; butan-2-one; 2-butoxyethoxy-ethanol; cyclohexanone; 2,6-dimethylaniline; 2,4- dimethylaniline; 1,2-dimethylcyclopentane; 3,4-dimethylhexan-2-one; 2,4- dimethyl-5H-1 ,3-oxazol-4-yl-methanol; 2,5-dimethyl-1 H-pyrrole; 2,6- dimethylpyrazine; 2-ethylhexane1-ol; 5-ethyl-5-methyloxolan-2-one; 3-ethyl-4- methylpyrrole-2, 5-dione; 5-ethyloxolan-2-one; 3-ethoxy-3-ethylpent-1-yne; heptan-3-one; heptan-4-one; E..hept-3-en-2-one or 3-Hepten-2-one; hexane- 3, 4-dione; E..hex-2-enyl . acetate; 1-methoxypropan-2-ol; 4-methylpent-3-en-2- one; 4-methylpent-3-enoic-acid; noan-2-one or 5-methylhexan-2-one; E..non-3- en-2-one UP; Z..oct-2-enoic-acid; pentan-2-one; pent-3-en-2-one; phenol; propan-2-ol; propan-2-one; 1,2,4-triazole-3,4-diamine; 2,3,3- trimethylcyclobutan-1-one; unknown (1S,5R..1 ,5-dimethyl-6,8-dioxabicyclo- 3,2,1 octane or E..hex-2-enyl . acetate); butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2-ylcyclohex-2-en-1-one); di-isobutyl cellosolve; 1 R,3S..1 ,3-dimethylcyclohexane; 1 S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2, 1 - octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1 ,.2-furan-2-ylcyclopropyl ethenone; guanidine; heptan-2-one; 2-methoxy-2-methylpropane; 1- methoxypropan-2-one; 2..4-methylcyclohexa-2,4-dien-1-yl-propan-2-ol;
Methyldisulfanylmethan; 3-methylheptan-2-one; 2-methylheptan-4-one; 5- methyl-2-propan-2-ylcyclohexan-1-ol-menthol; 5R..5-methyl-2-propan-2- ylidenecyclohexan-1-one (Pulegone); methylsulfonylmethane; oxime..methoxyphenyl; pentane-2, 3-dione; 2-prop-1-en-2-ylpyrazine; 1,2, 4, 5 tetrazine; 1 ,3,3- trimethyl-2-oxabicyclo-2,2,2-octane-eucalyptol;
(ii) comparing the quantity of the one or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is: a) an increase in one or more of the following biomarkers relative to its reference value: i. a biomarker present in the liquid phase selected from: 4-acetamidobutanoate; N-acetylaspartate; N1-acetylspermine; adenine; adipic acid; allotetrahydrocortisol; 3-amino-4-hydroxybenzoic acid; 3- amino-isobutanoic acid; 3 carboxypropyl trimethylammonium; carnitine; carnosine; cortexolone; cortisol; creatine; cCMP; cGMP; cystathionine; deoxyadenosine; 5-deoxy-5-methylthio-adenosine; dihydrofolic acid; dihydrouracil; 2,4-dihydroxyacetophenone; dodecanoylcarnitine; epinephrine; ethylmalonic.acid; galactose-1 -phosphate; galactose-6- phosphate; glucosamine-6-phosphate; glucose- 1 -phosphate; glucose-6- phosphate; glucuronic acid; guanosine; histidine; 3- hydroxybenzaldehyde; [3- hydroxy butyrate; 10-hydroxycapric acid; 12- hydroxydodecanoic acid; 3-hydroxy-3-methyl-glutaric.acid; 4- hydroxyphenyllactic acid (D-); 5-hydroxytryptophan; hypoxanthine; indolelactic acid; indole-3-methylacetate; isoleucine; kynurenic acid; malic acid; methionine-sulfoximine; 3-methylglutaric acid; 3- methyloxindole (A-); nicotinamide adenine dinucleotide (NAD); nitrotyrosine; norleucine; oxoadipic acid; phenyl acetate; phenylalanine; phosphatidylcholine; pyrocatechol; resorcinol-monoacetate; sarcosine; suberic acid; sphingomyelin; tyrosine-methyl-ester-4-sulfate; UBDM 1; UBDM 2; UBDM 3; Unknown metabolite 1 ; Unknown metabolite 4; Unknown metabolite 5; Unknown metabolite 7; Unknown metabolite 10; Unknown metabolite 14; uridine monophosphate (UMP); vanillylmandelic acid; xanthine; xanthurenic acid; and/or ii. a volatile organic compound selected from: acetic acid-1 R-2R or 2-methylcyclopentan-1-ol; acetoin; acetone; butan-
2-one; 2-butoxyethoxy-ethanoi; cyclohexanone; 2,6-dimethyianiline; 2,4- dimethylaniline; 1 ,2-dimethylcyclopentane; 3,4-dimethylhexan-2- one; 2,4-dimethyl-5H-1 ,3-oxazol-4-yl-methanol; 2,5-dimethyl-1 H- pyrrole; 2,6-dimethylpyrazine; 2-ethylhexane1-ol; 5-ethyl-5- methyloxolan-2-one; 3-ethyl-4-methylpyrrole-2, 5-dione; 5-ethyloxolan-2- one; 3-ethoxy-3-ethylpent-1-yne; heptan-3-one; heptan-4-one; E..hept-
3-en-2-one or 3-Hepten-2-one; hexane-3, 4-dione; E..hex-2- eny I.. acetate; 1-methoxypropan-2-ol; 4-methylpent-3-en-2-one; 4- methylpent-3-enoic-acid; noan-2-one or 5-methylhexan-2-one; E..non-3- en-2-one UP; Z..oct-2-enoic-acid; pentan-2-one; pent-3-en-2-one; phenol; propan-2-ol; propan-2-one; 1 ,2,4-triazole-3,4-diamine; 2,3,3- trimethylcyclobutan-1-one; unknown (1S,5R..1,5-dimethyl-6,8- dioxabicyclo-3,2,1 octane or E..hex-2-enyl..acetate); and/or b) a decrease in one or more of the following biomarkers relative to its reference value: i. a biomarker present in the liquid phase selected from: acetyl-lysine; adrenochrome; y-aminobutryic acid (GABA); 2- aminophenol; D-biotin; caffeic acid; caffeine; cortisol-21 -acetate; p- coumaric acid; creatinine; diethanolamine; dihydroxyacetone phosphate; dihydroxyphenylacetic acid; N.N.dimethyl-1 ,4- phenylenediamine; 1 ,2-dipalmitoyl-rac-glycero-3-phosphoethanolamine; L-dopa; dopamine; ferulic acid; gluconic acid; glutamine; L-glycyl-L- hydroxyproline; 3-hydroxyanthranilic acid; 4-hydroxyphenyllactic acid; 3- methyladenine; 4-methylcatechol; methyl-p-D-Galactoside; 2- methylmaleate; 6-methylmercaptopurine; methyl-N-a-methylbutyryl- glycine; 1-oleoyl-rac-glycerol; octanoic acid; paraxanthine; L-pipecolic acid; prolyl-threonine; quinic acid; rosmarinic acid; serotonin; tartaric acid; theobromine; theophylline; trigonelline; tryptamine; UBDM 4; Unknown metabolite 2; Unknown metabolite 3; Unknown metabolite 6; Unknown metabolite 8; Unknown metabolite 15; vanillactic acid; vanilloglycine; and/or ii. a volatile organic compound selected from: butane-2,3dione; butanoic acid; carvone (2 methyl-5-prop-1-en-2- ylcyclohex-2-en-1-one); di-isobutyl cellosolve; 1 R,3S..1 ,3- dimethylcyclohexane; 1S..1 ,5-dimethyl-6,8-dioxabicyclo-3,2,1-octane; 3S..3,7-dimethylocta-1 ,6-diene; 5..3,3-dimethyloxiran-2-yl..3- methylpent-1-en-3-ol; 3-ethylcyclopentan-1-one; 1-ethylpyrrolidine-2,5- dione; ethyl-2-hydroxy-2-methylpropanoate; 1. ,2-furan-2-ylcyclopropyl ethenone; guanidine; heptan-2-one; 2-methoxy-2-methylpropane; 1- methoxypropan-2-one; 2..4-methylcyclohexa-2,4-dien-1-yl-propan-2-ol; methyldisulfanylmethan; 3-methylheptan-2-one; 2-methylheptan-4-one; 5-methyl-2-propan-2-ylcyclohexan-1-ol-menthol; 5R..5-methyl-2-propan- 2-ylidenecyclohexan-1-one (Pulegone); methylsulfonylmethane; oxime.. methoxy-phenyl; pentane-2, 3-dione; 2-prop-1-en-2-ylpyrazine; 1 ,2,4,5 tetrazine; 1 ,3,3-trimethyl-2-oxabicyclo-2,2,2-octane-eucalyptol;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
Clause 2. A method of analysing the variance data obtained by testing a urine or blood sample obtained from a patient for the presence and quantity of one or more of the biomarkers listed step (i) of clause 1 ; the method comprising the steps of:
(i) collecting the quantification data for the one or more biomarkers listed step (i) of clause 1; (ii) comparing the quantity of the one or more biomarkers listed step (i) of clause 1 with a reference value for the biomarker concerned to determine whether there is: a. an increase in one or more of the biomarkers listed in step (ii) a) of clause 1 relative to its reference value; and/or b. a decrease in one or more of the following biomarkers listed in step (ii) b) of clause 1 relative to its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
Clause 3. A method according to clause 1 or clause 2, wherein, in step (i) of the method, the urine or blood sample is analysed using Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry LC-QTOF-MS for biomarkers in step (i) a) of clause 1 or Gas Chromatography-Mass Spectrometry (GC-MS) for biomarkers in step (i) b) of clause
1.
