DETECTION OF A RELAPSE IN A MULTIPLE SCLEROSIS PATIENT

Abstract

The present invention is directed to methods for confirming that a multiple sclerosis (MS) patient is suffering from a relapse. In particular, methods comprising: comparing a concentration of one or more metabolite(s) present in a sample obtained from the patient with the concentration of the same one or more metabolite(s) in a reference standard, wherein the one or more metabolite(s) are selected from: leucine, lysine, asparagine, phenylalanine, glucose, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, a lipoprotein having a CH2 group, and an N-acetylated glycoprotein; and confirming or not that the patient is suffering from a relapse.

Claims

1. A method for confirming that a multiple sclerosis (MS) patient is suffering from a relapse, or for determining prognosis of a relapse in a MS patient, or for monitoring a MS patient's response to therapy, the method comprising: a. comparing a concentration of one or more metabolite(s) present in a sample obtained from the patient with the concentration of the same one or more metabolite(s) in a reference standard, wherein the one or more metabolite(s) are selected from: lysine, leucine, asparagine, phenylalanine, glucose, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, a lipoprotein having a CH2 group, and an N-acetylated glycoprotein; or b. comparing an intensity of one or more chemical shift region(s) of a .sup.1H-NMR spectrum of a sample obtained from the patient with the intensity of the same one or more chemical shift region(s) of a .sup.1H-NMR reference standard, wherein the one or more chemical shift region(s) are selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, 1.53-1.61 ppm, 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm; and c. (i) confirming or not confirming that the patient is suffering a relapse based on the comparison, or (ii) determining or not determining that the patient's prognosis is poor based on the comparison or (iii) determining or not determining that the patient is responsive to the therapy based on the comparison.

2. The method according to claim 1, wherein the method is for confirming that a multiple sclerosis (MS) patient is suffering from a relapse, the method comprising: A. comparing a concentration of one or more metabolite(s) present in a sample obtained from the patient with the concentration of the same one or more metabolite(s) in a reference standard, wherein the one or more metabolite(s) are selected from: lysine, leucine, asparagine, phenylalanine, glucose, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH3 group of an HDL and/or LDL, a lipoprotein having a CH3 group of a VLDL, a lipoprotein having a (CH2)n group of an HDL and/or LDL, a lipoprotein having a CH2 group, and an N-acetylated glycoprotein, and a. confirming that the patient is suffering from a relapse when: i. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is the same or higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or b. not confirming that the patient is suffering from a relapse, or confirming that the patient is not suffering from a relapse, when: i. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is the same or lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or B. comparing an intensity of one or more chemical shift region(s) of a .sup.1H-NMR spectrum of a sample obtained from a patient with the intensity of the same one or more chemical shift region(s) of a .sup.1H-NMR reference standard, wherein the one or more chemical shift region(s) are selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, 1.53-1.61 ppm, 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm; and a. confirming the patient is suffering a relapse when: i. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is the same or higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or b. not confirming that the patient is suffering from a relapse, or confirming that the patient is not suffering from a relapse, when: i. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is the same or lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

3. The method according to claim 1, wherein the method is for determining prognosis of a relapse in a multiple sclerosis (MS) patient (preferably wherein the MS patient is in remission), the method comprising: a. comparing a concentration of one or more metabolite(s) present in a sample obtained from the patient with the concentration of the same one or more metabolite(s) in a reference standard, wherein the one or more metabolite(s) are selected from: lysine, leucine, asparagine, phenylalanine, glucose, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, a lipoprotein having a CH2 group, and an N-acetylated glycoprotein; and b. determining that the patient's prognosis is poor when: i. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is the same or higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or c. not determining that the patient's prognosis is poor, or determining that the patient's prognosis is good, when: i. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is the same or lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

4. The method according to claim 1, wherein the method is for determining prognosis of a relapse in a multiple sclerosis (MS) patient (preferably wherein the MS patient is in remission), the method comprising: a. comparing an intensity of one or more chemical shift region(s) of a .sup.1H-NMR spectrum of a sample obtained from a patient with the intensity of the same one or more chemical shift region(s) of a .sup.1H-NMR reference standard, wherein the one or more chemical shift region(s) are selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, 1.53-1.61 ppm, 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm; and b. determining the patient's prognosis is poor when: i. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is the same or higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or c. not determining that the patient's prognosis is poor, or determining that the patient's prognosis is good, when: i. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is the same or lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

5. The method according to claim 1, wherein the method is for monitoring a multiple sclerosis (MS) patient's response to therapy, wherein the patient is suffering or suspected of suffering from a relapse, or is at risk of suffering a relapse, the method comprising: a. providing a sample obtained from the patient, wherein the patient has received therapy for a relapse; b. comparing a concentration of one or more metabolite(s) present in the sample obtained from the patient with the concentration of the same one or more metabolite(s) in a reference standard, wherein the one or more metabolite(s) are selected from: lysine, leucine, asparagine, phenylalanine, glucose, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, a lipoprotein having a CH2 group, and an N-acetylated glycoprotein; and c. determining that the patient is responsive to the therapy when: i. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy; and/or ii. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy; or d. not determining that the patient is responsive to the therapy, or determining that the patient is not responsive to the therapy when: i. the concentration of one or more metabolite(s) selected from: lysine, asparagine, glucose, and a lipoprotein having a CH.sub.2 group is the same or higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy; and/or ii. the concentration of one or more metabolite(s) selected from: leucine, phenylalanine, -hydroxybutyrate, myo-inositol, a lipoprotein having a CH.sub.3 group of an HDL and/or LDL, a lipoprotein having a CH.sub.3 group of a VLDL, a lipoprotein having a (CH.sub.2).sub.n group of an HDL and/or LDL, and an N-acetylated glycoprotein is the same or lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy.

6. The method according to claim 1, wherein the method is for monitoring a multiple sclerosis (MS) patient's response to therapy, wherein the patient is suffering or suspected of suffering from a relapse, or is at risk of suffering a relapse, the method comprising: a. providing a sample obtained from the patient, wherein the patient has received therapy for a relapse; b. comparing an intensity of one or more chemical shift region(s) of a .sup.1H-NMR spectrum of the sample obtained from the patient with the intensity of the same one or more chemical shift region(s) of a .sup.1H-NMR reference standard, wherein the one or more chemical shift region(s) are selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, 1.53-1.61 ppm, 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm; and c. determining that the patient is responsive to the therapy when: i. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the intensity of said one or more chemical shift region(s) in a sample obtained from the patient pre-administration of the therapy; and/or ii. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the intensity of said one or more chemical shift region(s) in a sample obtained from the patient pre-administration of the therapy; or d. not determining that the patient is responsive to the therapy, or determining that the patient is not responsive to the therapy, when: i. the intensity of one or more chemical shift region(s) selected from: 1.37-1.55 ppm, 1.65-1.75 ppm, 1.83-1.94 ppm, 3.00-3.05 ppm, 2.80-3.00 ppm, 3.96-4.02 ppm, 3.17-3.95 ppm, 4.63-4.66 ppm, 5.22-5.25 ppm, and 1.53-1.61 ppm is the same or higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the intensity of said one or more chemical shift region(s) in a sample obtained from the patient pre-administration of the therapy; and/or ii. the intensity of one or more chemical shift region(s) selected from: 0.94-0.98 ppm, 1.62-1.78 ppm, 3.70-3.79 ppm, 7.32-7.44 ppm, 3.1-3.3 ppm, 3.9-4.0 ppm, 1.19-1.21 ppm, 2.27-2.45 ppm, 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm, 0.80-0.86 ppm, 0.86-0.92 ppm, 1.15-1.30 ppm, and 1.93-2.10 ppm is the same or lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the intensity of said one or more chemical shift region(s) in a sample obtained from the patient pre-administration of the therapy.

