GLX-derived molecule detection

Abstract

To provide means of diagnosis and / or prognosis of inflammatory diseases.SOLUTION: The present invention relates to biomarkers associated with immune-mediated inflammatory disease (IMID), particularly GLX molecules, and even more particularly GLX-related glycosaminglycans (GAGs) and GLX-related proteoglycans (PGs).SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to the diagnosis and / or prognosis of inflammatory diseases. More specifically, the measurement of biomarkers to predict the risk of developing and / or suffering from inflammatory diseases in a subject. Furthermore, the present invention relates to drugs or substances for the treatment or prevention of inflammatory diseases in individuals, monitoring of treatment success, and selection of treatments. [Background technology]

[0002] 2. Background of the Invention Immune-mediated inflammatory diseases (IMIDs) are any of a group of conditions or diseases that are characterized by a common inflammatory pathway leading to inflammation and may result from or be caused by dysregulation of the normal immune response. All IMIDs can cause end-organ damage and are associated with increased morbidity and / or mortality.

[0003] Inflammation is an increasingly important area of ​​biomedical research and healthcare because it mediates and is a primary driver of numerous medical disorders, such as autoimmune diseases (e.g., ankylosing spondylitis, psoriasis, systemic lupus erythematosus psoriatic arthritis, Behcet's disease, rheumatoid arthritis, inflammatory bowel disease (IBD), type I diabetes mellitus (T1DM) and allergies), as well as many cardiovascular diseases, neuromuscular diseases (e.g., amyotrophic lateral sclerosis (ALS)), neurodegenerative diseases (e.g., Parkinson's disease (PD)), multiple system atrophy (MSA), Alzheimer's disease (AD), psychiatric diseases (e.g., major depression, bipolar disorder, schizophrenia, anxiety disorders), neurodevelopmental diseases, and infectious diseases.

[0004] IMID is characterized by immune dysregulation, one underlying manifestation of which is the inappropriate activation of pro-inflammatory cytokines such as IL-12, IL-6 or TNF alpha, often accompanied by reduced production of anti-inflammatory cytokines, the effects of which lead to pathological outcomes.

[0005] Although evidence of disease association exists, progress has been limited because research has been driven in a disease-specific manner according to clinical features and has often focused on the later stages of the disease, thus early mechanisms surrounding the loss of immune tolerance or early inflammatory instigators have been overlooked.

[0006] Patent publication WO2009068685 broadly discloses the general principles, detection, diagnostic and high throughput devices, and the use of such devices for the above purposes.

[0007] Despite the advent of proteomics to assess plasma biomarkers, treatment strategies are challenged by the lack of distinctive biomarkers predicting disease severity, early signs of new disease recurrence, and response to treatment. Summary of the Invention [Problem to be solved by the invention]

[0008] The prevalence of IMID in Western societies is estimated to be 10%. Currently, there is no direct method to identify common disease characteristics, and therefore there remains an unmet need for biomarkers that can be used to predict the risk of developing IMID, which can potentially lead to organ dysfunction, failure and chronic disease. Furthermore, there is a need to further determine the specific type of inflammatory disease and in this context to initiate a targeted and effective course of treatment. [Means for solving the problem]

[0009] Summary of the Invention In a first aspect of the present invention, one or more of these objects are solved by an ex vivo method of diagnosing and / or prognosing at least one immune-mediated inflammatory disease (IMID) in a subject, said method comprising: a. performing an in vitro measurement of the level of one or more biomarkers selected from the group consisting of glycosaminoglycans (GAGs) and proteoglycans (PGs) of the glycocalyx in a first biological sample from the subject; b. optionally performing an in vitro measurement of the level of one or more biomarkers in a second or further biological sample from the subject obtained at a time later than said first biological sample; c. comparing the level of said one or more biomarkers to the level of one or more corresponding standards; d. Using the measurements to assess the condition of the subject; Includes.

[0010] In further embodiments, the glycosaminoglycan (GAG) is keratan sulfate and / or the proteoglycan (PG) is selected from biglycan, glypican-1, glypican-4, syndecan-3 and / or CD44.

[0011] In a further embodiment, the levels of more than one biomarker are measured.

[0012] In a further embodiment, the subject's status assessed in step d. is determined to be at risk of developing and / or suffering from a given IMID if the levels of two or more biomarkers are higher and / or lower than one or more corresponding reference levels, and is determined to be not at risk of developing or suffering from an IMID if the one or more levels are equal to one or more corresponding reference levels.

[0013] In yet another embodiment, the level of one or more biomarkers is significantly higher or lower than the corresponding reference value, at least with a P-value of less than 0.05.

[0014] The glycocalyx is a layer composed of proteoglycans, glycoproteins, and glycolipids that coats the plasma membrane of most epithelial animal cells. In general, components of the glycocalyx are involved in signal transduction, cell-cell recognition, communication, and cell-cell adhesion (with leukocytes and platelets), and maintain homeostasis of the vascular wall.

[0015] In blood vessels, the endothelial glycocalyx is located on the apical surface facing the lumen. The glycocalyx, located on the apical surface of endothelial cells, is composed of a negatively charged network of proteoglycans, glycoproteins, and glycolipids. When blood vessels are stained with cationic dyes, transmission electron microscopy reveals a diverse layer of irregular shapes that extends into the lumen of the vessel. It is present throughout various microvascular beds (including capillaries) and large blood vessels (arteries and veins).

[0016] The glycocalyx barrier fortifies blood vessels and organs against disease. Another major function of the glycocalyx in the vascular endothelium is to shield the vessel wall from direct exposure to the bloodstream, acting as a vascular permeability barrier. Its protective function is universal throughout the vascular system. Another protective function throughout the cardiovascular system is its ability to affect the filtration of interstitial fluid from the capillaries into the interstitial spaces.

[0017] Because the glycocalyx is so prominent throughout the cardiovascular system, disruption of this structure can have deleterious effects and lead to disease: initial dysfunction of the glycocalyx leads to internal fluid imbalance and potentially edema, leukocyte infiltration, disrupting the function of the barrier and potentially the endothelial cells themselves, leading to a deleterious cascade of inflammation.

[0018] Shedding of the glycocalyx can be triggered by inflammatory stimuli such as tumor necrosis factor-alpha, but whatever the stimulus, shedding of the glycocalyx leads to a series of dysregulations, dysfunctions, and allows damage to blood vessels and the organs they serve.

[0019] In one embodiment of the invention, the biological sample is a blood sample, such as whole blood, plasma, serum, or cerebrospinal fluid.

[0020] In a further embodiment of the invention, the at least one additional biomarker is selected from one or more of the group consisting of chondroitin sulfate (CS), heparan sulfate (HS), hyaluronic acid (HA) and dermatan sulfate (DS), keratan sulfate (KS), and / or selected from one or more of the group consisting of syndecan, glypican, and biglycan. The term "an additional biomarker" refers to a biomarker in addition to another biomarker, or to a biomarker in addition to two or more biomarkers.

[0021] In yet another embodiment, the syndecan is selected from one or more of the group consisting of syndecan-1, syndecan-2, syndecan-3 and syndecan-4, and / or the glypican is selected from one or more of the group consisting of glypican-1, glypican-2, glypican-3, glypican-4, glypican-5 and glypican-6, and / or the proteoglycan (PG) is selected from one or more of the group consisting of biglycan, CD44, perlecan, mimecan, decorin, hyalectans (aggrecan, versican, neurocan, brevican), betaglycan, lumincan, keratocan.

[0022] In certain embodiments of the invention, the biomarkers are two or more biomarkers selected from the group consisting of biglycan, CD44, keratan sulfate, GPC-1, GPC-4, hyaluronic acid, chondroitin sulfate, perlecan, heparan sulfate, syndecan-1, syndecan-2, syndecan-3, and syndecan-4.

[0023] It is believed that the immune-mediated inflammatory disease diagnosed or predicted according to the present invention may be or may progress to an autoimmune disease, a neuroinflammatory disease, a neurodegenerative disease, a psychiatric disease, an organ system disease, or more specifically may be or may progress to one of the diseases in Table 1 below, which may be diagnosed or predicted by the methods of the present invention. Individual diseases may be diagnosed or predicted alone or simultaneously in a single biological sample.

[0024] [Table 1]

[0025] [Table 2]

[0026] [Table 3]

[0027] [Table 4]

[0028] The amount of biological sample to be tested is 0.01 μl or more, for example 0.02 μl, 0.05 μl, 0.1 μl, 0.5 μl, 1 μl, 2 μl, 5 μl or 10 μl. Larger volumes may be used depending on the assay.