Clause 4. A method according to any one of preceding clauses, wherein, in step (i), the method involves detecting 1 to 75, 1 to 50, 1 to 45, 1 to 40, 1 to 35, 1 to 30, 1 to 25, 1 to 20, 1 to 15, 1 to 10, 2 to 75, 2 to 50, 2 to 45, 2 to 40, 2 to 35, 2 to 30, 2 to 25, 2 to 20, 2 to 15, 2 to 10, 4 to 75, 4 to 50, 4 to 45, 4 to 40, 4 to 35, 4 to 30, 4 to 25, 4 to 20, 4 to 15, 4 to 10, 6 to 75, 6 to 50, 6 to 45, 6 to 40, 6 to 35, 6 to 30, 6 to 25, 6 to 20, 6 to 15, 6 to 10,1,
2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers.
Clause 5. A method according to any one of preceding clauses, wherein the biomarker(s) detected in step (i) of the method may include a biomarker associated with one or more, two or more, three or more, four or more, five or more or six or more of the following: a) altered cellular energy metabolism b) disrupted mitochondrial fatty acid p-oxidation; c) increased muscle loss, muscle damage or rhabdomylosis; d) increased bone loss, bone damage or resorption; e) increased mitochondrial dysfunction; f) altered one carbon metabolism; g) decreased protein synthesis; h) decreased RNA synthesis; i) oxidative stress; j) altered nucleoside (purine, pyrimidine) metabolism; k) increased cell membrane breakdown; l) altered hormone production; m) altered amino acid metabolism; n) kynurenine pathway activation; and/or o) decreased oral intake (food and drink). p) altered DNA repair
Clause 6. A method according to any one of preceding clauses, wherein the biomarker(s) detected in step (i) of the method may include one or more two or more, three or more, four or more, five or more or six or more biomarkers selected from one or more of the following options: a) a biomarker associated altered cellular energy metabolism selected from one or more of NAD, creatine, creatine b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation selected from one or more of dicarboxylic acids (e.g. acetone, adipic acid, suberic acid, oxadipic acid, or ethylmalonic.acid), p-hydroxybutyrate, acetone, dodecanoylcarnitine or 12-hydroxy dodecanoic acid. c) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, nitrotyrosine, creatinine. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1, UBDM2, UBDM3 and UBDM4. e) a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl- glutaric.acid; f) a biomarker associated with altered one carbon metabolism selected from one or more of dihydrofolate, sarcosine, and cystathionine and the lack of glycine or dTMP. Altered one carbon metabolism has known consequences for purine biosynthesis including resultant decreased DNA synthesis and also the decreased production of sulfhydryl-containing reducing agents, impacting enzyme inhibition, enzyme reactivation or protection and labelling. g) a biomarker associated with decreased RNA synthesis selected from one or more of uridine monophosphate, guanosine and increased purine degradation products xanthine and hypoxanthine; with known consequences on decreased protein synthesis. h) a biomarker associated with decreased protein synthesis selected from one or more of essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine) and any biomarker associated with decreased RNA synthesis (see point (g)). i) a biomarker associated with oxidative stress for example nitrotyrosine; j) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism selected from one or more of adenine, guanosine, uridine monophosphate, galactose monophosphate, 5-deoxy, 5-methyl, thioadenosine, dihydrofolic acid, hypoxanthine, xanthine, 3-methyl deoxyadenosine, D-galactose-1-phosphate; k) a biomarker associated with increased cell membrane breakdown selected from one or more of various cholines (e.g. 1 ,2-dipalmitoyl-rac-glycreo-3- phosphoethanolamine, L-phosphatidycholine); l) a biomarker associated with altered hormone production selected from one or more of epinephrine (epinephrine, adrenochrome, vanillylmandelic.acid (VMA), pyrocatechol), GABA (GABA, acetamidobutanoate), cortisol (cortisol, allotetrahydrocortisol, cortexolone, cortisol-21 acetate), histamine (histidine), hydroxytryptophan, dopamine (dihydroxyphenylacetic acid, L- dopa) and serotonin; m) a biomarker associated with altered amino acid metabolism selected from one or more of alanine (eg dihydrouracil), cysteine (eg L- cystathionine) histidine, isoleucine, phenylalanine (eg phenylalanine, phenyl acetate), tryptophan (eg hydroxytryptophan, , kynurenic acid, oxoadipic acid) or tyrosine (eg tyrosine, 4.Hydroxyphenyllactic.acid, tyrosine methylene ketone, L-tyrosine-methyl-ester-4-sulfate). q) a biomarker associated with kynurenine pathway activation selected from one or more of 3-Hydroxyanthranilic acid or Kynurenic acid; r) a biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid. s) a biomarker associated with decreased DNA repair eg Hypoxia Inducible Factor 1 (HIF1), phosphorylated and unphosphorylated ATR, phosphorylated and unphosphorylated FANCG. Clause 7. A method according to any one of preceding clauses, wherein the biomarker(s) detected in step (i) of the method may include one or more biomarkers selected from one or more of the following: a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3- Phosphoethanolamine). b) a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl- glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1, UBDM2, UBDM3 and UBDM4. e) a biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
Clause 8. A method according to any one of clauses 5 to 7, wherein the method of the present invention also includes detecting the presence of one or more of the following compounds:
Metabolite name Mass RT
Unknown metabo 94.042 1.3
Unknown metabo 188.079 1.74
Unknown metabo 29.080 1.72
Unknown metabo 295.142 4.31
Unknown metabo 88.047 6.77
Unknown metabo 04.079 4.28
Unknown metabo 74.114 3.48
Unknown metabo 25.063 4.77
Clause 9. A method according to any one of preceding clauses, wherein step (i) of the method involves detecting a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine). b) a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol. d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4. e) a biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid. f) Unknown compound 5; and/or g) Unknown compound 7.
Clause 10. A method according to any one of preceding clauses, wherein step (i) of the method involves detecting a) Creatine; b) carnitine; c) pyrocatechol; d) gluconic acid; e) UBRM 4; f) Unknown compound 5; and/or g) Unknown compound 7.
Clause 11. A method according to any one of preceding clauses, wherein levels of the selected biomarkers in step (i) are compared with their respective reference values in step (ii) of the process, wherein the reference value is a mean level for the biomarker concerned that is generated from analysing urine or blood samples obtained from a cohort of individuals that are greater than three months from death.
Clause 12. A method according to any one of preceding clauses, wherein in step (iv), the prediction model is a Cox proportional hazards model with Least Absolute Shrinkage and Selection Operator (LASSO) penalty.
Clause 13. A method according to any one of the preceding clauses, wherein the patient is a cancer patient.
Clause 14. A method according to clause 13, wherein the patient is a patient suffering from lung cancer.
Clause 15. A method according to any one of the preceding clauses, wherein the method determines the likelihood that a patient is going to die within a period of up to 2 months, up to 1 month, up to 3 weeks, up to 2 weeks, up to 1 week, up to 6 days, up to 5 days up to 4 days, up to 3 days, up to 48 hours, up to 24 hours.
Clause 16. A method according to any one of the preceding clauses, wherein if the method predicts that a patient is within the final stages of their life, the method further comprises a step of implementing appropriate palliative care.
Setting and Participants
[0062] The study was conducted at 6 sites including hospitals and hospices in the North West of England from June 2016 to September 2018. Ethical approval was provided by North Wales (West) Research Ethics Committee (REC reference 15/WA/0464).