7. The method according to claim 1, further comprising: a. comparing a concentration of isoleucine and/or serum neurofilament light chain (NfL) present in a sample obtained from a patient with the concentration of isoleucine and/or NfL, respectively, in a reference standard; and b. confirming that the patient is suffering from a relapse when: i. the concentration of NfL is higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of isoleucine is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of NfL is the same or higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of isoleucine is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or c. not confirming that the patient is suffering from a relapse when: i. the concentration of NfL is the same or lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of isoleucine is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of NfL is lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of isoleucine is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

8. The method according to claim 1, further comprising: a. comparing an intensity of a chemical shift region of a .sup.1H-NMR spectrum of a sample obtained from a patient with the intensity of the same one or more chemical shift region of a .sup.1H-NMR reference standard, wherein the chemical shift region is 0.92-0.97 ppm, 1.00-1.03 ppm, 1.22-1.28 ppm, 1.43-1.51 ppm, 1.94-2.01 ppm, and/or 3.65-3.68 ppm; and b. confirming that the patient is suffering from a relapse when: i. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or c. not confirming that the patient is suffering from a relapse when: i. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

9. The method according to claim 1, further comprising: a. comparing a concentration of isoleucine and/or serum neurofilament light chain (NfL) present in a sample obtained from a patient with the concentration of isoleucine and/or NfL, respectively, in a reference standard; and b. determining the patient's prognosis is poor when: i. the concentration of NfL is higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of isoleucine is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of NfL is the same or higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of isoleucine is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or c. not determining the patient's prognosis is poor when: i. the concentration of NfL is the same or lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the concentration of isoleucine is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or iii. the concentration of NfL is lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; and/or iv. the concentration of isoleucine is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

10. The method according to claim 1, further comprising: a. comparing an intensity of a chemical shift region of a .sup.1H-NMR spectrum of a sample obtained from a patient with the intensity of the same one or more chemical shift region of a .sup.1H-NMR reference standard, wherein the chemical shift region is 0.92-0.97 ppm, 1.00-1.03 ppm, 1.22-1.28 ppm, 1.43-1.51 ppm, 1.94-2.01 ppm, and/or 3.65-3.68 ppm; and b. determining the patient's prognosis is poor when: i. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is lower in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is the same or lower in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard; or c. not determining the patient's prognosis is poor when: i. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is the same or higher in the sample relative to the reference standard, wherein the reference standard is a non-relapse reference standard; and/or ii. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is higher in the sample relative to the reference standard, wherein the reference standard is a relapse reference standard.

11. The method according to claim 1, further comprising: a. comparing a concentration of isoleucine and/or serum neurofilament light chain (NfL) present in a sample obtained from a patient with the concentration of isoleucine and/or NfL, respectively, in a reference standard; and b. determining that the patient is responsive to the therapy when: i. the concentration of NfL is lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy; or ii. the concentration of isoleucine is higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy; and/or c. not determining that the patient is responsive to the therapy when: i. the concentration of NfL is the same or higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy; and/or ii. the concentration of isoleucine is the same or lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the concentration of said one or more metabolite(s) in a sample obtained from the patient pre-administration of the therapy.

12. The method according to claim 1, further comprising: a. comparing an intensity of a chemical shift region of a .sup.1H-NMR spectrum of a sample obtained from a patient with the intensity of the same one or more chemical shift region of a .sup.1H-NMR reference standard, wherein the chemical shift region is 0.92-0.97 ppm, 1.00-1.03 ppm, 1.22-1.28 ppm, 1.43-1.51 ppm, 1.94-2.01 ppm, and/or 3.65-3.68 ppm; and b. determining that the patient is responsive to the therapy when: i. the intensity of said chemical shift region of the .sup.1H-NMR spectrum is higher in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the intensity of said one or more chemical shift region(s) in a sample obtained from the patient pre-administration of the therapy; or c. not determining that the patient is responsive to the therapy when: i. the intensity of said chemical shift region of the 1H-NMR spectrum is the same or lower in the sample relative to the reference standard, wherein the reference standard is a reference standard that is representative of the intensity of said one or more chemical shift region(s) in a sample obtained from the patient pre-administration of the therapy.

13. (canceled)

14. The method according to claim 1, wherein the sample is a biofluid sample, preferably wherein the sample is a blood sample.

15. (canceled)

16. The method according to claim 1, wherein the concentration of the one or more metabolites has been, or is, determined using .sup.1H-NMR spectroscopy.

17. The method according to claim 1, wherein the chemical shift region(s) is/are reported relative to lactate CH.sub.3 referenced at 1.33 ppm.

18. (canceled)

19. The method according to claim 1, wherein the method comprises comparing the concentration two or more metabolites or comparing the intensity of two or more chemical shift regions, respectively.

20. The method according to claim 1, wherein the method comprises comparing the concentration of three or more metabolites or comparing the intensity of three or more chemical shift regions, respectively.

21. The method according to claim 1, wherein the patient has relapsing remitting MS (RRMS).

22. The method according to claim 1 further comprising recording the output of at least one step on a data-storage medium.

23-24. (canceled)

25. A method of treating a relapse in a multiple sclerosis (MS) patient, the method comprising: a. obtaining the results of a method according to claim 1; and b. administering a therapy for treating relapse when a relapse is confirmed; and c. optionally administering a different therapy when a relapse is not confirmed.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0454] Embodiments of the invention will now be described, by way of example only, with reference to the following Figures and Examples, in which:

[0455] FIG. 1 shows representative OPLS-DA models generated from CPMG data of ln R vs. LR24 M patients. (A) Representative OPLS-DA scores plot (ln R=red circles, LR24 M=green triangles). (B) Box plots of predictive accuracies from the OPLS-DA models of ln R vs. LR24 M patients, against random class assignment. ****indicates p<0.0001 by Kolmogorov-Smirnov test.

[0456] FIG. 2 shows a bar graphs demonstrating the fold change in predictive accuracies of the OPLS-DA models of the different patient groups with respect to the reference comparator, i.e. LR24 M patients. The fold change of random chance is 1.0 as indicated by the dashed horizontal line. ****indicates p<0.0001 by post-hoc Bonferroni correction.