[0029] In one embodiment of the ex vivo method according to the invention, step a) consists in carrying out in vitro measurements of the levels of 2, 3, 4, 5, 10, 20 or more selected biomarkers. The measurements of the levels of two or more biomarkers can be carried out simultaneously or sequentially, e.g. multiplexing of two or more biomarkers.

[0030] In another aspect of the invention the object is solved by providing a substance for use in a method of treating a condition in an individual known to have at least one or more biomarkers selected from the group consisting of glycosaminoglycans (GAGs) and proteoglycans (PGs) of the glycocalyx, wherein the biomarkers are at a level higher than the corresponding reference level or levels, and the substance and the condition are selected from:

[0031] [Table 5]

[0032] In one embodiment the GAG ​​is keratan sulfate and / or the PG is selected from the list of biglycan, glypican-1, glypican-4, syndecan-3 and CD44.

[0033] Furthermore, the object is solved by providing a substance for use in a method of treating a condition in an individual known to have at least one or more biomarkers selected from the group consisting of glycosaminoglycans (GAGs) and proteoglycans (PGs) of the glycocalyx, wherein the substance and the condition are selected from:

[0034] [Table 6]

[0035] [Table 7]

[0036] [Table 8]

[0037] [Table 9]

[0038] and / or selected from:

[0039] [Table 10]

[0040] [Table 11]

[0041] [Table 12]

[0042] [Table 13]

[0043] [Table 14]

[0044] In certain embodiments, for use in a method of treating stroke, Parkinson's disease (PD), multiple system atrophy (MSA), amyotrophic lateral sclerosis (ALS), type I diabetes mellitus (T1DM) or Alzheimer's disease (AD) in an individual known to have at least one or more biomarkers selected from the group consisting of glycosaminoglycans (GAGs) and proteoglycans (PGs) of the glycocalyx, an agent selected from the group consisting of:

[0045] [Table 15]

[0046] [Table 16]

[0047] In one embodiment the GAG ​​is keratan sulfate and / or the PG is selected from the list of biglycan, glypican-1, glypican-4, syndecan-3 and CD44.

[0048] In another aspect of the present invention, the above object is to provide a method for the preparation of a - a binder for at least two biomarkers selected from the group consisting of glycosaminoglycans (GAGs) and proteoglycans (PGs) of the glycocalyx; - optionally, instructions for using the binder in the assessment of the IMID. The problem is solved by providing a kit comprising:

[0049] In one embodiment the GAG ​​is keratan sulfate and / or the PG is selected from the list of biglycan, glypican-1, glypican-4, syndecan-3 and / or CD44.

[0050] Suitably the kit comprises: - a binding agent for keratan sulfate, biglycan, glypican-1, glypican-4, syndecan-3 and / or CD44, - instructions for use of the binder in the assessment of stroke, Parkinson's disease (PD), multiple system atrophy (MSA), amyotrophic lateral sclerosis (ALS), diabetes mellitus type 1 (T1DM) or Alzheimer's disease (AD); may include.

[0051] In yet another aspect, a microfluidic detection chip for detecting IMID infection in a patient, said chip comprising one or more separation channels (2) coated with one or more binding agents for biomarkers selected from the group consisting of glycosaminoglycans (GAGs) and proteoglycans (PGs) of the glycocalyx.

[0052] In one embodiment, the GAG ​​is selected from keratan sulfate, chondroitin sulfate and heparan sulfate, and / or the proteoglycan (PG) is selected from biglycan, glypican-1, glypican-4, syndecan-3 and / or CD44.

[0053] In another embodiment, a microfluidic detection chip for detecting at least one IMID in a patient comprises one or more layers (3) having a plurality of separation channels (2) disposed within each layer, and at least one sample inlet port (4) in fluid communication with the separation channels (2) and into which a biological sample is introduced, one or more of the separation channels (2) having a plurality of three-dimensional (3D) separation zones (5) having intermittent distribution zones, and the chip is an optical chip.

[0054] In one embodiment, the kit according to the invention further comprises said microfluidic chip, wherein one or more separation channels are coated with said binding agents specific for one or more biomarkers of GAGs and PGs.

[0055] BRIEF DESCRIPTION OF THE DRAWINGS The invention is described in more detail below by way of non-limiting examples of embodiments and with reference to Figures 1 to 10. [Brief description of the drawings]

[0056] [Figure 1] Figure 1 shows the results of dot blot analysis of glycocalyx-related blood biomarkers for stroke. Biomarkers tested were: a) syndecan-3, b) chondroitin sulfate (CS), c) CD44, d) heparan sulfate (HS), e) syndecan-4, f) syndecan-1, g) glypican-1, and h) hyaluronic acid (HA). [Figure 2A] Glycocalyx (GLX) components detected in plasma of healthy subjects (HI) and longitudinally after ischemic stroke (ISS). Plasma from patients within 3 days, 7 days, and 90 days after HI and ISS were immunoassayed for GLX markers. [Figure 2B] Glycocalyx (GLX) components detected in plasma of healthy subjects (HI) and longitudinally after ischemic stroke (ISS). Plasma from patients within 3 days, 7 days, and 90 days after HI and ISS were immunoassayed for GLX markers. [Figure 2C] Glycocalyx (GLX) components detected in plasma of healthy subjects (HI) and longitudinally after ischemic stroke (ISS). Plasma from patients within 3 days, 7 days, and 90 days after HI and ISS were immunoassayed for GLX markers. [Diagram 3] Correlation heatmap profiles of GLX components detected in plasma of healthy subjects and longitudinally after ISS are shown. [Figure 4A] Figure 2 shows the GLX components detected in the plasma of patients with Parkinson's disease (PD), multiple system atrophy (MSA) and amyotrophic lateral sclerosis (ALS), as well as age-matched healthy controls (HC). [Figure 4B] Figure 2 shows the GLX components detected in the plasma of patients with Parkinson's disease (PD), multiple system atrophy (MSA) and amyotrophic lateral sclerosis (ALS), as well as age-matched healthy controls (HC). [Diagram 5] Correlation heatmap profiles of GLX components detected in plasma of patients suffering from Parkinson's disease (PD), multiple system atrophy (MSA) and amyotrophic lateral sclerosis (ALS), as well as age-matched healthy controls (HC). [Figure 6A] GLX components detected in plasma from patients with pediatric type 1 diabetes mellitus (T1DM) (ages 6-9) and from healthy control siblings without T1DM are shown. [Figure 6B] GLX components detected in plasma from patients with pediatric type 1 diabetes mellitus (T1DM) (ages 6-9) and from healthy control siblings without T1DM are shown. [Figure 7] Correlation heatmap profiles of GLX components detected in plasma from patients with pediatric type 1 diabetes mellitus (T1DM) (ages 6-9) and healthy control siblings without T1DM are shown. [Figure 8A] GLX components detected in plasma of patients with Alzheimer's disease (AD) and age-matched healthy controls (HC) are shown. [Figure 8B] GLX components detected in plasma of patients with Alzheimer's disease (AD) and age-matched healthy controls (HC) are shown. [Figure 9] Correlation heatmap profiles of GLX components detected in plasma of patients with Alzheimer's Disease (AD) and age-matched healthy controls (HC) and after 6 months of treatment (placebo vs. treatment) are shown. [Figure 10] 1 shows CD44 detected in plasma of patients with Alzheimer's Disease (AD) after 6 months of treatment versus placebo. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0057] Detailed Description In the following, embodiments of the invention are described in more detail, and unless otherwise stated, each particular variation of the features may be applied to other embodiments of the invention.

[0058] The biomarkers of the present invention are soluble components of the thick matrix lining of blood vessels called endothelial glycocalyx (GLX). This layer maintains vascular homeostasis, and multiple destructive stimuli result in the shedding of soluble and detectable components and thus loss of this layer. The decline in GLX in blood may be due, in part, to inflammation-driven degeneration and / or aging of blood vessels or organs and / or gene silencing. This leads to changes in plasma values, and we have developed highly sensitive assays to detect these changes using only minute amounts of blood. These biomarkers may predict risk or diagnose diseases of the vascular system and brain, such as Alzheimer's disease, generalized dementia, Parkinson's disease, ALS, depression, schizophrenia, traumatic brain injury, lupus, arthritis, inflammatory bowel disease, thyroiditis, Guillain-Barre syndrome, and vasculitis. There is a wide range of diseases that can benefit from such an invention, including many rare diseases of a particular type, such as, but not limited to, autoimmune diseases, chronic inflammatory diseases, neurological diseases, degenerative diseases, psychiatric diseases, and cardiovascular diseases. Specific diseases are listed in Table 2.