Participants were enrolled in the study as previously described [17], LC-QTOF-MS Method
[0063] Urine metabolomic studies were performed using Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry (LC-QTOF-MS) as previously described [16], In brief, analysis was performed on an Agilent 1290 Infinity LC coupled to an Agilent 6550 QTOF-MS equipped with a dual AJS electrospray ionization source (Agilent, UK), using two separate chromatographic methods.
Liquid chromatography conditions
[0064] Liquid chromatography (LC) methods were run in positive and negative polarity. LC method 1: employed an Atlantis dC18 column (3x100 mm, 3 pm, 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. LC method 2: used a BEH amide column (3x150 mm, 1.7 pm, 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 pL for both LC methods. The sampling needle was washed with a solution of water:methanol:isopropanol (45:45:10 v/v) between injections.
Mass spectrometry conditions
[0065] 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 previously [16],
Data Pre-processing and Quality Control
[0066] 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 [18],
[0067] 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 [16] (databases publicly available: https://doi.Org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time MS/MS was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al [19], 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.
Statistical analysis
[0068] Samples were normalised by reference feature, G-Log transformed and auto scaled (centered around the mean and divided by the standard deviation of each variable) using MetaboAnalyst [22], Missing values were replaced by 1/5 of the minimum positive value for each variable [24], Unequal variance was assumed. Principal component analyses was performed using MetaboAnalyst and Analysis of Variance (ANOVA) was performed using RStudio version 2021.09.0 [25], Only one sample per patient was included.
Prediction modelling
[0069] PROBAST guidelines for reporting prediction models were followed [26], A Cox proportional hazards model with lasso penalty to derive a prediction model was used for assessing the last days of life in our cohort [27], This approach is similar to the standard Cox model but shrinks parameter estimates towards zero, reducing over-fitting due to the large number of potential metabolites to consider as possible predictors of death. Only the last sample per patient was included. Administrative censoring was applied if the individual was still alive greater than 30 days after their sample was supplied.
[0070] A penalty parameter (lambda) was imposed to determine the amount of smoothing chosen when 10-fold cross validation was performed. The value of lambda that gave minimum mean cross-validated error was used for both the prediction model and internal validation. [0071] The model was internally validated using bootstrap resampling methods with 1000 bootstrap samples. The penalty parameter was fixed from the original cox lasso model fit to the whole dataset, and then for each bootstrap sample a Cox Lasso model was fit and time-dependent area under curve was calculated (24). Model calibration was assessed with each bootstrap sample by comparing the observed and expected survival probabilities, splitting the predicted risks into 3 groups (denoted low/medium/high survival). Calibration was performed at 10, 20 and 30 days. Kaplan-Meier curves were used to visually inspect the survival probabilities based on 30 day predicted risk. Log-rank tests were used to statistically compare the survival curves. Analysis was performed in R Studio Version 1.4.1717 and used the packages “glmnet”, “survival”, and “hdnom” [23],
[0072] As a sensitivity analysis, we also considered a model which selected a penalty parameter that gave a minimum error within one standard deviation of the optimal choice. This led to a model with fewer metabolites included, potentially reducing the risk of overfitting. These results are presented in the supplementary material.
Pathway Analysis
[0073] The pathway analysis was undertaken using MetaboAnalyst 5.0 [22] and combines powerful pathway enrichment analysis with pathway topology analysis to identify the most relevant pathways involved with the conditions under study. Metabolites that showed a significant difference between groups were collated into relevant human KEGG physiological pathways to visualise which significant pathways were altered towards dying. Metabolites were matched using HMDB, PUBCHEM and KEGG databases.
RESULTS Patients
[0074] Urine was prospectively collected from a total of 112 patients with mixed lung cancers from 6 institutions between June 2016 and August 2018. The demographic characteristics of those recruited are shown in (Table 1). Table 1 - Clinical Characteristics of the Patients number of samples collected
1 NSCLC Non-small cell lung cancer
* SCLC Small cell lung cancer Significant metabolites
ANOVA analysis
[0075] ANOVA analysis identified 76 metabolites that varied within the last weeks of life.
Eight metabolites were associated with decreased oral intake e.g. caffeine. 62 metabolites showed a difference in the last week of life (week 1) compared to week 2, week 3, week 4 or week 12+ (see Table S1 of the Supplementary Results section and Figure S1).
[0076] Volcano plot analysis
[0077] Volcano plot analysis identified 85 metabolites with a greater than 2-fold change for different time intervals; the last 4 weeks of life (0-4 weeks), last 2 weeks (0-2 weeks), last 5 days (0-5 days) and last 3 days (0-3 days) (see Tables S2 and S3 Supplementary Results). Twelve metabolites were associated with decreased oral intake, e.g. caffeine. Of the 73 metabolites, unrelated to oral intake, 41 were also identified by ANOVA.
Pathway analysis
[0078] The statistically significant metabolites identified reflect reduced flux though biosynthetic pathways or in the case of degradative products, increased breakdown of pathway intermediates. Several pathways are involved in the last weeks of life and summarised in Table 2.
[0079] Further KEGG pathway analysis using the significant metabolites for the last 2 weeks and last 3 days identified a number of associated biochemical pathways. The pathways are summarized in Table S4 (Supplementary Results).
Table 2 Pathways involved in the last weeks of life
^Disturbed represents a pathway where some metabolites increased and some metabolites decreased in abundance.
PREDICTION MODEL
[0080] Using a Cox Lasso logistic regression approach we derived a multivariable model predicting the probability of survival for each day in the last 30 days (Model 1). The model is based on 7 metabolites (see Table 3). We categorized predicted risks into Low, Medium and High risk of dying showing significant differences in survival probability on a Kaplan Meier curve (see Figure 1), log rank test p<0.001 . The High risk group predicts those in the last days of life. The Low risk group can predict those who are unlikely to be in the last 4 weeks of life. The 30 day model has high median AUC values for every day in the last 30 days (eg 0.86 at day 30, 0.88 at day20 and 0.85 at day 10) (see Figure S2). Calibration of the model at days 30, 20 and 10 was good (Figure S3).
[0081] As a sensitivity analysis, we developed a model which selected a penalty parameter that gave a minimum error within one standard deviation of the optimal choice (Model 2). The model is based on 2 metabolites (see Table S5 in supplementary). A Kaplan Meier curve (see Figure S4), demonstrated separation of High, Medium and Low risk groups. The 30 day model has good median AUC values (eg 0.81 at day 30, 0.83 at day20 and 0.79 at day 10) (Figure S5). Calibration of the model was good (Figure S6).
Table 3 - Table of metabolites for the 30 day Cox lasso logistic regression model and the corresponding Hazard Ratio/co-efficient.
Metabolite Hazard Ratios/Co-efficient
1 Creatine 0.506894
2 Gluconic acid -0.2887
3 Carnitine 0.166248
4 Pyrocatechol 0.051699
5 UBRM 4t -0.03056
6 Unknown Metabolite 5 0.461653 7 Unknown Metabolite 7§ 0.046582 t UBRM 4 Unidentified Bone Derived Metabolite with molecular mass 250.1066 and retention time 1.54. f Unknown Metabolite 5 with molecular mass 188.0472 and retention time 6.77 § Unknown Metabolite 7 with molecular mass 274.1148 and retention time 3.48
Example 1 - Supplemental Results
Pathway Analysis
Table 1 ANOVA
Table of metabolites that show an increase (f) or decrease (f) in abundance towards death identified from ANOVA analysis as significant in the last weeks of life v y _ . „ . n nn. ... . ... . D+,D- f 0.001 12+-Week 01; Week 12+-
[ [ ] | Week 02 i
; 2-Methylmaleate A- |. 0.009 ; Week 12+ vs Week 02; Week 12+ vs Week 03
[0082] Different pathways were identified from different LC-QTOF-MS protocols (A Amide column, D DC18 column, + Positive mode, - Negative mode). The table shows the FDR (False Discovery Rate) and Tukey's HSD (honestly significant difference) test. Metabolites were considered significant if the FDR adjusted p-value <0.05. Where the metabolite was shown by more than one method the P value is shown for the most significant method.
[0083] f UBRM 1 Unidentified Bone Derived Metabolite with molecular mass 1540.7544 and retention time 5.93. [0084] t UBRM 2 Unidentified Bone Derived Metabolite with molecular mass 228.1124 and retention time 1.52.