[0457] FIG. 3 shows a VIP score ranking plot obtained from the OPLS-DA models of ln R vs. LR24 M patients. The dashed red line indicates the VIP score threshold of 1.35, before a drop-off in VIP score.

[0458] FIG. 4 shows representative OPLS-DA models generated from AXINON lipoFIT data of ln R vs. LR24 M patients. (A) Representative OPLS-DA scores plot (ln R=red circles, LR24 M=green triangles). (B) Box plots of predictive accuracies from the OPLS-DA models of ln R vs. LR24 M patients, against random class assignment. ****indicates p<0.0001 by Kolmogorov-Smirnov test.

[0459] FIG. 5 shows VIP score ranking plot obtained from the OPLS-DA models of IR vs. LR2 years patients identifies isoleucine and leucine as the top two discriminatory metabolites.

[0460] FIG. 6 shows BCAA resonances in a CPMG spectrum. The zoom in panel shows that the spectral resonances overlap (e.g. isoleucine overlap with the broad methyl lipoprotein resonance; leucine resonance overlap with this broad lipoprotein signal, to a lesser extent); any masking due to overlap was overcome by targeted metabolomics (e.g. AXINON lipoFIT spectra data acquisition).

[0461] FIG. 7 shows a workflow schematic illustrating the selection of metabolites for ANOVA. *indicates metabolites detected by targeted metabolomics.

[0462] FIG. 8 shows bar graphs demonstrating significant metabolites on one-way ANOVA. (A and B) Lysine and asparagine levels were higher within ln R patients compared to LR24 M patients and decreased with time away from relapse. (C and D) In contrast, isoleucine and leucine concentrations were observed to be lower during relapses and increased with time away from relapse. **indicates p<0.01 and *indicates p<0.05 by post-hoc Holm-Sidak's test, with LR24 M patients as the reference comparator.

[0463] FIG. 9 shows ROC analysis of the four metabolite biomarkers (A-D) individually and (E) in combination.

[0464] FIG. 10 shows results from investigating the discriminatory ability of serum NfL levels across the patient groups. (A) One-way ANOVA showed that ln R patients had higher levels of serum NfL compared to LR24 M patients. ****indicates p<0.0001 by post-hoc Holm-Sidak's test, with LR24 M patients as the reference comparator. (B) ROC analysis of serum NfL. (C) ROC analysis using a combination of lysine, asparagine, isoleucine, leucine and NfL.

[0465] FIG. 11 shows results from investigating paired relapse-remission levels for the (A-D) four metabolite biomarkers, and for (E) serum NfL. *indicates p<0.05 on paired t-test.

[0466] FIG. 12 shows results from multivariate approaches to combine the four metabolites as a composite biomarker. (A) ROC analysis after multivariable logistic regression using the four metabolites. (B) Multivariable ROC analysis with the addition of serum NfL to the four metabolites. (C) OPLS-DA scores plot of ln R vs. LR24 M patients (ln R=circles, LR24 M=triangles), minus the in relapse data points of the nine patients with paired relapse-remission samples. (D) Insertion of data from paired relapse-remission samples into the OPLS-DA scores plot as a predictive set (relapse=diamonds within circles, remission=diamonds not within circles). Arrows indicate paired samples.

[0467] FIG. 13 shows results from investigating paired remission-relapse metabolites and serum NfL levels as predictive biomarkers of future relapses (A) Isoleucine, (B) leucine, and (C) serum NfL.

[0468] FIG. 14 shows results of analysis of plasma lipoprotein subclass particle number (-p), size, and cholesterol content (-c) of non-responders (defined as clinical relapse during 6 month follow-up) and responders (defined as no clinical relapse during follow-up) to first-line MS therapy using the AXINON lipoFIT platform. A significant increase in LDL particle number and cholesterol content was observed in the responder group relative to the non-responders.

[0469] FIG. 15 shows results of OPLS-DA NMR metabolomics analysis of plasma samples from a cohort of 44 people with MS receiving glatiramer acetate treatment provides additional evidence that plasma metabolites can distinguish between treatment responders (defined as no clinical relapse during 6 month follow up) and non-responders (defined as clinical relapse during 6 month follow up). A) A representative OPLS-DA scores plot (from the ensemble of 1000 cross-validated models generated) illustrates excellent discrimination between classes. B) A subset of the model validation metrics are provided including the predictive accuracy, sensitivity, specificity and cumulative Q.sub.2 to illustrate statistical significance of the model, robustness, and validation on independent data. Each box plot presents 1000 predictive values determined on randomly selected test sets. A permutation test generates the null distribution (1000 models) in order to validate the model and confirm discrimination has not occurred by chance. Kolmogorov-Smirnov test p-values<0.001 are represented by ***.

[0470] FIG. 16 shows results of unsupervised analysis of baseline blood samples from people with MS who go on to respond/not respond to 1st line therapy spontaneously clusters. This pilot data illustrates that significant differences in the blood metabolite profile exist which are predictive of response to 1st line treatments.

EXAMPLES

[0471] Materials and Methods

[0472] Patients

[0473] RRMS patients (under the MET cohort collection) were prospectively recruited from the John Radcliffe Hospital, Oxford University Hospital Trust. All patients fulfilled the 2017 revisions to the McDonald criteria for MS. Patients suspected to be having a relapse was first triaged by an experienced MS nurse via phone consultation and those suspected to have a relapse were then seen at the acute relapse clinic. Relapse status was established by MS neurologists and defined clinically (e.g. without the need for additional MRI confirmation) in accordance to the McDonald criteria, i.e. a monophasic clinical episode with patient-reported symptoms and objective findings typical of MS, reflecting a focal or multifocal inflammatory demyelinating event in the CNS, developing acutely or subacutely, with a duration of at least 24 hours, with or without recovery, and in the absence of fever or infection. Patients with pseudo-relapses were excluded. This was performed by systemic review of infective symptoms, temperature measurement and urine dipstick assessment to rule out urinary tract infections. Detailed information on smoking, alcohol intake, time from last meal and type of food for last meal were also collected at the time of recruitment.

[0474] Patients were divided into four groups according to the interval between their last relapse to blood sampling: (1) in relapse, defined as <1 month from the onset of relapse; (2) last relapse (LR)1 month to <6 months ago; (3) LR6 months to <24 months ago; and (4) LR24 months ago. These groups are henceforth referred to as ln R, LR 1-6 M, LR 6-24 M and LR24 M respectively.