[0059] GLX contains a combination of various proteoglycans, glycolipids and / or glycosaminoglycans. The combination of these molecules detected in a biological fluid or sample will vary depending on the type of disease, for example, Alzheimer's disease versus Parkinson's disease. In this way, different diseases present in different organs will be able to predict the course of the disease, predict stroke, and even diagnose the disease complementing other techniques such as MRI.

[0060] GLX-cleaving enzymes are thought to be activated during immune infiltration of affected organs or by autologous activated immune cells that lead to disease. Indeed, GLX-removing enzymes are increased in the blood in several diseases.

[0061] Furthermore, it is believed that a composite biomarker for GLX shedding could improve a personalized approach to disease monitoring and design therapeutics based on precision medicine. Thus, identification of the overall structure of GLX shedding components in blood and incorporating these markers into a composite biomarker, GLX profile or fingerprint or signature, allows detection of disease, severity and treatment response.

[0062] The mechanism of action of immune cells reaching endothelial cells is thought to be to secrete enzymes to remove GLX structures and extravasate into a given organ, causing damage. Furthermore, GLX shedding can be downregulated in the blood for gene silencing and organ degeneration.

[0063] Inflammation-based diseases are a prime target for diagnosis, and treatments that are anti-inflammatory or disease-modifying in nature may be of primary use to test therapeutic efficacy and separate therapeutic responders from non-responders.

[0064] Secondarily, a great many diseases result in damage to organs, followed by inflammation. There are a great many diseases in which secondary and even chronic inflammation is involved, for example this is the case in stroke and epilepsy and Parkinson's disease.

[0065] Thus, the present invention will be useful for tracking the damage that occurs, the extent of damage, and the reversal of the disease (as accompanied by a reduction in biomarkers) for this class of disease. Furthermore, this can be linked to the efficacy of treatment against the underlying disease, as inflammation is a secondary marker of disease severity, extending its value to separating treatment responders and non-responders.

[0066] Additionally, it is believed that biomarkers could be used in methods to differentiate between organ systems. This would be valuable to determine where in the body inflammation is coming from. For example, inflammation from a lupus patient could be from the kidneys or the skin.

[0067] Assays for the detection of biomarkers are based on antibodies, which bind to specific epitopes of the biomarkers. For example, labeled secondary antibodies are used to visualize the antibody-antigen complexes by HRP enzyme activity or chemiluminescence detection systems based on fluorescence or near-infrared emission. Alternatively, lanthanide conjugation can be used.

[0068] The method for the identification of GLX-biomarkers can be an immunostrip, which is an immunosensor, where the recognition agent is an antibody that binds to the analyte and the detection is by reflectance or fluorescence spectrophotometry.

[0069] Suitable detection systems for detecting the biomarkers of the present invention include, but are not limited to, immunoassays based on specific antibodies, lanthanides, lectins, GAG binding molecules (Table 2), Alcian blue, toluidine blue or dimethylmethylene blue.

[0070] [Table 17]

[0071] Another method for the identification of GLX-biomarkers can be mass spectrometry (MS), such as MALDI-MS, LC-MS, HPLC-MS or liquid chromatography-electrospray ionization-tandem mass spectrometry.

[0072] The platform for detecting one or more biomarkers in one sample (so-called multiplexing) can be in a chip-based device.

[0073] The kit may contain a panel of all GLX markers deemed to be valuable as biomarkers for a particular disease. These are detection molecules (e.g., antibodies) chemically bound to a surface (e.g., a plate or tube) that can specifically bind to each of the individual GLX markers. The kit takes one biological sample and tests for all GLX markers in one go and compares them to a reference value. This is a multiplex assay, providing a reading of each marker compared to the reference and allowing one to determine whether a given disease is active or in treatment response and / or in the case of autoimmune diseases, in remission.

[0074] In chip-based technology combining these elements, the device may transmit the generated signal via printout, Bluetooth, WIFi / Internet, and / or remote sensoring technologies.

[0075] The chip system can be a microfluidic chip.

[0076] definition Before discussing the present invention in further detail, the following terms and conventions are first defined.

[0077] Immune-mediated inflammatory diseases Immune-mediated inflammatory diseases (IMIDs) are any of a group of conditions or diseases that are characterized by a common inflammatory pathway that leads to inflammation and may result from or be caused by dysregulation of normal immune responses. All IMIDs can cause end-organ damage and are associated with increased morbidity and / or mortality. IMIDs can be any disease from the list shown in Table 1.

[0078] GLX molecule "GLX" or "glycocalyx" is the carbohydrate-rich outer portion of the cell surface of most cells in the body, including the luminal endothelium. This layer is the first interaction between blood and the blood vessel wall, both systemically and at the blood-brain barrier (BBB). As described herein, shedding of GLX may be an early predictor of autoimmunity and neurodegeneration, disease severity, and treatment efficacy. Examples of GLX molecules are glycosaminoglycans (GAGs) and proteoglycans (PGs). These may originate from the brain. Thus, the term "GLX-related" should be understood as a molecule that is (or was) related to the GLX structure. In other words, "GLX-related" may be understood as a molecule that is derived from the GLX structure.

[0079] Glycosaminoglycans Glycosaminoglycans (GAGs) or "mucopolysaccharides" are long-chain unbranched polysaccharides consisting of repeating disaccharide units. Examples of glycosaminoglycans (GAGs) that form part of the present invention are: - Keratan sulfate: Keratan sulfate (KS) is a β-1,3-linked poly-N-acetyllactosamine with sulfate residues at the 6-positions of the galatose and N-acetylglucosamine. - Chondroitin sulfate: Chondroitin sulfate (CS) is a sulfated glycosaminoglycan (GAG) composed of chains of alternating sugars (N20 acetylgalactosamine and glucuronic acid). It is usually found attached to proteins as part of proteoglycans. - Hyaluronic acid: Hyaluronic acid (HA), also called hyaluronan, is an anionic, non-sulfated GAG that is widely distributed throughout connective, epithelial, and neural tissues. It is unique among glycosaminoglycans in that it is found in its non-sulfated form in the plasma membrane rather than in the Golgi apparatus and can be very large, often reaching a molecular weight of one million. It is composed of a dimer of D-glucoronic acid and N-acetylglucosamine. - Heparan sulfate: Heparan sulfate (HS) is a linear polysaccharide found in all animal tissues. It exists as a proteoglycan (HSPG) with two or three HS chains attached in close proximity to cell surfaces or extracellular matrix proteins. It is sulfated and composed of dimers of glucuronic acid and N-acetylglucosamine. - Dermatan sulfate (DS): DS is a linear polysaccharide formed by a disaccharide containing GalNAc and IdoA via a β-linkage (1.4 or 1.3, respectively).

[0080] Proteoglycan Proteoglycans (PGs) are highly glycosylated proteins. The basic proteoglycan unit consists of a "core protein" with one or more covalently attached glycosaminoglycan (GAG) chains. Examples of proteoglycans that form part of the present invention are: - Syndecans: Syndecans are single transmembrane domain proteoglycans that are thought to act as co-receptors, particularly for G protein-coupled receptors. These core proteins possess heparan sulfate (HS) and chondroitin sulfate (CS) chains. The syndecan protein family has four members, syndecan 1-4. - Glypicans: The glypicans (GPCs or GPs) constitute one of two major families of heparan sulfate proteoglycans, the other being the syndecans. Six glypicans have been identified in mammals, designated GPC1 to GPC6 or GP1 to GP6. - Biglycan: Biglycan is a small leucine-rich repeat proteoglycan (SLRP) found in a variety of extracellular matrix tissues, often with attached GAG chains. - CD44 - CD44 is a cell surface PG involved in cell-cell interactions, cell adhesion and migration, and serves as a receptor for hyaluronan and other ligands. - Perlecan - a modular HSPG that regulates a variety of biological processes, primarily due to its widespread distribution. - Hyalectans (aggrecan, versican, neurocan, brevican) - PGs with a tridomain structure containing a hyaluronan-binding domain, a GAG side chain, and a lectin-binding domain. - Mimecan - also known as osteoglycin, is a PG related to KS GAGs and small leucine repeats. - Keratocan-3-chain KS, a KS PG containing highly sulfated linear polymers of N-acetyl-lactosamine covalently linked to asparagine residues by mannose-containing oligosaccharides. - Lumincan - a KS PG that interacts with Fas ligand and regulates sheddase activity. - Decorin - a small molecule leucine-rich PG that has functional activity in a variety of processes. - Betaglycan - is a membrane-bound PG with high transforming growth factor beta binding.