[0085] § UBRM 3 Unidentified Bone Derived Metabolite with molecular mass 260.1026 and retention time 1.42. Table of molecular mass and Retention Time (RT) for the Unknown metabolites. i Metabolite name Mass RT i Unknown metabol 94.0426 1.3
I Unknown metabol 188.079 1.74 i Unknown metabol 29.0801 1.72 i Unknown metabol 295.142 4.31
I Unknown metabol 88.0472 6.77 i Unknown metabol 04.0794 4.28 i Unknown metabol 74.1148 3.48
; Unknown metabol 25.0638 4.77 i Unknown metabol 79.0787 1.22
1 Unknown metabol 94.0425 1.29
: Unknown metabol 17.0793 1.64
: Unknown metabol 212.068 5.22
I Unknown metabol 22.0368 5.95
; Unknown metabol 05.0738 6.77 i Unknown metabol 95.0538 4.43
Table S2 Volcano plot analysis INCREASED Summary table of metabolites that Increase over 2-fold at different time intervals in the last month of life. Different pathways were identified from different LC-QTOF-MS protocols (A Amide column, D DC18 column, + Positive mode, - Negative mode). i L-Methionine-sulfoximine i 2.46 • Xanthurenic acid (A-) [ [ [ 2.20
+ Unknown metabolites 14 with molecular mass 205.0738 and retention time 6.77.
Table S3 Volcano plot analysis DECREASED
[0086] Summary table of metabolites that DECREASE over 2-fold at different time intervals in the last month of life. Different pathways were identified from different LC- QTOF-MS protocols (A Amide column, D DC18 column, + Positive mode, - Negative mode), f Metabolites associated with decreased oral intake. t Unknown metabolites 15 with molecular mass 195.0538 and retention time 4.43
Table S4 KEGG Pathway analysis
[0087] Biochemical pathways that change significantly in a) the last 2 weeks, b) shared in the last 2 weeks and last 3 days and c) the last 3 days of life. Different pathways were identified from different LC-QTOF-MS protocols (A Amide column, D DC18 column, + Positive mode, - Negative mode), t denotes a duplicated pathway identified using different methods. ^Disturbed represents a pathway where some metabolites increased and some metabolites decreased in abundance. P was <0.05. Table S5 Metabolites Model 2
Table of metabolites for the 30 day Cox lasso logistic regression model using a penalty parameter that gave a minimum error within one standard deviation (Model 2) and the corresponding Hazard Ratio/co-efficient.
Metabolite Hazard Ratios/Co-efficient
1 Creatine 0.3107244
2 Unknown Metabolite 5 0.1951167
DISCUSSION
[0088] We identified metabolites and associated pathways that change in the last weeks and days of life. From these urinary metabolites we developed an objective model predicting dying within the last weeks of life. By using a risk score we are able to categorize those patients who are imminently dying. It is the only model that predicts dying within the last 2 weeks of life.
The dying process
[0089] Towards the end of life there are changes in energy availability; within muscle increased creatine and decreased creatinine levels suggest a shortage of available phosphocreatine for muscular energy, likely resulting in a shortage of available high energy phosphate (ATP); increased NAD+ further supports the notion of reduced energy availability; as does evidence of disrupted mitochondrial fatty acid p-oxidation indicated by urinary dicarboxylic acids; and possible mitochondrial dysfunction, suggested by increased urinary carnitine (essential for fatty acid metabolism), methylglutaric acid and hydroxyl-3- methyl-glutaric acid.
[0090] Numerous changes suggest altered nucleic acid metabolism. Several critical building blocks accumulate, in particular UMP essential for RNA synthesis, as well as adenine and guanosine. In addition, purine degradation products xanthine, and hypoxanthine increase. Collectively these suggest depleting pools of substrates required for nucleic acid anabolism. Crucially this would suppress ribosomal biogenesis, impacting upon cellular capacity for protein synthesis, and importantly cellular stress monitoring and cell viability (23). In addition, ribosome synthesis makes high demands on cellular energy resources which appear to be low given the increase in NAD+. Other changes also likely impact nucleic acid metabolism; alterations in one carbon metabolism, indicated by accumulating dihydrofolic acid, sarcosine, and cystathionine, suggest a reduced ability to generate nucleotides for DNA synthesis. In addition, there will be reduced capacity to deal with reactive oxygen species through decreased production of sulfhydryl-containing reducing agents. Altered one carbon metabolism is highly associated with aging (24), however the consequences of changes in these processes are difficult to predict (25).
[0091] There was evidence of muscle damage or breakdown and an increase in amino acids starting approximately 3 weeks before death. Interestingly, increased muscle protein breakdown and efflux of amino acids is a fundamental response seen in critical illness (26).
[0092] Numerous hormones, including their essential intermediates were altered; cortisol, epinephrine, histamine and hydroxytryptophan increase; dopamine and serotonin decrease. Approximately 5% of tryptophan is converted to serotonin, therefore the low serotonin levels and increased kynurenine levels imply there is a diversion (27). Cortisol was previously shown to be increased in dying patients including a cohort of patients with lung cancer (28). In critically ill patients, increased cortisol was shown to be related to decreased breakdown in the liver (29). Increased urinary pyrocatechol, could be a result of reduced catecholamine synthesis and or increased release from cell or tissue damage with consequences for hormonal and neurotransmitter function (30).
[0093] Other processes affected include increased systemic inflammation, based on increased kynurenine pathway metabolites. Raised inflammatory markers eg CRP are known to be associated with dying in cancer patients irrespective of malignancy type [13],
Prediction Model
[0094] The Cox Lasso derived prediction model demonstrates it is possible to use urine metabolites to predict the dying process for each day within the last 30 days with good accuracy. The high risk score predicted the majority of patients imminently dying. The Low risk score identified those not in the last 2 weeks of life. The model calibration remains stable at 20 and 10 days; it slightly over predicts dying in those with a higher probability and slightly under predicts those with a lower probability. The low number of metabolites in the model suggests overfitting is limited.
Challenges for future research
[0095] We do not know how the dying process is regulated. We have tentative evidence that crucial proteins are turned off when dephosphorylated suggesting there may be a ‘tipping point’ beyond which the dying process is irreversible. Knowing what this ‘tipping point’ is opens the possibility of manipulation and extending life. Future work including different ‘-omics’ technologies is crucial to address this. [0096] Diagnosing when a patient with advanced cancer is likely to die is a challenge and currently no diagnostic test is available [36], Empirically there are different time frames of dying which is an added complication to predicting the last days of life. Usually when patients with cancer are recognized to be dying (‘actively dying’) they die over 2-3 days. However, some die within 24 hours and some take longer than a week. These different presentations of dying need to be addressed.
Conclusion
Our work represents the first attempt to use a metabolomic approach to describe the dying process and the first model to predict dying based on urinary metabolites. Accurate prognostic information is essential to co-ordinate and manage care in response to need, whilst avoiding burdensome and unnecessary interventions. The early recognition a person may be dying underpins all the priorities for improving people’s experience of care in the last days and hours of life. Prognostic tests, based on the metabolites identified in this study, have the potential to change clinical practice and improve the care of dying patients.
Example 2 - GC analysis of volatile organic compounds emanating from urine samples
Methods
Urine samples for GCMS VOC analysis.
5 ml of urine was collected from each volunteer and frozen at -20°C for storage. The collected urine was thawed at room temperature for 1-3 hours, then divided into 1ml aliquots contained within 10 ml headspace vials with magnetic screw caps (vials, SU860100 and screw caps SU860101 , Supelco from Merck, Dorset, UK). The new 1ml urine samples per volunteer were then refrozen and stored prior to sample preparation.
Urine sample preparation
1ml of urine was either freeze dried, or treated with acid or alkali.
1ml urine was freeze dried in an Edwards EF4 Modulyo freeze-dryer (Edwards High Vacuum, UK) operated at -35 °C and eight mbarwas used to freeze-dry for 18 h.
1ml of defrosted urine was treated with either 0.2 ml of 5M sulphuric acid solution (H2SO4) (#12963634, Fisher Scientific, Loughborough) or 5M sodium hydroxide (NaOH) solution (S8263-150ML, Sigma Aldrich, Dorset, UK), and vortexed, ready to be analysed for VOCs by headspace-SMPE-GC-MS. Headspace-SPME-GC-MS analysis
A Perkin Elmer Clarus 500 GC-MS quadruple bench tcp system (Beaccnsfield, UK) was used in ccmbinaticn with a Ccmbi PAL autcsampler (CTC Analytics, Switzerland). The GC cclumn used was a Zebrcn ZB-624 with inner diameter 0.25 mm, length 60 m, film thickness 1.4 pm (Phencmenex, Maccles field, UK). The carrier gas used was helium cf 99.996% purity (BOC, Sheffield, UK). A Divinylbenzene/Carbcxen/Pclydimethylsilcxane (DVB/CAR/PDMS) SPME fibre (needle size 23 ga, StableFlex, fcr use with autcsampler) was cbtained from Sigma-Aldrich, Dorset, UK and preconditioned before use.
Urine samples were placed in an incubation chamber at 60°C for 30 min, followed by the extraction of volatiles from the headspace of the vial and adsorption to the SPME fibre.