[0475] In more detail, a total of two hundred and one RRMS patients were included in this study: In R (n=38), LR 1-6 M (n=28), LR 6-24 M (n=34), and LR24 M (n=101). Demographic and clinical characteristics are shown in Table 1 below:

TABLE-US-00001 TABLE 1 Demographic and clinical details of study population. p value In R LR 1-6 M LR 6-24 M LR 24 M across (n = 38) (n = 28) (n = 34) (n = 101) groups Age in years, mean (SD) 38.3 (9.5)* 38.7 (7.0) 43.5 (9.7) 44.2 (9.9)* 0.002 Female, No. (%) 27 (71.1) 23 (82.1) 22 (64.7) 73 (72.3) 0.503 White ethnicity, No. (%) 37 (97.4) 25 (89.3) 31 (91.2) 92 (91.1) 0.589 Recent/current steroid use, No. (%) 5 (13.2)* 0 (0.0) 1 (2.9) 1 (1.0)* 0.011 DMT use, No. (%) 18 (47.4)* 12 (42.9) 20 (58.8) 75 (74.3)* 0.002 Alemtuzumab 1 (5.6) 1 (8.3) 1 (5.0) 2 (2.7) Dimethyl fumarate 6 (33.3) 2 (16.7) 6 (30.0) 13 (17.3) Fingolimod 2 (11.1) 2 (16.7) 2 (10.0) 10 (13.3) Glatiramer acetate 6 (33.3) 5 (41.7) 7 (35.0) 25 (33.3) Interferons 1 (5.6) 0 (0.0) 3 (15.0) 18 (24.0) Natalizumab 1 (5.6) 2 (16.7) 1 (5.0) 5 (6.7) Teriflunomide 1 (5.6) 0 (0.0) 0 (0.0) 2 (2.7) EDSS, median (range) 3.3 (1-7)*.sup..sup. 2.5 (1-6.5).sup. 2.3 (0-8.5).sup. 2.0 (0-7)* <0.001 Disease duration in years, median 11.1 (0.73-28.7) 7.5 (0.19-28.3) 4.4 (0.54-28.5).sup. 12.1 (2.3-47.3).sup. <0.001 (range) No comorbidities, No. (%) 16 (42.1) 10 (35.7) 8 (23.5) 42 (41.6) 0.271 Presence of new T.sub.2 lesion/s within 5/12 (41.7) 7/8 (87.5) 4/9 (44.4) 3/22 (13.6) 0.002 last 1 year, referenced to a baseline scan done 1 year apart, No. (%) Presence of GAD-enhancing 5/16 (31.3) 7/11 (63.6) 4/12 (33.3) 1/21 (4.8) 0.003 lesion/s within last 1 year, No. (%) BMI, median (range) 26.5 (20-49) 25.0 (19-38.7) 27.0 (19.8-57.4) 24.8 (15-42) 0.081 Current smoker, No. (%) 5 (13.2) 5 (17.9) 3 (8.8) 12 (11.9) 0.751 Alcohol intake in units/week, 0 (0-16) 1.5 (0-35) 0 (0-18) 1 (0-24) 0.394 median (range) Time from last meal in hours, 3.7 (1.3-18.5) 3.6 (0.8-20.9) 3.6 (0.4-16.3) 3.3 (0.9-16.7) 0.319 median (range) p values within the right most column indicates differences across the four groups of patients. Symbols indicate p < 0.05 for pair-wise comparison after post-hoc correction. *In R vs. LR 24 M; LR 1-6 M vs. LR 24 M; .sup.In R vs. LR 1-6 M; .sup.In R vs. LR 6-24 M; .sup.LR 6-24 M vs. LR 24 M.

[0476] Blood Collection, Serum Processing

[0477] The detailed protocol, as described in the paragraph below, was used for pre-analytical sample handling. Two hundred and fifty microlitres of serum was used for global metabolomics while 750 uL of serum from the same blood sample was used for targeted metabolomics.

[0478] During venepuncture, blood was collected into red-top (BD Vacutainer 367837) and green-top lithium-heparin tubes (BD Vacutainer 367375) for serum and plasma collection respectively. The blood sample-handling protocol used is consistent with those frequently employed in the metabolomics literature and involved the following steps (Bervoets et al., 2015; Yin et al., 2015). Once collected, blood was left to stand for 30 minutes (to give sufficient time for clot formation in the case of serum) at room temperature after which it was centrifuged at 1,300 g for 10 minutes at room temperature using a benchtop centrifuge (Medifuge, Thermo Fisher Scientific Inc.) for erythrocyte separation to obtain serum/plasma. The serum/plasma was then immediately aliquoted into polypropylene cryotubes (Crystal Clear, STARLAB UK Ltd) and stored at 80 C. until NMR sample preparation.

[0479] NMR Sample Preparation

[0480] For NMR sample preparation, human serum/plasma was thawed at room. Two hundred microlitres was then diluted with 400 L of 75 mM sodium phosphate buffer, to make up a total volume of 600 L. The sodium phosphate buffer was prepared by dissolving 62.5 mM of anhydrous sodium phosphate dibasic powder (CAS number 7558794) and 12.5 mM of anhydrous sodium phosphate monobasic powder (CAS number 7558807) in deuterium oxide (D.sub.2O) (CAS number 7789200), giving a final pH of 7.4 (all three reagents were obtained from Sigma-Aldrich, Dorset, UK). D.sub.2O was used as the NMR solvent for all NMR experiments.

[0481] In more detail, the NMR experiments were performed using a 700-MHz Bruker (Bruker BioSpin Gmbh, Rheinstetten, Germany) AVIII spectrometer operating at 16.4 T equipped with a .sup.1H [.sup.13C/.sup.15N] TCI cryoprobe at the Department of Chemistry, University of Oxford. Sample temperature was regulated at 310 K.

[0482] For all serum/plasma NMR samples used, .sup.1H NMR spectra were first acquired using a 1D NOESY presaturation scheme for suppression of the water resonance with a 2 second (s) presaturation, 8 data collections, an acquisition time of 1.5 s, and a fixed receiver gain. Following this, a spin-echo Carr-Purcell-Meiboom-Gill (CPMG) sequence was applied, with a interval of 400 s, 80 loops, 32 data collections, an acquisition time of 1.5 s, a relaxation delay of 2 s and a fixed receiver gain, was used to suppress broad signals arising from large molecular weight serum/plasma components (e.g. albumin). In this way, the CPMG pulse sequence retains resonances from small molecular weight metabolites and mobile side chains of lipoproteins, providing an accurate measurement of these parameters in the serum/plasma sample. All serum/plasma spectral data acquisitions were therefore performed using the CPMG pulse sequence, as per standard NMR metabolomics literature (Soininen et al., 2009).

[0483] Free induction decays of the pulse sequences were zero-filled by a factor of 2 and multiplied by an exponential function corresponding to 0.30 Hertz (Hz) line broadening prior to Fourier transformation. For quality control (QC), pooled serum/plasma samples were spread throughout the run to monitor technical variation. .sup.1H COSY spectra were acquired on at least one sample in each disease classification to aid in metabolite identification. When required, further confirmation was achieved by 1D TOCSY or 2D TOCSY NMR experiments, or with spiking experiments with known candidate compounds. Metabolite assignments were further confirmed by referencing to literature values and the Human metabolome database (HMDB) (Wishart et al., 2018).

[0484] The buffered NMR samples were then stored at 80 C. until NMR analysis. Immediately before NMR experiments, the NMR samples were thawed at room temperature, briefly vortexed, and then transferred to a 5 mm borosilicate glass tube (Norell 502-7) via a glass pipette.