[0081] Reference Level In the context of the present invention, the term "reference level" relates to a standard related to an amount, to which other values ​​or characteristics can be compared. In one embodiment of the present invention, the reference level can be determined by examining the abundance (e.g., elevated or decreased levels with respect to the reference level) of one or more of the biomarkers according to the present invention in a biological or blood sample from a healthy individual. By applying different statistical means, such as multivariate analysis, one or more reference levels can be calculated. Based on these results, a cut-off can be obtained that indicates the relationship between the detection level and the patient at risk. The cut-off can then be used to determine the amount of one or more biomarkers that corresponds, for example, to an increased risk of PD.

[0082] A reference can consist of the biomarker level of a single sample or can include an average value of the biomarker levels from two or more samples, or two or more single samples can each be considered as two or more references.

[0083] Furthermore, the biomarker levels of several biomarkers can be compared to their corresponding reference levels to obtain a "profile" of the analyzed sample. Such a profile can include more than one dimension, e.g., increased and decreased levels of several biomarkers, and / or increased or decreased relationships between markers.

[0084] risk assessment The inventors have succeeded in developing a novel method for predicting the risk of a subject to develop stroke, AD, ALS, PD, T1DM and / or MSA. The results presented in the Examples show that the described biomarkers (alone or in combination) appear to be efficient biomarkers for determining whether a patient is at increased risk of developing stroke, AD, ALS, PD, T1DM and / or MSA.

[0085] A cut-off must be set to determine whether a patient is at increased risk of developing stroke, AD, ALS, PD, T1DM and / or MSA, or of experiencing acute inflammatory events such as lupus attacks. This cut-off can be set by the laboratory, by the physician, or individually for each patient. The cut-off level can be set using several methods, including multivariate statistical tests (e.g., partial least squares discriminant analysis (PLS-DA), random forest, support vector machine, etc.), percentiles, mean + / - standard deviation, median, fold change. Multivariate discriminant analysis and other risk assessments can be performed in free or commercially available computer statistical packages (SAS, SPSS, Matlab, R, etc.), or other statistical software packages or screening software known to those skilled in the art.

[0086] As will be clear to those skilled in the art, in any of the above embodiments, changing the risk cut-off level can change the result of the discrimination analysis of each subject.Statistics allows the significance of each level to be evaluated.The conventional statistical test applied to the data set includes t-test, f-test, or even more advanced data comparison test or method.The use of such test or method allows the determination of whether two or more samples are significantly different.

[0087] Significance can be determined by standard statistical methodology known to those skilled in the art. The selected reference level can vary depending on the mammal / subject to which the test is applied. Preferably, the subject according to the present invention is a human subject, for example, a subject who is suffering from or is considered to be at risk of developing a stroke.

[0088] The reference level selected can be varied, if desired, to provide different specificity or sensitivity, as known in the art. Sensitivity and specificity are statistics commonly used to describe and quantify how good and reliable a biomarker or diagnostic test is. Sensitivity assesses how good a biomarker or diagnostic test is at detecting disease, and specificity assesses how likely it is that an individual (i.e., control group, disease-free patients) can be correctly identified as not having the disease.

[0089] Several terms are used along with descriptions of sensitivity and specificity: true positive (TP), true negative (TN), false negative (FN) and false positive. If the disease is proven to be present in a diseased patient and the diagnostic test confirms the presence of the disease, the result of the diagnostic test is considered a TP. If the disease is not present in an individual (i.e., control group, disease-free patients) and the diagnostic test confirms the absence of the disease, the test result is a TN. If the diagnostic test indicates the presence of the disease in such disease-free individuals, the test result is an FP. Finally, if the diagnostic test indicates the absence of the disease in a diseased patient, the test result is an FN.

[0090] sensitivity Sensitivity = TP / (TP+FN) = number of true positive assessments / number of all samples from patients with disease. As used herein, sensitivity refers to a measure of the proportion of actual positives that are correctly identified as positive, similar to a diagnostic test (i.e., the proportion of people with lower than normal PaO2 who are identified as having lower than normal PaO2).

[0091] specificity Specificity = TN / (TN+FP) = number of true negative assessments / number of all samples from the control group. As used herein, specificity refers to a measure of the proportion of negatives that are correctly identified. The relationship between both sensitivity and specificity can be evaluated by the ROC curve. This graphical display helps determine the optimal model through the determination of the best threshold or cutoff for a diagnostic test or biomarker candidate.

[0092] As would be generally understood by one of skill in the art, the methodology for screening is a decision-making process, and thus the specificity and sensitivity selected will depend on what is believed to be the optimal outcome for a given assay.

[0093] It will be apparent to one of skill in the art that in most cases it may be advantageous to choose higher sensitivity at the expense of lower specificity in order to identify as many patients at risk for disease as possible.

[0094] In a preferred embodiment the present invention relates to a method having high specificity, such as at least 70%, such as at least 80%, such as at least 90%, such as at least 95%, such as 100%. In another preferred embodiment the present invention relates to a method having high sensitivity, such as at least 80%, such as at least 90%, such as 100%. EXAMPLES

[0095] Working Example Example 1: Blood from human stroke patients (Figure 1) To demonstrate that GLX structural biomarkers are detectable in blood samples and that these biomarkers increase in response to disease and inflammation, blood samples obtained from patients 1 day (within 72 hours), 10 days, and 90 days after an acute stroke event were examined and compared with levels of GLX markers in blood samples from healthy individuals (reference samples are indicated by the abbreviations of the GLX markers on the x-axis of Figures 1a)-h).

[0096] Materials and Methods Plasma dot blotting Antibody-based dot blots were used to assess GLX markers longitudinally. 2 μl of plasma was dotted in duplicate onto cationic nitrocellulose membranes (Hybond N+, Amersham, GE Healthcare, Brondby, Denmark) and dried. The membranes were incubated for 60 min at room temperature in blocking buffer: 5% skim milk powder (Sigma-Aldrich) in Tris-buffered saline + 0.05% Tween 20 (TBS-T; Sigma-Aldrich). The membranes were then incubated overnight at 4°C with the primary antibodies at their respective dilutions. The membranes were then washed in TBS-T and incubated for 60 min with secondary antibodies coupled to horseradish peroxidase, diluted in blocking buffer at their respective dilutions, and raised against the source of the primary antibodies. The membranes were washed extensively in TBS-T and finally in TBS. The membranes were visualized using a Supersignal West femtoluminescent substrate and a Chemidoc XRS CCD camera (Bio-Rad Laboratories). Chemiluminescence was quantified by densitometry after normalization to background with ImageJ software.

[0097] Primary antibodies: heparan sulfate (HS) (1:1500, Millipore), syndecan-1 (R&D Systems), syndecan-4 (Santa Cruz), chondroitin sulfate (CS) (1:1000, Sigma-Aldrich), syndecan-3 (1:1000, R&D Systems), HA (Bio-Rad), glypican-1 (R&D Systems), CD44 (1:200, DAKO / Aglient).

[0098] Secondary antibodies: HRP-conjugated: anti-rabbit (1:2000), anti-rat (1:4000), anti-mouse (1:3000) (DAKO / Agilent).

[0099] Visualization and optical density analysis are performed as described above using secondary antibody-HRP conjugate and chemiluminescence (femtogram resolution). The following biomarkers were tested: Proteoglycans: Syndecan-1 (Figure 1f) Syndecan-3 (Figure 1a) Syndecan-4 (Figure 1e) Glypican-1 (Figure 1g) CD44 (Figure 1c) Glycosaminoglycans: Chondroitin sulfate (CS) (Figure 1b) Heparan sulfate (HS) (Figure 1d) Hyaluronic acid (HA) (Figure 1h)

[0100] Data Analysis: Data sets were tested for normality (Shapiro-Wilk) and equal variance before statistical analysis was performed (one-way ANOVA). Non-normal data were log-transformed and retested for normality and equal variance. Data sets were then analyzed with parametric or non-parametric statistics based on normality after transformation. P values ​​less than 0.05 were reported as statistically significantly different. Data are presented as mean ± SEM for normal data and median ± interquartile range for non-normal data.

[0101] result As can be seen in Figure 1a)-h), all GLX markers are detectable above background in all patient and healthy control samples. All GLX markers display unique signatures between patients and healthy controls and across time scales within each patient. As can be seen in Figure 1, syndecan-3, chondroitin sulfate (CS), CD44 and heparan sulfate (HS) are particularly effective in separating healthy control samples from stroke patient samples over a 7 day time interval.

