Biomarkers for multiple sclerosis

A method for monitoring disease risk, progression, or regression of multiple sclerosis (MS) in a subject, the method comprising:

• detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one biomarker is selected from GLX-related glycosaminoglycans (GAGs), and at least one biomarker is selected from GLX-related proteoglycans (PGs);
• comparing the levels of the at least two biomarkers in the first biological sample to reference levels for the at least two biomarkers;
• determining a cut-off level using a multivariate statistical analysis comprising machine learning, which transforms the levels of the at least two biomarkers detected in the first biological sample to the cut-off level;
• and
• determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the reference levels for the at least two biomarkers with respect to the cut-off level,
• wherein the cut-off level indicates a risk of MS or the progression or regression of MS, and
• wherein the at least two biomarkers in the first biological sample are detected by a method selected from the group consisting of:
• binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy.

A method for monitoring disease progression or regression of multiple sclerosis (MS) in a subject, the method comprising:

• detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related glycosaminoglycans (GAGs);
• detecting the level of the same at least two biomarkers in a second biological sample from the subject, wherein said second biological sample has been obtained at a different time point than said first biological sample from said subject;
• comparing the levels of the at least two biomarkers detected in the second biological sample;
• determining a cut-off level using a multivariate statistical analysis comprising machine learning, wherein the machine learning transforms the levels of the at least two biomarkers detected in the first and/or second samples to the cut-off level;
• and
• determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the levels of the at least two biomarkers detected in the second biological sample with respect to the cut-off level,
• wherein the cut-off level indicates the progression or regression of MS, and
• wherein the at least two biomarkers in the first biological sample and the same at least two biomarkers in the second biological sample are detected by a method selected from the group consisting of:
• binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy.

A method for monitoring disease progression or regression of multiple sclerosis (MS) in a subject, the method comprising:

• detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related proteoglycans (PGs);
• detecting the level of the same at least two biomarkers in a second biological sample from the subject, wherein said second biological sample has been obtained at a different time point than said first biological sample from said subject;
• comparing the levels of the at least two biomarkers detected in the second biological sample;
• determining a cut-off level using a multivariate statistical analysis comprising machine learning, wherein the machine learning transforms the levels of the at least two biomarkers detected in the first and/or second samples to the cut-off level;
• and
• determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the levels of the at least two biomarkers detected in the second biological sample with respect to the cut-off level,
• wherein the cut-off level indicates the progression or regression of MS, and
• wherein the at least two biomarkers in the first biological sample and the same at least two biomarkers in the second biological sample are detected by a method selected from the group consisting of:
• binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy.

A method for monitoring disease risk, progression, or regression of multiple sclerosis (MS) in a subject, the method comprising:

• detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related glycosaminoglycans (GAGs);
• comparing the levels of the at least two biomarkers in the first biological sample to reference levels for the at least two biomarkers;
• determining a cut-off level using a multivariate statistical analysis comprising machine learning, which transforms the levels of the at least two biomarkers detected in the first biological sample to the cut-off level;
• and
• determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the reference levels for the at least two biomarkers with respect to the cut-off level,
• wherein the cut-off level indicates a risk of MS or the progression or regression of MS, and
• wherein the at least two biomarkers in the first biological sample are detected by a method selected from the group consisting of:
• binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy.

A method for monitoring disease risk, progression, or regression of multiple sclerosis (MS) in a subject, the method comprising:

• detecting the level of at least two biomarkers in a first biological sample from a subject considered as having multiple sclerosis or at risk of developing multiple sclerosis, whereby at least one of the biomarkers is selected from GLX-related proteoglycans (PGs);
• comparing the levels of the at least two biomarkers in the first biological sample to reference levels for the at least two biomarkers;
• determining a cut-off level using a multivariate statistical analysis comprising machine learning, which transforms the levels of the at least two biomarkers detected in the first biological sample to the cut-off level;
• and
• determining that the subject has a progression or a regression of MS if the levels of the at least two biomarkers detected in the first biological sample differ from the reference levels for the at least two biomarkers with respect to the cut-off level,
• wherein the cut-off level indicates a risk of MS or the progression or regression of MS, and
• wherein the at least two biomarkers in the first biological sample are detected by a method selected from the group consisting of:
• binding agents, immunoassays, antibody recognition, multiplexing, dot blotting, beads, microspheres, single-molecule array technology, mass spectrometry, HPLC, Raman spectroscopy, NIR spectroscopy, and NMR spectroscopy.