[0485] Global MetabolomicsCPMG Spectra Data Acquisition

[0486] All .sup.1H NMR experiments for global metabolomics were performed in-house at the Department of Chemistry, University of Oxford using a 700-MHz Bruker AVIII spectrometer, with the CPMG relaxation editing pulse sequence for spectra acquisition. Technical specifications of the NMR experiments and details of data handling are as described in Materials and Methods. In all, 191 metabolite bins were available for multivariate statistical analysis.

[0487] Targeted MetabolomicsAXINON lipoFIT Spectra Data Acquisition

[0488] Targeted metabolomics was performed with the AXINON lipoFIT system at numares AG, Regensburg, Germany, using a 500-MHz Bruker NMR spectrometer with the NOESY pulse sequence for spectra acquisition. This test system deconvolutes the broad methyl lipoprotein resonance of the .sup.1H NMR NOESY spectrum into its constituent parts, allowing for the direct measurement and quantification of the cholesterol content, number of particles, and mean particle diameter of each lipoprotein subpopulation. Lipoprotein groups measured include VLDL, LDL, IDL, and HDL, with each group further divided into large and small subpopulations. Additionally, the AXINON lipoFIT system provides absolute quantification of glucose as well as metabolites located close to the .sup.1H NMR lipoprotein resonances, as the resonances of these metabolites overlap with those arising from lipoproteins. These metabolites include lactate, glucose, alanine, as well as the BCAAisoleucine, leucine and valine. In all, 29 variables from targeted metabolomics were available for multivariate statistical analysis.

[0489] Serum NfL Level Determination

[0490] Serum NfL levels were measured using the Simoa assay (Quanterix, Massachusetts, USA) performed at the University of Basel, in collaboration with Dr Jens Kuhle. Assay techniques and principles have been previously described (Disanto et al., 2017; incorporated herein be reference). All laboratory personnel were blinded to the assignment of patient groups.

[0491] NMR Spectral Data Processing

[0492] All spectra were processed in Topspin 3.6.1 (Bruker BioSpin Gmbh, Rheinstetten, Germany) in which they were phased, baseline corrected using a third degree polynomial, and chemical shifts referenced to the lactate doublet resonance centred at chemical shift 1.33 ppm, a commonly used method of spectra referencing (Lu et al., 2018; Pontes et al., 2019; Verwaest et al., 2011). The NMR spectra were then visually inspected for errors in baseline correction, referencing, spectral distortion, or contamination with exogenous products (e.g. ethanol, propylene glycol). Processed spectra were then transferred to ACD/Labs Spectrus Processor Academic Edition 12.01 (Advanced Chemistry Development, Inc., Toronto, Canada) whereby the water signal from 4.20-5.20 ppm was excluded due to baseline distortion, as were signal-free noise regions. The remaining regions of the spectra between 0.80-4.20 ppm and 5.20-8.50 ppm were split into 0.02 ppm fixed-width bins. Integral values of each individual spectral bin were obtained and normalised to the integral value of the entire spectrum (i.e. constant-sum-normalisation) such that the integral value of the whole spectrum is 1 (Worley et al., 2013). This data matrix was used for multivariate statistical analysis.

[0493] Multivariate Statistical Analysis

[0494] OPLS-DA was used to interrogate the CPMG (global metabolomics) and AXINON lipoFIT (targeted metabolomics) data sets to identify metabolic differences between the groups of patients as defined above. Details of the OPLS-DA approach are as described in the paragraph below.

[0495] Multivariate analysis/model building was performed in R software using the ropls package. Orthogonal partial least squares discriminant analysis (OPLS-DA) with 10-fold external cross validation and repetition was used to identify linear combinations of metabolites which distinguish between the groups of interest. Models were validated on independent test data and by permutation testing. To identify which are the most discriminatory variables driving the separation between classes, variable importance in projection (VIP) scores derived from the OPLS-DA models are computed.

[0496] Univariate Statistical Analysis

[0497] All other statistical analyses (comparative, logistic regression, receiver operating curve [ROC], correlation analyses) were performed with STATA software (Release 14, College Station, TX: Statacorp LP, USA) and GraphPad Prism (version 6, California, USA). Comparative analyses between two groups were performed using Mann-Whitney U test or two-sample t-test as appropriate for continuous variables. For comparisons between three groups, one-way ANOVA or Kruskal Wallis test were used, with pair-wise post-hoc corrections using Bonferroni/Tukey and Dunn tests respectively. Chi-square or Fisher exact tests were used for categorical variables depending on the size of the expected frequency, with Bonferroni correction when comparing 3 groups. Two-way ANOVA was used to compare across groups and time points/treatment, with Sidak's test for multiple comparison corrections. Pearson's or Spearman's correlation was used to explore correlations depending on data normality. Two-tailed p values of <0.05 were considered statistically significant.

[0498] In other words, univariate analysis of demographic, clinical, and metabolite data was performed using STATA software (release 14, Texas, USA) and GraphPad Prism (version 6, California, USA). Comparative analyses between patient groups were performed using one-way ANOVA or Kruskal Wallis test as appropriate for continuous variables, with pair-wise post-hoc corrections using Bonferroni and Dunn tests respectively. Chi-square or Fisher exact tests were used for categorical variables as appropriate, with Bonferroni correction when comparing 3 groups. Pearson's or Spearman's correlation was used to explore correlations depending on data normality. Two-tailed p values of <0.05 were considered statistically significant and data was presented as meanSD unless stated otherwise.

Example 1Global Metabolomics (CPMG Spectral Data)

[0499] ln R vs. LR24 M Patients

[0500] To identify global metabolic signatures and perturbations reflective of clinical relapses, OPLS-DA was used to construct discriminatory models using CPMG spectral data to distinguish between ln R and LR24 M patients. The representative OPLS-DA scores plot showed a moderate separation between ln R and LR24 M patients (FIG. 1A). The mean predictive accuracy for the ensemble of the OPLS-DA models of ln R vs. LR24 M patients was significantly higher than the mean predictive accuracy of the ensemble created by random class assignments (meanSD, 62.64.8% vs. 50.98.2%; p<0.0001) (FIG. 1B), validating the metabolic differences seen between the two groups of patients.

[0501] LR 1-6 M Vs. LR24 M Patients, and LR 6-24 M Vs. LR24 M Patients

[0502] To explore how long global metabolic perturbations persist after relapses, OPLS-DA models were constructed using CPMG spectral data for LR 1-6 M as well as for LR 6-24 M patients against the reference comparator, i.e. LR24 M patients. The mean predictive accuracy for the ensemble of the OPLS-DA models for LR 1-6 M vs. LR24 M patients was significantly higher than the mean predictive accuracy of the random class ensemble (meanSD, 61.27.5% vs. 48.88.5%; p<0.0001). In contrast, the mean accuracy of the OPLS-DA models for LR 6-24 M vs. LR24 M patients was not different from the mean accuracy of the random class ensemble (meanSD, 50.56.7% vs. 50.67.8%; p=0.971).