[0102] conclusion In this series of experiments, it is shown that GLX components across a range of classes are elevated and variable among stroke patients and over time within each patient, and should therefore be considered potential biomarkers of disease, disease severity, and treatment response. The classes of GLX shown to be substantially relevant are glycosaminoglycans (GAGs) and proteoglycans (PGs).

[0103] In summary, evidence is provided herein for the identification in biological samples of GLX components consisting of GAGs: chondroitin sulfate, hyaluronic acid, and heparan sulfate, and PGs: syndecans and glypicans, which represent the majority of the GLX structure and are all identified as indicating biomarkers of stroke disease.

[0104] Example 2: Blood from human stroke patients (Figures 2 and 3) To demonstrate that GLX structural biomarkers are detectable in blood samples and that these biomarkers are increased or decreased in response to disease and inflammation, blood samples obtained from patients within 3 days, 7 days, and 90 days after an acute stroke event were examined and compared with levels of GLX markers in blood samples from age-matched healthy individuals (HI).

[0105] Figure 2: GLX markers in blood are altered as a result of inflammatory diseases. GLX markers were measured in human plasma in healthy subjects (HI) and in ischemic stroke patients within 3 days (samples taken within 72 hours), 7 days, and 90 days after the stroke event and shown as box plots (10th to 90th percentiles, "+" for median line and mean). Data are significant when p<0.05, 0.01, or <0.001, respectively. * , ** , *** Significance was indicated using the following formula: GLX markers in blood are altered as a result of inflammatory disease.

[0106] Figure 3: GLX markers in blood change as a result of inflammatory disease. Correlation heatmap of glycocalyx (GLX) markers in a) healthy controls and at b) day 1 (within 72 hours after stroke), c) day 7, and d) day 90 after ischemic stroke. GLX markers were measured in human plasma and Pearson correlations were mapped on a -1 to 1 scale. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX markers in blood change as a result of inflammatory disease and return to baseline (day 90) once stroke damage has been reversed, representing a soluble profile.

[0107] Materials and Methods patient Plasma was stored at -20°C after informed consent from ISS patients within 3 days (within 72 hours, N=13), 7 days (N=9), and 90 days (N=13) in the acute stroke unit, and from healthy controls (HI, N=8). Patients were treated according to international stroke guidelines and received antithrombotic statins and, where appropriate, antihypertensive treatment. Exclusion criteria: transient cerebral ischemia, inflammatory diseases and cancer; cytostatic / immunosuppressive therapy; and cerebral, cardiac, ocular or peripheral infarction within 3 months. Neurological disability was estimated using the National Institutes of Health Stroke Scale (NIHSS).

[0108] Plasma GLX analysis Two microliters of plasma were dotted in duplicate onto nylon membranes (Hybond N, Amersham, RPN203B). Membranes were blocked in blocking buffer (5% skim milk + TBS-0.05% Tween 20), incubated overnight at 4°C in blocking buffer + primary antibody, washed (TBS-0.05% Tween 20), incubated with blocking buffer + secondary antibody, washed, incubated with SuperSignal Femto Reagent (Thermo Scientific), imaged with a CCD camera (LAS 4000, GE), and analyzed for raw integrated density using ImageJ. -5 The images were converted to a magnification of 100x. Primary antibody targets: glycosaminoglycans (GAGs): chondroitin sulfate (CS, Sigma), heparin sulfate (HS, Millipore), keratin sulfate (KS, US-Biologicals), and hyaluronic acid (HA, Bio-Rad); and proteoglycans (PGs): CD44 (DAKO), syndecan (Syn)-1 (R&D), -2 (R&D), -3 (R&D), -4 (Santa Cruz), glypican-1 (R&D), and biglycan (Abcam). Secondary antibody targets: anti-mouse, anti-goat, anti-rat, anti-rabbit-HRP (DAKO).

[0109] Data analysis Datasets were tested for normality (Shapiro-Wilk). Non-normal datasets were log-transformed and retested. Datasets that remained non-normal were tested with non-parametric statistics. Parametric data were tested with one-way ANOVA and non-parametric data with Kruskal-Wallis test. Day 7 appeared to be the most dynamic sampling time. Therefore, we tested whether NISS within 3 days predicted levels at day 7. Datasets were tested using least squares regression modeling with ROUT (robust regression and outlier removal) correction (Q=1%) to approach a robust line of best fit. Residuals were tested for normality (QQ plot). Correlations were tested for normality and Pearson coefficients were reported. Significant differences are reported when p<0.05 (GraphPad Prism 8).

[0110] A correlation heatmap was created from the GLX measurements, mapping the Pearson correlation between markers on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. A) Glycosaminoglycans: Keratan sulfate (KS) Chondroitin sulfate (CS) Heparan sulfate (HS) Hyaluronic Acid (HA) B) Proteoglycans: CD44 Syndecan-1 Syndecan-2 Syndecan-3 Syndecan-4 Glypican-1 Biglycan

[0111] result As seen in Figure 2A) and B), all GLX markers are detectable above background in all patient and healthy control samples. All GLX markers display unique signatures between patients and healthy controls and across time scales in each patient. Five of the eleven GLX markers (KS, CS, HS, CD44, and syndecan-3) were significantly increased after ISS, and one was decreased (syndecan-2). Differences in normal data were tested by one-way ANOVA, and data that were non-normal after log transformation were tested by Kruskal-Wallis. * , ** , *** , and **** Represent P values ​​of <0.05, 0.01, 0.001 and 0.0001, respectively. Data presentation: median (line) / box (25 th ~75 th Percentiles) and whiskers (10 th ~90 th percentiles); remaining data points as dots; and mean (+) for parametric raw data.

[0112] Keratan sulfate, chondroitin sulfate, heparan sulfate, CD44, syndecan-3 and syndecan-2 are particularly effective in separating healthy control samples from stroke patient samples over a 7 day time interval.

[0113] Keratan sulfate and syndecan-2 are particularly effective in separating healthy control samples from stroke patient samples over a 90 day time interval.

[0114] As seen in Figure 2C), soluble CD44 at day 7 significantly correlates with early neurological impairment after minor stroke. Neurological impairment was estimated by NIHSS within 72 hours (within 3 days) of the ischemic stroke event. Plasma CD44 at day 7 significantly correlated with NIHSS (normalized data, Pearson's r: 0.72, P<0.05; robust linear regression fit: 0.52).

[0115] As seen in Figure 3, the relationship between each set of biomarkers is analyzed for samples of healthy controls and stroke patients 1 day, 7 days and 90 days after stroke. The heatmap correlation profile within 3 days after stroke clearly shows an increase in the positive relationship between the levels of CD44 in combination with either biglycan, Syn1 or Syn3. The heatmap 1 day after stroke also clearly shows an increase in the levels of KS in combination with either biglycan, Syn1 or Syn3, an increase in the levels of GPC-1 in combination with Syn3, and an increase in the levels of Syn-3 in combination with biglycan, CD44, KS, HA and Syn1. Other combinations in which the levels of the biomarker combinations are increased or decreased 1 day after stroke can be found in Figure 3.

[0116] The heat map correlation profile 7 days after stroke clearly shows the increased levels of CD44 in combination with either biglycan, Syn1, Syn3 or Syn4. The heat map 7 days after stroke also clearly shows the increased positive relationship between the levels of KS in combination with either biglycan, Syn1, Syn3 or Syn4, the increased levels of GPC-1 in combination with either Syn1 or Syn2, and the increased levels of Syn-3 in combination with CD44, KS Syn-1 and Syn-4. Other combinations not described above in which the levels of biomarker combinations are increased or decreased 7 days after stroke are disclosed in FIG. 3.

[0117] The heatmap 90 days after stroke shows only a decrease in the number of correlated biomarkers, and the biomarker profile returns to being similar to, but not yet identical to, the profile in the heatmap of the healthy controls.

[0118] conclusion In this series of experiments, it is shown that GLX components across a range of types are elevated and variable between stroke patients and over time within each patient, and should therefore be considered potential biomarkers of disease, disease severity, and treatment response. GLX components shown to be substantially related are keratan sulfate, CD44, syndecan-3, and syndecan-2. Furthermore, GLX profiles (heat maps) showing blood GLX profiles after stroke and over time with each other, and from healthy controls, can distinguish disease from remission from disease.

[0119] We further conclude that one or more sets of two biomarkers are elevated on days 1 and / or 7 after stroke, indicating that there are specific relationships between some of the biomarkers, as shown in Figure 3.