The fibre was then inserted into the GC component for desorption at 220°C for 5 mins. The initial temperature of the GC oven was set at 40°C, held for 2 min before increasing to 220°C at a rate of 5 °C/min and then held for 4 min, with a total run time of 42 min. A solvent delay was set for the first 4 min and the MS was operated in positive electron impact ionization EI+ mode, scanning from ion mass fragments 10-300 m/z, with an interscan delay of 0.1s and a resolution of 1000 at FWHM (Full Width at Half Maximum). The helium gas flow rate was set at 1 ml/min. All samples were randomly injected. This was exactly the same as Aggio et al. 2016 [2],
System suitability and quality control
To warrant for potential contaminants or fluctuations in GC-MS measurement we used a set of system suitability samples processed under the same experimental conditions as ‘real’ samples: ‘blank’ samples, ‘laboratory air’ (uncapped, empty vial, where the SPME fibre sat and sampled the air for 20 minutes at room temperature). We ran blank samples (capped, empty vials) after every 8 real samples, and laboratory air before every batch.
The analytical sequence of freeze-dried urine samples was randomised.
The analytical sequence of acid and alkaline treated urine sample was designed according to published guidance [3], We produced a set of QC samples by making a single pooled sample from a representative subset (n=50) of real urine samples in our study (WHAT SAMPLES WERE USED FOR THIS?? Table X). We then prepared 1ml aliquots from that single pooled sample. One pooled QC urine sample treated with acid or alkali was run through the GC-MS per batch of real samples, these acted as technical replicates and as a measurement of systemic stability of the SPME-GC-MS over time.
GC-MS VOC Library building and data analysis
After SPME-GC-MS, chromatograms were analysed for individual peaks by the computer software Automated Mass Spectral Deconvolution and Identification System (AMDIS) (version 2.71), in conjunction with the National Institute of Standards and Technology (NIST) mass spectral library software (version 17). Peaks were added to the library if there was a forward match greater than 800/1000.
The freeze-dried urine:
The freeze-dried urine library was built by examining 10% of the ‘real’ samples. The final library contained xxx unique VOCs (Table X). A batch report was generated from AMDIS using our library with deconvolution settings as follows: component width of 10, adjacent peak subtraction of one, high resolution, high sensitivity, and low shape requirements.
Acid/alkali treated urine:
An acid-alkali urine library was built by examining all the pooled QC samples run and 10% of the ‘real’ samples per treatment group. The final library contained 173 unique VOCs (Table X). A batch report was generated from AMDIS using our library with deconvolution settings as follows: component width of 10, adjacent peak subtraction of one, low resolution, low sensitivity, and high shape requirements.
R package Metab was used to generate a spreadsheet of VOCs per sample, using a half a minute time-window [4],
Compounds appearing in either lab air or blank samples revealed impurities and were considered contaminants if they were present in >50%; their removal from statistical analysis ensured that VOCs originated from urine samples being tested and prevented carry-over of VOCs on the SPME fibre between samples [5], Contaminants identified with system suitability tests were removed before statistical analysis. (Table of contaminants).
Statistics were performed using MetaboAnalyst or R software [6, 7],
Categorisation of VOCs
Acid and Alkali Library of VOCs were classified by adapting the MeSH (Medical Subject Headings) database, which is found on the PubChem website. https://pubchem.ncbi.nlm.nih.gov/source/11939 https://www.nlm.nih.gov/mesh/meshhome.html Table of VOC classification.
Acid Analysis
Fold Change and Univariate Statistics = Volcano Plots
We proceeded data analysis with 82 VOCs at 20% threshold in any one “week” category were analysed. Missing values were imputed with half min VOC numbers, PQN normalised, and no batch correction.
Anything over 1.5-fold changed toward death was considered. Adjusted p value < 0.05. Considering the 144 samples - samples closest to death.
We split the sample data into 7 different groups. The number of samples in each group is in the following table. Table Legend: Volcano plot examined of VOCs in H2SO4 urine.
Survl, compares samples from days 0-1 to days 2+
Surv3, compares samples from days 0-3 to days 4+.
Surv5, compares samples from days 0-5 to days 6+.
Surv10, compares samples from days 0-10 to days 11+. Surv15, compares samples from days 0-15 to days 16+.
Surv17, compares samples from days 0-17 to days 18+.
Surv28, compares samples from days 0-28 to days 29+.
If we assume equal group variance: see this table below from: We performed volcano plots on 7 different groups - see Figure 2:
7 of the compounds increase towards death and this seems to be a trend from day 7 or day 15.
Many more are seen to decrease if we assume unequal distribution - Figure 3.
The following 7 compounds seem to increase towards death: propan.2. one
4.methylpent.3.enoic.acid nonan.2.one or 5.methylhexan.2.one
E..non.3.en.2.one
5.ethyl.5.methyloxolan.2.one
Z..oct.2.enoic.acid
1.2.4. triazole.3.4. diamine
One compound seems to consistently decrease from day 7 or day 15 towards death: 5..3.3.dimethyloxiran.2.yl..3.methylpent.1.en.3.ol
3.4.dimethylhexan.2.one_818_52 seems to peak around day 10 -15.
Box plots
Box plots of the above-identified VOCs were prepared and 9 VOCs which appear to change towards death with statistical significance were:
7 that increase towards death.
1 that decreases towards death.
1 that increases around days 10 -15.
Cox-proportional hazards model with a lasso penalty Model
This is based on the ‘Acid treated’ GC-MS identified VOCs. The modelling following uses the VOCs identified from the volcano plots and used a logistic regression with lasso penalty.
Figures 4 and 5
The model was tested for each of the last days of life (30days - last day) and shows an AUC of below 0.8 increasing in accuracy in the last 2 weeks to approximately 0.8. To get an idea of the prediction error, bootstrapping was used.
The model can place patients into low, medium and high risks groups and their survival predicted. The Kaplan Meier curve shows patients in different risk groups have different survival risks.
Freeze-dried method analysis
Volcano plots
(using the ShinyApp, filtered at 20% present)
26 VOCs. 17 increased, 7 decreased, 2 increased but only at 28 days. acetic.acid.1 R.2R..2. methylcyclopentan.1.ol INCREASED AT 28 DAYS
Acetoin INCREASED AT 28 DAYS (3.hydroxybutan.2.one)
Acetone UP
Butan.2.one UP
2. butoxyethoxy. ethanol UP
Cyclohexanone UP
1.2. dimethylcyclopentane UP
2.4. dimethyl.5H.1.3. oxazol.4. yl. methanol UP
2.5. dimethyl.1H. pyrrole UP 3. ethyl.4. methylpyrrole.2.5. dione UP
3. ethoxy.3. ethylpent. I.yne UP heptan.3.one UP
E..hept.3.en.2.one..3.Hepten.2.one. UP
4.methylpent.3.en.2.one UP pent.3.en.2.one UP
Phenol UP
Propan.2. ol UP
2.3.3.trimethylcyclobutan.1.one UP
Unknown.3....1S.5R..1.5. dimethyl.6.8. dioxabicyclo.3.2.1. octane OR
E..hex.2.enyl.. acetate UP
Butane.2.3. dione DOWN
1 S..1 ,5.dimethyl.6.8. dioxabicyclo.3.2.1.octane DOWN
3.ethylcyclopentan.1.one DOWN
1 ,ethylpyrrolidine.2.5.dione.S DOWN
1. methoxypropan.2. one DOWN
Methylsulfonylmethane DOWN pentane.2.3.dione DOWN
Cox-proportional hazards model with lasso penalty model 2 (Freeze Dry method to identify VOCs)
Figure 6
In this model, the AUC increases from 0.8 in the last 3-4 weeks to >0.9 in the last days.
Figure 7 The Kaplan Meier curve shows that if we group the risks into high/medium/low, there are clearly different survival risks. Alkali method analysis
Fold Change
Fold Change was examined, the closer to death examined, the more VOCs were found to be significantly changed
Increased
1) Propanone,
2) 5.methyl.2. propan.2. ylcyclohexan.I .ol. Menthol,
3) cyclohexanone,
4) nonan-2-one or 5methylhexan-2-one,
5) 3-ethylhexan-1-ol.
Decreased
The following 7 seemed to decrease towards death:
1) X22.68_2.methylheptan.4.one_912_62_ALK
2) X15.79_. methyldisulfanyl. methane_899_98
3) X32.62_2.4.dimethylaniline_834_51_ALK
4) X31.23_3.6. dimethyl.4.5.6.7. tetrahydro.1.benzofuran_827_78_ALKALI
5) X33.25_1 ..2..furan.2. yl. cyclopropyl. ethanone_824_93_ALK
6) X34.49_.5R..5.methyl.2.propan.2.ylidenecyclohexan.1.one..Pulegone._849_41
7) X34.77_2. methyl.5. prop.1 ,en.2.ylcyclohex.2.en.1 ,one.CARVONE._784_74
See Figure 8 - Fold change examined of VOCs in alkaline urine.