[0503] The fold change in the predictive accuracy (normalised to the accuracy of random chance, i.e. 50%) of each patient group, using LR2 years patients as reference comparator, is shown in FIG. 2. Taking these findings in totality, this implies that global metabolic perturbations last for at least 6 months after a clinical relapse.

[0504] Identifying Discriminatory Metabolites from the in R Vs. LR24 M OPLS-DA Models

[0505] To identify the top discriminatory metabolites (and potential metabolite biomarkers of relapse) from the ln R vs. LR24 M OPLS-DA models, VIP scores were generated. The VIP score cut-off at 1.35 was determined by identifying a drop-off on the VIP ranking plot (FIG. 3). Metabolites with VIP scores above this cut-off are detailed in Table 2. These consisted predominantly of lipoproteins, amino acids and glucose. As most (i.e. two thirds) of these discriminatory metabolites were detected by the AXINON lipoFIT system, targeted metabolomics was performed next. Although targeted metabolomics covers a smaller region of the NMR spectra, it allows for detailed lipoproteins, amino acids and glucose analyses, as well as absolute quantification of these metabolites. The additional metabolites measured by the AXINON lipoFIT system that were not identified to be highly discriminatory by global metabolomics included alanine, isoleucine, lactate and valine.

TABLE-US-00002 TABLE 2 Top discriminatory metabolites from global (CPMG) metabolomics distinguishing In R vs. LR 24 M patients. Chemical shift of contributing Discriminatory metabolites spectral bins (VIP score, VIP rank) Lipoprotein CH3 0.80-0.86 ppm (1.36, 12) (HDL/LDL dominated) Lipoprotein CH3 0.86-0.92 ppm (1.62, 5) (VLDL dominated) Leucine* 0.94-0.98 ppm (1.36, 13); 1.62-1.78 ppm; 3.70-3.79 ppm Lipoprotein (CH.sub.2).sub.n 1.15-1.30 ppm (HDL/LDL dominated) Lysine 1.37-1.55 ppm; 1.65-1.75 ppm; 1.83-1.94 ppm; 3.00-3.05 ppm Lipoprotein CH.sub.2 1.53-1.61 ppm (1.63, 3) N-acetylated glycoprotein 1.93-2.10 ppm (e.g. NAC/CHCH.sub.2CH.sub.2)* Asparagine 2.80-3.00 ppm; 3.96-4.02 ppm Glucose* 3.17-3.95 ppm; 4.63-4.66 ppm; 5.22-5.25 ppm Phenylalanine 7.32-7.44 ppm (1.60, 7); 3.1-3.3 ppm; 3.9-4.0 ppm -hydroxybutyrate 1.19-1.21, 2.27-2.45 Myo-inositol 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm *indicates metabolites measured and quantified by AXINON lipoFIT system (targeted metabolomics, see Example 2). Chemical shift ranges are reported relative to lactate CH.sub.3 referenced at 1.33 ppm.

Example 2Targeted Metabolomics (AXINON lipoFIT Spectral Data)

[0506] ln R Vs. LR24 M Patients

[0507] To identify if there were metabolomics perturbations in relapses using a targeted metabolomics approach, OPLS-DA was employed to interrogate the AXINON lipoFIT parameters obtained from ln R and LR24 M patients. The mean predictive accuracy for the ensemble of the OPLS-DA models of ln R vs. LR24 M patients was significantly higher than the mean predictive accuracy of the ensemble created by random class assignments (meanSD, 58.15.5% vs. 50.57.0%; p<0.0001) (FIG. 4), confirming the presence of targeted metabolic differences between the two groups of patients. The decrease in predictive accuracy as well as the less obvious separation of the patient groups on the OPLS-DA scores plot is likely due to lesser amount of data available within a targeted region of the NMR spectra, as compared to the OPLS-DA models generated by global metabolomics.

[0508] Identifying Discriminatory Metabolites from the in R Vs. LR24 M OPLS-DA Model

[0509] VIP scores from the ln R and LR24 M OPLS-DA models were generated to elucidate the principal discriminatory metabolites from targeted metabolomics. The VIP ranking plot revealed isoleucine and leucine (both are BCAA) as the two most important metabolites (FIG. 5), with VIP scores of 2.05 and 2.01 respectively. BCAA resonances are shown in FIG. 6.

[0510] Isoleucine was identified as a top discriminatory metabolite in targeted metabolomics. Of note, the top lipoprotein parameter from the AXINON lipoFIT was LDL-s (mean diameter of LDL particles) which was ranked third based on its VIP score of 1.35.

[0511] Outcome of Examples 1 and 2

[0512] A principle outcome of Examples 1 and 2 is that the following metabolites have been identified a biomarkers (e.g. based on significant concentration change ln R vs. LR24 M) for confirming that MS patient is suffering a relapse:

TABLE-US-00003 TABLE 3 Metabolite Fold change In R vs. LR 24 M % Chemical Shift (PPM) Leucine 4.3 (e.g. decreased in relapse) 0.94-0.98; 1.62-1.78; 3.70-3.79 Lysine 5.2 (e.g. increased in relapse) 1.37-1.55; 1.65-1.75; 1.83-1.94; 3.00-3.05 Asparagine 5.9 (e.g. increased in relapse) 2.80-3.00; 3.96-4.02 Phenylalanine 6.5 (e.g. decreased in relapse) 7.32-7.44; 3.1-3.3; 3.9-4.0 Glucose 4.2 (e.g. increased in relapse) 3.17-3.95; 4.63-4.66; 5.22-5.25 -hydroxybutyrate 5.2 (e.g. decreased in relapse) 1.19-1.21 ppm, 2.27-2.45 ppm Myo-inositol 4.9 (e.g. decreased in relapse) 3.63-3.65 ppm, 3.53-3.58 ppm, 3.93-3.98 ppm, 3.25-3.29 ppm Lipoprotein CH3 6.4 (e.g. decreased in relapse) 0.80-0.86 (HDL/LDL dominated) Lipoprotein CH3 (VLDL 7.5 (e.g. decreased in relapse) 0.86-0.92 dominated) Lipoprotein (CH.sub.2).sub.n 8.1 (e.g. decreased in relapse) 1.15-1.30 (HDL/LDL dominated) Lipoprotein CH.sub.2 3.8 (e.g. increased in relapse) 1.53-1.61 N-acetylated glycoprotein 4.5 (e.g. decreased in relapse) 1.93-2.10 (e.g. NAC1/CHCH2CH2) Isoleucine 5.1 (e.g. decreased in relapse) 0.92-0.97; 1.00-1.03; 1.22-1.28; 1.43-1.51; 1.94-2.01; 3.65-3.68

Example 3Further Exploration of Metabolite Biomarkers of Clinical Relapses

[0513] As demonstrated above, using the OPLS-DA analytical approach, global metabolic perturbations can be observed during relapses and up to six months after relapses. The top discriminatory metabolites from targeted (isoleucine and leucine) and global metabolomics (metabolites listed in Table 2 not marked by an asterisk) distinguishing ln R vs. LR24 M patients were shortlisted for further demonstration of the associated of metabolite biomarkers with clinical relapses. One-way ANOVA was performed for each of these shortlisted metabolite across the four patient groups, using LR24 M patients as the reference group. The workflow for choosing the metabolites for ANOVA is illustrated in FIG. 7. For consistency, discriminatory metabolomics from global metabolomics (i.e. lipoproteins, leucine and glucose) that are already covered in-depth by targeted metabolomics (AXINON lipoFIT system) were not included in this analysis.