[0120] Taken together, evidence is provided herein for the identification of the GLX components keratan sulfate, chondroitin sulfate, heparan sulfate, CD44, syndecan-3 and syndecan-2 in biological samples from stroke patients, which represent the majority of the GLX structure and are all identified as indicating biomarkers of stroke disease.

[0121] Example 3: Blood from patients with neurodegenerative disorders (Figures 4 and 5) To demonstrate that GLX structural biomarkers are detectable in blood samples and that concentrations of these biomarkers change in response to disease and inflammation, blood samples obtained from patients with Parkinson's disease (PD), multiple system atrophy (MSA) and amyotrophic lateral sclerosis (ALS) were examined and compared with levels of GLX markers in blood samples from age-matched healthy controls (HC) (y-axis: arbitrary OD).

[0122] Figure 4: GLX markers in blood are altered as a result of inflammatory diseases. GLX markers were measured in human plasma in age / sex-matched healthy controls (HC), as well as in patients with Parkinson's disease (PD), multiple system atrophy (MSA), and amyotrophic lateral sclerosis (ALS), and are shown as box plots (10th to 90th percentiles, "+" to median line and mean). Data are significant when p<0.05, 0.01, or <0.001, respectively. * , ** , *** Significance was indicated using the GLX marker in blood. GLX markers are altered as a result of inflammatory diseases.

[0123] Figure 5: GLX markers in blood are altered as a result of inflammatory diseases. Correlation heatmaps of glycocalyx (GLX) markers in a) age / gender-matched healthy controls, and in b) Parkinson's disease (PD), c) multiple system atrophy (MSA), d) amyotrophic lateral sclerosis (ALS). GLX markers were measured in human plasma and Pearson correlations were mapped on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX markers in blood are altered as a result of inflammatory diseases and represent the soluble GLX profile.

[0124] Materials and Methods patient Plasma samples were obtained from patients with PD, MSA, ALS and matched healthy controls (HC) (N=20).

[0125] Plasma GLX analysis Two microliters of plasma were dotted in duplicate onto nylon membranes (Hybond N, Amersham, RPN203B). Membranes were blocked in blocking buffer (5% skim milk + TBS-0.05% Tween 20), incubated in blocking buffer + primary antibody for 1 h at room temperature, washed (TBS-0.05% Tween 20), incubated with blocking buffer + secondary antibody, washed, incubated with SuperSignal Femto Reagent (Thermo Scientific), imaged with a CCD camera (LAS 4000, GE) and analyzed for raw integrated density using ImageJ. Primary antibody targets: glycosaminoglycans (GAGs): chondroitin sulfate (CS, Sigma), heparin sulfate (HS, Millipore), keratin sulfate (KS, US-Biologicals), and hyaluronic acid (HA, Bio-Rad); and proteoglycans (PGs): CD44 (DAKO), syndecan (Syn)-1 (R&D), -2 (R&D), -3 (R&D), -4 (Santa Cruz), glypican-1 (R&D), glypican-4 (Us Biologicals), and biglycan (Abcam). Secondary antibody targets: anti-mouse, anti-goat, anti-rabbit-HRP (DAKO), and rat-HRP (Sigma).

[0126] Data analysis Datasets were tested for normality (Shapiro-Wilk). Non-normal datasets were log-transformed and retested. Datasets that remained non-normal were tested with non-parametric statistics. Parametric data were tested with one-way ANOVA and non-parametric data with the Kruskal-Wallis test. Significant differences are reported when p<0.05 (GraphPad Prism 8). * , ** , *** , and **** represent P values ​​of <0.05, 0.01, 0.001 and 0.0001, respectively.

[0127] A correlation heatmap was created from the GLX measurements, mapping the Pearson correlation between markers on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX Structure: Keratan sulfate (KS) CD44 GPC-4 Biglycan Hyaluronic Acid (HA) Chondroitin sulfate (CS) Heparan sulfate (HS) GPC-1 Syndecan-1 Syndecan-2 Syndecan-3 Syndecan-4

[0128] result As can be seen in Figure 4, all GLX markers are detectable above background in all patient and healthy control samples. All GLX markers display unique signatures between patients and healthy controls.

[0129] In PD patients, 10 of the 12 GLX markers, namely KS, CD44, GP4, biglycan, HA, HS, GPC-1, Syn2, Syn3 and Syn4, are significantly increased compared to healthy controls, with the strongest increase observed for the biomarkers KS, HA and Syn4.

[0130] In MSA patients, 10 of the 12 GLX markers, namely KS, CD44, GP4, HA, chondroitin sulfate, HS, GPC-1, Syn2, Syn3, and Syn4, are significantly increased compared to healthy controls. The strongest increases are observed for the biomarkers KS, CD44, GP4, HA, HS, Syn2, Syn3, and Syn4.

[0131] In ALS patients, two of the 12 GLX markers, namely Syn2 and Syn4, are significantly increased compared to healthy controls. The strongest increase is observed for the biomarker Syn2.

[0132] The biomarkers that are significantly increased in all samples from the three diseases (PD, MSA, and ALS) are Syn2 and Syn4. The biomarkers that are increased for two of the three diseases, PD and MSA, are KS, CD44, GP4, HA, HS, GPC-1, and Syn3.

[0133] As can be seen in FIG. 5, the associations between each set of two biomarkers were analyzed for healthy control samples and for patient samples with Parkinson's disease, MSA, and ALS.

[0134] Heatmap profiling in FIG. 5b) for PD patients clearly shows an increased positive correlation between the levels of Syn3 and biglycan, CD44 and biglycan, Syn4 and biglycan, Syn4 and GPC4, CD44 and Syn3, CD44 and Syn4, Syn4 and GPC1, and CD44 and GPC1.

[0135] The heatmap in FIG. 5c) for MSA patients clearly shows an overall increase of the analyzed biomarkers. Particularly positively increased sets of biomarkers are the sets HA and biglycan, biglycan and GPC-1, biglycan and Syn3, CD44 and GPC-1, CD44 and Syn4, GP4 and Syn1, KS and Syn4, HA and GPC-1, HA and Syn1, HA and Syn2, HA and Syn3, CS and Syn3, CS and Syn1, CS and Syn2, CS and GPC1, HS and CD44, HS and GPC-1, HS and Syn1, HS and Syn2, HS and Syn3, GPC-1 and GPC-1 and Syn3, GPC-1 and Syn4, and Syn3 and Syn4.

[0136] The heatmap correlation profile in FIG. 5d) for ALS patients clearly shows increased correlation in levels for the following sets: biglycan and CD44, biglycan and HA, biglycan and CS, biglycan and HS, biglycan and Syn2, CD44 and CS, CD44 and HS, CD44 and Syn2, HA and Syn2, CS and Syn1, CS and Syn2, HS and Syn2, GPC1 and Syn4, and Syn3 and Syn4.

[0137] Other combinations not listed above in which the levels of combinations of biomarkers for PD, MSA and ALS are elevated or decreased are disclosed in FIG.

[0138] As can be seen in Figures 5a)-d), the heat maps for PD, MSA and ALS show specific patterns for each disease.

[0139] conclusion In this series of experiments, it is shown that GLX components across a range of types are elevated and variable for PD, MSA and ALS patients, and therefore should be considered as potential biomarkers of disease.GLX components that have been shown to be substantially related to PD patients are keratan sulfate, CD44, biglycan, GPC-4, GPC-1, HA, Syn2, Syn3 and Syn4.GLX components that have been shown to be substantially related to MSA patients are keratan sulfate, CD44, GPC-4, GPC-1, HA, Syn2, Syn3 and Syn4.GLX components that have been shown to be substantially related to ALS patients are Syn2 and Syn4.

[0140] We further conclude that for each of the diseases PD, ALS and MSA, one or more sets of two biomarkers are specifically elevated, indicating that there are specific relationships between some of the biomarkers as shown in Figure 5. Furthermore, for each of the diseases PD, ALS and MSA, the typical patterns of correlations can be used as "fingerprints" to determine the PD, ALS and / or MSA status of a patient.

[0141] Taken together, evidence is provided herein for the identification of GLX components in biological samples from PD, MSA and / or ALS patients, all of which are identified as being indicative biomarkers for PD, MSA and / or ALS.

[0142] Example 4: Blood from a patient with autoimmune disease, type I diabetes (Figures 6 and 7) To demonstrate that GLX structural biomarkers are detectable in blood samples and that concentrations of these biomarkers change in response to disease and inflammation, blood samples obtained from children aged 6-9 years with autoimmune type 1 diabetes mellitus (T1DM) were examined and compared with levels of GLX markers in blood samples from healthy controls (siblings) without T1DM (y-axis: arbitrary OD).