The 33 VOCs were found to be present in over 20% of any one “week” category were analysed.
Missing values were imputed with half min VOC numbers, PQN normalised, and no batch correction. Anything over 1.5-fold changed toward death was considered. Those that increase towards death are indicated in green, those that decrease towards death are indicated in red. Cells in grey, did not reach 1.5-fold at that given time point. Fold Change was examined, the closer to death examined, the more VOCs were found to be significantly changed. Surv3, compares samples from days 0-3 to days 4+.
Surv5, compares samples from days 0-5 to days 6+.
SurvIO, compares samples from days 0-10 to days 11+.
Surv15, compares samples from days 0-15 to days 16+.
Surv28, compares samples from days 0-28 to days 29+.
Univariate Statistics and Volcano Plots
Only 33 VOCs were found to be present in over 20% of any one “week” category, this decreased to just 13 VOCs when the threshold was increased to 50%.
Missing values were imputed with half min VOC numbers, PQN normalised, and no batch correction.
See Figure 9.
Box plots of the above-identified VOCs were prepared and 9 VOCs which appear to change towards death with statistical significance were: propan-2-one;
4-methylpent-3-en-2-one; nonan-2-one or 5-methylhexan-2-one; non-3-en-2-one;
2-ethylhexan-1-ol.
Example 3 - DNA damage repair
Urine samples were collected 3 times a week from a patient and stored frozen.
A western blot analysis was performed as per previous published work (Chen X, Wilson JB, McChesney P, Williams SA, Kwon Y, Longerich S, Marriott AS, Sung P, Jones NJ, Kupfer GM. The Fanconi anemia proteins FANCD2 and FANCJ interact and regulate each other's chromatin localization. J Biol Chem. 2014 Sep 12;289(37):25774-82. doi: 10.1074/jbc.M114.552570. Epub 2014 Jul 28. PMID: 25070891; PMCID: PMC4162179.)
Results
See Figure 10.
The plot shows serial samples for the last 6 weeks of life from one patient. The p-Actin and ATG lanes are controls to demonstrate equal loading of each well.
Hypoxia Inducible Factor 1 (HIF1) levels decrease before dying. Phosphorylated ATR which is active in DNA repair becomes de-phosphorylated and therefore inactive in the last weeks.
Phosphorylated FANCG which is active in DNA repair becomes de-phosphorylated and therefore inactive in the last weeks.
Example 4 - Blood ketone data - B-hydroxybutyrate
Little is known about the biology of dying. Mouse models of old age show rapid weight loss in the last days of life - this is because of fat loss. Fat loss can be monitored by measuring serum ketone levels, easily achieved using a glucometer.
This project asked how the levels of body fat changed in people in the last days, weeks and months of life. To do this serial serum ketone (p-hydroxybutyrate) levels were taken in people with advanced diseases (predominantly cancer). They were under the care of a palliative care team in a hospice or hospital.
Methods
Capillary blood was obtained using a lancet pen/device. Ketones were measured done with a glucometer (Freestyle Optium Neo glucometer, Abbott) using dedicated blood B- Ketone test strips (Abbott). An estimate of a patient’s food intake was recorded as being Normal, Reduced, Minimal or None. The participants Phase of Illness was also recorded (Masso M, Allingham SF, Banfield M, et al. Palliative Care Phase: inter-rater reliability and acceptability in a national study. Palliat Med. 2015;29(1):22-30. doi:10.1177/0269216314551814). This is a concept in palliative medicine consisting of five distinct phases (Stable, Unstable, Deteriorating, Dying and Bereavement). Sampling occurred up to 3 times a week depending on patient preference and researcher availability.
Statistical Analysis
All statistics and figures were produced using R software. Nonparametric data was analysed using krustal- etc and post-hoc analysis using holm method (R Core Team. (2014) A Language and Environment for Statistical Computing. ed.)Aeds.), R Foundation for Statistical Computing, Vienna, Austria).
Results
Ninety people were recruited from June 2019 to March 2020 at five sites (4 hospices and 1 hospital). Number of people recruited, ketone measurements and urine samples collected to the Ketone study at different time points
Demographics
Disease zSHF - End Stage Heart Failure COPD - Chronic Obstructive Pulmonary Disease
MND - Motor Neuron Disease
UKP - Unknown Primary
H&N - Head and Neck
CML - Chronic Myeloid Leukemia
Ketone Concentrations
A boxplot (see Figure 1) shows an increase in ketone concentrations in the last 3 weeks. The increase is greatest in the last 3 days of life (see Figure 2). The data is not normally distributed, therefore a Kruskal-Wallis test was used. Post-hoc analysis, to see where the differences in the groups were, was performed using a Dunn test; P-values were adjusted with the Holm method.
Results
Figure 11
Boxplot of serum ketone concentrations in the last weeks of life compared to controls (Days30+)
** There is a statistical difference between Week 1 and Weeks 3 and 4+ (p <0.001)
* There is a statistical difference between Week 2 and Week 4+ (p <0.001)
Figure 12
Boxplot of serum ketone concentrations in the last days of life
** statistical difference from Days 30+ (p=<0.01)
Figure 13
Individual ketone concentrations during the last 30 days of life
A scatterplot of the ketone concentrations in the last month of life is shown in Figure 16. A non-parametric regression model is plotted with the 95% confidence interval marked by the transparent gray-shaded area. Ketone concentrations gradually increase for the majority of participants in the last days of life. There is a cohort where the increase occurs in the last 3 weeks. Abbreviations
ANOVA Analysis of Variance
FDR False Discovery Rate
LASSO Least Absolute Shrinkage and Selection Operator PCA Principal Component Analysis
Example 5 - Validation of the 7 biomarker panel identified in Table 3 of Example 1
[0097] To further validate the seven biomarker panel identified in Example 1 above, a further validation study was conducted on urine samples obtained from a cohort of 50 patients.
[0098] The clinical characteristics of the patients included in the validation cohort are shown below:
Clinical characteristics of the Patients included in the validation cohort. Total number of unique samples n=50 t NSCLC Non-small cell lung cancer; t SCLC Small cell lung cancer; § Based on Multidisciplinary Team discussion
[0099] The patients in this cohort had a mixture of different lung cancer pathologies.
[00100] The seven biomarkers analysed were as follows: creatine, gluconic acid, carnitine, pyrocatechol, UBRM 4, Unknown Metabolite 5 and Unknown Metabolite 7. [00101] The analysis of the urine samples, LC-QTOF-MS methodology and conditions, data processing, statistical analysis and prediction modelling are as previously described in Example 1.
RESULTS [00102] The median AUC for the last 30 days was 0.849, with a maximum of 0.906 and minimum of 0.803. The median AUC for the last 7 days was excellent at 0.88, with a maximum of 0.906 and minimum of 0.853. The results are shown graphically in Figure 14. Example 6 - MS Spectrum for Unknown Metabolite 5
[00103] The MS spectrum for Unknown Metabolite 5 is shown in the table below. It contains peaks observed at 3 different collision energies with abundance values. References
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Claims

Claims
1. A method of determining the likelihood that an individual is going to die within a time period of up to 3 months, the method comprising:
(i) analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) a biomarker associated altered cellular energy metabolism; b) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) a biomarker associated with increased bone loss, bone damage or bone resorption; e) a biomarker associated with increased mitochondrial dysfunction; f) a biomarker associated with altered one carbon metabolism; g) a biomarker associated with decreased RNA synthesis; h) a biomarker associated with decreased protein synthesis; i) a biomarker associated with oxidative stress; j) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) a biomarker associated with increased cell membrane breakdown; l) a biomarker associated with altered hormone production; m) a biomarker associated with altered amino acid metabolism; n) a biomarker associated with kynurenine pathway activation; o) a biomarker associated with decreased oral intake (food and drink); p) a biomarker associated with decreased DNA repair; q) one or more unknown metabolites selected from Unknown metabolites 1 to 15:
Metabolite name Mass
Unknown metabolite 1 194.04 Unknown metabolite 2 188.08
Unknown metabolite 3 129.08
Unknown metabolite 4 295.14
Unknown metabolite 5 188.05
Unknown metabolite 6 304.08
Unknown metabolite 7 274.11
Unknown metabolite 8 225.06
Unknown metabolite 9 179.08
Unknown metabolite 10 194.04
Unknown metabolite 11 117.08
Unknown metabolite 12 212.07
Unknown metabolite 13 122.04
Unknown metabolite 14 205.07
Unknown metabolite 15 195.05
(ii) comparing the quantity of the three or more, four or more or five or more biomarkers listed in step (i) above with a reference value for the biomarker concerned to determine whether there is any variance between the detected quantity of the biomarker and its reference value;
(iii) compiling the variance data obtained for the one or more biomarkers selected from the options listed above relative to their respective reference value(s); and
(iv) analysing the variance data in a prediction model to determine the likelihood of a patient dying within a specified period of time of up to three months.