[0514] Lysine and asparagine (from global metabolomics), as well as isoleucine and leucine (from targeted metabolomics) were significant on one-way ANOVA. Both lysine and asparagine were higher in ln R vs. LR24 M patients, and showed a decreasing trend over time (FIGS. 8A and B). The converse was observed for isoleucine and leucine: lower levels in ln R compared to LR24 M patients and increasing with time away from relapse (FIGS. 8C and D). Taking these observations in totality, this demonstrates that lysine, asparagine, isoleucine and leucine are particularly advantageous metabolite biomarkers of clinical relapses.

[0515] On ROC analysis to distinguish between ln R and LR24 M patients, the univariate AUC of the four metabolite biomarkers ranged from 0.610 to 0.697 (FIG. 9A-D). Next, to explore if the combination of the four metabolites could enable higher discriminatory ability, the four metabolites were used as independent variables in a multivariable logistic regression model followed by ROC analysis. This resulted in an improved AUC of 0.758 (FIG. 9E).

Example 4Exploring Serum NfL as a Potential Biomarker of Clinical Relapses

[0516] The previous sections demonstrated that using both global and targeted metabolomics approaches, four metabolites in particular examined further to demonstrate they serve as biomarkers of relapse. Serum NfL has been suggested to be a potential biomarker to inform on MS inflammatory activity and is reported to be elevated in clinical relapses, thus its diagnostic performance in this cohort of patients was explored. One-way ANOVA of serum NfL levels, using LR24 M patients as the reference group, showed that ln R patients had higher levels of serum NfL compared to LR24 M patients, although this appeared to be driven by outliers with very elevated levels of serum NfL (FIG. 10A). Indeed, when the three highest serum NfL values (i.e. outliers) from the ln R group were removed, the one-way ANOVA was no longer statistically significant (p=0.054) and the post-hoc Holm-Sidak's test comparing ln R vs. LR24 M was also not statistically significant (p=0.061). ROC analysis of all data points showed an AUC of 0.575 for serum NfL in distinguishing between ln R vs. LR24 M patients (FIG. 10B).

[0517] That being said, it was next explored if the ability to distinguish ln R and LR24 M patients could be increased by combining 4 metabolites and NfL. A multivariable logistic regression model was constructed followed ROC analysis (FIG. 10C), showing a slightly increased AUC of 0.789, as compared to using the combination of the four metabolites alone.

Example 5Metabolites as Individualised, Responsive Biomarkers of Relapses

[0518] From the previous sections, lysine, asparagine, isoleucine and leucine were identified to be advantageous metabolite biomarkers of relapses. It was next explored whether these metabolites would be: (1) applicable in an individualised manner (i.e. within an individual patient), and (2) be responsive enough such that the change in its levels between diseased states must be observable within a clinically useful time frame.

[0519] To this end, nine patients (under the MET cohort collection) who had paired relapse-remission samples (i.e. relapse first followed by remission), and with the remission sample collected within 6 months of relapse onset, were identified. The clinical characteristics of these nine patients are shown in Table 4. Of note, none of these patients received steroids at the time of relapse blood sampling.

TABLE-US-00004 TABLE 4 Clinical characteristics of the nine patients with paired relapse- remission samples, with data collected at the relapse time point. Patients with paired relapse-remission (n = 9) Age in years, mean (SD) 40.2 (7.1) Female, No. (%) 6 (66.7) White, No. (%) 9 (100.0) Recent/current steroid use, No. (%) 0 (0.0) DMT use, No (%) 4 (44.4) Alemtuzumab 1 (25.0) Glatiramer acetate 2 (50.0) Interferons 1 (25.0) EDSS, median (range) 4.0 (2.0-6.5)* Disease duration in years, median (range) 14.8 (1.9-23.5) No comorbidities, No. (%) 4 (44.4) BMI, median (range) 30.3 (24.2-46.4) Interval between relapse onset and 3.6 (2.3-5.6) remission sampling in months, median (range) Presence of new T.sub.2 lesion/s within Not known as none had last 1 year, referenced to baseline earlier baseline reference scan done 1 year apart, No. (%) scan done 1 year Presence of GAD enhancing lesion/s 1/5 (20) within last 1 year, No. (%) Presence of new T.sub.2 lesion/s within 2/4 (50) next 6 months, referenced to baseline scan 1 year apart, No. (%) Presence of GAD enhancing lesion/s 3/6 (50) within next 6 months, No. (%) *Median EDSS at remission sampling was 2.5 (range 1.0-6.5).

[0520] Univariate Analysis

[0521] On paired t-test, all four metabolites showed significant differences in their levels during relapse and in remission (FIGS. 11A-D), and the direction of change was consistent with that observed when comparing at group level across the four groups of patients (FIGS. 8A-D). There was no difference in serum NfL levels within this 6-month time frame (FIG. 11E). Six out of the eight patients (one patient had missing data for the relapse sample and was excluded from analysis) had lower serum NfL levels during remission while two had higher levels. It was observed that the change in serum NfL levels was gradual and there were considerable inter-individual differences in NfL levels during relapses, consistent with observations from group comparisons (FIG. 8).

[0522] Multivariate Analysis

[0523] The majority of these patients had complete concordance in the direction of change for all four metabolites. Given these observations, multivariate approaches were employed to determine if a combination of these four metabolites (i.e. a metabolic constellation), rather than individual metabolites, could be used as a composite biomarker. Using the four metabolites as independent variables in a multivariable logistic regression approach followed by ROC analysis, an AUC of 0.911 was achieved (FIG. 12A). The addition of serum NfL into this multivariable regression model resulted in slight decrease in AUC to 0.896 (FIG. 12B).

[0524] To further extend this demonstration of a composite metabolic biomarker, a vector-based OPLS-DA approach was employed. A representative OPLS-DA scores plot (generated using 7-fold internal cross-validation) of ln R vs. LR24 M patients was first constructed (FIG. 12C) using the same data used to generate the scores plot in FIG. 1A, however excluding the relapse data of the nine patients who had paired relapse-remission samples. This was done to allow the data (from these nine patients) to be used as an independent predictive set. The x-axis of the OPLS-DA scores plot (FIG. 12C), i.e. along the first component, can be thought to represent inflammation, with a leftward vector signifying decreasing inflammatory activity. Data from the paired relapse-remission samples were then inserted into this OPLS-DA scores plot as a predictive set (FIG. 12D). All remission samples, except one, moved in a leftward direction (along the first component) with respect to their paired relapse sample, consistent with the vector representing decreasing inflammation.