[0143] Figure 6: GLX markers were measured in human plasma in age / gender-matched healthy controls (siblings) and type I diabetes mellitus (T1DM) and shown as box plots (10th-90th percentiles, "+" for median line and mean). Healthy controls are healthy siblings who have a T1DM sibling in the family. Data are reported relative to mean if p<0.05, 0.01, or <0.001, respectively. * , ** , *** Significance was indicated using the following formula: GLX markers in blood are altered as a result of inflammatory disease.

[0144] Figure 7: GLX markers in blood are altered as a result of inflammatory diseases. Correlation heatmap of glycocalyx (GLX) markers in a) age / gender-matched healthy controls and b) autoimmune disease type I diabetes mellitus (T1DM). GLX markers were measured in human plasma and Pearson correlations were mapped on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX markers in blood are altered as a result of inflammatory diseases and represent the soluble GLX profile.

[0145] Materials and Methods patient Plasma was obtained from patients with autoimmune T1DM and matched controls from siblings who were healthy but had a sibling with T1DM (N=33, 36, respectively).

[0146] Plasma GLX analysis Two microliters of plasma were dotted in duplicate onto nylon membranes (Hybond N, Amersham, RPN203B). Membranes were blocked in blocking buffer (5% skim milk + TBS-0.05% Tween 20), incubated overnight at 4C in blocking buffer + primary antibody, washed (TBS-0.05% Tween 20), incubated with blocking buffer + secondary antibody, washed, incubated with SuperSignal Femto Reagent (Thermo Scientific), imaged with a CCD camera (LAS 4000, GE) and analyzed for raw integrated density using ImageJ. Primary antibody targets: glycosaminoglycans (GAGs): chondroitin sulfate (CS, Sigma), heparin sulfate (HS, Millipore), keratin sulfate (KS, US-Biologicals), and hyaluronic acid (HA, Bio-Rad); and proteoglycans (PGs): CD44 (DAKO), syndecan (Syn)-1 (R&D), -2 (R&D), -3 (R&D), -4 (Santa Cruz), glypican-4 (Us Biologicals), and biglycan (Abcam). Secondary antibody targets: anti-mouse, anti-goat, anti-rabbit-HRP (DAKO), and rat-HRP (Sigma).

[0147] Data analysis Datasets were tested for normality (Shapiro-Wilk). Non-normal datasets were log-transformed and retested. Datasets that remained non-normal were tested with non-parametric statistics. Parametric data were tested with one-way ANOVA and non-parametric data with the Kruskal-Wallis test. Significant differences are reported when p<0.05 (GraphPad Prism 8). * , ** , *** , and **** represent P values ​​of <0.05, 0.01, 0.001 and 0.0001, respectively.

[0148] A correlation heatmap was created from the GLX measurements, mapping the Pearson correlation between markers on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX Structure: Keratan sulfate (KS) CD44 GPC-4 Biglycan Chondroitin sulfate (CS) Heparan sulfate (HS) Hyaluronic Acid (HA) Syndecan-1 Syndecan-2 Syndecan-3 Syndecan-4

[0149] result As can be seen in Figure 6, all GLX markers are detectable above background in all patient and healthy control samples. All GLX markers display unique signatures between patients and healthy controls.

[0150] In T1DM patients, 7 of the 11 GLX markers, namely KS, CD44, GP4, biglycan, HA, Syn1, and Syn3, are significantly increased compared to healthy controls. The strongest increases are observed for the biomarkers KS, biglycan, GP4, and Syn3.

[0151] As can be seen in Figure 7, the associations between each set of two biomarkers were analyzed for sibling healthy controls (Figure 7a)) and T1DM patient samples (Figure 7b).

[0152] Heat maps for T1DM patients clearly show elevated levels of the following sets: biglycan and CD44, biglycan and Syn3, biglycan and Syn4, CD44 and Syn3, CD44 and Syn4, HA and biglycan, HA and Syn3, HA and Syn4, and Syn3 and Syn4.

[0153] Other combinations not listed above in which the levels of combinations of biomarkers for T1DM are elevated or decreased are disclosed in FIG.

[0154] conclusion In this series of experiments, a range of GLX components are shown to be elevated and variable in T1DM patients and should therefore be considered potential biomarkers of the disease. GLX components shown to be substantially relevant in T1DM patients are keratan sulfate, CD44, biglycan, GPC-4, Syn1 and Syn3.

[0155] We further conclude that for T1DM, one or more sets of two biomarkers are particularly elevated and that there are specific relationships between some of the biomarkers as shown in Figure 7, which may distinguish between health and disease.

[0156] Taken together, evidence is provided herein for the identification of GLX components in biological samples from T1DM patients. The GLX components keratan sulfate, CD44, biglycan, GPC-4, Syn1 and Syn3 are all identified as showing to be biomarkers for T1DM.

[0157] Example 5: Blood from Alzheimer's disease patients (Figures 8 and 9) To demonstrate that GLX structural biomarkers are detectable in blood samples and that the concentrations of these biomarkers change in response to disease and inflammation, blood samples obtained from Alzheimer's disease patients (AD, grey bars) were examined and compared (y-axis: arbitrary OD) with the levels of GLX markers in blood samples from age-matched healthy controls (HC, white bars).

[0158] Figure 8: GLX markers in blood are altered as a result of inflammatory diseases. GLX markers were measured in human plasma in age / gender-matched healthy controls (white) and Alzheimer's disease (AD; grey) and shown as box plots (10th to 90th percentiles, "+" to median line and mean). Data are expressed as significant if p<0.05, 0.01, or <0.001, respectively. * , ** , *** Significance was indicated using the following formula: GLX markers in blood are altered as a result of inflammatory disease.

[0159] Figure 9: GLX markers in blood change as a result of inflammatory disease and respond to disease-modifying drug treatment. Correlation heatmap of glycocalyx (GLX) markers in a) age / gender-matched healthy controls, and b) Alzheimer's disease (AD), and AD patients after 6 months of c) treatment with placebo and d) disease-modifying drug treatment. GLX markers were measured in human plasma and Pearson correlations were mapped on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX markers in blood change as a result of inflammatory disease and treatment and represent the soluble GLX profile.

[0160] Materials and Methods patient Plasma was obtained from AD patients at baseline (N=32), healthy controls (N=20), and the AD patients were followed up after 6 months receiving either placebo (N=19) or disease treatment (N=13).

[0161] Plasma GLX analysis Two microliters of plasma were dotted in duplicate onto nylon membranes (Hybond N, Amersham, RPN203B). Membranes were blocked in blocking buffer (5% skim milk + TBS-0.05% Tween 20), incubated in blocking buffer + primary antibody for 1 h at room temperature, washed (TBS-0.05% Tween 20), incubated with blocking buffer + secondary antibody, washed, incubated with SuperSignal Femto Reagent (Thermo Scientific), imaged with a CCD camera (LAS 4000, GE) and analyzed for raw integrated density using ImageJ. Primary antibody targets: glycosaminoglycans (GAGs): chondroitin sulfate (CS, Sigma), heparin sulfate (HS, Millipore), keratin sulfate (KS, US-Biologicals), and hyaluronic acid (HA, Bio-Rad); and proteoglycans (PGs): CD44 (DAKO), syndecan (Syn)-1 (R&D), -2 (R&D), -3 (R&D), -4 (Santa Cruz), glypican-1 (R&D), glypican-4 (Us Biologicals), perlecan (R&D), and biglycan (Abcam). Secondary antibody targets: anti-mouse, anti-goat, anti-rabbit-HRP (DAKO), and rat-HRP (Sigma).

[0162] Data analysis Datasets were tested for normality (Shapiro-Wilk). Non-normal datasets were log-transformed and retested. Datasets that remained non-normal were tested with non-parametric statistics. Parametric data were tested with one-way ANOVA and non-parametric data with the Kruskal-Wallis test. Significant differences are reported when p<0.05. * , ** , *** , and **** represent P values ​​of <0.05, 0.01, 0.001 and 0.0001, respectively (GraphPad Prism 8).

[0163] A correlation heatmap was created from the GLX measurements, mapping the Pearson correlation between markers on a scale of -1 to 1. Values ​​below zero are plotted in red and values ​​above zero are plotted in blue. GLX Structure: Keratan sulfate (KS) CD44 GPC-4 Biglycan Chondroitin sulfate (CS) Heparan sulfate (HS) Hyaluronic acid GPC-1 Syndecan-1 Syndecan-2 Syndecan-3 Syndecan-4 Pearlcan

[0164] result As can be seen in Figure 8, all GLX markers are detectable above background in all patient and healthy control samples. All GLX markers display unique signatures between patients and healthy controls.