2. A method according to claim 1 , wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with disrupted mitochondrial fatty acid 0- oxidation; c) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) one biomarker associated with increased bone loss, bone damage or bone resorption; e) one biomarker associated with increased mitochondrial dysfunction; f) one biomarker associated with altered one carbon metabolism; g) one biomarker associated with decreased RNA synthesis; h) one biomarker associated with decreased protein synthesis; i) one biomarker associated with oxidative stress; j) one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k) one biomarker associated with increased cell membrane breakdown; l) one biomarker associated with altered hormone production; m) one biomarker associated with altered amino acid metabolism; n) one biomarker associated with kynurenine pathway activation; o) one biomarker associated with decreased oral intake (food and drink); p) one biomarker associated with decreased DNA repair; q) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein. A method according to claim 1 or claim, wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of at least the following biomarkers: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with altered hormone production; e) one biomarker associated with decreased oral intake (food and drink); f) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein. A method according to any one of the preceding claims, wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with disrupted mitochondrial fatty acid p- oxidation; c) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d) one biomarker associated with increased bone loss, bone damage or bone resorption; e) one biomarker associated with increased mitochondrial dysfunction; f) one biomarker associated with oxidative stress; g) one biomarker associated with altered hormone production; h) one biomarker associated with decreased oral intake (food and drink); i) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein. A method according to any one of the preceding claims, wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated altered cellular energy metabolism; b) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c) one biomarker associated with increased bone loss, bone damage or bone resorption; d) one biomarker associated with increased mitochondrial dysfunction; e) one biomarker associated with altered hormone production; f) one biomarker associated with decreased oral intake (food and drink); g) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
6. A method according to any one of the preceding claims, wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of three or more, four or more, five or more, six or more or seven or more biomarkers selected from the following options: a) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; b) one biomarker associated with increased bone loss, bone damage or bone resorption; c) one biomarker associated with altered hormone production; d) one biomarker associated with decreased oral intake (food and drink); e) one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
7. A method according to any one of claims 1 to 6, wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of:
(i) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with decreased RNA synthesis; f. a biomarker associated with decreased protein synthesis; g. a biomarker associated with oxidative stress; h. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; i. a biomarker associated with increased cell membrane breakdown; j. a biomarker associated with altered hormone production; k. a biomarker associated with altered amino acid metabolism; l. a biomarker associated with kynurenine pathway activation; m. a biomarker associated with decreased oral intake (food and drink); n. a biomarker associated with decreased DNA repair; o. one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein;
(ii) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with oxidative stress; f. a biomarker associated with increased cell membrane breakdown; g. a biomarker associated with altered hormone production; h. a biomarker associated with altered amino acid metabolism; i. a biomarker associated with kynurenine pathway activation; j. a biomarker associated with decreased oral intake (food and drink); k. one or more of unknown metabolites selected from Unknown metabolites 1 to
15 disclosed herein;
(iii) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with altered one carbon metabolism; e. a biomarker associated with oxidative stress; f. a biomarker associated with altered hormone production; g. a biomarker associated with decreased oral intake (food and drink); h. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein;
(iv) creatine and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with increased bone loss, bone damage or bone resorption; b. a biomarker associated with altered hormone production; c. a biomarker associated with increased mitochondrial dysfunction; d. a biomarker associated with decreased oral intake (food and drink); e. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein;
(v) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with decreased RNA synthesis; e. a biomarker associated with decreased protein synthesis; f. a biomarker associated with oxidative stress; g. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; h. a biomarker associated with increased cell membrane breakdown; i. a biomarker associated with altered hormone production; j. a biomarker associated with altered amino acid metabolism; k. a biomarker associated with kynurenine pathway activation; l. a biomarker associated with decreased oral intake (food and drink); m. a biomarker associated with decreased DNA repair; n. one or more unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein;
(vi) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with oxidative stress; e. a biomarker associated with increased cell membrane breakdown; f. a biomarker associated with altered hormone production; g. a biomarker associated with altered amino acid metabolism; h. a biomarker associated with kynurenine pathway activation; i. a biomarker associated with decreased oral intake (food and drink); j. one or more of unknown metabolites selected from Unknown metabolites 1 to
15 disclosed herein;
(vii) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. a biomarker associated with increased bone loss, bone damage or bone resorption; c. a biomarker associated with altered one carbon metabolism; d. a biomarker associated with oxidative stress; e. a biomarker associated with altered hormone production; f. a biomarker associated with decreased oral intake (food and drink); g. one or more of unknown metabolites selected from Unknown metabolites 1 to
15 disclosed herein;
(viii) creatine, carnitine and one or more, two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated with increased bone loss, bone damage or bone resorption; b. a biomarker associated with altered hormone production; c. a biomarker associated with decreased oral intake (food and drink); d. one or more of unknown metabolites selected from Unknown metabolites 1 to 15 disclosed herein.
8. A method according to any one of claims 1 to 6, wherein, in step (i), the method comprises analysing a urine or blood sample obtained from the individual to detect the presence and quantity of:
(i) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. a biomarker associated altered cellular energy metabolism; b. a biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. a biomarker associated with increased bone loss, bone damage or bone resorption; e. a biomarker associated with increased mitochondrial dysfunction; f. a biomarker associated with altered one carbon metabolism; g. a biomarker associated with decreased RNA synthesis; h. a biomarker associated with decreased protein synthesis; i. a biomarker associated with oxidative stress; j. a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k. a biomarker associated with increased cell membrane breakdown; l. a biomarker associated with altered hormone production; m. a biomarker associated with altered amino acid metabolism; n. a biomarker associated with kynurenine pathway activation; o. a biomarker associated with decreased oral intake (food and drink); p. a biomarker associated with decreased DNA repair; q. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(ii) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated altered cellular energy metabolism; b. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. one biomarker associated with increased bone loss, bone damage or bone resorption; e. one biomarker associated with increased mitochondrial dysfunction; f. one biomarker associated with altered one carbon metabolism; g. one biomarker associated with decreased RNA synthesis; h. one biomarker associated with decreased protein synthesis; i. one biomarker associated with oxidative stress; j. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; k. one biomarker associated with increased cell membrane breakdown; l. one biomarker associated with altered hormone production; m. one biomarker associated with altered amino acid metabolism; n. one biomarker associated with kynurenine pathway activation; o. one biomarker associated with decreased oral intake (food and drink); p. one biomarker associated with decreased DNA repair; q. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(iii) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated altered cellular energy metabolism; b. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; c. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; d. one biomarker associated with increased bone loss, bone damage or bone resorption; e. one biomarker associated with increased mitochondrial dysfunction; f. one biomarker associated with altered hormone production; g. one biomarker associated with decreased oral intake (food and drink); h. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(iv) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated altered cellular energy metabolism; b. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; c. one biomarker associated with increased bone loss, bone damage or bone resorption; d. one biomarker associated with increased mitochondrial dysfunction; e. one biomarker associated with altered hormone production; f. one biomarker associated with decreased oral intake (food and drink); g. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(v) Unknown Metabolite 5 and two or more, three or more, four or more, five or more, or six or more biomarkers selected from: a. one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(vi) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered one carbon metabolism; e. one biomarker associated with decreased RNA synthesis; f. one biomarker associated with decreased protein synthesis; g. one biomarker associated with oxidative stress; h. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; i. one biomarker associated with increased cell membrane breakdown; j. one biomarker associated with altered hormone production; k. one biomarker associated with altered amino acid metabolism; l. one biomarker associated with kynurenine pathway activation; m. one biomarker associated with decreased oral intake (food and drink); n. one biomarker associated with decreased DNA repair; o. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein; (vii) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered one carbon metabolism; e. one biomarker associated with oxidative stress; f. one biomarker associated with increased cell membrane breakdown; g. one biomarker associated with altered hormone production; h. one biomarker associated with decreased oral intake (food and drink); i. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(viii) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered one carbon metabolism; e. one biomarker associated with altered hormone production; f. one biomarker associated with decreased oral intake (food and drink); g. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(ix) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(x) Unknown Metabolite 5, creatine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with increased bone loss, bone damage or bone resorption; b. one biomarker associated with altered hormone production; c. one biomarker associated with increased mitochondrial dysfunction; d. one biomarker associated with decreased oral intake (food and drink); e. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(xi) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with decreased RNA synthesis; e. one biomarker associated with decreased protein synthesis; f. one biomarker associated with oxidative stress; g. one biomarker associated with altered nucleoside (purine, pyrimidine) metabolism; h. one biomarker associated with increased cell membrane breakdown; i. one biomarker associated with altered hormone production; j. one biomarker associated with altered amino acid metabolism; k. one biomarker associated with kynurenine pathway activation; l. one biomarker associated with decreased oral intake (food and drink); m. one biomarker associated with decreased DNA repair; n. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(xii) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with oxidative stress; e. one biomarker associated with increased cell membrane breakdown; f. one biomarker associated with altered hormone production; g. one biomarker associated with decreased oral intake (food and drink); h. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(xiii) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered one carbon metabolism; d. one biomarker associated with altered hormone production; e. one biomarker associated with decreased oral intake (food and drink); f. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(xiv) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with disrupted mitochondrial fatty acid p-oxidation; b. one biomarker associated with increased bone loss, bone damage or bone resorption; c. one biomarker associated with altered hormone production; d. one biomarker associated with decreased oral intake (food and drink); e. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein;
(xv) Unknown Metabolite 5, creatine, carnitine and one or more, two or more, three or more, four or more, or five or more biomarkers selected from: a. one biomarker associated with increased bone loss, bone damage or bone resorption; b. one biomarker associated with altered hormone production; c. one biomarker associated with decreased oral intake (food and drink); d. one or more unknown metabolites selected from Unknown metabolites 1 to 4 or 6 to 15 disclosed herein.