Example 6Demonstrating Utilisation of Metabolites as Predictive Biomarkers of Relapses

[0525] Isoleucine and leucine were chosen for further analysis to explore whether the biomarkers find utility in predicting onset of a relapse.

[0526] To explore if the identified metabolites can be predictive biomarkers of relapses, three patients (from the MET cohort) who had remission samples prior to relapse samples were identified. The mean interval between remission and relapse was 14.1 months. Two of the three patients had higher isoleucine levels during remission compared to in relapse, while the levels remained the same for one patient (FIG. 13A). For leucine, two patients showed higher levels during remission (FIG. 13B). The leucine measurement in remission for one patient was not available and was thus excluded from analysis. The direction of change was consistent with that described in the previous sections. For serum NfL, most patients showed lower levels in remission (FIG. 13C). This observation could mean that isoleucine and leucine are reflective of the state of immune activation.

Example 7Addressing Potential Confounders for the Identified Metabolite Biomarkers

[0527] As illustrated in Table 1, several baseline characteristics were different across the four patient groups; notably age, steroid use, and DMT use were dissimilar between ln R vs. LR24 M patients on post-hoc analysis. Two approaches were used to address these potential confounders: (1) for quantitative variables, correlation of the potential confounding variable with each of the four identified metabolite biomarkers was performed, and (2) for categorical variables, the levels of the four metabolites were explored stratified by the potential confounding variable (i.e. steroid use and DMT use).

[0528] There were no correlations between any of the four metabolite biomarkers with age, EDSS and disease duration (the highest R.sup.2 was 0.113 between age and lysine) across the entire cohort of patients (n=201). There were no differences in any of the metabolite concentrations stratified by steroid use in the whole cohort (7 steroid users, 194 non-steroid users) and indeed within ln R patients (5 steroid users, 33 non-steroid users). For DMT use, there were higher isoleucine levels in DMT users (meanSD, 82.325.6 mol/L vs. 72.818.7 mol/L; p=0.011) across the entire cohort (125 DMT users, 76 non-DMT users). However, no differences in isoleucine levels were observed between DMT users and non-users within ln R patients (18 DMT users, 20 non-DMT users) (p=0.120) as well as within LR24 M patients (75 DMT users, 26 non-DMT users) (p=0.304). There were also no significant associations/correlations of the discriminatory metabolites with smoking status, alcohol intake, and time from last meal.

Example 8Association of Metabolite Biomarkers with MRI Indices of Inflammatory Activity

[0529] The association of certain identified metabolite biomarkers with MRI indices of inflammation, i.e. the presence of new T.sub.2 lesion/s (referenced to baseline scan done 51 year apart) as well as the presence of GAD-enhancing lesion/s within the last 1 year, were explored. Interestingly, lysine (meanSD, 25.510.sup.42.110.sup.4 AU vs. 23.710.sup.42.410.sup.4 AU; p=0.008) and asparagine (meanSD, 9.610.sup.41.210.sup.4 AU vs. 8.910.sup.41.210.sup.4 AU; p=0.048) levels were significantly higher in patients who had GAD-enhancing lesions.

Example 9Demonstrating Treatment Response Via Metabolomics

[0530] It was explored whether metabolomics analysis can be used to monitor a patient's response to a treatment, e.g. to distinguish a responder from a non-responder. Associated data are provided in FIGS. 14-16.

[0531] FIG. 14 shows results of analysis of plasma lipoprotein subclass particle number (-p), size, and cholesterol content (-c) of non-responders (defined as clinical relapse during 6 month follow-up) and responders (defined as no clinical relapse during follow-up) to first-line MS therapy using the AXINON lipoFIT platform. A significant increase in LDL particle number and cholesterol content was observed in the responder group relative to the non-responders.

[0532] FIG. 15 shows results of OPLS-DA NMR metabolomics analysis of plasma samples from a cohort of 44 people with MS receiving glatiramer acetate treatment provides additional evidence that plasma metabolites can distinguish between treatment responders (defined as no clinical relapse during 6 month follow up) and non-responders (defined as clinical relapse during 6 month follow up). A) A representative OPLS-DA scores plot (from the ensemble of 1000 cross-validated models generated) illustrates excellent discrimination between classes. B) A subset of the model validation metrics are provided including the predictive accuracy, sensitivity, specificity and cumulative Q.sub.2 to illustrate statistical significance of the model, robustness, and validation on independent data. Each box plot presents 1000 predictive values determined on randomly selected test sets. A permutation test generates the null distribution (1000 models) in order to validate the model and confirm discrimination has not occurred by chance. Kolmogorov-Smirnov test p-values<0.001 are represented by ***.

[0533] FIG. 16 shows results of unsupervised analysis of baseline blood samples from people with MS who go on to respond/not respond to 1st line therapy spontaneously clusters. This pilot data illustrates that significant differences in the blood metabolite profile exist which are predictive of response to 1st line treatments.

[0534] Discussion of Examples

[0535] The above Examples demonstrate that: (1) metabolic perturbations are present in patients during relapses using both global and targeted metabolomics, as compared to patients with no relapses for the past two years, (2) in particular, four discriminatory metabolites that were significant on ANOVA (lysine, asparagine, isoleucine and leucine) across the different patient groups showed a consistent trend (either increasing or decreasing) with time away from relapse, and advantageously, (3) these metabolites are informative in an individualised manner within a clinically useful time frame. Taking these observations in totality, this demonstrates that the identified metabolites are advantageous biomarkers of clinical relapses.

[0536] Without wishing to be bound by theory, it is believed the metabolic perturbation seen in MS relapses is likely to be due to the summative effects of various immunopathological processes that occur during these inflammatory events: (1) activation of peripheral T cells and monocytes, and their subsequent access into the CNS, (2) activation of resident microglial and astrocytes, (3) initiation of injurious effector mechanisms leading to the production of ROS and mitochondrial stress, and (4) demyelination with possible axonal injury. Advantageously, while many/most of these pathophysiological processes may occur within the CNS, the metabolic perturbations can be detected in alternative samples (e.g. alternatively/additionally to cerebrospinal fluid), such as serum. Two possible explanations can account for this observation: (1) CNS metabolites involved in these pathophysiological processes can cross the blood brain barrier, BBB (and indeed in the opposite direction) facilitated by increased permeability on a background of an inflamed BBB, and/or (2) the metabolic perturbation is contributed mostly by peripheral processes, namely the activation of peripheral immune cells and the peripheral response to CNS injury which is mediated primarily by the liver. The postulated roles of the identified metabolite biomarkers in these processes will now be discussed.

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[0546] All publications mentioned in the above specification are herein incorporated by reference. Various modifications and variations of the described methods and system of the present invention will be apparent to those skilled in the art without departing from the scope and spirit of the present invention. Although the present invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in biochemistry and biotechnology or related fields are intended to be within the scope of the following claims.