[0165] In AD patients, 7 of the 13 GLX markers are significantly altered compared to healthy controls: KS (increased), CD44 (increased), GP4 (increased), biglycan (decreased), GPC-1 (decreased), chondroitin sulfate (decreased), heparan sulfate (decreased). The strongest increases are observed for the biomarkers KS, CD44 and GPC-1.

[0166] As can be seen in Figure 9, the heatmap of AD patients (Figure 9b)) shows different correlations between biomarkers compared to the heatmap of healthy controls (Figure 9a)). Sets of biomarkers with increased positive associations are e.g., CD44 and CS, CD44 and HS, GP4 and HS, GP4 and Syn2, KS and HA, KS and Syn2, biglycan and HA, HA and Syn4, CS and Syn3, CS and Syn4, and HS and Syn3.

[0167] The biomarker heatmap for the 6-month placebo sample (Figure 9c) is similar, but not identical, to the heatmap for untreated AD patients (Figure 9b). In contrast, the biomarker heatmap for the 6-month treatment sample (Figure 9d) is relatively similar, but not identical, to the healthy control group and completely dissimilar to the placebo sample.

[0168] Other combinations not listed above in which the levels of combinations of biomarkers for AD are elevated or decreased are disclosed in FIG.

[0169] As can be seen in FIG. 10, the presence of CD44 in AD patients receiving 6 months of treatment is reduced compared to the levels of CD44 in AD patients receiving placebo only.

[0170] conclusion In this series of experiments, it is shown that a range of GLX components are significantly increased or decreased and therefore distinct for AD patients and should therefore be considered potential biomarkers of disease, disease severity and treatment response. The GLX components shown to be substantially relevant for AD patients are keratan sulfate, CD44, GP4, biglycan, GPC-1 and chondroitin sulfate.

[0171] We further conclude that for AD, one or more sets of two biomarkers are particularly correlated, and that there is a particular relationship between some of the biomarkers, as shown in Figure 9. Furthermore, the analyzed biomarkers can be used to monitor the progress of treatment compared to placebo only treatment. It can also be used to distinguish treated patients from those who have not been treated at all or those who have been treated inadequately. Illustratively, the level of CD44 can distinguish treated patients from placebo patients after only 6 months.

[0172] Taken together, evidence is provided herein for the identification of GLX components in biological samples from AD patients. The GLX components KS, CD44, GP4, biglycan, GPC-1 and chondroitin sulfate are all identified as showing to be biomarkers for AD.

Claims

1. 1. An ex vivo method for aiding in the diagnosis and / or prognosis of at least one immune-mediated inflammatory disease (IMID) in a subject, comprising: the IMID is selected from an autoimmune disease, a neuroinflammatory disease, a cardiovascular disease, a neurodegenerative disease, a psychiatric disease, or an organ system disease; The method further comprising: a. a component of endothelial glycocalyx (GLX) in a blood sample from the subject; and (i) glycosaminoglycans (GAGs) of GLX and proteoglycans (PGs) of GLX; or (ii) two or more PGs of GLX; performing an in vitro measurement of a composite biomarker comprising a combination of levels of two or more biomarkers, b. comparing the levels of the composite biomarkers with corresponding reference levels; Including, c. A method wherein the measurement is used to assess a condition of said subject.

2. Step (a) a. performing an in vitro measurement of said composite biomarkers, comprising levels of said two or more biomarkers that are components of GLX, in a first blood sample from said subject; and said method further comprises: performing an in vitro measurement of said composite biomarkers comprising levels of said two or more biomarkers that are components of GLX in a second or further blood sample from said subject obtained at a time later than said first blood sample; The method of claim 1 , comprising:

3. The ex vivo method described in claim 1 or 2, wherein the blood sample is whole blood, plasma, or serum. (i) the PG of GLX is a PG selected from the group consisting of syndecan, CD44, perlecan, biglycan, glypican, hyalectan, mimecan, decorin, betaglycan, lumincan and keratocan, or 4. The ex vivo method according to any one of claims 1 to 3, wherein (ii) the two or more PGs of GLX are selected from the group consisting of syndecan, CD44, perlecan, biglycan, glypican, hyalectan, mimecan, decorin, betaglycan, lumincan and keratocan.

5. The PG, or the two or more PGs, independently, (i) a syndecan selected from the group consisting of syndecan-2, syndecan-4, syndecan-1 and syndecan-3; (ii) a glypican selected from the group consisting of glypican-1, glypican-4, glypican-5, glypican-6, glypican-2 and glypican-3, and / or (iii) a hyalectan selected from the group consisting of versican, brevican, neurocan and aggrecan; The ex vivo method of claim 4, wherein

6. The ex vivo method described in any one of claims 1 to 5, wherein the GAG ​​of GLX is selected from the group consisting of hyaluronic acid, keratan sulfate, chondroitin sulfate, heparan sulfate, and dermatan sulfate.

7. An ex vivo method according to any one of claims 1 to 6, wherein the levels of two or more biomarkers which are components of GLX are measured simultaneously or sequentially.

8. The ex vivo method of claim 1, wherein the measurement value used to assess the subject's condition in step (c) is associated with the risk of developing and / or suffering from the IMID.

9. The ex vivo method according to claim 1, wherein the level of the composite biomarker is higher or lower than a reference value.

10. The ex vivo method described in any one of claims 1 to 9, wherein the IMID is stroke, Parkinson's disease (PD), Alzheimer's disease (AD), multiple system atrophy (MSA), amyotrophic lateral sclerosis (ALS), and type I diabetes.

11. An ex vivo method described in any one of claims 1 to 10, further comprising determining the risk of IMID from the corresponding reference levels as cut-off levels in biological fluids from healthy patients using multivariate statistics.

12. The ex vivo method of claim 11, wherein the cutoff level is set by a laboratory, by a physician, or individually for each patient by a method selected from the list of multivariate statistical tests, percentiles, mean + / - standard deviation, median, and fold change. (i) a component of the endothelial glycocalyx (GLX), and (i.i) glycosaminoglycan (GAG) of GLX and proteoglycan (PG) of GLX, or (i.ii) two or more PGs of GLX; A binding agent for a combination of two or more biomarkers, comprising: (ii) instructions for using the binding agent in the assessment of an IMID that is an autoimmune disease, a neuroinflammatory disease, a cardiovascular disease, a neurodegenerative disease, a psychiatric disease, or an organ system disease; and Kit including:

14. The method of claim 13, wherein the PG of GLX or the two or more PGs are independently selected from the group consisting of syndecan, CD44, perlecan, biglycan, glypican, hyalectan, mimecan, decorin, betaglycan, lumincan, and keratocan; The kit of claim 13, wherein (ii) the GAG ​​of GLX is selected from the group consisting of hyaluronic acid, keratan sulfate, chondroitin sulfate, heparan sulfate, and dermatan sulfate.

15. The kit described in claim 13 or 14, wherein the IMID is stroke, Parkinson's disease (PD), Alzheimer's disease (AD), multiple system atrophy (MSA), amyotrophic lateral sclerosis (ALS), and type I diabetes.

16. A microfluidic detection device (1) for detecting an IMID in a patient, comprising: the IMID is selected from stroke, Parkinson's disease (PD), Alzheimer's disease (AD), multiple system atrophy (MSA), amyotrophic lateral sclerosis (ALS), or type I diabetes; The device, (i) glycosaminoglycans (GAGs) of GLX and proteoglycans (PGs) of GLX, or (ii) two or more PGs of GLX; A microfluidic detection device (1) comprising two or more binding agents for 17. The method of claim 17, wherein the PG of GLX or the two or more PGs are independently selected from the group consisting of syndecan, CD44, perlecan, biglycan, glypican, hyalectan, mimecan, decorin, betaglycan, lumincan, and keratocan; The microfluidic detection device (1) of claim 16, wherein (ii) the GAG ​​of GLX is selected from the group consisting of hyaluronic acid, keratan sulfate, chondroitin sulfate, heparan sulfate, and dermatan sulfate.

18. The microfluidic detection device (1) of claim 16 or 17, wherein the IMID is stroke, Parkinson's disease (PD), Alzheimer's disease (AD), multiple system atrophy (MSA), amyotrophic lateral sclerosis (ALS) and type I diabetes.

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