9. A method according to any one of the preceding claims, wherein:
(i) a biomarker associated altered cellular energy metabolism is selected from one or more of NAD or creatine;
(ii) a biomarker associated with disrupted mitochondrial fatty acid p-oxidation is selected from one or more of dicarboxylic acids (e.g. acetone, adipic acid, suberic acid, oxadipic acid, or ethylmalonic.acid), p-hydroxybutyrate, acetone, dodecanoylcarnitine or 12-hydroxy dodecanoic acid;
(iii) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis is selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, nitrotyrosine, creatinine;
(iv) a biomarker associated with increased bone loss, bone damage or bone resorption is selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4;
(v) a biomarker associated with increased mitochondrial dysfunction is selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl- glutaric.acid;
(vi) a biomarker associated with altered one carbon metabolism is selected from one or more of dihydrofolate, sarcosine, and cystathionine and the lack of glycine or dTMP (vii) a biomarker associated with decreased RNA synthesis is selected from one or more of uridine monophosphate, guanosine and increased purine degradation products xanthine and hypoxanthine; with known consequences on decreased protein synthesis.
(viii) a biomarker associated with decreased protein synthesis is selected from one or more of essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine).
(ix) a biomarker associated with oxidative stress for example nitrotyrosine;
(x) a biomarker associated with altered nucleoside (purine, pyrimidine) metabolism is selected from one or more of adenine, guanosine, uridine monophosphate, galactose monophosphate, 5-deoxy, 5-methyl, thioadenosine, dihydrofolic acid, hypoxanthine, xanthine, 3-methyl deoxyadenosine, D-galactose-1-phosphate;
(xi) a biomarker associated with increased cell membrane breakdown is selected from one or more of various cholines (e.g. 1,2-dipalmitoyl-rac-glycreo-3- phosphoethanolamine, L-phosphatidycholine);
(xii) a biomarker associated with altered hormone production is selected from one or more of epinephrine (epinephrine, adrenochrome, vanillylmandelic.acid (VMA), pyrocatechol), GABA (GABA, acetamidobutanoate), cortisol
(cortisol, allotetrahydrocortisol, cortexolone, cortisol-21 acetate), histamine (histidine), hydroxytry ptophan, dopamine (dihydroxyphenylacetic acid, L-dopa) and serotonin;
(xiii) a biomarker associated with altered amino acid metabolism is selected from one or more of alanine (eg dihydrouracil), cysteine (eg L-cystathionine) histidine, isoleucine, phenylalanine (eg phenylalanine, phenyl acetate), tryptophan (eg hydroxytryptophan, , kynurenic acid, oxoadipic acid) or tyrosine (eg tyrosine, 4.Hydroxyphenyllactic.acid, tyrosine methylene ketone, L-tyrosine-methyl-ester- 4-sulfate);
(xiv) a biomarker associated with kynurenine pathway activation is selected from one or more of 3-Hydroxyanthranilic acid or Kynurenic acid;
(xv) a biomarker associated with decreased oral intake (food and drink) is selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid; (xvi) a biomarker associated with decreased DNA repair eg Hypoxia Inducible Factor 1 (HIF1), phosphorylated and unphosphorylated ATR, phosphorylated and unphosphorylated FANCG; and
(xvii) the unknown metabolites are selected from:
Metabolite name Mass
Unknown metabolite 1 194.0426
Unknown metabolite 2 188.079
Unknown metabolite 3 129.0801
Unknown metabolite 4 295.142
Unknown metabolite 5 188.0472
Unknown metabolite 6 304.0794
Unknown metabolite 7 274.1148
Unknown metabolite 8 225.0638
Unknown metabolite 9 179.0787
Unknown metabolite 10 194.0425
Unknown metabolite 11 117.0793
Unknown metabolite 12 212.068
Unknown metabolite 13 122.0368
Unknown metabolite 14 205.0738
Unknown metabolite 15 195.0538
10. A method according to any one of preceding claims, wherein the biomarker(s) detected in step (i) comprise at least three biomarkers selected from the following groups: a) a biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, creatine and nitrotyrosine or metabolites of cell membrane breakdown (e.g. Phosphatidylcholine, 1 ,2-Dipalmitoyl-Rac-Glycreo-3- Phosphoethanolamine); b) a biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl- glutaric.acid; c) a biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol; d) a biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1, UBDM2, UBDM3 and UBDM4; e) a biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid.
11. A method according to any one of preceding claims, wherein step (i) of the method involves detecting h) one biomarker associated with increased muscle loss, muscle damage or rhabdomylosis selected from one or more of creatine, sarcosine, carnitine, carnosine, 3-amino-isobutanoic-acid, 3-carboxypropyl-trimethylammonium, and nitrotyrosine or metabolites of cell membrane breakdown (e.g Phosphatidylcholine, 1,2-Dipalmitoyl-Rac-Glycreo-3-Phosphoethanolamine); i) one biomarker associated with increased mitochondrial dysfunction selected from one or more of carnitine, methylglutaric acid and hydroxyl-3-methyl-glutaric.acid; j) one biomarker associated with altered hormone production selected from one or more of epinephrine, adrenochrome, vanillylmandelic.acid (VMA) and pyrocatechol; k) one biomarker associated with increased bone loss, bone damage or bone resorption selected from one or more of UBDM1 , UBDM2, UBDM3 and UBDM4; l) one biomarker associated with decreased oral intake (food and drink) selected from one or more of caffeic acid, caffeine, ferulic acid, paraxanthine, quinic acid, rosmarinic acid, tartaric acid, theobromine, theophylline, trigonelline or gluconic acid; m) Unknown compound 5; and/or n) Unknown compound 7.
12. A method according to any one of preceding claims, wherein step (i) of the method involves detecting h) creatine; i) carnitine; j) pyrocatechol; k) gluconic acid; l) UBRM 4; m) Unknown compound 5; and/or n) Unknown compound 7.
13. A method according to any one of preceding claims, wherein the selected biomarkers in step (i) are detected and quantified by Liquid Chromatography Mass Spectrometry LC-MS, preferably Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry (LC-QTOF-MS).
14. A method according to any one of preceding claims, wherein, in step (ii), the variance between the detected quantity of the biomarker and its reference value is an increase or decrease in quantity relative to the reference value.
15. A method according to any one of preceding claims, wherein levels of the selected biomarkers in step (i) are compared with their respective reference values in step (ii) of the process, wherein the reference value is a mean level for the biomarker concerned that is generated from analysing urine or blood samples obtained from a cohort of individuals that are greater than three months from death.
16. A method according to any one of preceding claims, wherein in step (iv), the prediction model is a Cox proportional hazards model with Least Absolute Shrinkage and Selection Operator (LASSO) penalty.
17. A method according to any one of the preceding claims, wherein the individual is a cancer patient.
18. A method according to claim 17, wherein the patient is a patient suffering from lung cancer.
19. A method according to any one of the preceding claims, wherein the method determines the likelihood that a patient is going to die within a period of up to 2 months, up to 1 month, up to 3 weeks, up to 2 weeks, up to 1 week, up to 6 days, up to 5 days up to 4 days, up to 3 days, up to 48 hours, up to 24 hours.
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